ChatParrotLink Class — langchain Architecture

Architecture documentation for the ChatParrotLink class in custom_chat_model.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4["ChatParrotLink"]
  48aa29b8_65e7_522f_a445_a441eeb6baff["BaseChatModel"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|extends| 48aa29b8_65e7_522f_a445_a441eeb6baff
  e8c09d7d_da16_210c_b993_d1fd1cb09051["custom_chat_model.py"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|defined in| e8c09d7d_da16_210c_b993_d1fd1cb09051
  8acc69e5_bdb4_2351_c878_2f8a36d8b3c3["_generate()"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|method| 8acc69e5_bdb4_2351_c878_2f8a36d8b3c3
  ea8bbe3d_c7fc_58bc_8614_aed85432697f["_stream()"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|method| ea8bbe3d_c7fc_58bc_8614_aed85432697f
  f0f57b7f_37fd_95ca_4715_cd163af3cb60["_llm_type()"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|method| f0f57b7f_37fd_95ca_4715_cd163af3cb60
  1c024976_5d02_41e2_d92a_8da264f528fd["_identifying_params()"]
  827d4990_b3c8_3ba7_bcd4_0a554daa3db4 -->|method| 1c024976_5d02_41e2_d92a_8da264f528fd

Relationship Graph

Source Code

libs/standard-tests/tests/unit_tests/custom_chat_model.py lines 18–188

class ChatParrotLink(BaseChatModel):
    """Chat Parrot Link.

    A custom chat model that echoes the first `parrot_buffer_length` characters
    of the input.

    When contributing an implementation to LangChain, carefully document
    the model including the initialization parameters, include
    an example of how to initialize the model and include any relevant
    links to the underlying models documentation or API.

    Example:
    ```python
    model = ChatParrotLink(parrot_buffer_length=2, model="bird-brain-001")
    result = model.invoke([HumanMessage(content="hello")])
    result = model.batch(
        [
            [HumanMessage(content="hello")],
            [HumanMessage(content="world")],
        ]
    )
    ```
    """

    model_name: str = Field(alias="model")
    """The name of the model"""
    parrot_buffer_length: int
    """The number of characters from the last message of the prompt to be echoed."""
    temperature: float | None = None
    max_tokens: int | None = None
    timeout: int | None = None
    stop: list[str] | None = None
    max_retries: int = 2

    @override
    def _generate(
        self,
        messages: list[BaseMessage],
        stop: list[str] | None = None,
        run_manager: CallbackManagerForLLMRun | None = None,
        **kwargs: Any,
    ) -> ChatResult:
        """Override the _generate method to implement the chat model logic.

        This can be a call to an API, a call to a local model, or any other
        implementation that generates a response to the input prompt.

        Args:
            messages: the prompt composed of a list of messages.
            stop: a list of strings on which the model should stop generating.
                  If generation stops due to a stop token, the stop token itself
                  SHOULD BE INCLUDED as part of the output. This is not enforced
                  across models right now, but it's a good practice to follow since
                  it makes it much easier to parse the output of the model
                  downstream and understand why generation stopped.
            run_manager: A run manager with callbacks for the LLM.
            **kwargs: Additional keyword arguments.

        """
        # Replace this with actual logic to generate a response from a list
        # of messages.
        _ = stop  # Mark as used to avoid unused variable warning
        _ = run_manager  # Mark as used to avoid unused variable warning
        _ = kwargs  # Mark as used to avoid unused variable warning
        last_message = messages[-1]
        tokens = last_message.content[: self.parrot_buffer_length]
        ct_input_tokens = sum(len(message.content) for message in messages)
        ct_output_tokens = len(tokens)
        message = AIMessage(
            content=tokens,
            additional_kwargs={},  # Used to add additional payload to the message
            response_metadata={  # Use for response metadata
                "time_in_seconds": 3,
                "model_name": self.model_name,
            },
            usage_metadata={
                "input_tokens": ct_input_tokens,
                "output_tokens": ct_output_tokens,
                "total_tokens": ct_input_tokens + ct_output_tokens,
            },
        )