langchain_community.callbacks.argilla_callback
.ArgillaCallbackHandler¶
- class langchain_community.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_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 – 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
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)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.
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]¶
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_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) None [source]¶
Do nothing when tool ends.