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6dbd5bb10488-1 | Run on retriever error.
on_retriever_start(serialized, query, *, run_id)
Run on retriever start.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id, tags])
Run on arbitrary text.
on_tool_end(output, *, run_id[, ...])
Run when tool ends running.
on_tool_error(error, *, run_id[, ...])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__() → None[source]¶
async aiter() → AsyncIterator[str][source]¶
async on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on agent action.
async on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on agent end.
async on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain ends running.
async on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html |
6dbd5bb10488-2 | Run when chain errors.
async on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when chain starts running.
async on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
async on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
async on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
async on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
async on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on retriever end.
async on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on retriever error. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html |
6dbd5bb10488-3 | Run on retriever error.
async on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run on retriever start.
async on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
async on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on arbitrary text.
async on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool ends running.
async on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool errors.
async on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when tool starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html |
7328c4053cbb-0 | langchain.callbacks.utils.import_spacy¶
langchain.callbacks.utils.import_spacy() → Any[source]¶
Import the spacy python package and raise an error if it is not installed. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.import_spacy.html |
66058a758b4f-0 | langchain.callbacks.manager.CallbackManagerForChainRun¶
class langchain.callbacks.manager.CallbackManagerForChainRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Callback manager for chain run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_agent_action(action, **kwargs)
Run when agent action is received.
on_agent_finish(finish, **kwargs)
Run when agent finish is received.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_retry(retry_state, **kwargs)
Run on a retry event. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForChainRun.html |
66058a758b4f-1 | on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received.
__init__(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
get_child(tag: Optional[str] = None) → CallbackManager¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
The child callback manager.
Return type
CallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run when agent action is received.
Parameters
action (AgentAction) – The agent action.
Returns | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForChainRun.html |
66058a758b4f-2 | Parameters
action (AgentAction) – The agent action.
Returns
The result of the callback.
Return type
Any
on_agent_finish(finish: AgentFinish, **kwargs: Any) → Any[source]¶
Run when agent finish is received.
Parameters
finish (AgentFinish) – The agent finish.
Returns
The result of the callback.
Return type
Any
on_chain_end(outputs: Union[Dict[str, Any], Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Union[Dict[str, Any], Any]) – The outputs of the chain.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
Examples using CallbackManagerForChainRun¶
Custom chain | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForChainRun.html |
0ad720816253-0 | langchain.callbacks.manager.env_var_is_set¶
langchain.callbacks.manager.env_var_is_set(env_var: str) → bool[source]¶
Check if an environment variable is set.
Parameters
env_var (str) – The name of the environment variable.
Returns
True if the environment variable is set, False otherwise.
Return type
bool | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.env_var_is_set.html |
7d0cdd3ee543-0 | langchain.callbacks.manager.CallbackManagerForRetrieverRun¶
class langchain.callbacks.manager.CallbackManagerForRetrieverRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Callback manager for retriever run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retriever_end(documents, **kwargs)
Run when retriever ends running.
on_retriever_error(error, **kwargs)
Run when retriever errors.
on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForRetrieverRun.html |
7d0cdd3ee543-1 | on_text(text, **kwargs)
Run when text is received.
__init__(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
get_child(tag: Optional[str] = None) → CallbackManager¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
The child callback manager.
Return type
CallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_retriever_end(documents: Sequence[Document], **kwargs: Any) → None[source]¶
Run when retriever ends running.
on_retriever_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when retriever errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForRetrieverRun.html |
7d0cdd3ee543-2 | Run when retriever errors.
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
Examples using CallbackManagerForRetrieverRun¶
Retrieve as you generate with FLARE | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForRetrieverRun.html |
d33a8932b903-0 | langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler¶
class langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler(evaluators: Sequence[RunEvaluator], client: Optional[Client] = None, example_id: Optional[Union[str, UUID]] = None, skip_unfinished: bool = True, project_name: Optional[str] = 'evaluators', **kwargs: Any)[source]¶
A tracer that runs a run evaluator whenever a run is persisted.
Parameters
evaluators (Sequence[RunEvaluator]) – The run evaluators to apply to all top level runs.
client (LangSmith Client, optional) – The LangSmith client instance to use for evaluating the runs.
If not specified, a new instance will be created.
example_id (Union[UUID, str], optional) – The example ID to be associated with the runs.
project_name (str, optional) – The LangSmith project name to be organize eval chain runs under.
example_id¶
The example ID associated with the runs.
Type
Union[UUID, None]
client¶
The LangSmith client instance used for evaluating the runs.
Type
Client
evaluators¶
The sequence of run evaluators to be executed.
Type
Sequence[RunEvaluator]
executor¶
The thread pool executor used for running the evaluators.
Type
ThreadPoolExecutor
futures¶
The set of futures representing the running evaluators.
Type
Set[Future]
skip_unfinished¶
Whether to skip runs that are not finished or raised
an error.
Type
bool
project_name¶
The LangSmith project name to be organize eval chain runs under.
Type
Optional[str]
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html |
d33a8932b903-1 | ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
name
raise_error
run_inline
Methods
__init__(evaluators[, client, example_id, ...])
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, inputs])
End a trace for a chain run.
on_chain_error(error, *[, inputs])
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html |
d33a8932b903-2 | Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
wait_for_futures()
Wait for all futures to complete.
__init__(evaluators: Sequence[RunEvaluator], client: Optional[Client] = None, example_id: Optional[Union[str, UUID]] = None, skip_unfinished: bool = True, project_name: Optional[str] = 'evaluators', **kwargs: Any) → None[source]¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any) → Run¶
End a trace for a chain run.
on_chain_error(error: BaseException, *, inputs: Optional[Dict[str, Any]] = None, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a chain run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html |
d33a8932b903-3 | Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → Run¶
End a trace for an LLM run.
on_llm_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Run¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for an LLM run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html |
d33a8932b903-4 | Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → Run¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → Run¶
End a trace for a tool run.
on_tool_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a tool run.
wait_for_futures() → None[source]¶
Wait for all futures to complete. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html |
8e8ef8143b68-0 | langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler¶
class langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler(logger: Logger, handler: Any)[source]¶
Callback Handler for logging to WhyLabs. This callback handler utilizes
langkit to extract features from the prompts & responses when interacting with
an LLM. These features can be used to guardrail, evaluate, and observe interactions
over time to detect issues relating to hallucinations, prompt engineering,
or output validation. LangKit is an LLM monitoring toolkit developed by WhyLabs.
Here are some examples of what can be monitored with LangKit:
* Text Quality
readability score
complexity and grade scores
Text Relevance
- Similarity scores between prompt/responses
- Similarity scores against user-defined themes
- Topic classification
Security and Privacy
- patterns - count of strings matching a user-defined regex pattern group
- jailbreaks - similarity scores with respect to known jailbreak attempts
- prompt injection - similarity scores with respect to known prompt attacks
- refusals - similarity scores with respect to known LLM refusal responses
Sentiment and Toxicity
- sentiment analysis
- toxicity analysis
For more information, see https://docs.whylabs.ai/docs/language-model-monitoring
or check out the LangKit repo here: https://github.com/whylabs/langkit
—
:param api_key: WhyLabs API key. Optional because the preferred
way to specify the API key is with environment variable
WHYLABS_API_KEY.
Parameters
org_id (Optional[str]) – WhyLabs organization id to write profiles to.
Optional because the preferred way to specify the organization id is
with environment variable WHYLABS_DEFAULT_ORG_ID.
dataset_id (Optional[str]) – WhyLabs dataset id to write profiles to.
Optional because the preferred way to specify the dataset id is | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-1 | Optional because the preferred way to specify the dataset id is
with environment variable WHYLABS_DEFAULT_DATASET_ID.
sentiment (bool) – Whether to enable sentiment analysis. Defaults to False.
toxicity (bool) – Whether to enable toxicity analysis. Defaults to False.
themes (bool) – Whether to enable theme analysis. Defaults to False.
Initiate the rolling logger.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(logger, handler)
Initiate the rolling logger.
close()
Close any loggers to allow writing out of any profiles before exiting.
flush()
Explicitly write current profile if using a rolling logger.
from_params(*[, api_key, org_id, ...])
Instantiate whylogs Logger from params.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-2 | Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__(logger: Logger, handler: Any)[source]¶
Initiate the rolling logger.
close() → None[source]¶
Close any loggers to allow writing out of any profiles before exiting.
flush() → None[source]¶
Explicitly write current profile if using a rolling logger. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-3 | flush() → None[source]¶
Explicitly write current profile if using a rolling logger.
classmethod from_params(*, api_key: Optional[str] = None, org_id: Optional[str] = None, dataset_id: Optional[str] = None, sentiment: bool = False, toxicity: bool = False, themes: bool = False, logger: Optional[Logger] = None) → WhyLabsCallbackHandler[source]¶
Instantiate whylogs Logger from params.
Parameters
api_key (Optional[str]) – WhyLabs API key. Optional because the preferred
way to specify the API key is with environment variable
WHYLABS_API_KEY.
org_id (Optional[str]) – WhyLabs organization id to write profiles to.
If not set must be specified in environment variable
WHYLABS_DEFAULT_ORG_ID.
dataset_id (Optional[str]) – The model or dataset this callback is gathering
telemetry for. If not set must be specified in environment variable
WHYLABS_DEFAULT_DATASET_ID.
sentiment (bool) – If True will initialize a model to perform
sentiment analysis compound score. Defaults to False and will not gather
this metric.
toxicity (bool) – If True will initialize a model to score
toxicity. Defaults to False and will not gather this metric.
themes (bool) – If True will initialize a model to calculate
distance to configured themes. Defaults to None and will not gather this
metric.
logger (Optional[Logger]) – If specified will bind the configured logger as
the telemetry gathering agent. Defaults to LangKit schema with periodic
WhyLabs writer.
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-4 | Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM ends running.
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-5 | Run when LLM errors.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) –
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
8e8ef8143b68-6 | Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when tool starts running.
Examples using WhyLabsCallbackHandler¶
WhyLabs | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html |
cb6f76aa1113-0 | langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler¶
class langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]¶
A callback handler that writes to a Streamlit app.
Create a StreamlitCallbackHandler instance.
Parameters
parent_container – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers – The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(parent_container, *[, ...])
Create a StreamlitCallbackHandler instance.
on_agent_action(action[, color])
Run on agent action. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html |
cb6f76aa1113-1 | on_agent_action(action[, color])
Run on agent action.
on_agent_finish(finish[, color])
Run on agent end.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run on new LLM token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text[, color, end])
Run on arbitrary text.
on_tool_end(output[, color, ...])
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html |
cb6f76aa1113-2 | Run when tool starts running.
__init__(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]¶
Create a StreamlitCallbackHandler instance.
Parameters
parent_container – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers – The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
on_agent_action(action: AgentAction, color: Optional[str] = None, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, color: Optional[str] = None, **kwargs: Any) → None[source]¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html |
cb6f76aa1113-3 | Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) –
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html |
cb6f76aa1113-4 | Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, color: Optional[str] = None, end: str = '', **kwargs: Any) → None[source]¶
Run on arbitrary text.
on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
Examples using StreamlitCallbackHandler¶
Streamlit
GPT4All | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html |
f8cac7746be5-0 | langchain.callbacks.manager.AsyncCallbackManagerForChainGroup¶
class langchain.callbacks.manager.AsyncCallbackManagerForChainGroup(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, parent_run_id: uuid.UUID | None = None, *, parent_run_manager: AsyncCallbackManagerForChainRun, **kwargs: Any)[source]¶
Initialize callback manager.
Attributes
is_async
Return whether the handler is async.
Methods
__init__(handlers[, inheritable_handlers, ...])
Initialize callback manager.
add_handler(handler[, inherit])
Add a handler to the callback manager.
add_metadata(metadata[, inherit])
add_tags(tags[, inherit])
configure([inheritable_callbacks, ...])
Configure the async callback manager.
copy()
Copy the callback manager.
on_chain_end(outputs, **kwargs)
Run when traced chain group ends.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs[, run_id])
Run when chain starts running.
on_chat_model_start(serialized, messages, ...)
Run when LLM starts running.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_start(serialized, query[, ...])
Run when retriever starts running.
on_tool_start(serialized, input_str[, ...])
Run when tool starts running.
remove_handler(handler)
Remove a handler from the callback manager.
remove_metadata(keys)
remove_tags(tags)
set_handler(handler[, inherit])
Set handler as the only handler on the callback manager.
set_handlers(handlers[, inherit])
Set handlers as the only handlers on the callback manager. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainGroup.html |
f8cac7746be5-1 | Set handlers as the only handlers on the callback manager.
__init__(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, parent_run_id: uuid.UUID | None = None, *, parent_run_manager: AsyncCallbackManagerForChainRun, **kwargs: Any) → None[source]¶
Initialize callback manager.
add_handler(handler: BaseCallbackHandler, inherit: bool = True) → None¶
Add a handler to the callback manager.
add_metadata(metadata: Dict[str, Any], inherit: bool = True) → None¶
add_tags(tags: List[str], inherit: bool = True) → None¶
classmethod configure(inheritable_callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, local_callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, verbose: bool = False, inheritable_tags: Optional[List[str]] = None, local_tags: Optional[List[str]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None, local_metadata: Optional[Dict[str, Any]] = None) → AsyncCallbackManager¶
Configure the async callback manager.
Parameters
inheritable_callbacks (Optional[Callbacks], optional) – The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional) – The local callbacks.
Defaults to None.
verbose (bool, optional) – Whether to enable verbose mode. Defaults to False.
inheritable_tags (Optional[List[str]], optional) – The inheritable tags.
Defaults to None.
local_tags (Optional[List[str]], optional) – The local tags.
Defaults to None.
inheritable_metadata (Optional[Dict[str, Any]], optional) – The inheritable
metadata. Defaults to None.
local_metadata (Optional[Dict[str, Any]], optional) – The local metadata.
Defaults to None.
Returns | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainGroup.html |
f8cac7746be5-2 | Defaults to None.
Returns
The configured async callback manager.
Return type
AsyncCallbackManager
copy() → T¶
Copy the callback manager.
async on_chain_end(outputs: Union[Dict[str, Any], Any], **kwargs: Any) → None[source]¶
Run when traced chain group ends.
Parameters
outputs (Union[Dict[str, Any], Any]) – The outputs of the chain.
async on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
async on_chain_start(serialized: Dict[str, Any], inputs: Union[Dict[str, Any], Any], run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForChainRun¶
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) – The serialized chain.
inputs (Union[Dict[str, Any], Any]) – The inputs to the chain.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The async callback managerfor the chain run.
Return type
AsyncCallbackManagerForChainRun
async on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any) → List[AsyncCallbackManagerForLLMRun]¶
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) – The serialized LLM.
messages (List[List[BaseMessage]]) – The list of messages.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The list ofasync callback managers, one for each LLM Run
corresponding to each inner message list.
Return type
List[AsyncCallbackManagerForLLMRun] | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainGroup.html |
f8cac7746be5-3 | Return type
List[AsyncCallbackManagerForLLMRun]
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → List[AsyncCallbackManagerForLLMRun]¶
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) – The serialized LLM.
prompts (List[str]) – The list of prompts.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The list of asynccallback managers, one for each LLM Run corresponding
to each prompt.
Return type
List[AsyncCallbackManagerForLLMRun]
async on_retriever_start(serialized: Dict[str, Any], query: str, run_id: Optional[UUID] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForRetrieverRun¶
Run when retriever starts running.
async on_tool_start(serialized: Dict[str, Any], input_str: str, run_id: Optional[UUID] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForToolRun¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) – The serialized tool.
input_str (str) – The input to the tool.
run_id (UUID, optional) – The ID of the run. Defaults to None.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
Returns
The async callback managerfor the tool run.
Return type
AsyncCallbackManagerForToolRun
remove_handler(handler: BaseCallbackHandler) → None¶
Remove a handler from the callback manager.
remove_metadata(keys: List[str]) → None¶
remove_tags(tags: List[str]) → None¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainGroup.html |
f8cac7746be5-4 | remove_tags(tags: List[str]) → None¶
set_handler(handler: BaseCallbackHandler, inherit: bool = True) → None¶
Set handler as the only handler on the callback manager.
set_handlers(handlers: List[BaseCallbackHandler], inherit: bool = True) → None¶
Set handlers as the only handlers on the callback manager. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainGroup.html |
7d8c58a50b01-0 | langchain.callbacks.tracers.stdout.elapsed¶
langchain.callbacks.tracers.stdout.elapsed(run: Any) → str[source]¶
Get the elapsed time of a run.
Parameters
run – any object with a start_time and end_time attribute.
Returns
A string with the elapsed time in seconds ormilliseconds if time is less than a second. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.elapsed.html |
1877565e8f0c-0 | langchain.callbacks.tracers.langchain_v1.get_headers¶
langchain.callbacks.tracers.langchain_v1.get_headers() → Dict[str, Any][source]¶
Get the headers for the LangChain API. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.langchain_v1.get_headers.html |
47145536a72e-0 | langchain.callbacks.infino_callback.InfinoCallbackHandler¶
class langchain.callbacks.infino_callback.InfinoCallbackHandler(model_id: Optional[str] = None, model_version: Optional[str] = None, verbose: bool = False)[source]¶
Callback Handler that logs to Infino.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__([model_id, model_version, verbose])
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing.
on_chain_end(outputs, **kwargs)
Do nothing when LLM chain ends.
on_chain_error(error, **kwargs)
Need to log the error.
on_chain_start(serialized, inputs, **kwargs)
Do nothing when LLM chain starts.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log the latency, error, token usage, and response to Infino.
on_llm_error(error, **kwargs)
Set the error flag.
on_llm_new_token(token, **kwargs)
Do nothing when a new token is generated.
on_llm_start(serialized, prompts, **kwargs)
Log the prompts to Infino, and set start time and error flag.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html |
47145536a72e-1 | Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Do nothing.
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
__init__(model_id: Optional[str] = None, model_version: Optional[str] = None, verbose: bool = False) → None[source]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when LLM chain ends.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Need to log the error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when LLM chain starts. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html |
47145536a72e-2 | Do nothing when LLM chain starts.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log the latency, error, token usage, and response to Infino.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Set the error flag.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Log the prompts to Infino, and set start time and error flag.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html |
47145536a72e-3 | Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing.
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Examples using InfinoCallbackHandler¶
Infino | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html |
125e6232c6ab-0 | langchain.callbacks.utils.hash_string¶
langchain.callbacks.utils.hash_string(s: str) → str[source]¶
Hash a string using sha1.
Parameters
s (str) – The string to hash.
Returns
The hashed string.
Return type
(str) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.hash_string.html |
707d6c705b48-0 | langchain.callbacks.argilla_callback.ArgillaCallbackHandler¶
class langchain.callbacks.argilla_callback.ArgillaCallbackHandler(dataset_name: str, workspace_name: Optional[str] = None, api_url: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Callback Handler that logs into Argilla.
Parameters
dataset_name – name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset in
Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url – URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
will be used.
Raises
ImportError – if the argilla package is not installed.
ConnectionError – if the connection to Argilla fails.
FileNotFoundError – if the FeedbackDataset retrieval from Argilla fails.
Examples
>>> from langchain.llms import OpenAI
>>> from langchain.callbacks import ArgillaCallbackHandler
>>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0, | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-1 | ... )
>>> llm = OpenAI(
... temperature=0,
... callbacks=[argilla_callback],
... verbose=True,
... openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
... "What is the best NLP-annotation tool out there? (no bias at all)",
... ])
"Argilla, no doubt about it."
Initializes the ArgillaCallbackHandler.
Parameters
dataset_name – name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset
in Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url – URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
will be used.
Raises
ImportError – if the argilla package is not installed.
ConnectionError – if the connection to Argilla fails.
FileNotFoundError – if the FeedbackDataset retrieval from Argilla fails.
Attributes
BLOG_URL
DEFAULT_API_URL
ISSUES_URL
REPO_URL
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-2 | ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(dataset_name[, workspace_name, ...])
Initializes the ArgillaCallbackHandler.
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing
on_chain_end(outputs, **kwargs)
If either the parent_run_id or the run_id is in self.prompts, then log the outputs to Argilla, and pop the run from self.prompts.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
If the key input is in inputs, then save it in self.prompts using either the parent_run_id or the run_id as the key.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log records to Argilla when an LLM ends.
on_llm_error(error, **kwargs)
Do nothing when LLM outputs an error.
on_llm_new_token(token, **kwargs)
Do nothing when a new token is generated.
on_llm_start(serialized, prompts, **kwargs)
Save the prompts in memory when an LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-3 | on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Do nothing
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
__init__(dataset_name: str, workspace_name: Optional[str] = None, api_url: Optional[str] = None, api_key: Optional[str] = None) → None[source]¶
Initializes the ArgillaCallbackHandler.
Parameters
dataset_name – name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset
in Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url – URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
will be used.
Raises
ImportError – if the argilla package is not installed.
ConnectionError – if the connection to Argilla fails. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-4 | ConnectionError – if the connection to Argilla fails.
FileNotFoundError – if the FeedbackDataset retrieval from Argilla fails.
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
If either the parent_run_id or the run_id is in self.prompts, then
log the outputs to Argilla, and pop the run from self.prompts. The behavior
differs if the output is a list or not.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
If the key input is in inputs, then save it in self.prompts using
either the parent_run_id or the run_id as the key. This is done so that
we don’t log the same input prompt twice, once when the LLM starts and once
when the chain starts.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log records to Argilla when an LLM ends.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-5 | on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Save the prompts in memory when an LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
707d6c705b48-6 | Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Examples using ArgillaCallbackHandler¶
Argilla | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html |
12d59fd07b55-0 | langchain.callbacks.aim_callback.AimCallbackHandler¶
class langchain.callbacks.aim_callback.AimCallbackHandler(repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True)[source]¶
Callback Handler that logs to Aim.
Parameters
repo (str, optional) – Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) – Sets Run’s experiment property.
‘default’ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) – Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) – Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run
and then logs the response to Aim.
Initialize callback handler.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__([repo, experiment_name, ...])
Initialize callback handler.
flush_tracker([repo, experiment_name, ...])
Flush the tracker and reset the session.
get_custom_callback_meta()
on_agent_action(action, **kwargs) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html |
12d59fd07b55-1 | get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Run when agent is ending.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
reset_callback_meta()
Reset the callback metadata.
setup(**kwargs) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html |
12d59fd07b55-2 | reset_callback_meta()
Reset the callback metadata.
setup(**kwargs)
__init__(repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True) → None[source]¶
Initialize callback handler.
flush_tracker(repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True, langchain_asset: Any = None, reset: bool = True, finish: bool = False) → None[source]¶
Flush the tracker and reset the session.
Parameters
repo (str, optional) – Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) – Sets Run’s experiment property.
‘default’ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) – Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) – Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
langchain_asset – The langchain asset to save.
reset – Whether to reset the session.
finish – Whether to finish the run.
Returns – None
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html |
12d59fd07b55-3 | Run when chain ends running.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html |
12d59fd07b55-4 | Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
setup(**kwargs: Any) → None[source]¶
Examples using AimCallbackHandler¶
Aim | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html |
b0c020ec70da-0 | langchain.callbacks.openai_info.get_openai_token_cost_for_model¶
langchain.callbacks.openai_info.get_openai_token_cost_for_model(model_name: str, num_tokens: int, is_completion: bool = False) → float[source]¶
Get the cost in USD for a given model and number of tokens.
Parameters
model_name – Name of the model
num_tokens – Number of tokens.
is_completion – Whether the model is used for completion or not.
Defaults to False.
Returns
Cost in USD. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.get_openai_token_cost_for_model.html |
1d73365b3f19-0 | langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler¶
class langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler(example_id: Optional[Union[UUID, str]] = None, **kwargs: Any)[source]¶
A tracer that collects all nested runs in a list.
This tracer is useful for inspection and evaluation purposes.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Initialize the RunCollectorCallbackHandler.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
name
raise_error
run_inline
Methods
__init__([example_id])
Initialize the RunCollectorCallbackHandler.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, inputs])
End a trace for a chain run.
on_chain_error(error, *[, inputs])
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html |
1d73365b3f19-1 | Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
__init__(example_id: Optional[Union[UUID, str]] = None, **kwargs: Any) → None[source]¶
Initialize the RunCollectorCallbackHandler.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html |
1d73365b3f19-2 | on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any) → Run¶
End a trace for a chain run.
on_chain_error(error: BaseException, *, inputs: Optional[Dict[str, Any]] = None, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → Run¶
End a trace for an LLM run.
on_llm_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html |
1d73365b3f19-3 | Handle an error for an LLM run.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Run¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → Run¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → Run¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html |
1d73365b3f19-4 | End a trace for a tool run.
on_tool_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a tool run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html |
47b2042bf717-0 | langchain.callbacks.base.BaseCallbackHandler¶
class langchain.callbacks.base.BaseCallbackHandler[source]¶
Base callback handler that can be used to handle callbacks from langchain.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...]) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackHandler.html |
47b2042bf717-1 | on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__()¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackHandler.html |
47b2042bf717-2 | Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM ends running.
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) – | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackHandler.html |
47b2042bf717-3 | information. (containing content and other) –
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackHandler.html |
47b2042bf717-4 | Run when tool ends running.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when tool starts running.
Examples using BaseCallbackHandler¶
Ollama
Custom callback handlers
Multiple callback handlers
Async callbacks
Streaming final agent output | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackHandler.html |
16106fbe5720-0 | langchain.callbacks.tracers.schemas.ToolRun¶
class langchain.callbacks.tracers.schemas.ToolRun[source]¶
Bases: BaseRun
Class for ToolRun.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param action: str [Required]¶
param child_chain_runs: List[langchain.callbacks.tracers.schemas.ChainRun] [Optional]¶
param child_execution_order: int [Required]¶
param child_llm_runs: List[langchain.callbacks.tracers.schemas.LLMRun] [Optional]¶
param child_tool_runs: List[langchain.callbacks.tracers.schemas.ToolRun] [Optional]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param output: Optional[str] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param tool_input: str [Required]¶
param uuid: str [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ToolRun.html |
16106fbe5720-1 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ToolRun.html |
16106fbe5720-2 | classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ToolRun.html |
c8d99b661ecb-0 | langchain.callbacks.llmonitor_callback.identify¶
langchain.callbacks.llmonitor_callback.identify(user_id: str, user_props: Any = None) → UserContextManager[source]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.llmonitor_callback.identify.html |
c6558953cadb-0 | langchain.callbacks.tracers.schemas.RunTypeEnum¶
langchain.callbacks.tracers.schemas.RunTypeEnum() → RunTypeEnum[source]¶
RunTypeEnum. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.RunTypeEnum.html |
2d2ef813a3f5-0 | langchain.callbacks.manager.AsyncCallbackManagerForToolRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForToolRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Async callback manager for tool run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForToolRun.html |
2d2ef813a3f5-1 | on_tool_error(error, **kwargs)
Run when tool errors.
__init__(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
get_child(tag: Optional[str] = None) → AsyncCallbackManager¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
The child callback manager.
Return type
AsyncCallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForToolRun.html |
2d2ef813a3f5-2 | Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
async on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (str) – The output of the tool.
async on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
Examples using AsyncCallbackManagerForToolRun¶
Defining Custom Tools | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForToolRun.html |
29b76a19d9ae-0 | langchain.callbacks.manager.AsyncRunManager¶
class langchain.callbacks.manager.AsyncRunManager(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Async Run Manager.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncRunManager.html |
29b76a19d9ae-1 | on_text(text, **kwargs)
Run when text is received.
__init__(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_retry(retry_state: RetryCallState, **kwargs: Any) → None[source]¶
Run on a retry event.
async on_text(text: str, **kwargs: Any) → Any[source]¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncRunManager.html |
620ee1a16b5b-0 | langchain.callbacks.openai_info.OpenAICallbackHandler¶
class langchain.callbacks.openai_info.OpenAICallbackHandler[source]¶
Callback Handler that tracks OpenAI info.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
completion_tokens
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
prompt_tokens
raise_error
run_inline
successful_requests
total_cost
total_tokens
Methods
__init__()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Collect token usage.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Print out the token.
on_llm_start(serialized, prompts, **kwargs)
Print out the prompts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
620ee1a16b5b-1 | Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__()¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
620ee1a16b5b-2 | Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Collect token usage.
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Print out the token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Print out the prompts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
620ee1a16b5b-3 | Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when tool starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
5b75d92f6c53-0 | langchain.callbacks.tracers.wandb.WandbRunArgs¶
class langchain.callbacks.tracers.wandb.WandbRunArgs[source]¶
Arguments for the WandbTracer.
job_type: Optional[str]¶
dir: Optional[StrPath]¶
config: Union[Dict, str, None]¶
project: Optional[str]¶
entity: Optional[str]¶
reinit: Optional[bool]¶
tags: Optional[Sequence]¶
group: Optional[str]¶
name: Optional[str]¶
notes: Optional[str]¶
magic: Optional[Union[dict, str, bool]]¶
config_exclude_keys: Optional[List[str]]¶
config_include_keys: Optional[List[str]]¶
anonymous: Optional[str]¶
mode: Optional[str]¶
allow_val_change: Optional[bool]¶
resume: Optional[Union[bool, str]]¶
force: Optional[bool]¶
tensorboard: Optional[bool]¶
sync_tensorboard: Optional[bool]¶
monitor_gym: Optional[bool]¶
save_code: Optional[bool]¶
id: Optional[str]¶
settings: Union[WBSettings, Dict[str, Any], None]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbRunArgs.html |
4b8ec927227a-0 | langchain.callbacks.labelstudio_callback.LabelStudioMode¶
class langchain.callbacks.labelstudio_callback.LabelStudioMode(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
PROMPT = 'prompt'¶
CHAT = 'chat'¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.labelstudio_callback.LabelStudioMode.html |
1d1c06b96bd7-0 | langchain.callbacks.aim_callback.import_aim¶
langchain.callbacks.aim_callback.import_aim() → Any[source]¶
Import the aim python package and raise an error if it is not installed. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.import_aim.html |
832683ae0041-0 | langchain.callbacks.confident_callback.DeepEvalCallbackHandler¶
class langchain.callbacks.confident_callback.DeepEvalCallbackHandler(metrics: List[Any], implementation_name: Optional[str] = None)[source]¶
Callback Handler that logs into deepeval.
Parameters
implementation_name – name of the implementation in deepeval
metrics – A list of metrics
Raises
ImportError – if the deepeval package is not installed.
Examples
>>> from langchain.llms import OpenAI
>>> from langchain.callbacks import DeepEvalCallbackHandler
>>> from deepeval.metrics import AnswerRelevancy
>>> metric = AnswerRelevancy(minimum_score=0.3)
>>> deepeval_callback = DeepEvalCallbackHandler(
... implementation_name="exampleImplementation",
... metrics=[metric],
... )
>>> llm = OpenAI(
... temperature=0,
... callbacks=[deepeval_callback],
... verbose=True,
... openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
... "What is the best evaluation tool out there? (no bias at all)",
... ])
"Deepeval, no doubt about it."
Initializes the deepevalCallbackHandler.
Parameters
implementation_name – Name of the implementation you want.
metrics – What metrics do you want to track?
Raises
ImportError – if the deepeval package is not installed.
ConnectionError – if the connection to deepeval fails.
Attributes
BLOG_URL
ISSUES_URL
REPO_URL
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.confident_callback.DeepEvalCallbackHandler.html |
832683ae0041-1 | Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(metrics[, implementation_name])
Initializes the deepevalCallbackHandler.
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing
on_chain_end(outputs, **kwargs)
Do nothing when chain ends.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
Do nothing when chain starts
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log records to deepeval when an LLM ends.
on_llm_error(error, **kwargs)
Do nothing when LLM outputs an error.
on_llm_new_token(token, **kwargs)
Do nothing when a new token is generated.
on_llm_start(serialized, prompts, **kwargs)
Store the prompts
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Do nothing
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.confident_callback.DeepEvalCallbackHandler.html |
832683ae0041-2 | Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
__init__(metrics: List[Any], implementation_name: Optional[str] = None) → None[source]¶
Initializes the deepevalCallbackHandler.
Parameters
implementation_name – Name of the implementation you want.
metrics – What metrics do you want to track?
Raises
ImportError – if the deepeval package is not installed.
ConnectionError – if the connection to deepeval fails.
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when chain ends.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when chain starts
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log records to deepeval when an LLM ends. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.confident_callback.DeepEvalCallbackHandler.html |
832683ae0041-3 | Log records to deepeval when an LLM ends.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Store the prompts
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.confident_callback.DeepEvalCallbackHandler.html |
832683ae0041-4 | on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Examples using DeepEvalCallbackHandler¶
Confident | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.confident_callback.DeepEvalCallbackHandler.html |
2bed9a72461d-0 | langchain.callbacks.flyte_callback.FlyteCallbackHandler¶
class langchain.callbacks.flyte_callback.FlyteCallbackHandler[source]¶
This callback handler that is used within a Flyte task.
Initialize callback handler.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__()
Initialize callback handler.
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...]) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html |
2bed9a72461d-1 | on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Run when agent is ending.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
reset_callback_meta()
Reset the callback metadata.
__init__() → None[source]¶
Initialize callback handler.
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html |
2bed9a72461d-2 | Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html |
2bed9a72461d-3 | Run when tool ends running.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
Examples using FlyteCallbackHandler¶
Flyte | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html |
7ba21ae4d03f-0 | langchain.callbacks.tracers.schemas.TracerSessionBase¶
class langchain.callbacks.tracers.schemas.TracerSessionBase[source]¶
Bases: TracerSessionV1Base
Base class for TracerSession.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html |
7ba21ae4d03f-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html |
7ba21ae4d03f-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html |
fd5a95c83479-0 | langchain.callbacks.streamlit.mutable_expander.ChildType¶
class langchain.callbacks.streamlit.mutable_expander.ChildType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
The enumerator of the child type.
MARKDOWN = 'MARKDOWN'¶
EXCEPTION = 'EXCEPTION'¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.mutable_expander.ChildType.html |
fa46d69dd32d-0 | langchain.callbacks.tracers.stdout.FunctionCallbackHandler¶
class langchain.callbacks.tracers.stdout.FunctionCallbackHandler(function: Callable[[str], None], **kwargs: Any)[source]¶
Tracer that calls a function with a single str parameter.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
name
raise_error
run_inline
Methods
__init__(function, **kwargs)
get_breadcrumbs(run)
get_parents(run)
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, inputs])
End a trace for a chain run.
on_chain_error(error, *[, inputs])
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.FunctionCallbackHandler.html |
fa46d69dd32d-1 | Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
__init__(function: Callable[[str], None], **kwargs: Any) → None[source]¶
get_breadcrumbs(run: Run) → str[source]¶
get_parents(run: Run) → List[Run][source]¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any) → Run¶
End a trace for a chain run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.FunctionCallbackHandler.html |
fa46d69dd32d-2 | End a trace for a chain run.
on_chain_error(error: BaseException, *, inputs: Optional[Dict[str, Any]] = None, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → Run¶
End a trace for an LLM run.
on_llm_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Run¶
Run on new LLM token. Only available when streaming is enabled. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.FunctionCallbackHandler.html |
fa46d69dd32d-3 | Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → Run¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → Run¶
End a trace for a tool run.
on_tool_error(error: BaseException, *, run_id: UUID, **kwargs: Any) → Run¶
Handle an error for a tool run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.FunctionCallbackHandler.html |
fa46d69dd32d-4 | Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any) → Run¶
Start a trace for a tool run. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.FunctionCallbackHandler.html |
426df4f851db-0 | langchain.callbacks.streamlit.streamlit_callback_handler.LLMThought¶
class langchain.callbacks.streamlit.streamlit_callback_handler.LLMThought(parent_container: DeltaGenerator, labeler: LLMThoughtLabeler, expanded: bool, collapse_on_complete: bool)[source]¶
A thought in the LLM’s thought stream.
Initialize the LLMThought.
Parameters
parent_container – The container we’re writing into.
labeler – The labeler to use for this thought.
expanded – Whether the thought should be expanded by default.
collapse_on_complete – Whether the thought should be collapsed.
Attributes
container
The container we're writing into.
last_tool
The last tool executed by this thought
Methods
__init__(parent_container, labeler, ...)
Initialize the LLMThought.
clear()
Remove the thought from the screen.
complete([final_label])
Finish the thought.
on_agent_action(action[, color])
on_llm_end(response, **kwargs)
on_llm_error(error, **kwargs)
on_llm_new_token(token, **kwargs)
on_llm_start(serialized, prompts)
on_tool_end(output[, color, ...])
on_tool_error(error, **kwargs)
on_tool_start(serialized, input_str, **kwargs)
__init__(parent_container: DeltaGenerator, labeler: LLMThoughtLabeler, expanded: bool, collapse_on_complete: bool)[source]¶
Initialize the LLMThought.
Parameters
parent_container – The container we’re writing into.
labeler – The labeler to use for this thought.
expanded – Whether the thought should be expanded by default.
collapse_on_complete – Whether the thought should be collapsed.
clear() → None[source]¶
Remove the thought from the screen. A cleared thought can’t be reused. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.LLMThought.html |
426df4f851db-1 | Remove the thought from the screen. A cleared thought can’t be reused.
complete(final_label: Optional[str] = None) → None[source]¶
Finish the thought.
on_agent_action(action: AgentAction, color: Optional[str] = None, **kwargs: Any) → Any[source]¶
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
on_llm_start(serialized: Dict[str, Any], prompts: List[str]) → None[source]¶
on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.LLMThought.html |