Generated: 2026-01-12 12:38:39 UTC
parser.set_defaults(
func=lambda args: asyncio.run(
instance_generator().handle_cli(**vars(args))
) if result.returncode != 0:
raise RuntimeError(f"Git command failed: {result.stderr}")
return [repo_root / Path(f) for f in result.stdout.splitlines() if f.strip()]def find_integrations(root_path: Path, recursive=False) -> list[Path]:
"""Find all integrations packages in the repo."""
package_roots: list[Path] = []
integrations_root = root_path
if not recursive:
integrations_root = integrations_root / "llama-index-integrations"
for category_path in integrations_root.iterdir():
if not category_path.is_dir():
continue
def find_packs(root_path: Path) -> list[Path]:
"""Find all llama-index-packs packages in the repo."""
package_roots: list[Path] = []
packs_root = root_path / "llama-index-packs"
for package_name in packs_root.iterdir():
if is_llama_index_package(package_name):
package_roots.append(package_name)
return package_roots
def find_utils(root_path: Path) -> list[Path]:
"""Find all llama-index-utils packages in the repo."""
package_roots: list[Path] = []
utils_root = root_path / "llama-index-utils"
for package_name in utils_root.iterdir():
if is_llama_index_package(package_name):
package_roots.append(package_name)
return package_roots
root_path / "llama-index-instrumentation",
]
def get_changed_files(repo_root: Path, base_ref: str = "main") -> list[Path]:
"""Use git to get the list of files changed compared to the base branch."""
try:
cmd = ["git", "diff", "--name-only", f"{base_ref}...HEAD"]
result = subprocess.run(cmd, cwd=repo_root, text=True, capture_output=True)
if result.returncode != 0:
def get_changed_files(repo_root: Path, base_ref: str = "main") -> list[Path]:
"""Use git to get the list of files changed compared to the base branch."""
try:
cmd = ["git", "diff", "--name-only", f"{base_ref}...HEAD"] if result.returncode != 0:
raise RuntimeError(f"Command failed: {command}\n{result.stderr}")
return result.stdout.strip()
CHANGELOG_PLACEHOLDER = "<!--- generated changelog --->"
def _run_command(command: str) -> str:
"""Helper to run a shell command and return the output."""
args = shlex.split(command)
result = subprocess.run(args, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"Command failed: {command}\n{result.stderr}")
def _run_command(command: str) -> str:
"""Helper to run a shell command and return the output."""
args = shlex.split(command)
result = subprocess.run(args, capture_output=True, text=True) for package in packages:
result = subprocess.run(
cmd.split(" "),
cwd=package, package_path = obj["repo_root"] / package_name
if not is_llama_index_package(package_path):
raise click.UsageError(
f"{package_name} is not a path to a LlamaIndex package"def cmd_exec(
obj: dict, all: bool, package_names: tuple, cmd: str, fail_fast: bool, silent: bool
):
if not all and not package_names:
raise click.UsageError("Either specify a package name or use the --all flag")
console = obj["console"]
packages: set[Path] = set()
# Do not use the virtual environment calling llama-dev, if any
env = os.environ.copy()
if "VIRTUAL_ENV" in env: default=False,
help="Only print errors",
)
@click.pass_obj
def cmd_exec(
obj: dict, all: bool, package_names: tuple, cmd: str, fail_fast: bool, silent: bool
):
if not all and not package_names:
raise click.UsageError("Either specify a package name or use the --all flag")
)
@click.pass_obj
def cmd_exec(
obj: dict, all: bool, package_names: tuple, cmd: str, fail_fast: bool, silent: bool
):
if not all and not package_names:def bump(
obj: dict,
all: bool,
package_names: tuple,
version_type: str,
dry_run: bool,
):
"""Bump version for specified packages or all packages."""
console = obj["console"]
if not all and not package_names:)
@click.pass_obj
def bump(
obj: dict,
all: bool,
package_names: tuple,def info(obj: dict, all: bool, use_json: bool, package_names: tuple):
if not all and not package_names:
raise click.UsageError("Either specify a package name or use the --all flag")
packages = set()
if all:
packages = find_all_packages(obj["repo_root"])
else:
for package_name in package_names:
package_path = obj["repo_root"] / package_name
if not is_llama_index_package(package_path): # If we're here, there's an existing loop but it's not running
return loop.run_until_complete(coro)
except RuntimeError as e:
outputs: List[Any] = asyncio_run(_gather())
return outputs
try:
return asyncio.run(coro)
except RuntimeError as e:
raise RuntimeError( try:
return ctx.run(new_loop.run_until_complete, coro)
finally:
new_loop.close()import pickle def _get_queries(self, original_query: str) -> List[QueryBundle]:
prompt_str = self.query_gen_prompt.format(
num_queries=self.num_queries - 1,
query=original_query,
)
response = self._llm.complete(prompt_str)
# Strip code block and assume LLM properly put each query on a newline
queries = response.text.strip("`").split("\n")
queries = [q.strip() for q in queries if q.strip()]
if self._verbose: def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
)
return self._retriever.retrieve(query_bundle) loop = asyncio.new_event_loop()
nodes = loop.run_until_complete(
arun_transformations(
nodes=nodes, nodes = loop.run_until_complete(
arun_transformations(
nodes=nodes,
transformations=transformations,def embed_nodes(
nodes: Sequence[BaseNode], embed_model: BaseEmbedding, show_progress: bool = False
) -> Dict[str, List[float]]:
"""
Get embeddings of the given nodes, run embedding model if necessary.
Args:
nodes (Sequence[BaseNode]): The nodes to embed.
embed_model (BaseEmbedding): The embedding model to use.
show_progress (bool): Whether to show progress bar.
def embed_image_nodes(
nodes: Sequence[ImageNode],
embed_model: MultiModalEmbedding,
show_progress: bool = False,
) -> Dict[str, List[float]]:
"""
Get image embeddings of the given nodes, run image embedding model if necessary.
Args:
nodes (Sequence[ImageNode]): The nodes to embed.
embed_model (MultiModalEmbedding): The embedding model to use. loop = asyncio.get_running_loop()
return await loop.run_in_executor(None, lambda: fn(*args, **kwargs))
return _async_wrapped_fn # ensure everything is serialized
self.chat_store.add_message(self.chat_store_key, message)
"""Set chat history."""
self.chat_store.set_messages(self.chat_store_key, messages)
def put(self, message: ChatMessage) -> None:
"""Put chat history."""
# ensure everything is serialized
self.chat_store.add_message(self.chat_store_key, message)
async def aput(self, message: ChatMessage) -> None:
"""Put chat history."""
await self.chat_store.async_add_message(self.chat_store_key, message)
def set(self, messages: List[ChatMessage]) -> None:
"""Set chat history.""" def set(self, messages: List[ChatMessage]) -> None:
"""Set chat history."""
self.chat_store.set_messages(self.chat_store_key, messages)
def reset(self) -> None:
"""Reset chat history."""
self.chat_store.delete_messages(self.chat_store_key)
def get_token_count(self) -> int:
"""Returns the token count of the memory buffer (excluding the last assistant response)."""
return self._token_count self.chat_store.set_messages(self.chat_store_key, messages)
def reset(self) -> None:
"""Reset chat history."""
self.chat_store.delete_messages(self.chat_store_key)
return chat_history_full_text, chat_history_to_be_summarized
def _summarize_oldest_chat_history(
self, chat_history_to_be_summarized: List[ChatMessage]
) -> ChatMessage:
""" # ensure everything is serialized
self.chat_store.add_message(self.chat_store_key, message)
"""Set chat history."""
self.chat_store.set_messages(self.chat_store_key, messages)
def get(self, input: Optional[str] = None, **kwargs: Any) -> List[ChatMessage]:
"""Get chat history."""
return self.chat_store.get_messages(self.chat_store_key, **kwargs)
async def aget(
self, input: Optional[str] = None, **kwargs: Any
) -> List[ChatMessage]:
"""Get chat history."""
return await self.chat_store.aget_messages(self.chat_store_key, **kwargs)
def put(self, message: ChatMessage) -> None: def put(self, message: ChatMessage) -> None:
"""Put chat history."""
# ensure everything is serialized
self.chat_store.add_message(self.chat_store_key, message)
async def aput(self, message: ChatMessage) -> None:
"""Put chat history."""
# ensure everything is serialized
await self.chat_store.async_add_message(self.chat_store_key, message)
def set(self, messages: List[ChatMessage]) -> None: def set(self, messages: List[ChatMessage]) -> None:
"""Set chat history."""
self.chat_store.set_messages(self.chat_store_key, messages)
async def aset(self, messages: List[ChatMessage]) -> None:
"""Set chat history."""
# ensure everything is serialized
await self.chat_store.aset_messages(self.chat_store_key, messages)
def reset(self) -> None:
"""Reset chat history.""" await self.chat_store.aset_messages(self.chat_store_key, messages)
def reset(self) -> None:
"""Reset chat history."""
self.chat_store.delete_messages(self.chat_store_key)
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Postprocess nodes."""
try:
import pandas as pd
except ImportError:
raise ImportError(
"pandas is required for this function. Please install it with `pip install pandas`." def run_llm(self, messages: Sequence[ChatMessage]) -> ChatResponse:
return self.llm.chat(messages)
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
if query_bundle is None:
raise ValueError("Query bundle must be provided.")
items = {
"query": query_bundle.query_str,
"hits": [{"content": node.get_content()} for node in nodes], def run_llm(self, messages: Sequence[ChatMessage]) -> ChatResponse:
return self.llm.chat(messages)
async def arun_llm(self, messages: Sequence[ChatMessage]) -> ChatResponse:
return await self.llm.achat(messages)
def _clean_response(self, response: str) -> str:
new_response = ""
for c in response:
if not c.isdigit():
new_response += " " def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
"""Optimize a node text given the query by shortening the node text."""
if query_bundle is None:
return nodes
for node_idx in range(len(nodes)):
text = nodes[node_idx].node.get_content(metadata_mode=MetadataMode.LLM) def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
if query_bundle is None:
raise ValueError("Query bundle must be provided.")
if len(nodes) == 0:
return []
initial_results: List[NodeWithScore] = [] def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
dispatcher.event(
ReRankStartEvent(
query=query_bundle,
nodes=nodes,
top_n=self.top_n,
model_name=self.llm.metadata.model_name,import pickleimport pickle
agent_response = self._llm.predict_and_call(
[tool],
chat_history=messages, self._prompt = prompt
def __call__(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
image_documents: Optional[List[Union[ImageBlock, ImageNode]]] = None, return Prompt(self.template_str).text(data=mapped_all_kwargs)
def format_messages(
self, llm: Optional[BaseLLM] = None, **kwargs: Any
) -> List[ChatMessage]:
del llm # unused ) -> RESPONSE_TEXT_TYPE:
text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
single_text_chunk = "\n".join(text_chunks)
truncated_chunks = self._prompt_helper.truncate(
prompt=text_qa_template,
text_chunks=[single_text_chunk],
llm=self._llm,
)
response: RESPONSE_TEXT_TYPE
if not self._streaming:
response = self._llm.predict(
text_qa_template, return response
def get_response(
self,
query_str: str,
text_chunks: Sequence[str], def get_response(
self,
query_str: str,
text_chunks: Sequence[str],
**response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
# NOTE: ignore text chunks and previous response
del text_chunks
if not self._streaming:
return self._llm.predict( """Give response given a query and a corresponding text chunk."""
text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
text_chunks = self._prompt_helper.repack(
text_qa_template, [text_chunk], llm=self._llm
)
response: Optional[RESPONSE_TEXT_TYPE] = None
program = self._program_factory(text_qa_template)
# TODO: consolidate with loop in get_response_default
for cur_text_chunk in text_chunks:
query_satisfied = False
if response is None and not self._streaming:
try:
structured_response = cast(
StructuredRefineResponse,
program(
context_str=cur_text_chunk,
**response_kwargs,
),
)
query_satisfied = structured_response.query_satisfied
if query_satisfied:
response = structured_response.answer
except ValidationError as e:
logger.warning(
f"Validation error on structured response: {e}", exc_info=True
)
elif response is None and self._streaming:
response = self._llm.stream(
text_qa_template, # NOTE: partial format refine template with query_str and existing_answer here
refine_template = self._refine_template.partial_format(
query_str=query_str, existing_answer=response
)
# compute available chunk size to see if there is any available space
# determine if the refine template is too big (which can happen if
# prompt template + query + existing answer is too large)
avail_chunk_size = self._prompt_helper._get_available_chunk_size(
refine_template
)
if avail_chunk_size < 0:
# if the available chunk size is negative, then the refine template
# is too big and we just return the original response
return response
# obtain text chunks to add to the refine template
text_chunks = self._prompt_helper.repack(
refine_template, text_chunks=[text_chunk], llm=self._llm
)
program = self._program_factory(refine_template)
for cur_text_chunk in text_chunks:
query_satisfied = False
if not self._streaming:
try:
structured_response = cast(
StructuredRefineResponse,
program(
context_msg=cur_text_chunk,
**response_kwargs,
),
)
query_satisfied = structured_response.query_satisfied
if query_satisfied:
response = structured_response.answer
except ValidationError as e:
logger.warning(
f"Validation error on structured response: {e}", exc_info=True
)
else:
# TODO: structured response not supported for streaming
if isinstance(response, Generator):
response = "".join(response)
refine_template = self._refine_template.partial_format(
query_str=query_str, existing_answer=response
)
response = self._llm.stream(
refine_template, def _give_response_single(
self,
query_str: str,
text_chunk: str,
**response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
"""Give response given a query and a corresponding text chunk."""
text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
text_chunks = self._prompt_helper.repack(
text_qa_template, [text_chunk], llm=self._llm
) return response
def _refine_response_single(
self,
response: RESPONSE_TEXT_TYPE,
query_str: str, """Get tree summarize response."""
summary_template = self._summary_template.partial_format(query_str=query_str)
# repack text_chunks so that each chunk fills the context window
text_chunks = self._prompt_helper.repack(
summary_template, text_chunks=text_chunks, llm=self._llm
)
if self._verbose:
print(f"{len(text_chunks)} text chunks after repacking")
# give final response if there is only one chunk
if len(text_chunks) == 1:
response: RESPONSE_TEXT_TYPE
if self._streaming:
response = self._llm.stream(
summary_template, context_str=text_chunks[0], **response_kwargs def get_response(
self,
query_str: str,
text_chunks: Sequence[str],
**response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
"""Get tree summarize response."""
summary_template = self._summary_template.partial_format(query_str=query_str)
# repack text_chunks so that each chunk fills the context window
text_chunks = self._prompt_helper.repack(
summary_template, text_chunks=text_chunks, llm=self._llm try:
subprocess.run(
[ try:
subprocess.run(
[
"llamaindex-cli", metric_dict[metric_key].append(mean_score)
return pd.DataFrame(metric_dict)
def _download_llama_dataset_from_hub(llama_dataset_id: str) -> "LabelledRagDataset":
"""Uses a subprocess and llamaindex-cli to download a dataset from llama-hub."""
from llama_index.core.llama_dataset import LabelledRagDataset
with tempfile.TemporaryDirectory() as tmp:
try:
def _download_llama_dataset_from_hub(llama_dataset_id: str) -> "LabelledRagDataset":
"""Uses a subprocess and llamaindex-cli to download a dataset from llama-hub."""
from llama_index.core.llama_dataset import LabelledRagDataset
def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
streaming: bool = False,
) -> RESPONSE_TYPE:
image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
context_str = "\n\n".join(
[r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
) return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)
def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
llm_input = self._condense_prompt_template.format(
chat_history=chat_history_str, question=latest_message
)
return str(self._llm.complete(llm_input))
def _condense_question(
self, chat_history: List[ChatMessage], last_message: str
) -> str:
"""
Generate standalone question from conversation context and last message.
"""
if not chat_history:
# Keep the question as is if there's no conversation context.
return last_message
chat_history_str = messages_to_history_str(chat_history) def chat_repl(self) -> None:
"""Enter interactive chat REPL."""
print("===== Entering Chat REPL =====")
print('Type "exit" to exit.\n')
self.reset()
message = input("Human: ")
while message != "exit":
response = self.chat(message)
print(f"Assistant: {response}\n")
message = input("Human: ")
@trace_method("chat")
def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentChatResponse:
if chat_history is not None:
@trace_method("chat")
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
if chat_history is not None:
llm_input = self._condense_prompt_template.format(
chat_history=chat_history_str, question=latest_message
)
return str(self._multi_modal_llm.complete(llm_input))
def synthesize(
self,
query_str: str,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
streaming: bool = False,
) -> RESPONSE_TYPE:
image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
context_str = "\n\n".join(
[r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
) return context_source, context_nodes
def synthesize(
self,
query_str: str,
nodes: List[NodeWithScore], def generate(
self, tools: Sequence[ToolMetadata], query: QueryBundle
) -> List[SubQuestion]:
tools_str = build_tools_text(tools)
query_str = query.query_str
prediction = self._llm.predict(
prompt=self._prompt,
tools_str=tools_str,
query_str=query_str,
)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
assert self.messages_to_prompt is not None
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen: def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat endpoint for LLM."""
# TODO:
# NOTE: we are wrapping existing messages in a ChatPromptTemplate to
# make this work with our FunctionCallingProgram, even though
# the messages don't technically have any variables (they are already formatted)
chat_prompt = ChatPromptTemplate(message_templates=messages)
output = self.llm.structured_predict( def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
chat_prompt = ChatPromptTemplate(message_templates=messages)
stream_output = self.llm.stream_structured_predict(
output_cls=self.output_cls, prompt=chat_prompt, llm_kwargs=kwargs
)
for partial_output in stream_output:
yield ChatResponse(
message=ChatMessage(
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat endpoint for LLM."""
# TODO:
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
chat_prompt = ChatPromptTemplate(message_templates=messages) )
tool_calls = self.get_tool_calls_from_response(
response, error_on_no_tool_call=error_on_no_tool_call
) def chat_with_tools(
self,
tools: Sequence["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
tool_required: bool = False, # if required, LLM should only call tools, and not return a response
**kwargs: Any,
) -> ChatResponse:
"""Chat with function calling.""" def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
r = super().chat(copy.deepcopy(messages), **kwargs)
self.last_chat_messages = messages
self.last_called_chat_function.append("chat")
return r
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
r = super().stream_chat(copy.deepcopy(messages), **kwargs) """Get nodes for response."""
graph_store_query = self.generate_query(query_bundle.query_str)
if self._verbose:
with self.callback_manager.event(
CBEventType.RETRIEVE,
payload={EventPayload.QUERY_STR: graph_store_query}, def generate_query(self, query_str: str) -> str:
"""Generate a Graph Store Query from a query bundle."""
# Get the query engine query string
graph_store_query: str = self._llm.predict(
self._graph_query_synthesis_prompt,
query_str=query_str,
schema=self._graph_schema,
)
return graph_store_query def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Get nodes for response."""
graph_store_query = self.generate_query(query_bundle.query_str)
if self._verbose:
print_text(f"Graph Store Query:\n{graph_store_query}\n", color="yellow")
logger.debug(f"Graph Store Query:\n{graph_store_query}")
with self.callback_manager.event(
CBEventType.RETRIEVE,
payload={EventPayload.QUERY_STR: graph_store_query},
) as retrieve_event: def _run(self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle:
"""Run query transform."""
query_str = query_bundle.query_str
sql_query = metadata["sql_query"]
sql_query_response = metadata["sql_query_response"]
new_query_str = self._llm.predict(
self._sql_augment_transform_prompt,
query_str=query_str,
sql_query_str=sql_query,
sql_response_str=sql_query_response,
) def _query_sql_other(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Query SQL database + other query engine in sequence."""
# first query SQL database
sql_response = self._sql_query_tool.query_engine.query(query_bundle)
if not self._use_sql_join_synthesis:
return sql_response
sql_query = (
sql_response.metadata["sql_query"] if sql_response.metadata else None
)
if self._verbose: def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
) -> RESPONSE_TYPE:
image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
context_str = "\n\n".join(
[r.get_content(metadata_mode=MetadataMode.LLM) for r in text_nodes]
)
fmt_prompt = self._text_qa_template.format( return self._apply_node_postprocessors(nodes, query_bundle=query_bundle)
def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore], )
new_query = self.query_transformer.run(query_bundle, {"evaluation": eval})
logger.debug("New query: %s", new_query.query_str) query_transformer = FeedbackQueryTransformation()
new_query = query_transformer.run(query_bundle, {"evaluation": eval})
return new_query_engine.query(new_query)
)
new_query = self.query_transformer.run(query_bundle, {"evaluation": eval})
logger.debug("New query: %s", new_query.query_str)
return new_query_engine.query(new_query)
qa_pairs_all = run_async_tasks(tasks)
qa_pairs_all = cast(List[Optional[SubQuestionAnswerPair]], qa_pairs_all)
else: def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata ) -> RESPONSE_TYPE:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata """Answer a query."""
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
) ) -> RESPONSE_TYPE:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
) """Answer a query."""
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
) def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
)
return self._query_engine.retrieve(query_bundle)
def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None, def synthesize(
self,
query_bundle: QueryBundle,
nodes: List[NodeWithScore],
additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
) -> RESPONSE_TYPE:
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
)
return self._query_engine.synthesize(
query_bundle=query_bundle, def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Answer a query."""
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata
)
return self._query_engine.query(query_bundle)
async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Answer a query."""
query_bundle = self._query_transform.run(
query_bundle, metadata=self._transform_metadata """Retrieve using generated spec."""
retrieval_spec = self.generate_retrieval_spec(query_bundle)
retriever, new_query_bundle = self._build_retriever_from_spec(retrieval_spec) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve using generated spec."""
retrieval_spec = self.generate_retrieval_spec(query_bundle)
retriever, new_query_bundle = self._build_retriever_from_spec(retrieval_spec)
return retriever.retrieve(new_query_bundle)
async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve using generated spec asynchronously."""
retrieval_spec = await self.agenerate_retrieval_spec(query_bundle)
retriever, new_query_bundle = self._build_retriever_from_spec(retrieval_spec)
return await retriever.aretrieve(new_query_bundle) parsed = self._prompt.output_parser.parse(prediction)
return _structured_output_to_selector_result(parsed)
async def _aselect(def generate_qa_embedding_pairs(
nodes: List[TextNode],
llm: Optional[LLM] = None,
qa_generate_prompt_tmpl: str = DEFAULT_QA_GENERATE_PROMPT_TMPL,
num_questions_per_chunk: int = 2,
) -> EmbeddingQAFinetuneDataset:
"""Generate examples given a set of nodes."""
llm = llm or Settings.llm
node_dict = {
node.node_id: node.get_content(metadata_mode=MetadataMode.NONE)
for node in nodes def insert(
self,
response: str,
query_tasks: List[QueryTask],
answers: List[str],
prev_response: Optional[str] = None,
) -> str:
"""Insert answers into response."""
prev_response = prev_response or ""
query_answer_pairs = "" def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Query and get response."""
print_text(f"Query: {query_bundle.query_str}\n", color="green")
cur_response = ""
source_nodes = []
for iter in range(self._max_iterations):
if self._verbose:
print_text(f"Current response: {cur_response}\n", color="blue")
# generate "lookahead response" that contains "[Search(query)]" tags
# e.g.
# The colors on the flag of Ghana have the following meanings. Red is return relevant_lookahead_resp
def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
"""Query and get response."""
print_text(f"Query: {query_bundle.query_str}\n", color="green")
cur_response = "" def evaluate(
self,
query: Union[str, None] = None,
response: Union[str, None] = None,
contexts: Union[Sequence[str], None] = None,
image_paths: Union[List[str], None] = None,
image_urls: Union[List[str], None] = None,
**kwargs: Any,
) -> EvaluationResult:
"""Evaluate whether the response is faithful to the multi-modal contexts."""
del query # Unused self._refine_template = prompts["refine_template"]
def evaluate(
self,
query: Union[str, None] = None,
response: Union[str, None] = None, def evaluate(
self,
query: Union[str, None] = None,
response: Union[str, None] = None,
contexts: Union[Sequence[str], None] = None,
image_paths: Union[List[str], None] = None,
image_urls: Union[List[str], None] = None,
**kwargs: Any,
) -> EvaluationResult:
"""Evaluate whether the multi-modal contexts and response are relevant to the query."""
del kwargs # Unused self._refine_template = prompts["refine_template"]
def evaluate(
self,
query: Union[str, None] = None,
response: Union[str, None] = None, if ctx is not None and ctx.is_running:
return super().run(
ctx=ctx,
**kwargs, )
return super().run(
start_event=start_event,
ctx=ctx, def run(
self,
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
ctx: Optional[Context] = None,
max_iterations: Optional[int] = None,
early_stopping_method: Optional[Literal["force", "generate"]] = None,
start_event: Optional[AgentWorkflowStartEvent] = None,
**kwargs: Any,
) -> WorkflowHandler: if ctx is not None and ctx.is_running:
return super().run(
ctx=ctx,
**kwargs, )
return super().run(
start_event=start_event,
ctx=ctx, def run(
self,
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
memory: Optional[BaseMemory] = None,
ctx: Optional[Context] = None,
max_iterations: Optional[int] = None,
early_stopping_method: Optional[Literal["force", "generate"]] = None,
start_event: Optional[AgentWorkflowStartEvent] = None,
**kwargs: Any,
) -> WorkflowHandler: def build_semantic_nodes_from_documents(
self,
documents: Sequence[Document],
show_progress: bool = False,
) -> List[BaseNode]:
"""Build window nodes from documents."""
all_nodes: List[BaseNode] = []
for doc in documents:
text = doc.text
text_splits = self.sentence_splitter(text)
def _calculate_distances_between_sentence_groups(
self, sentences: List[SentenceCombination]
) -> List[float]:
distances = []
for i in range(len(sentences) - 1):
embedding_current = sentences[i]["combined_sentence_embedding"]
embedding_next = sentences[i + 1]["combined_sentence_embedding"]
similarity = self.embed_model.similarity(embedding_current, embedding_next)
distance = 1 - similarity
numbers = extract_numbers_given_response(response, n=self.child_branch_factor)
if numbers is None:
debug_str = (
numbers = extract_numbers_given_response(response, n=self.child_branch_factor)
if numbers is None:
debug_str = ( )
def _query_with_selected_node(
self,
selected_node: BaseNode,
query_bundle: QueryBundle, def _get_query_text_embedding_similarities(
self, query_bundle: QueryBundle, nodes: List[BaseNode]
) -> List[float]:
"""
Get query text embedding similarity.
Cache the query embedding and the node text embedding.
"""
if query_bundle.embedding is None:
query_bundle.embedding = self._embed_model.get_agg_embedding_from_queries( node1 = TextNode(text=summary1)
self.index_graph.insert(node1, children_nodes=half1)
truncated_chunks = self._prompt_helper.truncate( node2 = TextNode(text=summary2)
self.index_graph.insert(node2, children_nodes=half2)
# insert half1 and half2 as new children of parent_node )
numbers = extract_numbers_given_response(response)
if numbers is None or len(numbers) == 0:
# NOTE: if we can't extract a number, then we just insert under parent def _insert_node(
self, node: BaseNode, parent_node: Optional[BaseNode] = None
) -> None:
"""Insert node."""
cur_graph_node_ids = self.index_graph.get_children(parent_node)
cur_graph_nodes = self._docstore.get_node_dict(cur_graph_node_ids)
cur_graph_node_list = get_sorted_node_list(cur_graph_nodes)
# if cur_graph_nodes is empty (start with empty graph), then insert under
# parent (insert new root node)
if len(cur_graph_nodes) == 0:
self._insert_under_parent_and_consolidate(node, parent_node) self._docstore = docstore or get_default_docstore()
def _insert_under_parent_and_consolidate(
self, text_node: BaseNode, parent_node: Optional[BaseNode]
) -> None:
""" else:
raw_response_str, metadata = self._sql_database.run_sql(sql_query_str)
def _run_with_sql_only_check(
self, sql_query_str: str
) -> Tuple[str, Dict[str, Any]]:
"""Don't run sql if sql_only is true, else continue with normal path."""
if self._sql_only:
metadata: Dict[str, Any] = {}
raw_response_str = sql_query_str
else:
raw_response_str, metadata = self._sql_database.run_sql(sql_query_str)
return raw_response_str, metadata def _run_with_sql_only_check(self, sql_query_str: str) -> Tuple[str, Dict]:
"""Don't run sql if sql_only is true, else continue with normal path."""
if self._sql_only:
metadata: Dict[str, Any] = {}
raw_response_str = sql_query_str
else:
raw_response_str, metadata = self._sql_database.run_sql(sql_query_str)
return raw_response_str, metadata
def _query(self, query_bundle: QueryBundle) -> Response: def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer a query."""
table_desc_str = self._get_table_context(query_bundle)
logger.info(f"> Table desc str: {table_desc_str}")
response_str = self._llm.predict(
self._text_to_sql_prompt,
query_str=query_bundle.query_str,
schema=table_desc_str,
dialect=self._sql_database.dialect,
) def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer a query."""
schema = self._get_schema_context()
json_path_response_str = self._llm.predict(
self._json_path_prompt,
schema=schema,
query_str=query_bundle.query_str,
)
if self._verbose: return json.dumps(self._json_schema)
def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer a query."""
schema = self._get_schema_context()
self._embed_model = embed_model
def parse_response_to_sql(self, response: str, query_bundle: QueryBundle) -> str:
"""Parse response to SQL."""
sql_query_start = response.find("SQLQuery:")
if sql_query_start != -1: if handle_llm_prompt_template is not None:
response = self._llm.predict(
handle_llm_prompt_template, )
keywords = extract_keywords_given_response(
response, start_token="KEYWORDS:", lowercase=False
) )
enitities_llm = extract_keywords_given_response(
response, start_token=result_start_token, lowercase=False
) def _get_keywords(self, query_str: str) -> List[str]:
"""Extract keywords."""
response = self._llm.predict(
self.query_keyword_extract_template,
max_keywords=self.max_keywords_per_query,
question=query_str,
)
keywords = extract_keywords_given_response(
response, start_token="KEYWORDS:", lowercase=False
)
return list(keywords) def _retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Get nodes for response."""
node_visited = set()
keywords = self._get_keywords(query_bundle.query_str)
if self._verbose:
print_text(f"Extracted keywords: {keywords}\n", color="green")
rel_texts = []
cur_rel_map = {} def _process_entities(
self,
query_str: str,
handle_fn: Optional[Callable],
handle_llm_prompt_template: Optional[BasePromptTemplate],
cross_handle_policy: Optional[str] = "union",
max_items: Optional[int] = 5,
result_start_token: str = "KEYWORDS:",
) -> List[str]:
"""Get entities from query string."""
assert cross_handle_policy in [ return keywords
def _retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]: """Extract keywords from text."""
response = self._llm.predict(
self.kg_triplet_extract_template, def _build_index_from_nodes(
self, nodes: Sequence[BaseNode], **build_kwargs: Any
) -> KG:
"""Build the index from nodes."""
# do simple concatenation
index_struct = self.index_struct_cls()
nodes_with_progress = get_tqdm_iterable(
nodes, self._show_progress, "Processing nodes"
)
for n in nodes_with_progress:
triplets = self._extract_triplets( def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
for n in nodes:
triplets = self._extract_triplets(
n.get_content(metadata_mode=MetadataMode.LLM)
)
logger.debug(f"Extracted triplets: {triplets}")
for triplet in triplets:
subj, _, obj = triplet
triplet_str = str(triplet)
self.upsert_triplet(triplet) return index_struct
def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
"""Insert a document."""
for n in nodes:
triplets = self._extract_triplets( def _get_keywords(self, query_str: str) -> List[str]:
"""Extract keywords."""
response = self._llm.predict(
self.query_keyword_extract_template,
max_keywords=self.max_keywords_per_query,
question=query_str,
)
keywords = extract_keywords_given_response(response, start_token="KEYWORDS:")
return list(keywords)
"""Extract keywords from text."""
response = self._llm.predict(
self.keyword_extract_template, )
return extract_keywords_given_response(response, start_token="KEYWORDS:")
async def _async_extract_keywords(self, text: str) -> Set[str]: if self._use_async:
nodes = asyncio.run(
arun_transformations(
nodes, self._kg_extractors, show_progress=self._show_progress else:
nodes = run_transformations(
nodes, self._kg_extractors, show_progress=self._show_progress
) nodes = asyncio.run(
arun_transformations(
nodes, self._kg_extractors, show_progress=self._show_progress
) if self._use_async:
embeddings = asyncio.run(
self._embed_model.aget_text_embedding_batch(
node_texts, show_progress=self._show_progress def _insert_nodes(self, nodes: Sequence[BaseNode]) -> Sequence[BaseNode]:
"""Insert nodes to the index struct."""
if len(nodes) == 0:
return nodes
# run transformations on nodes to extract triplets
if self._use_async:
nodes = asyncio.run(
arun_transformations(
nodes, self._kg_extractors, show_progress=self._show_progress
) return None
def _insert_nodes(self, nodes: Sequence[BaseNode]) -> Sequence[BaseNode]:
"""Insert nodes to the index struct."""
if len(nodes) == 0:
return nodes return new_node_dict
def build_index_from_nodes(
self,
index_graph: IndexGraph,
cur_node_ids: Dict[int, str], def _get_embeddings(
self, query_bundle: QueryBundle, nodes: List[BaseNode]
) -> Tuple[List[float], List[List[float]]]:
"""Get top nodes by similarity to the query."""
if query_bundle.embedding is None:
query_bundle.embedding = self._embed_model.get_agg_embedding_from_queries(
query_bundle.embedding_strs
)
node_embeddings: List[List[float]] = []
nodes_embedded = 0 def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve nodes."""
node_ids = self._index.index_struct.nodes
results = []
for idx in range(0, len(node_ids), self._choice_batch_size):
node_ids_batch = node_ids[idx : idx + self._choice_batch_size]
nodes_batch = self._index.docstore.get_nodes(node_ids_batch)
query_str = query_bundle.query_str
fmt_batch_str = self._format_node_batch_fn(nodes_batch)
# call each batch independently def _retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve nodes."""
summary_ids = self._index.index_struct.summary_ids
all_summary_ids: List[str] = []
all_relevances: List[float] = []
for idx in range(0, len(summary_ids), self._choice_batch_size):
summary_ids_batch = summary_ids[idx : idx + self._choice_batch_size] def _retrieve(
self,
query_bundle: QueryBundle,
) -> List[NodeWithScore]:
"""Retrieve nodes."""
if self._vector_store.is_embedding_query:
if query_bundle.embedding is None:
query_bundle.embedding = (
self._embed_model.get_agg_embedding_from_queries(
query_bundle.embedding_strs
) def _resynthesize_query(
self, query_str: str, response: str, feedback: Optional[str]
) -> str:
"""Resynthesize query given feedback."""
if feedback is None:
return query_str
else:
new_query_str = self.llm.predict(
self.resynthesis_prompt,
query_str=query_str,
response=response, """Run query processor."""
return self.run(query_bundle_or_str, metadata=metadata)
"""Run query processor."""
return self.run(query_bundle_or_str, metadata=metadata)
query_str = query_bundle.query_str
hypothetical_doc = self._llm.predict(self._hyde_prompt, context_str=query_str)
embedding_strs = [hypothetical_doc]
if self._include_original: def __call__(
self,
query_bundle_or_str: QueryType,
metadata: Optional[Dict] = None,
) -> QueryBundle:
"""Run query processor."""
return self.run(query_bundle_or_str, metadata=metadata)
class IdentityQueryTransform(BaseQueryTransform):
""" def _run(self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle:
"""Run query transform."""
# TODO: support generating multiple hypothetical docs
query_str = query_bundle.query_str
hypothetical_doc = self._llm.predict(self._hyde_prompt, context_str=query_str)
embedding_strs = [hypothetical_doc]
if self._include_original:
embedding_strs.extend(query_bundle.embedding_strs)
return QueryBundle(
query_str=query_str,
custom_embedding_strs=embedding_strs, def _run(self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle:
"""Run query transform."""
# currently, just get text from the index structure
index_summary = cast(str, metadata.get("index_summary", "None"))
# given the text from the index, we can use the query bundle to generate
# a new query bundle
query_str = query_bundle.query_str
new_query_str = self._llm.predict(
self._decompose_query_prompt,
query_str=query_str, def _run(self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle:
"""Run query transform."""
index_summary = cast(
str,
metadata.get("index_summary", "None"),
)
prev_reasoning = cast(Response, metadata.get("prev_reasoning"))
fmt_prev_reasoning = f"\n{prev_reasoning}" if prev_reasoning else "None"
# given the text from the index, we can use the query bundle to generate
# a new query bundle new_cur_fields = self._clean_and_validate_fields(cur_fields)
fields.update(new_cur_fields)
struct_datapoint = StructDatapoint(fields)
if struct_datapoint is not None: def insert_datapoint_from_nodes(self, nodes: Sequence[BaseNode]) -> None:
"""Extract datapoint from a document and insert it."""
text_chunks = [
node.get_content(metadata_mode=MetadataMode.LLM) for node in nodes
]
fields = {}
for i, text_chunk in enumerate(text_chunks):
fmt_text_chunk = truncate_text(text_chunk, 50)
logger.info(f"> Adding chunk {i}: {fmt_text_chunk}")
# if embedding specified in document, pass it to the Node
schema_text = self._get_schema_text() def generate_retrieval_spec(
self, query_bundle: QueryBundle, **kwargs: Any
) -> BaseModel:
# prepare input
info_str = self._vector_store_info.model_dump_json(indent=4)
schema_str = VectorStoreQuerySpec.model_json_schema()
# call LLM
output = self._llm.predict(
self._prompt,
schema_str=schema_str, """
return asyncio.run(self.acall(nodes, show_progress=show_progress, **kwargs))
async def _apredict_without_props(self, text: str) -> str: """Extract triples from nodes."""
return asyncio.run(self.acall(nodes, show_progress=show_progress, **kwargs))
async def _aextract(self, node: BaseNode) -> BaseNode: """Extract triplets from nodes."""
return asyncio.run(self.acall(nodes, show_progress=show_progress, **kwargs))
def _prune_invalid_props( def _get_vector_store_query(self, query_bundle: QueryBundle) -> VectorStoreQuery:
if query_bundle.embedding is None:
query_bundle.embedding = self._embed_model.get_agg_embedding_from_queries(
query_bundle.embedding_strs
)
return VectorStoreQuery(
query_embedding=query_bundle.embedding,
similarity_top_k=self._similarity_top_k,
filters=self._filters,
**self._retriever_kwargs, def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
schema = self._graph_store.get_schema_str()
question = query_bundle.query_str
response = self.llm.predict(
self.text_to_cypher_template,
schema=schema,
question=question,
)
parsed_cypher_query = self._parse_generated_cypher(response) def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
question = query_bundle.query_str
response = self.llm.structured_predict(
self.output_cls, PromptTemplate(question)
)
cypher_response = self._graph_store.structured_query(
self.cypher_query,
param_map=response.model_dump(),
) def _snapshot_messages(self, ctx: Context, chat_history: List[ChatMessage]) -> None:
# inject tool calls into the assistant message
for msg in chat_history:
if msg.role == "assistant":
tool_calls = self.llm.get_tool_calls_from_response(
ChatResponse(message=msg), error_on_no_tool_call=False
)
if tool_calls:
msg.additional_kwargs["ag_ui_tool_calls"] = [
{
"id": tool_call.tool_id, self.system_prompt = system_prompt
def _snapshot_messages(self, ctx: Context, chat_history: List[ChatMessage]) -> None:
# inject tool calls into the assistant message
for msg in chat_history:
if msg.role == "assistant": _model: Any = PrivateAttr()
def __init__(
self,
model_name: str = DEFAULT_ENTITY_MODEL,
prediction_threshold: float = 0.5, def __init__(
self,
model: str = DEFAULT_OPENAI_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
max_output_tokens: Optional[int] = None,
reasoning_options: Optional[Dict[str, Any]] = None,
include: Optional[List[str]] = None,
instructions: Optional[str] = None,
track_previous_responses: bool = False,
store: bool = False,
built_in_tools: Optional[List[dict]] = None, def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
client = self._get_client()
message_dicts = to_openai_message_dicts(
messages,
model=self.model,
)
if self.reuse_client:
response = client.chat.completions.create(
messages=message_dicts,
stream=False, def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**{**self.additional_kwargs, **kwargs})
chat_messages, all_kwargs = prepare_messages_before_chat(
messages=messages, **all_kwargs
)
response = self._client.chat.completions.create(
project_id=self.project_id, messages=chat_messages, **all_kwargs
)
if not response.choices: def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**{**self.additional_kwargs, **kwargs})
chat_messages, all_kwargs = prepare_messages_before_chat(
messages=messages, **all_kwargs
)
response_generator = self._client.chat.completions.create(
project_id=self.project_id,
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**{**self.additional_kwargs, **kwargs})
chat_messages, all_kwargs = prepare_messages_before_chat( )
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**{**self.additional_kwargs, **kwargs}) ) -> CompletionResponse:
return chat_to_completion_decorator(self.chat)(prompt, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
oci_params = self._provider.messages_to_oci_params(messages)
oci_params["is_stream"] = False
tools = kwargs.pop("tools", None)
all_kwargs = self._get_all_kwargs(**kwargs)
chat_params = {**all_kwargs, **oci_params}
if tools:
chat_params["tools"] = [
self._provider.convert_to_oci_tool(tool) for tool in tools
] def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
oci_params = self._provider.messages_to_oci_params(messages)
oci_params["is_stream"] = True
tools = kwargs.pop("tools", None)
all_kwargs = self._get_all_kwargs(**kwargs)
chat_params = {**all_kwargs, **oci_params}
if tools:
chat_params["tools"] = [
self._provider.convert_to_oci_tool(tool) for tool in tools def gen() -> ChatResponseGen:
content = ""
tool_calls_accumulated = []
for event in response.data.events():
content_delta = self._provider.chat_stream_to_text(
json.loads(event.data)
)
content += content_delta
try:
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
oci_params = self._provider.messages_to_oci_params(messages)
oci_params["is_stream"] = False
tools = kwargs.pop("tools", None) )
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
oci_params = self._provider.messages_to_oci_params(messages) response = self._client.chat(request)
def gen() -> ChatResponseGen:
content = ""
tool_calls_accumulated = []
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
from llama_index.llms.langchain.utils import (
from_lc_messages,
to_lc_messages,
)
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
if hasattr(self._llm, "stream"):
def gen() -> Generator[ChatResponse, None, None]:
from llama_index.llms.langchain.utils import (
from_lc_messages,
to_lc_messages, ) -> CompletionResponse:
return chat_to_completion_decorator(self.chat)(prompt, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
think = kwargs.pop("think", None) or self.thinking
format = kwargs.pop("format", "json" if self.json_mode else None)
response = self.client.chat(
model=self.model,
messages=ollama_messages,
stream=False, def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
think = kwargs.pop("think", None) or self.thinking
format = kwargs.pop("format", "json" if self.json_mode else None)
def gen() -> ChatResponseGen:
response = self.client.chat(
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
ollama_messages = self._convert_to_ollama_messages(messages)
tools = kwargs.pop("tools", None)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
ollama_messages = self._convert_to_ollama_messages(messages) format = kwargs.pop("format", "json" if self.json_mode else None)
def gen() -> ChatResponseGen:
response = self.client.chat(
model=self.model,
messages=ollama_messages, def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs) def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""
Complete by LLM.
Args:
prompt: Prompt for completion.
formatted: Whether the prompt is formatted by wrapper.
kwargs: Other kwargs for complete.
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
""" def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
response = self._runner.send_chat_completion_request(request)
return CompletionResponse(
text=response.choices[0].message.content, text=response.choices[0].message.content,
logprobs=extract_logprobs(response),
)
streamer = self._runner.send_chat_completion_request(request)
def gen() -> CompletionResponseGen: def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
if self._has_messages_to_prompt:
messages = self.messages_to_prompt(messages)
else:
messages = llama_index_to_mistralrs_messages(messages) def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
if self._has_messages_to_prompt:
messages = self.messages_to_prompt(messages)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
try:
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try:
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
try: logger.debug(f"Calling complete with prompt: {prompt}")
response = self.client.generate(
prompt=prompt, text = ""
for response in self.client.generate(
prompt=prompt, logger.debug(f"Calling chat with messages: {messages}")
response = self.client.chat(
messages=_to_message_dicts( tool_calls = []
for response in self.client.chat(
messages=_to_message_dicts( def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""
Generate a chat completion based on the input messages.
Args:
messages (Sequence[ChatMessage]): A sequence of chat messages.
**kwargs: Additional keyword arguments.
Returns:
ChatResponse: The chat response from the LLM.
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""
Generate a chat completion based on the input messages.
if stream:
return client.predict_streaming(prompt, **kwargs)
else: else:
return client.predict(prompt, **kwargs)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.complete(messages=messages, **all_kwargs)
blocks: List[TextBlock | ThinkingBlock | ToolCallBlock] = []
response = self._client.chat.stream(messages=messages, **all_kwargs)
if stop:
response = self._client.fim.complete(
model=self.model, prompt=prompt, suffix=suffix, stop=stop else:
response = self._client.fim.complete(
model=self.model, prompt=prompt, suffix=suffix def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# convert messages to mistral ChatMessage
messages = to_mistral_chatmessage(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.complete(messages=messages, **all_kwargs)
blocks: List[TextBlock | ThinkingBlock | ToolCallBlock] = []
if self.model in MISTRAL_AI_REASONING_MODELS:
thinking_txt, response_txt = self._separate_thinking(
response.choices[0].message.content or [] def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# convert messages to mistral ChatMessage
messages = to_mistral_chatmessage(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.stream(messages=messages, **all_kwargs)
def gen() -> ChatResponseGen:
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
# convert messages to mistral ChatMessage
messages = to_mistral_chatmessage(messages)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
# convert messages to mistral ChatMessage del generation_kwargs["use_completions"]
response = self._model.generate(
prompt=prompt,
stream_response = self._model.generate_text_stream(
prompt=prompt, def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
params, generation_kwargs = self._split_generation_params(kwargs)
stream_response = self._model.generate_text_stream(
prompt=prompt,
params=self._text_generation_params or params,
**generation_kwargs,
)
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
message_dicts = [to_watsonx_message_dict(message) for message in messages]
params, generation_kwargs = self._split_chat_generation_params(kwargs)
response = self._model.chat(
messages=message_dicts,
params=params,
tools=generation_kwargs.get("tools"),
tool_choice=generation_kwargs.get("tool_choice"),
tool_choice_option=generation_kwargs.get("tool_choice_option"),
) def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
message_dicts = [to_watsonx_message_dict(message) for message in messages]
params, generation_kwargs = self._split_chat_generation_params(kwargs)
stream_response = self._model.chat_stream(
messages=message_dicts,
params=params,
tools=generation_kwargs.get("tools"),
tool_choice=generation_kwargs.get("tool_choice"), def gen() -> CompletionResponseGen:
content = ""
if kwargs.get("raw_response"):
for stream_delta in stream_response:
stream_delta_text = self._model._return_guardrails_stats(
stream_delta
).get("generated_text", "")
content += stream_delta_text
yield CompletionResponse(
text=content, delta=stream_delta_text, raw=stream_delta
) return await achat_fn(messages, **kwargs)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
message_dicts = [to_watsonx_message_dict(message) for message in messages] assert self._generator is not None
response_text = self._generator.chat(
prompt=prompt, assert self._generator is not None
response_iter = self._generator.chat(
prompt=prompt,
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
assert self._generator is not None
prompt = messages[-1].content if len(messages) > 0 else ""
history = [xinference_message_to_history(message) for message in messages[:-1]]
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
assert self._generator is not None
output: ChatCompletionOutput = self._sync_client.chat_completion(
messages=self._to_huggingface_messages(messages), cur_index = -1
for chunk in self._sync_client.chat_completion(
messages=self._to_huggingface_messages(messages), def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self.task == "conversational" or self.task is None:
model_kwargs = self._get_model_kwargs(**kwargs)
output: ChatCompletionOutput = self._sync_client.chat_completion(
messages=self._to_huggingface_messages(messages),
**model_kwargs,
)
content = output.choices[0].message.content or ""
tool_calls = output.choices[0].message.tool_calls or [] def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self.task == "conversational" or self.task is None:
model_kwargs = self._get_model_kwargs(**kwargs)
def gen() -> ChatResponseGen:
response = ""
tool_call_strs = []
cur_index = -1
for chunk in self._sync_client.chat_completion( )
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
if self.task == "conversational" or self.task is None:
model_kwargs = self._get_model_kwargs(**kwargs)
return completion_response_to_chat_response(completion)
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if self.task == "conversational": )
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self.task == "conversational" or self.task is None: model_kwargs = self._get_model_kwargs(**kwargs)
def gen() -> ChatResponseGen:
response = ""
tool_call_strs = []
cur_index = -1 Dict[str, Any]: The API response.
"""
def perform_request():
response = requests.post(
self.get_url(endpoint),
json={
**payload,
"stream": False, str: The streaming response from the API.
"""
def perform_request():
response = requests.post(
self.get_url(endpoint),
json={
**payload,
"stream": True, def __init__(
self,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
query_wrapper_prompt: Union[str, PromptTemplate] = "{query_str}",
model_id_or_path: str = DEFAULT_HUGGINGFACE_MODEL,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
device_map: Optional[str] = "auto",
stopping_ids: Optional[List[int]] = None,
tokenizer_kwargs: Optional[dict] = None, )
def __init__(
self,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS, adapter_model = self._client.LLM(uri=f"hf://{self.adapter_id}")
result = base_llm_deployment.with_adapter(model=adapter_model).generate(
prompt=prompt, else:
result = base_llm_deployment.generate(
prompt=prompt, try:
response = lorax_client.generate(
prompt=prompt, try:
response = lorax_client.generate(
prompt=prompt, try:
response = lorax_client.generate(
prompt=prompt, """
raw_message = self.client.generate.create(
messages=messages, def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response_gen = self.stream_complete(prompt, formatted=True, **kwargs)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
model_kwargs = {**self.model_kwargs, **kwargs}
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
model_kwargs = {**self.model_kwargs, **kwargs} request_body = self.content_handler.serialize_input(prompt, model_kwargs)
def gen() -> CompletionResponseGen:
raw_text = ""
prev_clean_text = ""
for response in self._client.invoke_endpoint_with_response_stream( ) -> CompletionResponse:
return chat_to_completion_decorator(self.chat)(prompt, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
messages_dict = self._convert_to_llm_messages(messages)
raw_response = self._client.chat.completions.create(
model=self.model,
messages=messages_dict,
stream=False,
tools=kwargs.get("tools"),
tool_choice=kwargs.get("tool_choice"),
stop=kwargs.get("stop"),
timeout=self.timeout,
extra_body=self.model_kwargs, def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
messages_dict = self._convert_to_llm_messages(messages)
def gen() -> ChatResponseGen:
raw_response = self._client.chat.completions.create(
model=self.model,
messages=messages_dict,
stream=True,
tools=kwargs.get("tools"),
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
messages_dict = self._convert_to_llm_messages(messages)
raw_response = self._client.chat.completions.create(
model=self.model,
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
messages_dict = self._convert_to_llm_messages(messages) messages_dict = self._convert_to_llm_messages(messages)
def gen() -> ChatResponseGen:
raw_response = self._client.chat.completions.create(
model=self.model,
messages=messages_dict,
return super().complete(prompt, **kwargs)
return super().chat(messages, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat with the model."""
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
return super().chat(messages, **kwargs)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return super().complete(prompt, **kwargs)
return super().chat(messages, **kwargs)
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat with the model."""
if not self.metadata.is_chat_model:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
return super().chat(messages, **kwargs)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
yield self.complete(prompt, **kwargs)
) -> ChatResponseGen:
yield self.chat(messages, **kwargs) def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
yield self.chat(messages, **kwargs) all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.complete(messages=messages, **all_kwargs)
response = self._client.complete(messages=messages, stream=True, **all_kwargs)
if not allow_parallel_tool_calls:
force_single_tool_call(response)
return response
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
messages = to_inference_message(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.complete(messages=messages, **all_kwargs)
response_message = from_inference_message(response.choices[0].message)
return ChatResponse(
message=response_message,
raw=response.as_dict(),
) def chat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
tool_required: bool = False,
**kwargs: Any,
) -> ChatResponse:
"""Predict and call the tool."""
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
messages = to_inference_message(messages) def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs) def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""
Send a chat request to the Reka API.
Args:
messages (Sequence[ChatMessage]): A sequence of chat messages.
**kwargs: Additional keyword arguments for the API call.
Returns:
ChatResponse: The response from the Reka API.
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""
Send a chat request to the Reka API.
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
"""
Send a completion request to the Reka API.
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
"""
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
"""
Send a streaming completion request to the Reka API.
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
self.generate_kwargs.update({"stream": False})
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
self.generate_kwargs.update({"stream": True}) params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params( params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params( params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params(
self.model, messages, self.file_mode, self._client, **params params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params(
self.model, messages, self.file_mode, self._client, **params contents_and_names = [
asyncio.run(chat_message_to_gemini(message, self.file_mode, self._client))
for message in messages
] contents_and_names = [
asyncio.run(
chat_message_to_gemini(message, self.file_mode, self._client)
) contents_and_names = [
asyncio.run(
chat_message_to_gemini(message, self.file_mode, self._client)
) def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any):
generation_config = {
**(self._generation_config or {}),
**kwargs.pop("generation_config", {}),
}
params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params(
self.model, messages, self.file_mode, self._client, **params
)
) def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
generation_config = {
**(self._generation_config or {}),
**kwargs.pop("generation_config", {}),
}
params = {**kwargs, "generation_config": generation_config}
next_msg, chat_kwargs, file_api_names = asyncio.run(
prepare_chat_params(
self.model, messages, self.file_mode, self._client, **params def structured_predict_without_function_calling(
self,
output_cls: Type[Model],
prompt: PromptTemplate,
llm_kwargs: Optional[Dict[str, Any]] = None,
**prompt_args: Any,
) -> Model:
"""Structured predict."""
llm_kwargs = llm_kwargs or {}
messages = prompt.format_messages(**prompt_args) def structured_predict(
self,
output_cls: Type[Model],
prompt: PromptTemplate,
llm_kwargs: Optional[Dict[str, Any]] = None,
**prompt_args: Any,
) -> Model:
"""Structured predict."""
llm_kwargs = llm_kwargs or {}
if self.pydantic_program_mode == PydanticProgramMode.DEFAULT: def stream_structured_predict(
self,
output_cls: Type[Model],
prompt: PromptTemplate,
llm_kwargs: Optional[Dict[str, Any]] = None,
**prompt_args: Any,
) -> Generator[Union[Model, FlexibleModel], None, None]:
"""Stream structured predict."""
llm_kwargs = llm_kwargs or {}
if self.pydantic_program_mode == PydanticProgramMode.DEFAULT:
@llm_retry_decorator
def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any):
generation_config = {
**(self._generation_config or {}),
**kwargs.pop("generation_config", {}), return await self._achat(messages, **kwargs)
def _stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
generation_config = {
@dispatcher.span
def structured_predict_without_function_calling(
self,
output_cls: Type[Model],
prompt: PromptTemplate,
@dispatcher.span
def stream_structured_predict(
self,
output_cls: Type[Model],
prompt: PromptTemplate, file_api_names = [name for it in contents_and_names for name in it[1]]
def gen() -> Generator[Union[Model, FlexibleModel], None, None]:
flexible_model = create_flexible_model(output_cls)
response_gen = self._client.models.generate_content_stream(
model=self.model, if args.torch_compile and model.config.model_type == "llama":
model = get_torch_compiled_model(model)
# if args.assistant_model is not None:
# assistant_model = get_torch_compiled_model(assistant_model) if args.torch_compile and model.config.model_type == "llama":
model = get_torch_compiled_model(model)
# if args.assistant_model is not None:
# assistant_model = get_torch_compiled_model(assistant_model) # assistant_model = get_torch_compiled_model(assistant_model)
return model, assistant_model
def setup_distributed_model(args, model_dtype, model_kwargs, logger):
import deepspeed
logger.info("DeepSpeed is enabled.")
deepspeed.init_distributed(dist_backend="hccl")
config = AutoConfig.from_pretrained(
def setup_model(args, model_dtype, model_kwargs, logger):
logger.info("Single-device run.")
if args.assistant_model is None:
assistant_model = None
def setup_distributed_model(args, model_dtype, model_kwargs, logger):
import deepspeed
logger.info("DeepSpeed is enabled.") def chat(
self,
messages: Sequence[ChatMessage],
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> ChatResponse:
"""Chat endpoint for LLM."""
prompt = "".join([str(m) for m in messages])
try:
response = (
self._model.predict_by_bytes( def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.completions.create(
stream=False,
**get_chat_request(messages),
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT, content=response.choices[0].message.content def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
stream = self._client.chat.completions.create(
stream=True,
**get_chat_request(messages),
**all_kwargs,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.chat.completions.create(
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs) request_options = self._request_options or kwargs.pop("request_options", None)
result = self._model.generate_content(
prompt, request_options=request_options, **kwargs text = ""
it = self._model.generate_content(
prompt, stream=True, request_options=request_options, **kwargs text = ""
it = await self._model.generate_content_async(
prompt, stream=True, request_options=request_options, **kwargs def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
request_options = self._request_options or kwargs.pop("request_options", None)
merged_messages = merge_neighboring_same_role_messages(messages)
*history, next_msg = map(chat_message_to_gemini, merged_messages)
chat = self._model.start_chat(history=history)
response = chat.send_message(
next_msg,
request_options=request_options,
**kwargs,
)
return chat_from_gemini_response(response) def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
request_options = self._request_options or kwargs.pop("request_options", None)
merged_messages = merge_neighboring_same_role_messages(messages)
*history, next_msg = map(chat_message_to_gemini, merged_messages)
chat = self._model.start_chat(history=history)
response = chat.send_message(
next_msg, stream=True, request_options=request_options, **kwargs
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
request_options = self._request_options or kwargs.pop("request_options", None)
merged_messages = merge_neighboring_same_role_messages(messages)
*history, next_msg = map(chat_message_to_gemini, merged_messages)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
request_options = self._request_options or kwargs.pop("request_options", None)
blocks, citations = self._get_blocks_and_tool_calls_and_thinking(response)
return AnthropicChatResponse( def chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> AnthropicChatResponse:
anthropic_messages, system_prompt = messages_to_anthropic_messages(
messages, self.cache_idx, self.model
)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.messages.create(
messages=anthropic_messages,
stream=False, def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> Generator[AnthropicChatResponse, None, None]:
anthropic_messages, system_prompt = messages_to_anthropic_messages(
messages, self.cache_idx, self.model
)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.messages.create(
messages=anthropic_messages, system=system_prompt, stream=True, **all_kwargs
) ] = PrivateAttr()
def __init__(
self,
model: str = DEFAULT_ANTHROPIC_MODEL,
temperature: float = DEFAULT_TEMPERATURE,
@llm_chat_callback()
def chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> AnthropicChatResponse:
anthropic_messages, system_prompt = messages_to_anthropic_messages(
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> AnthropicCompletionResponse:
chat_message = ChatMessage(role=MessageRole.USER, content=prompt)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> Generator[AnthropicChatResponse, None, None]:
anthropic_messages, system_prompt = messages_to_anthropic_messages( engine_buffer = f.read()
decoder = tensorrt_llm.runtime.GenerationSession(
model_config, engine_buffer, runtime_mapping, debug_mode=False
) def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try:
import torch
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try: def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint."""
full_prompt = prompt
if not formatted:
if self.query_wrapper_prompt:
full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
if self.completion_to_prompt:
full_prompt = self.completion_to_prompt(full_prompt)
elif self.system_prompt: def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint.""" def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response) messages = [message_to_ai21_message(message) for message in messages]
response = self._client.chat.completions.create(
messages=messages, system, messages = message_to_ai21_j2_message(messages)
response = self._client.chat.create(
system=system, messages = [message_to_ai21_message(message) for message in messages]
response = self._client.chat.completions.create(
messages=messages, def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if self._is_j2_model():
return self._j2_chat(messages, **all_kwargs)
messages = [message_to_ai21_message(message) for message in messages]
response = self._client.chat.completions.create(
messages=messages,
stream=False,
**all_kwargs, def _j2_chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
system, messages = message_to_ai21_j2_message(messages)
response = self._client.chat.create(
system=system,
messages=messages,
stream=False,
**kwargs,
)
return ChatResponse(
message=ChatMessage( def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_j2_model():
raise ValueError("Stream chat is not supported for J2 models.")
all_kwargs = self._get_all_kwargs(**kwargs)
messages = [message_to_ai21_message(message) for message in messages]
response = self._client.chat.completions.create(
messages=messages,
stream=True, return await astream_complete_fn(prompt, **kwargs)
def _j2_chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
system, messages = message_to_ai21_j2_message(messages)
response = self._client.chat.create(
system=system,
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
if self._is_j2_model(): current_llm = self._get_current_llm()
return current_llm.chat(messages, **kwargs)
except Exception as e: current_llm = self._get_current_llm()
return current_llm.stream_chat(messages, **kwargs)
except Exception as e: current_llm = self._get_current_llm()
return current_llm.complete(prompt, formatted, **kwargs)
except Exception as e: current_llm = self._get_current_llm()
return current_llm.stream_complete(prompt, formatted, **kwargs)
except Exception as e: def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
"""Chat with the AI Gateway by delegating to the current LLM."""
while True:
try:
current_llm = self._get_current_llm()
return current_llm.chat(messages, **kwargs)
except Exception as e:
# Try next LLM on failure
logger.warning(
f"It seems that the current LLM is not working with the AI Gateway. Error: {e}"
) def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
"""Stream chat with the AI Gateway by delegating to the current LLM."""
while True:
try:
current_llm = self._get_current_llm()
return current_llm.stream_chat(messages, **kwargs)
except Exception as e:
# Try next LLM on failure
logger.warning( def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Complete a prompt using the AI Gateway by delegating to the current LLM."""
while True:
try:
current_llm = self._get_current_llm()
return current_llm.complete(prompt, formatted, **kwargs)
except Exception as e:
# Try next LLM on failure
logger.warning( def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Stream complete a prompt using the AI Gateway by delegating to the current LLM."""
while True:
try:
current_llm = self._get_current_llm()
return current_llm.stream_complete(prompt, formatted, **kwargs)
except Exception as e:
# Try next LLM on failure
logger.warning( """
completion = palm.generate_text(
model=self.model_name, with GigaChat(**self._gigachat_kwargs) as giga:
response = giga.chat(
Chat( with GigaChat(**self._gigachat_kwargs) as giga:
response = giga.chat(
Chat( def chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any,
) -> ChatResponse:
"""Get chat."""
with GigaChat(**self._gigachat_kwargs) as giga:
response = giga.chat(
Chat(
model=self.model,
messages=[
@llm_completion_callback()
def complete(
self,
prompt: str,
formatted: bool = False,
@llm_chat_callback()
def chat(
self,
messages: Sequence[ChatMessage],
**kwargs: Any, data["values"] = list(map(self._parse_item_values, list(item["column_values"])))
return data
def _perform_request(self, board_id) -> Dict[str, str]:
headers = {"Authorization": self.api_key}
query = """
query{
boards(ids: [%d]){
name, def load(self) -> List[Document]:
"""Load data into Document objects..."""
try:
import oracledb
except ImportError as e:
raise ImportError(
"Unable to import oracledb, please install with "
"`pip install -U oracledb`."
) from e
ncols = 0
@staticmethod
def read_file(conn: Connection, file_path: str, params: dict) -> Document:
"""
Read a file using OracleReader
Args: self.params = json.loads(json.dumps(params))
def load(self) -> List[Document]:
"""Load data into Document objects..."""
try:
import oracledb self._pytesseract_model_kwargs = pytesseract_model_kwargs
def load_data(
self,
file: Path,
extra_info: Optional[Dict] = None, docs = []
responses = asyncio.run(self.fetch_items(urls))
for response in responses: docs = []
responses = asyncio.run(self.fetch_items(urls))
for response in responses:
content = self._get_content(response) """
return asyncio.run(self.aload_data(urls)) """
return asyncio.run(self.aload_data(urls)) smart_df = SmartDataframe(initial_df, config=self._pandasai_config)
return smart_df.chat(query=query)
"""Parse file."""
result = self.run_pandas_ai(
initial_df, query, is_conversational_answer=is_conversational_answer def run_pandas_ai(
self,
initial_df: pd.DataFrame,
query: str,
is_conversational_answer: bool = False,
) -> Any:
"""Load dataframe."""
smart_df = SmartDataframe(initial_df, config=self._pandasai_config)
return smart_df.chat(query=query)
def load_data( def load_data(
self,
initial_df: pd.DataFrame,
query: str,
is_conversational_answer: bool = False,
) -> List[Document]:
"""Parse file."""
result = self.run_pandas_ai(
initial_df, query, is_conversational_answer=is_conversational_answer
)
if is_conversational_answer: try:
return asyncio.run(self.aload_data(file_path, extra_info))
except RuntimeError as e:
if nest_asyncio_err in str(e): try:
return asyncio.run(self.aget_json(file_path, extra_info))
except RuntimeError as e:
if nest_asyncio_err in str(e): return results
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
return dataframes
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
return all_msgs
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
return "GoogleDriveReader"
def _get_credentials(self) -> Tuple[Credentials]:
"""
Authenticate with Google and save credentials.
Download the service_account_key.json file with these instructions: https://cloud.google.com/iam/docs/keys-create-delete. return results
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
self.ocr = PaddleOCR(use_angle_cls=use_angle_cls, lang=lang)
def extract_text_from_image(self, image_data):
"""
Extract text from image data using PaddleOCR
"""
documents = self._loop.run_until_complete(
self._generate_documents(
blobs_and_paths=blobs_and_paths,
documents = self._loop.run_until_complete(
self._generate_documents(
blobs_and_paths=blobs_and_paths, def load_data(
self,
) -> List[Document]:
"""
GitHub repository collaborators reader.
Retrieves the list of collaborators in a GitHub repository and converts them to documents.
Each collaborator is converted to a document by doing the following:
- The text of the document is the login. self._github_client = github_client
def load_data(
self,
) -> List[Document]:
""" def load_data(
self,
state: Optional[IssueState] = IssueState.OPEN,
labelFilters: Optional[List[Tuple[str, FilterType]]] = None,
) -> List[Document]:
"""
Load issues from a repository and converts them to documents.
Each issue is converted to a document by doing the following:
- The text of the document is the concatenation of the title and the body of the issue. try:
result = subprocess.run(cli_command, capture_output=True, text=True)
result.check_returncode()
return result.stdoutfrom llama_index.core.schema import Document
class PDFNougatOCR(BaseReader):
def nougat_ocr(self, file_path: Path) -> str:
cli_command = ["nougat", "--markdown", "pdf", str(file_path), "--out", "output"]
try:
result = subprocess.run(cli_command, capture_output=True, text=True)
result.check_returncode()
class PDFNougatOCR(BaseReader):
def nougat_ocr(self, file_path: Path) -> str:
cli_command = ["nougat", "--markdown", "pdf", str(file_path), "--out", "output"]
try: if not self.path.endswith("/"):
asyncio.run(download_file_from_opendal(self.op, temp_dir, self.path))
else:
asyncio.run(download_dir_from_opendal(self.op, temp_dir, self.path))
jarr = self.run(q)
if jarr is None:
return []
jarr = self.run(q)
if jarr is None:
return [] """
return asyncio.run(
self.aevaluate_run( """
return asyncio.run(
self.aevaluate_run(
queries=queries, client = cast(genai.GenerativeServiceClient, self._client)
response = genaix.generate_answer(
prompt=query_str, def get_response(
self,
query_str: str,
text_chunks: Sequence[str],
**response_kwargs: Any,
) -> SynthesizedResponse:
"""
Generate a grounded response on provided passages.
Args:
query_str: The user's question. def identify_fields(
self, nodes: List[BaseNode], topic: str, fields_top_k: int = 5
) -> List:
"""
Identify fields from nodes.
Will extract fields independently per node, and then
return the top k fields.
Args:
nodes (List[BaseNode]): List of nodes to extract fields from. def extract_datapoints_with_fn(
self,
nodes: List[BaseNode],
topic: str,
sample_k: int = 5,
fields_top_k: int = 5,
) -> List[Dict]:
"""Extract datapoints from a list of nodes, given a topic."""
idxs = list(range(len(nodes)))
sample_k = min(sample_k, len(nodes))
subset_idxs = random.sample(idxs, sample_k) text = output.text
return self.output_cls.parse_raw(text) try:
asyncio.run(sentinel_client.execute_command("ping"))
except redis.exceptions.AuthenticationError:
exception_info = sys.exc_info() return result
def delete(self, key: str, collection: str = DEFAULT_COLLECTION) -> bool:
"""
Delete a value from the store.
if self.on_audio_callback:
asyncio.run(self.on_audio_callback(mic_chunk))
else:
time.sleep(0.05) def output(self) -> None:
"""Process microphone audio and call back when new audio is ready."""
while not self._stop_event.is_set():
if not self.mic_queue.empty():
mic_chunk = self.mic_queue.get()
if self.on_audio_callback:
asyncio.run(self.on_audio_callback(mic_chunk))
else:
time.sleep(0.05)
def receive(self, data: bytes, *args, **kwargs) -> None: with self._driver.session(database=self._database) as session:
data = session.run(
neo4j.Query(text=query, timeout=self._timeout), param_map
) return triples
def structured_query(
self,
query: str,
param_map: Optional[Dict[str, Any]] = None, with self._driver.session(database=self._database) as session:
data = session.run(
neo4j.Query(text=query, timeout=self._timeout), param_map with self._driver.session(database=self._database) as session:
data = session.run(
neo4j.Query(text=query, timeout=self._timeout), param_map
) with self._driver.session(database=self._database) as session:
session.run(
(
"MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.id = $subj AND n2.id" with self._driver.session(database=self._database) as session:
session.run(
"MATCH (n:%s) WHERE n.id = $entity DELETE n" % self.node_label,
{"entity": entity}, with self._driver.session(database=self._database) as session:
is_exists_result = session.run(
"MATCH (n1:%s)--() WHERE n1.id = $entity RETURN count(*)"
% (self.node_label), return self.schema
def query(self, query: str, param_map: Optional[Dict[str, Any]] = None) -> Any:
param_map = param_map or {}
try:
data, _, _ = self._driver.execute_query( )
def delete_entity(entity: str) -> None:
with self._driver.session(database=self._database) as session:
session.run(
"MATCH (n:%s) WHERE n.id = $entity DELETE n" % self.node_label, with self._driver.session(database=self._database) as session:
result = session.run(query, param_map)
return [record.data() for record in result] with self._driver.session(database=self._database) as session:
result = session.run(query, param_map)
return [record.data() for record in result]
def query(self, query: str, param_map: Optional[Dict[str, Any]] = {}) -> Any:
"""Execute a Cypher query."""
with self._driver.session(database=self._database) as session:
result = session.run(query, param_map)
return [record.data() for record in result]
def get(self, subj: str) -> List[List[str]]:
"""Get triplets."""
query = f"""
MATCH (n1:{self.node_label})-[r]->(n2:{self.node_label})
WHERE n1.id = $subj def structured_query(
self, query: str, param_map: Optional[Dict[str, Any]] = None
) -> Any:
param_map = param_map or {}
with self._driver.session(database=self._database) as session:
result = session.run(query, param_map)
full_result = [d.data() for d in result]
if self.sanitize_query_output:
return [value_sanitize(el) for el in full_result] return dict(rel_map)
def delete(self, subj: str, rel: str, obj: str) -> None:
"""Delete triplet."""
with Session(self._engine) as session:
stmt = delete(self._rel_model).where( def get(
self,
properties: Optional[dict] = None,
ids: Optional[List[str]] = None,
) -> List[LabelledNode]:
"""Get nodes."""
with Session(self._engine) as session:
query = session.query(self._node_model)
if properties:
for key, value in properties.items():
query = query.filter(self._node_model.properties[key] == value) def get_triplets(
self,
entity_names: Optional[List[str]] = None,
relation_names: Optional[List[str]] = None,
properties: Optional[dict] = None,
ids: Optional[List[str]] = None,
) -> List[Triplet]:
"""Get triplets."""
# if nothing is passed, return empty list
if not ids and not properties and not entity_names and not relation_names:
return [] def delete(
self,
entity_names: Optional[List[str]] = None,
relation_names: Optional[List[str]] = None,
properties: Optional[dict] = None,
ids: Optional[List[str]] = None,
) -> None:
"""Delete matching data."""
with Session(self._engine) as session:
# 1. Delete relations
relation_stmt = delete(self._relation_model) return NodeModel, RelationModel
def get(
self,
properties: Optional[dict] = None,
ids: Optional[List[str]] = None, return nodes
def get_triplets(
self,
entity_names: Optional[List[str]] = None,
relation_names: Optional[List[str]] = None, session.commit()
def delete(
self,
entity_names: Optional[List[str]] = None,
relation_names: Optional[List[str]] = None, raise NotImplementedError("TiDB does not support cypher queries.")
def vector_query(
self, query: VectorStoreQuery, **kwargs: Any
) -> Tuple[List[LabelledNode], List[float]]:
"""Query the graph store with a vector store query.""" with self._driver.session(database=self._database) as session:
session.run(
(
"MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.id = $subj AND n2.id" with self._driver.session(database=self._database) as session:
session.run(
"MATCH (n:%s) WHERE n.id = $entity DETACH DELETE n"
% self.node_label, """Delete triplet from the graph."""
def delete_rel(subj: str, obj: str, rel: str) -> None:
with self._driver.session(database=self._database) as session:
session.run(
( )
def delete_entity(entity: str) -> None:
with self._driver.session(database=self._database) as session:
session.run(
"MATCH (n:%s) WHERE n.id = $entity DETACH DELETE n"
@staticmethod
def create_and_save_openvino_model(
model_name_or_path: str,
output_path: str,
export_kwargs: Optional[dict] = None, self._ensure_llm()
return self.llm.chat(messages)
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
dispatcher.event(
ReRankStartEvent(
query=query_bundle,
nodes=nodes,
top_n=self.top_n,
model_name=self.llm.metadata.model_name, def run_llm(self, messages: Sequence[ChatMessage]) -> ChatResponse:
self._ensure_llm()
return self.llm.chat(messages)
def _clean_response(self, response: str) -> str:
new_response = ""
for c in response:
if not c.isdigit():
new_response += " "
else:
new_response += c ]
results = self._watsonx_rerank.generate(
query=query_bundle.query_str, return start_time_in_ms, end_time_in_ms
def _ensure_run(self, should_print_url: bool = False) -> None:
"""
Ensures an active W&B run exists.
) -> List[BaseNode]:
return asyncio.get_event_loop().run_until_complete(
self._aparse_nodes(documents, show_progress, **kwargs) return chunks
def build_localised_splits(
self,
chunks: List[str],
) -> List[Dict[str, str]]: return re.split(r"\n\s*\n", text)
def proposition_transfer(self, paragraph: str) -> List[str]:
"""
Convert a paragraph into a list of self-sustaining statements using LLM.
""" return []
def is_same_topic_llm(self, current_chunk: List[str], new_proposition: str) -> bool:
"""
Use zero-shot classification with LLM to determine if the new proposition belongs to the same topic.
""" def _embed(self, sentences: List[str]) -> List[List[float]]:
"""Embed sentences."""
try:
from clarifai.client.input import Inputs
except ImportError:
raise ImportError("ClarifaiEmbedding requires `pip install clarifai`.")
embeddings = []
try:
for i in range(0, len(sentences), self.embed_batch_size):
batch = sentences[i : i + self.embed_batch_size] def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._model.generate_embeddings(model=self.model_name, text=query)[
"embedding"
]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
return await self._model.aget_embedding(query)
def _get_text_embedding(self, text: str) -> List[float]: def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._model.embed_content(
model=self.model_name,
content=query,
title=self.title,
task_type=self.task_type,
)["embedding"]
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding.""" def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._langchain_embedding.embed_query(query)
async def _aget_query_embedding(self, query: str) -> List[float]:
try:
return await self._langchain_embedding.aembed_query(query)
except NotImplementedError:
# Warn the user that sync is being used
self._async_not_implemented_warn_once()
return self._get_query_embedding(query) )
def __init__(
self,
model: str = "embedding",
embed_batch_size: int = 100,
@classmethod
def create_and_save_optimum_model(
cls,
model_name_or_path: str,
output_path: str, def _get_query_embedding(self, query: str) -> List[float]:
query_embeddings: list[np.ndarray] = list(self._model.query_embed(query))
return query_embeddings[0].tolist()
async def _aget_query_embedding(self, query: str) -> List[float]:
return await asyncio.to_thread(self._get_query_embedding, query) def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._model.embed_content(
model=self.model_name,
content=query,
title=self.title,
task_type=self.task_type,
request_options=self._request_options,
)["embedding"]
def _get_text_embedding(self, text: str) -> List[float]:def main():
"""Demonstrate basic usage of Heroku embeddings."""
# Initialize the embedding model. This assumes the presence of EMBEDDING_MODEL_ID,
# EMBEDDING_KEY, and EMBEDDING_URL in the host environment
embedding_model = HerokuEmbedding()
# Example texts to embed
texts = [
"Hello, world!",
"This is a test document about artificial intelligence.", self.dimensionality = dimensionality
self._model.eval()
def _embed(self, sentences: List[str]) -> List[List[float]]: _model: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_device: str = PrivateAttr()
def __init__(
self,
model_name: Optional[str] = None,
tokenizer_name: Optional[str] = None,
pooling: Union[str, Pooling] = "cls",
max_length: Optional[int] = None, _device: str = PrivateAttr()
def __init__(
self,
model_name: Optional[str] = None,
tokenizer_name: Optional[str] = None, """
return asyncio.run(self._aget_query_embedding(query))
"""
return asyncio.run(self._aget_text_embedding(text))
"""
return asyncio.run(self._aget_query_embedding(query))
def _get_text_embedding(self, text: str) -> Embedding: """
return asyncio.run(self._aget_text_embedding(text))
def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]: def _get_query_embedding(self, query: str) -> Embedding:
"""
Embed the input query synchronously.
NOTE: a new asyncio event loop is created internally for this.
"""
return asyncio.run(self._aget_query_embedding(query))
def _get_text_embedding(self, text: str) -> Embedding:
"""
Embed the text query synchronously.def main():
"""Demonstrate basic usage of Isaacus embeddings."""
# Initialize the embedding model. This assumes the presence of ISAACUS_API_KEY
# in the host environment
embedding_model = IsaacusEmbedding()
# Example legal texts to embed
texts = [
"The parties hereby agree to the terms and conditions set forth in this contract.",
"This agreement shall be governed by the laws of the State of California.",
def main():
"""Demonstrate basic usage of Isaacus embeddings."""
# Initialize the embedding model. This assumes the presence of ISAACUS_API_KEY """
return asyncio.run(self._aget_query_embedding(query))
"""
return asyncio.run(self._aget_text_embedding(text))
"""
return asyncio.run(self._aget_query_embedding(query))
def _get_text_embedding(self, text: str) -> Embedding: """
return asyncio.run(self._aget_text_embedding(text))
def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]: def _embed_with_retry(
self,
inputs: List[Union[str, BytesIO]],
prompt_name: Optional[str] = None,
) -> List[List[float]]:
"""
Generates embeddings with retry mechanism.
Args:
inputs: List of texts or images to embed
prompt_name: Optional prompt type def _get_query_embedding(self, query: str) -> Embedding:
"""
Embed the input query synchronously.
NOTE: a new asyncio event loop is created internally for this.
"""
return asyncio.run(self._aget_query_embedding(query))
def _get_text_embedding(self, text: str) -> Embedding:
"""
Embed the text query synchronously. def _get_query_embedding(self, query: str) -> Embedding:
texts = _get_embedding_request(
texts=[query],
embed_mode=self.embed_mode,
is_query=True,
model_name=self.model_name,
)
embeddings = self._model.get_embeddings(texts, **self.additional_kwargs)
return embeddings[0].values
async def _aget_query_embedding(self, query: str) -> Embedding: model.load_state_dict(
torch.load(
os.path.join(input_path, "pytorch_model.bin"),
map_location=torch.device("cpu"), def load(cls, input_path: str) -> "BaseAdapter":
"""Load model."""
with open(os.path.join(input_path, "config.json")) as fIn:
config = json.load(fIn)
model = cls(**config)
model.load_state_dict(
torch.load(
os.path.join(input_path, "pytorch_model.bin"),
map_location=torch.device("cpu"),
)
) """Forward pass."""
def save(self, output_path: str) -> None:
"""Save model."""
os.makedirs(output_path, exist_ok=True)
with open(os.path.join(output_path, "config.json"), "w") as fOut: def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
import torch
query_embedding = self._base_embed_model._get_query_embedding(query)
if self._transform_query:
query_embedding_t = torch.tensor(query_embedding).to(self._target_device)
query_embedding_t = self._adapter.forward(query_embedding_t)
query_embedding = query_embedding_t.tolist()
return query_embedding def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._embed_model.embed_query(text=query, params=self.params)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._get_query_embedding(query=text)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return self._embed_model.embed_documents(texts=texts, params=self.params)
@staticmethod
def create_and_save_openvino_model(
model_name_or_path: str,
output_path: str,
export_kwargs: Optional[dict] = None,
@staticmethod
def create_and_save_openvino_model(
model_name_or_path: str,
output_path: str,
export_kwargs: Optional[dict] = None, def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
results = []
for text in texts:
try:
import clip
except ImportError:
raise ImportError(
"ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch."
)
text_embedding = self._model.encode_text(
clip.tokenize(text).to(self._device)
response = self.client.images.generate(
prompt=text, try:
return self.run(query, fetch, **kwargs)
except Exception as e:
tables_data = self.run(tables_query, fetch="all")
# Fetch filtered column details
columns_data = self.run(columns_query, fetch="all")
# Fetch filtered index details
indexes_data = self.run(indexes_query, fetch="all")
return tables_data, columns_data, indexes_data ) -> Optional[str]:
result = db.run(
f"""SELECT comment
FROM system_schema.tables cluster_info = []
results = db.run(
f"""SELECT column_name, type, kind, clustering_order, position
FROM system_schema.columns indexes = []
results = db.run(
f"""SELECT index_name, kind, options
FROM system_schema.indexes try:
return self.run(query, fetch, **kwargs)
except Exception as e:
return str(e)
result = self.run(query, fetch="all")
return "\n".join(str(row) for row in result)
except Exception as e: def run_no_throw(
self,
query: str,
fetch: str = "all",
**kwargs: Any,
) -> Union[str, Sequence[Dict[str, Any]], ResultSet]:
"""Execute a CQL query and return the results."""
try:
return self.run(query, fetch, **kwargs)
except Exception as e:
return str(e) documents = []
result = self.db.run_no_throw(query, fetch="Cursor")
for row in result: def cassandra_db_query(self, query: str) -> List[Document]:
"""
Execute a CQL query and return the results as a list of Documents.
Args:
query (str): A CQL query to execute.
Returns:
List[Document]: A list of Document objects, each containing data from a row.
"""
self._try_display(run_result.to_pandas_df())
# create documents based on returned rows
return self._run_query(query, False)
def _get_summarization(self, original_ask: str, documents) -> Any:
return self._run_query(query, True)
def generate_query_only(self, ask: str) -> str: """
result = subprocess.run([sys.executable, "-c", code], capture_output=True)
return f"StdOut:\n{result.stdout}\nStdErr:\n{result.stderr}" """
spec_functions = ["code_interpreter"]
def code_interpreter(self, code: str):
"""
A function to execute python code, and return the stdout and stderr.
You should import any libraries that you wish to use. You have access to any libraries the user has installed.
spec_functions = ["code_interpreter"]
def code_interpreter(self, code: str):
"""
A function to execute python code, and return the stdout and stderr.
try:
msg_result = slack_client.chat_postMessage(
channel=channel_id, def send_message(
self,
channel_id: str,
message: str,
) -> None:
"""Send a message to a channel given the channel ID."""
slack_client = self.reader._client
try:
msg_result = slack_client.chat_postMessage(
channel=channel_id,
text=message, elif isinstance(run_args, BaseModel):
handler = workflow.run(**run_args.model_dump())
elif isinstance(run_args, dict):
start_event = StartEventCLS.model_validate(run_args) start_event = StartEventCLS.model_validate(run_args)
handler = workflow.run(start_event=start_event)
else:
raise ValueError(f"Invalid start event type: {type(run_args)}") )
return asyncio.run(func_async(*args, **kwargs))
return patched_sync with self.graph_store.client.session() as session:
result = session.run(neo4j_query, params)
output = [r.values() for r in result] print("Retrying")
return self.run_request(
question, print("Retrying")
return self.run_request(
question,
[ def query_graph_db(self, neo4j_query, params=None):
"""
Queries the Neo4j database.
Args:
neo4j_query (str): The Cypher query to be executed.
params (dict, optional): Parameters for the Cypher query. Defaults to None.
Returns:
list: The query results.
return output
def construct_cypher_query(self, question, history=None):
"""
Constructs a Cypher query based on a given question and history.
return results
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
return self.search_messages(query="")
def _get_credentials(self) -> Any:
"""
Get valid user credentials from storage.
"""
setattr(self, action_name, function_action)
self.spec_functions.append(action_name)
def list_actions(self):
response = requests.get(
"https://nla.zapier.com/api/v1/dynamic/exposed/", headers=self._headers
)
return response.text
# Generate audio
audio = client.generate(
text=text, voice=voice_id, voice_settings=voice_settings, model=model_id try:
response = openai.Image.create(prompt=prompt, n=n, size=size)
return [image["url"] for image in response["data"]] openai.api_key = api_key
def generate_images(
self, prompt: str, n: Optional[int] = 1, size: Optional[str] = "256x256"
) -> List[str]:
"""
return extract_output_from_stream(response)
except Exception as e:
return f"Error executing code: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error executing command: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error reading files: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error listing files: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error deleting files: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error writing files: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error starting command: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error getting task status: {e!s}"
return extract_output_from_stream(response)
except Exception as e:
return f"Error stopping task: {e!s}" return self.list_files(directory_path=directory_path, thread_id=thread_id)
def delete_files(
self,
paths: List[str],
thread_id: str = "default", return self.delete_files(paths=paths, thread_id=thread_id)
def write_files(
self,
files: List[Dict[str, str]],
thread_id: str = "default", ) -> Tuple[List[NodeWithScore], str]:
spec = self.generate_retrieval_spec(query_bundle)
vectara_retriever, new_query = self._build_retriever_from_spec( def _vectara_query(
self,
query_bundle: QueryBundle,
**kwargs: Any,
) -> Tuple[List[NodeWithScore], str]:
spec = self.generate_retrieval_spec(query_bundle)
vectara_retriever, new_query = self._build_retriever_from_spec(
VectorStoreQuerySpec(
query=spec.query, filters=spec.filters, top_k=self._similarity_top_k
)
) index._docs_pos_to_node_id = docs_pos_to_node_id
index._multi_embed_store = pickle.load(
open(Path(persist_dir) / "multi_embed_store.pkl", "rb")
)import pickle def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve from the platform."""
search_filters_inference_schema = OMIT
if self._search_filters_inference_schema is not None:
search_filters_inference_schema = (
self._search_filters_inference_schema.model_json_schema()
)
results = self._client.pipelines.run_search(
query=query_bundle.query_str,
pipeline_id=self.pipeline.id,
dense_similarity_top_k=self._dense_similarity_top_k, response_dict = get(base_url, headers, params)
return response_dict.get("id", "")
def run_ingestion(request_url: str, headers: dict, verbose: bool = False):
ingestion_status = ""
failed_docs = []
while True:
response = requests.get( def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
return asyncio.get_event_loop().run_until_complete(
self.adelete(ref_doc_id, **delete_kwargs) def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
async def aquery(
self, query: VectorStoreQuery, **kwargs: Any
) -> VectorStoreQueryResult:
filters = MetadataFiltersToFilters.metadata_filters_to_filters(
query.filters if query.filters else []
)
if query.query_str:
request = VectorSearchQueryRequest( return [node.node_id for node in nodes]
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
return asyncio.get_event_loop().run_until_complete(
self.adelete(ref_doc_id, **delete_kwargs)
) await self.adelete_nodes([ref_doc_id], **delete_kwargs)
def delete_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None, def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""
Query Moorcheh vector store.
Args:
query (VectorStoreQuery): query object
Returns:
VectorStoreQueryResult: query result
""" def get_generative_answer(
self,
query: str,
top_k: int = 5,
ai_model: str = "anthropic.claude-3-7-sonnet-20250219-v1:0",
llm: Optional[LLM] = None,
**kwargs: Any,
) -> str:
"""
Get a generative AI answer using Moorcheh's built-in RAG capability.
raise
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""
Query Moorcheh vector store.
import pickle # No running loop: create a temporary loop and close cleanly
asyncio.run(self.async_client.close())
else:
# Running loop: schedule async close (not awaited) # No running loop: create a temporary loop and close cleanly
asyncio.run(self._async_search_client.close())
else:
# Running loop: schedule async close (not awaited) try:
return new_loop.run_until_complete(coroutine)
finally:
new_loop.close() except RuntimeError:
result = asyncio.run(coroutine)
else:
if threading.current_thread() is threading.main_thread(): if not loop.is_running():
result = loop.run_until_complete(coroutine)
else:
with ThreadPoolExecutor() as pool: )
self.run(q)
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: q += self._sanitize_input(metadata_fields) + ")"
self.run(q)
def add_text( q = "textcol " + podstorevcol
js = self.run(q)
if js == "":
return "" q += "','" + text + "')"
js = self.run(q, False)
zid = js["zid"]
else: if filecol != "":
js = self.run(q, True)
else:
js = self.run(q, False) else:
js = self.run(q, False)
zid = js["zid"]
jarr = self.run(q)
if jarr is None: q = "truncate store " + podstore
self.run(q)
def drop(self) -> None: q = "drop store " + podstore
self.run(q)
def prt(self, msg: str) -> None: return ids
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete nodes using with ref_doc_id.
self.run(q)
def add_text(
self,
text: str,
embedding: List[float], self.run(q)
def drop(self) -> None:
"""
Drop or remove a store in jaguardb.
"""
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""
Query index for top k most similar nodes.
Args:
query (VectorStoreQuery): contains
query_embedding (List[float]): query embedding
similarity_top_k (int): top k most similar nodes
filters (Optional[MetadataFilters]): filter result
Returns: return self._vdb_client
def clear(self) -> None:
"""
Clear all nodes from Baidu VectorDB table.
This method deletes the table. with self._driver.session(database=self._database) as session:
data = session.run(neo4j.Query(text=query), params)
return [r.data() for r in data] with self._driver.session(database=self._database) as session:
data = session.run(neo4j.Query(text=query), params)
return [r.data() for r in data]
def database_query(
self,
query: str,
params: Optional[Dict[str, Any]] = None,
) -> Any:
params = params or {}
try:
data, _, _ = self._driver.execute_query(
query, database_=self._database, parameters_=params
)
return [r.data() for r in data] self.database_query(fts_index_query)
def database_query(
self,
query: str,
params: Optional[Dict[str, Any]] = None, if not loop.is_running():
asyncio.run(self._async_engine.dispose())
else:
# If already in a running loop, create a new thread to run the disposal # If no event loop exists, create one
asyncio.run(self._async_engine.dispose())
self._is_initialized = False
"""
return asyncio.get_event_loop().run_until_complete(
self.adelete(ref_doc_id, **delete_kwargs) """Query vector store."""
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""Query vector store."""
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
async def aquery(
self, query: VectorStoreQuery, **kwargs: Any
) -> VectorStoreQueryResult: return [node.node_id for node in nodes]
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete nodes using with ref_doc_id.
"""
asyncio.get_event_loop().run_until_complete(
self.adelete(ref_doc_id, **delete_kwargs) """
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
def __init__(
self,
host: str,
port: int,
username: str,
password: str,
index: str,
dimension: int,
text_field: str = "content",
max_chunk_bytes: int = 1 * 1024 * 1024,
os_client: Optional[OSClient] = None, def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""
Query index for top k most similar nodes.
Synchronous wrapper,using asynchronous logic of async_add function in synchronous way.
Args:
query (VectorStoreQuery): Store query object.
"""
return asyncio.get_event_loop().run_until_complete(self.aquery(query, **kwargs))
return [result.node_id for result in nodes]
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete nodes using a ref_doc_id.
Synchronous wrapper,using asynchronous logic of async_add function in synchronous way. """
return asyncio.get_event_loop().run_until_complete(
self.adelete(ref_doc_id, **delete_kwargs) """
return asyncio.get_event_loop().run_until_complete(
self.aquery(query, custom_query, es_filter, **kwargs) def query(
self,
query: VectorStoreQuery,
custom_query: Optional[
Callable[[Dict, Union[VectorStoreQuery, None]], Dict]
] = None,
es_filter: Optional[List[Dict]] = None,
metadata_keyword_suffix: str = ".keyword",
**kwargs: Any,
) -> VectorStoreQueryResult:
""" )
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete node from Elasticsearch index.
)
def delete_nodes(
self,
node_ids: Optional[List[str]] = None,
filters: Optional[MetadataFilters] = None, return nodes
def clear(self) -> None:
"""
Clear all nodes from Elasticsearch index.
This method deletes and recreates the index. return "PinconeVectorStore"
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any, def _get_query_embedding(self, query: str) -> SparseEmbedding:
results = self._model.query_embed(query)
return self._fastembed_to_dict(results)[0]
async def _aget_query_embedding(self, query: str) -> SparseEmbedding:
return self._get_query_embedding(query) def smart_batching_collate(self, batch: List) -> Tuple[Any, Any]:
"""Smart batching collate."""
import torch
from torch import Tensor
query_embeddings: List[Tensor] = []
text_embeddings: List[Tensor] = []
for query, text in batch:
query_embedding = self.embed_model.get_query_embedding(query)
text_embedding = self.embed_model.get_text_embedding(text)def train_model(
model: BaseAdapter,
data_loader: torch.utils.data.DataLoader,
device: torch.device,
epochs: int = 1,
steps_per_epoch: Optional[int] = None,
warmup_steps: int = 10000,
optimizer_class: Type[Optimizer] = torch.optim.AdamW,
optimizer_params: Dict[str, Any] = {"lr": 2e-5},
output_path: str = "model_output",
max_grad_norm: float = 1,def generate_qa_embedding_pairs(
nodes: List[TextNode],
llm: LLM,
qa_generate_prompt_tmpl: str = DEFAULT_QA_GENERATE_PROMPT_TMPL,
num_questions_per_chunk: int = 2,
retry_limit: int = 3,
on_failure: str = "continue", # options are "fail" or "continue"
save_every: int = 500,
output_path: str = "qa_finetune_dataset.json",
verbose: bool = True,
) -> EmbeddingQAFinetuneDataset: def finetune(self) -> None:
"""Finetune model."""
if self._validate_json:
if self.training_path:
reformat_jsonl(self.training_path)
if self.validation_path:
reformat_jsonl(self.validation_path)
# upload file
with open(self.training_path, "rb") as f:
train_file = self._client.files.upload(file=f) def get_finetuned_model(self, engine: str, **model_kwargs: Any) -> LLM:
"""
Get finetuned model.
- engine: This will correspond to the custom name you chose
for your deployment when you deployed a model.
"""
current_job = self.get_current_job()
return AzureOpenAI(
engine=engine or current_job.fine_tuned_model, **model_kwargsdef generate_ce_fine_tuning_dataset(
documents: List[Document],
questions_list: List[str],
max_chunk_length: int = 1000,
llm: Optional[LLM] = None,
qa_doc_relevance_prompt: str = DEFAULT_QUERY_DOC_RELEVANCE_PROMPT,
top_k: int = 8,
) -> List[CrossEncoderFinetuningDatasetSample]:
ce_dataset_list = []
node_parser = TokenTextSplitter(
def generate_synthetic_queries_over_documents(
documents: List[Document],
num_questions_per_chunk: int = 5,
max_chunk_length: int = 3000, pass
def push_to_hub(self, repo_id: Any = None) -> None:
"""
Saves the model and tokenizer to HuggingFace hub.
""" def finetune(self) -> None:
"""Finetune model."""
if self._validate_json:
validate_json(self.data_path)
# TODO: figure out how to specify file name in the new API
# file_name = os.path.basename(self.data_path)
# upload file
with open(self.data_path, "rb") as f:
output = self._client.files.create(file=f, purpose="fine-tune") # download image to temporary file
asyncio.get_event_loop().run_until_complete(
_screenshot_page(import pickle
prompt = CATEGORIZER_PROMPT.format(
question=query, category_info=self.matrix.get_all_category_info()
)
response = str(self.llm.complete(prompt)) # type: ignore
"""Runs pipeline."""
return asyncio_run(self._wf.run(query_str=query)) result = asyncio_run(
self._wf.run(
data_dir=self._data_dir,
llm=self._llm, """Runs pipeline."""
return asyncio_run(self._wf.run(query_str=query)) def run(self, query: str, *args: t.Any, **kwargs: t.Any) -> t.Any:
"""Runs pipeline."""
return asyncio_run(self._wf.run(query_str=query)) """Generates a differentially private synthetic example."""
return asyncio.run(
self.agenerate_dp_synthetic_example(
label=label, def run(
self,
sizes: Union[int, Dict[str, int]],
t_max: int = 1,
sigma: float = 0.5,
num_splits: int = 5,
num_samples_per_split: int = 1,
) -> LabelledSimpleDataset:
"""Main run method."""
if num_samples_per_split < 1:
raise ValueError( return str(response)
def generate_instructions_gen(self, chunk, x=5) -> List[str]:
"""
Generates `x` questions / use cases for `chunk`. Used when the input document is of general types
`pdf`, `json`, or `txt`. return [node.get_content() for node in nodes]
def add_chunk_to_dataset(
self,
chunks: List,
chunk: str, def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentCitationsChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
self._memory.put(ChatMessage(content=message, role="user"))
context_str_template, nodes = self._generate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
all_messages = self._memory.get_all() def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentCitationsChatResponse:
if chat_history is not None:
self._memory.set(chat_history)
self._memory.put(ChatMessage(content=message, role="user"))
context_str_template, nodes = self._generate_context(message)
prefix_messages = self._get_prefix_messages_with_context(context_str_template)
all_messages = self._memory.get_all()
documents_list = convert_nodes_to_documents_list(nodes)
@trace_method("chat")
def chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> AgentCitationsChatResponse:
if chat_history is not None:
@trace_method("chat")
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentCitationsChatResponse:
if chat_history is not None: """Get propositions."""
sub_nodes = asyncio.run(
run_jobs(
[self._aget_proposition(node) for node in nodes],
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
import streamlit as st def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
import streamlit as st
from streamlit_pills import pills
st.set_page_config(
page_title=f"Chat with {self.wikipedia_page}'s Wikipedia page, powered by LlamaIndex",
page_icon="🦙",
layout="centered",
initial_sidebar_state="auto",
menu_items=None, return {}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
import streamlit as st
from streamlit_pills import pills # Insert nodes into the index
self.index.insert_nodes(nodes)
def run(self, query_str: str, user: Any, **kwargs: Any) -> Any: def add(self, documents: List[Document], user: Any) -> None:
"""Insert Documents of a user into index."""
# Add metadata to documents
for document in documents:
document.metadata["user"] = user
# Create Nodes using IngestionPipeline
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(chunk_size=512, chunk_overlap=20),
]
)import pickle if len(documents) > 0:
asyncio.run(self.insert(documents))
def _get_embeddings_per_level(self, level: int = 0) -> List[float]: """Runs the configured pipeline for a specified task and reasoning modules."""
return asyncio_run(self.workflow.run(task=task, llm=self.llm)) def run(self, message: str, **kwargs: Any) -> Any:
"""Run the pipeline."""
# tailored for query engine input/output, using "user" role
chat = [{"role": "user", "content": message}]
prompt = self._moderation_prompt_for_chat(chat)
inputs = self.tokenizer([prompt], return_tensors="pt").to(self.device)
output = self.model.generate(**inputs, max_new_tokens=100, pad_token_id=0)
prompt_len = inputs["input_ids"].shape[-1]
return self.tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
class LlamaGuardModeratorPack(BaseLlamaPack):
def __init__(
self,
custom_taxonomy: str = DEFAULT_TAXONOMY,
) -> None: }
def run(self, message: str, **kwargs: Any) -> Any:
"""Run the pipeline."""
# tailored for query engine input/output, using "user" role
chat = [{"role": "user", "content": message}] """Run the pipeline."""
return asyncio_run(self._wf.run(query_str=query_str)) """Run the pipeline."""
return asyncio_run(self._wf.run(query_str=query_str)) def run(self, query_str: str, **kwargs: Any) -> Any:
"""Run the pipeline."""
return asyncio_run(self._wf.run(query_str=query_str)) self.dprmodel = DPRReader.from_pretrained(dprmodel_path)
self.dprmodel.eval()
self.dprmodel.to(self.device)
self.dprmodel.eval()
self.dprmodel.to(self.device)
def _get_answer(self, query, texts, title):def _have_seen_or_not(model_cross_encoder, query_item, query_seen_list, query_type):
if "Unsolved" in query_type:
return False
for query_seen in query_seen_list:
with torch.no_grad():
if model_cross_encoder.predict([(query_seen, query_item)]) > 0.5:
return True
return False
class SearChainPack(BaseLlamaPack): def execute(self, data_path, start_idx):
data = open(data_path)
for k, example in enumerate(data):
if k < start_idx:
continue
example = json.loads(example)
q = example["question"]
round_count = 0
message_keys_list = [
ChatMessage(
role="user",
class SearChainPack(BaseLlamaPack):
"""Simple short form SearChain pack."""
def __init__(
self,
data_path: str,
dprtokenizer_path: str = "facebook/dpr-reader-multiset-base", # download from https://huggingface.co/facebook/dpr-reader-multiset-base,
dprmodel_path: str = "facebook/dpr-reader-multiset-base", # download from https://huggingface.co/facebook/dpr-reader-multiset-base,
crossencoder_name_or_path: str = "microsoft/MiniLM-L12-H384-uncased", # down load from https://huggingface.co/microsoft/MiniLM-L12-H384-uncased, """Simple short form SearChain pack."""
def __init__(
self,
data_path: str,
dprtokenizer_path: str = "facebook/dpr-reader-multiset-base", # download from https://huggingface.co/facebook/dpr-reader-multiset-base, return "Sorry, I still cannot solve this question!"
def execute(self, data_path, start_idx):
data = open(data_path)
for k, example in enumerate(data):
if k < start_idx: """Run the pipeline."""
return self.llm.complete(*args, **kwargs) try:
context.run(active_span_id.reset, token)
except ValueError as e:
# TODO: Since the context is created in a sync context no in async task, c = c.parent
def span(self, func: Callable[..., _R]) -> Callable[..., _R]:
# The `span` decorator should be idempotent.
try:
if hasattr(func, DISPATCHER_SPAN_DECORATED_ATTR): )
def handle_future_result(
future: asyncio.Future,
span_id: str,
bound_args: inspect.BoundArguments, """Run tuning."""
return asyncio.run(self.atune())
def _format_dataset(
self, dataset: EmbeddingQAFinetuneDataset, corpus: Dict[str, str]
):
"""
Convert the dataset into NUDGE format.
Args:
dataset (EmbeddingQAFinetuneDataset): Dataset to convert.
"""
try: # Get the SQL query with text-to-SQL prompt
response_str = llm.predict(
prompt=prompt,def default_jsonalyzer(
list_of_dict: List[Dict[str, Any]],
query_bundle: QueryBundle,
llm: LLM,
table_name: str = DEFAULT_TABLE_NAME,
prompt: BasePromptTemplate = DEFAULT_JSONALYZE_PROMPT,
sql_parser: BaseSQLParser = DefaultSQLParser(),
) -> Tuple[str, Dict[str, Any], List[Dict[str, Any]]]:
"""
Default JSONalyzer that executes a query on a list of dictionaries.
def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer an analytical query on the JSON List."""
query = query_bundle.query_str
if self._verbose:
print_text(f"Query: {query}\n", color="green")
# Perform the analysis
sql_query, table_schema, results = self._analyzer(
self._list_of_dict,
query_bundle,
self._llm, def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer a query."""
context = self._get_table_context()
polars_response_str = self._llm.predict(
self._polars_prompt,
df_str=context,
query_str=query_bundle.query_str,
instruction_str=self._instruction_str,
)
def _query(self, query_bundle: QueryBundle) -> Response:
"""Answer a query."""
context = self._get_table_context()
pandas_response_str = self._llm.predict(
self._pandas_prompt,
df_str=context,
query_str=query_bundle.query_str,
instruction_str=self._instruction_str,
)
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
return asyncio.run(self._aretrieve(query_bundle)) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
return asyncio.run(self._aretrieve(query_bundle)) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
return asyncio.run(self._aretrieve(query_bundle))Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'ConfigAnalyzer' object has no attribute 'file_exists'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Implement watermarking for model outputs
Use cryptographic watermarks for model weights
Track watermark verification for model theft detection
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Implement data validation pipelines
Verify data source integrity
Monitor for anomalies in training data
Detection failed: 'bool' object has no attribute 'lower'
Implement rate limiting on API endpoints
Add query logging and anomaly detection
Monitor for extraction patterns
Implement drift detection with evidently or alibi-detect
Monitor input data distribution changes
Set up automated alerts for drift events
Implement anomaly detection on model inputs
Monitor for unusual query patterns
Use statistical methods or ML-based detection
Implement adversarial input detection
Use adversarial robustness toolkits
Add input perturbation analysis
Detection failed: 'bool' object has no attribute 'lower'
Use Evidently or alibi-detect for drift monitoring
Set up automated alerts for significant drift
Implement automatic retraining pipelines
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Implement adversarial testing for bias
Test across demographic groups
Use TextAttack or CheckList for NLP bias testing
Detection failed: 'ConfigAnalyzer' object has no attribute 'file_exists'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Detection failed: 'bool' object has no attribute 'lower'
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required 4. Use allowlists for permitted operations 5. Consider structured output formats (JSON) instead
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Secure Data Loading: 1. Use safetensors instead of pickle for model weights 2. For torch.load, use weights_only=True 3. Verify checksums/signatures before loading 4. Only load data from trusted, verified sources 5. Implement content scanning before deserialization 6. Consider using JSON/YAML for configuration data
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for SQL Injection: 1. Use parameterized queries: cursor.execute(query, (param,)) 2. Never concatenate LLM output into SQL 3. Use ORM query builders (SQLAlchemy, Django ORM)
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required 4. Use allowlists for permitted operations 5. Consider structured output formats (JSON) instead
Mitigations for Code Execution: 1. Never pass LLM output to eval() or exec() 2. Use safe alternatives (ast.literal_eval for data) 3. Implement sandboxing if code execution is required 4. Use allowlists for permitted operations 5. Consider structured output formats (JSON) instead
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Code Execution Security: 1. NEVER execute LLM-generated code directly with exec()/eval() 2. If code execution is necessary, use sandboxed environments (Docker, VM) 3. Implement strict code validation and static analysis before execution 4. Use allowlists for permitted functions/modules 5. Set resource limits (CPU, memory, time) for execution 6. Parse and validate code structure before running 7. Consider using safer alternatives (JSON, declarative configs) 8. Log all code execution attempts with full context 9. Require human review for generated code 10. Use tools like RestrictedPython for safer Python execution
NEVER directly execute LLM-generated code: 1. Remove direct execution: - Do not use eval(), exec(), or os.system() - Avoid dynamic code execution - Use safer alternatives (allow-lists) 2. If code generation is required: - Generate code for review only - Require human approval before execution - Use sandboxing (containers, VMs) - Implement strict security policies 3. Use structured outputs: - Return data, not code - Use JSON schemas - Define clear interfaces 4. Add safeguards: - Static code analysis before execution - Whitelist allowed operations - Rate limiting and monitoring
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Mitigations: 1. Use structured prompt templates (e.g., LangChain PromptTemplate) 2. Implement input sanitization to remove prompt injection patterns 3. Use separate 'user' and 'system' message roles (ChatML format) 4. Apply input validation and length limits 5. Use allowlists for expected input formats 6. Consider prompt injection detection libraries
Mitigations for Command Injection: 1. Never pass LLM output to shell commands 2. Use subprocess with shell=False and list arguments 3. Apply allowlist validation for expected values 4. Use shlex.quote() if shell execution is unavoidable 5. Consider alternative APIs that don't use shell
Model DoS Mitigations: 1. Implement rate limiting per user/IP (@limiter.limit('10/minute')) 2. Validate and limit input length (max 1000 chars) 3. Set token limits (max_tokens=500) 4. Configure timeouts (timeout=30 seconds) 5. Avoid LLM calls in unbounded loops 6. Implement circuit breakers for cascading failures 7. Monitor and alert on resource usage 8. Use queuing for batch processing 9. Implement cost controls and budgets
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Secure Data Loading: 1. Use safetensors instead of pickle for model weights 2. For torch.load, use weights_only=True 3. Verify checksums/signatures before loading 4. Only load data from trusted, verified sources 5. Implement content scanning before deserialization 6. Consider using JSON/YAML for configuration data
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Secure Tool/Plugin Implementation: 1. NEVER execute shell commands from LLM output directly 2. Use allowlists for permitted commands/operations 3. Validate all file paths against allowed directories 4. Use parameterized queries - never raw SQL from LLM 5. Validate URLs against allowlist before HTTP requests 6. Implement strict input schemas (JSON Schema, Pydantic) 7. Add rate limiting and request throttling 8. Log all tool invocations for audit 9. Use principle of least privilege 10. Implement human-in-the-loop for destructive operations
Protect model artifacts from unauthorized access: 1. Implement strict access control: - Require authentication for model downloads - Use role-based access control (RBAC) - Log all artifact access attempts 2. Store artifacts securely: - Use private S3 buckets with signed URLs - Never store in /static or /public directories - Encrypt at rest and in transit 3. Model obfuscation: - Use model encryption - Implement model quantization - Remove unnecessary metadata 4. Add legal protection: - Include license files with models - Add watermarks to model outputs - Use model fingerprinting 5. Monitor access: - Track who downloads models - Alert on unauthorized access - Maintain audit logs
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical financial decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical financial decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical financial, security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical legal decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical financial decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Secure Serialization: 1. Use safer alternatives like safetensors 2. Never deserialize from untrusted sources 3. Consider using ONNX for model exchange
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical security, data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards
Critical data_modification decision requires human oversight: 1. Implement human-in-the-loop review: - Add review queue for high-stakes decisions - Require explicit human approval before execution - Log all decisions for audit trail 2. Add verification mechanisms: - Cross-reference with trusted sources - Implement multi-step verification - Use confidence thresholds 3. Include safety checks: - Set limits on transaction amounts - Require secondary confirmation - Implement rollback mechanisms 4. Add disclaimers: - Inform users output may be incorrect - Recommend professional consultation - Document limitations clearly 5. Monitor and review: - Track decision outcomes - Review failures and near-misses - Continuously improve safeguards