ArgillaCallbackHandler#

class langchain_community.callbacks.argilla_callback.ArgillaCallbackHandler(dataset_name: str, workspace_name: str | None = None, api_url: str | None = None, api_key: str | None = None)[source]#

Callback Handler that logs into Argilla.

Parameters:

dataset_name (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (str | None) – 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 (str | None) – 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 (str | None) – 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_community.llms import OpenAI
>>> from langchain_community.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,
...     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 (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (str | None) – 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 (str | None) – 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 (str | None) – 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_custom_event`	Ignore custom event.
`ignore_llm`	Whether to ignore LLM callbacks.
`ignore_retriever`	Whether to ignore retriever callbacks.
`ignore_retry`	Whether to ignore retry callbacks.
`raise_error`	Whether to raise an error if an exception occurs.
`run_inline`	Whether to run the callback 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_custom_event`(name, data, *, run_id[, ...])	Override to define a handler for a custom event.
`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)	Run when the 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: str | None = None, api_url: str | None = None, api_key: str | None = None) → None[source]#

Initializes the ArgillaCallbackHandler.

Parameters:

dataset_name (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (str | None) – 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 (str | None) – 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 (str | None) – 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.

Return type:

None

on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]#

Do nothing when agent takes a specific action.

Parameters:

action (AgentAction)
kwargs (Any)

Return type:

Any

on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]#

Do nothing

Parameters:

finish (AgentFinish)
kwargs (Any)

Return type:

None

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.

Parameters:

outputs (Dict[str, Any])
kwargs (Any)

Return type:

None

on_chain_error(error: BaseException, **kwargs: Any) → None[source]#

Do nothing when LLM chain outputs an error.

Parameters:

error (BaseException)
kwargs (Any)

Return type:

None

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.

Parameters:

serialized (Dict[str, Any])
inputs (Dict[str, Any])
kwargs (Any)

Return type:

None

on_chat_model_start(serialized: dict[str, Any], messages: list[list[BaseMessage]], *, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any) → Any#

Run when a chat model starts running.

ATTENTION: This method is called for chat models. If you’re implementing: a handler for a non-chat model, you should use on_llm_start instead.

Parameters:

serialized (Dict[str, Any]) – The serialized chat model.
messages (List[List[BaseMessage]]) – The messages.
run_id (UUID) – The run ID. This is the ID of the current run.
parent_run_id (UUID) – The parent run ID. This is the ID of the parent run.
tags (Optional[List[str]]) – The tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
kwargs (Any) – Additional keyword arguments.

Return type:

Any

on_custom_event(name: str, data: Any, *, run_id: UUID, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any) → Any#

Override to define a handler for a custom event.

Parameters:

name (str) – The name of the custom event.
data (Any) – The data for the custom event. Format will match the format specified by the user.
run_id (UUID) – The ID of the run.
tags (list[str] | None) – The tags associated with the custom event (includes inherited tags).
metadata (dict[str, Any] | None) – The metadata associated with the custom event (includes inherited metadata).
kwargs (Any)

Return type:

Any

Added in version 0.2.15.

on_llm_end(response: LLMResult, **kwargs: Any) → None[source]#

Log records to Argilla when an LLM ends.

Parameters:

response (LLMResult)
kwargs (Any)

Return type:

None

on_llm_error(error: BaseException, **kwargs: Any) → None[source]#

Do nothing when LLM outputs an error.

Parameters:

error (BaseException)
kwargs (Any)

Return type:

None

on_llm_new_token(token: str, **kwargs: Any) → None[source]#

Do nothing when a new token is generated.

Parameters:

token (str)
kwargs (Any)

Return type:

None

on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]#

Save the prompts in memory when an LLM starts.

Parameters:

serialized (Dict[str, Any])
prompts (List[str])
kwargs (Any)

Return type:

None

on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) → Any#

Run when Retriever ends running.

Parameters:

documents (Sequence[Document]) – The documents retrieved.
run_id (UUID) – The run ID. This is the ID of the current run.
parent_run_id (UUID) – The parent run ID. This is the ID of the parent run.
kwargs (Any) – Additional keyword arguments.

Return type:

Any

on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) → Any#

Run when Retriever errors.

Parameters:

error (BaseException) – The error that occurred.
run_id (UUID) – The run ID. This is the ID of the current run.
parent_run_id (UUID) – The parent run ID. This is the ID of the parent run.
kwargs (Any) – Additional keyword arguments.

Return type:

Any

on_retriever_start(serialized: dict[str, Any], query: str, *, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any) → Any#

Run when the Retriever starts running.

Parameters:

serialized (Dict[str, Any]) – The serialized Retriever.
query (str) – The query.
run_id (UUID) – The run ID. This is the ID of the current run.
parent_run_id (UUID) – The parent run ID. This is the ID of the parent run.
tags (Optional[List[str]]) – The tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
kwargs (Any) – Additional keyword arguments.

Return type:

Any

on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) → Any#

Run on a retry event.

Parameters:

retry_state (RetryCallState) – The retry state.
run_id (UUID) – The run ID. This is the ID of the current run.
parent_run_id (UUID) – The parent run ID. This is the ID of the parent run.
kwargs (Any) – Additional keyword arguments.

Return type:

Any

on_text(text: str, **kwargs: Any) → None[source]#

Do nothing

Parameters:

text (str)
kwargs (Any)

Return type:

None

on_tool_end(output: Any, observation_prefix: str | None = None, llm_prefix: str | None = None, **kwargs: Any) → None[source]#

Do nothing when tool ends.

Parameters:

output (Any)
observation_prefix (str | None)
llm_prefix (str | None)
kwargs (Any)

Return type:

None

on_tool_error(error: BaseException, **kwargs: Any) → None[source]#

Do nothing when tool outputs an error.

Parameters:

error (BaseException)
kwargs (Any)

Return type:

None

on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]#

Do nothing when tool starts.

Parameters:

serialized (Dict[str, Any])
input_str (str)
kwargs (Any)

Return type:

None

Examples using ArgillaCallbackHandler

Argilla