langchain_community.callbacks.confident_callback.DeepEvalCallbackHandler

class langchain_community.callbacks.confident_callback.DeepEvalCallbackHandler(metrics: List[Any], implementation_name: Optional[str] = None)

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_community.llms import OpenAI
>>> from langchain_community.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.
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)	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

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

Do nothing when agent takes a specific action.

on_agent_finish(finish: AgentFinish, **kwargs: Any) → None

Do nothing

on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None

Do nothing when chain ends.

on_chain_error(error: BaseException, **kwargs: Any) → None

Do nothing when LLM chain outputs an error.

on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None

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

Log records to deepeval when an LLM ends.

on_llm_error(error: BaseException, **kwargs: Any) → None

Do nothing when LLM outputs an error.

on_llm_new_token(token: str, **kwargs: Any) → None

Do nothing when a new token is generated.

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

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

Do nothing

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

Do nothing when tool ends.

on_tool_error(error: BaseException, **kwargs: Any) → None

Do nothing when tool outputs an error.

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

Do nothing when tool starts.

Examples using DeepEvalCallbackHandler

• Confident