langchain_community.callbacks.confident_callback.DeepEvalCallbackHandler

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

Callback Handler that logs into deepeval.

Parameters
  • implementation_name (Optional[str]) – name of the implementation in deepeval

  • metrics (List[Any]) – 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 (Optional[str]) – Name of the implementation you want.

  • metrics (List[Any]) – 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[source]

Initializes the deepevalCallbackHandler.

Parameters
  • implementation_name (Optional[str]) – Name of the implementation you want.

  • metrics (List[Any]) – What metrics do you want to track?

Raises
  • ImportError – if the deepeval package is not installed.

  • ConnectionError – if the connection to deepeval fails.

Return type

None

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

Do nothing when agent takes a specific action.

Parameters
Return type

Any

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

Do nothing

Parameters
Return type

None

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

Do nothing when chain ends.

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]

Do nothing when 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: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = 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]) –

  • messages (List[List[BaseMessage]]) –

  • run_id (UUID) –

  • parent_run_id (Optional[UUID]) –

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

Any

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

Log records to deepeval when an LLM ends.

Parameters
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]

Store the prompts

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: Optional[UUID] = None, **kwargs: Any) Any

Run when Retriever ends running.

Parameters
  • documents (Sequence[Document]) –

  • run_id (UUID) –

  • parent_run_id (Optional[UUID]) –

  • kwargs (Any) –

Return type

Any

on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) Any

Run when Retriever errors.

Parameters
  • error (BaseException) –

  • run_id (UUID) –

  • parent_run_id (Optional[UUID]) –

  • kwargs (Any) –

Return type

Any

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.

Parameters
  • serialized (Dict[str, Any]) –

  • query (str) –

  • run_id (UUID) –

  • parent_run_id (Optional[UUID]) –

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

Return type

Any

on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) Any

Run on a retry event.

Parameters
  • retry_state (RetryCallState) –

  • run_id (UUID) –

  • parent_run_id (Optional[UUID]) –

  • kwargs (Any) –

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: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) None[source]

Do nothing when tool ends.

Parameters
  • output (Any) –

  • observation_prefix (Optional[str]) –

  • llm_prefix (Optional[str]) –

  • 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 DeepEvalCallbackHandler