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    Pythonlangchain-coreoutput_parsersopenai_functionsPydanticOutputFunctionsParser
    Class●Since v0.1

    PydanticOutputFunctionsParser

    Parse an output as a Pydantic object.

    This parser is used to parse the output of a chat model that uses OpenAI function format to invoke functions.

    The parser extracts the function call invocation and matches them to the Pydantic schema provided.

    An exception will be raised if the function call does not match the provided schema.

    Copy
    PydanticOutputFunctionsParser(
      self,
      *args: Any = (),
      **kwargs: Any = {}
    )

    Bases

    OutputFunctionsParser

    Example:

    message = AIMessage(
        content="This is a test message",
        additional_kwargs={
            "function_call": {
                "name": "cookie",
                "arguments": json.dumps({"name": "value", "age": 10}),
            }
        },
    )
    chat_generation = ChatGeneration(message=message)
    
    class Cookie(BaseModel):
        name: str
        age: int
    
    class Dog(BaseModel):
        species: str
    
    # Full output
    parser = PydanticOutputFunctionsParser(
        pydantic_schema={"cookie": Cookie, "dog": Dog}
    )
    result = parser.parse_result([chat_generation])

    Used in Docs

    • LangSmith LLM runs integration

    Attributes

    attribute
    pydantic_schema: type[BaseModel] | dict[str, type[BaseModel]]

    The Pydantic schema to parse the output with.

    If multiple schemas are provided, then the function name will be used to determine which schema to use.

    Methods

    method
    validate_schema

    Validate the Pydantic schema.

    method
    parse_result

    Parse the result of an LLM call to a JSON object.

    Inherited fromOutputFunctionsParser

    Attributes

    Aargs_only: bool
    —

    Whether to only return the arguments to the function call.

    Inherited fromBaseGenerationOutputParser

    Attributes

    AInputType: AnyAOutputType: Any

    Methods

    Minvoke
    —

    Invoke the retriever to get relevant documents.

    Mainvoke
    —

    Asynchronously invoke the retriever to get relevant documents.

    Inherited fromBaseLLMOutputParser

    Methods

    Maparse_result
    —

    Parse a list of candidate model Generation objects into a specific format.

    Inherited fromRunnableSerializable

    Attributes

    Aname: str
    —

    The name of the function.

    Amodel_config

    Methods

    Mto_json
    —

    Convert the graph to a JSON-serializable format.

    Mconfigurable_fieldsMconfigurable_alternatives
    —

    Configure alternatives for Runnable objects that can be set at runtime.

    Inherited fromSerializable

    Attributes

    Alc_secrets: dict[str, str]
    —

    A map of constructor argument names to secret ids.

    Alc_attributes: dict
    —

    List of attribute names that should be included in the serialized kwargs.

    Amodel_config

    Methods

    Mis_lc_serializable
    —

    Return True as this class is serializable.

    Mget_lc_namespace
    —

    Get the namespace of the LangChain object.

    Mlc_id
    —

    Return a unique identifier for this class for serialization purposes.

    Mto_json
    —

    Convert the graph to a JSON-serializable format.

    Mto_json_not_implemented
    —

    Serialize a "not implemented" object.

    Inherited fromRunnable

    Attributes

    Aname: str
    —

    The name of the function.

    AInputType: AnyAOutputType: AnyAinput_schema: type[BaseModel]
    —

    The type of input this Runnable accepts specified as a Pydantic model.

    Aoutput_schema: type[BaseModel]
    —

    Output schema.

    Aconfig_specs: list[ConfigurableFieldSpec]

    Methods

    Mget_nameMget_input_schemaMget_input_jsonschema
    —

    Get a JSON schema that represents the input to the Runnable.

    Mget_output_schemaMget_output_jsonschema
    —

    Get a JSON schema that represents the output of the Runnable.

    Mconfig_schema
    —

    The type of config this Runnable accepts specified as a Pydantic model.

    Mget_config_jsonschema
    —

    Get a JSON schema that represents the config of the Runnable.

    Mget_graphMget_prompts
    —

    Return a list of prompts used by this Runnable.

    Mpipe
    —

    Pipe Runnable objects.

    Mpick
    —

    Pick keys from the output dict of this Runnable.

    Massign
    —

    Merge the Dict input with the output produced by the mapping argument.

    Minvoke
    —

    Invoke the retriever to get relevant documents.

    Mainvoke
    —

    Asynchronously invoke the retriever to get relevant documents.

    MbatchMbatch_as_completed
    —

    Run invoke in parallel on a list of inputs.

    MabatchMabatch_as_completed
    —

    Run ainvoke in parallel on a list of inputs.

    MstreamMastreamMastream_log
    —

    Stream all output from a Runnable, as reported to the callback system.

    Mastream_events
    —

    Generate a stream of events.

    MtransformMatransformMbind
    —

    Bind arguments to a Runnable, returning a new Runnable.

    Mwith_configMwith_listeners
    —

    Bind lifecycle listeners to a Runnable, returning a new Runnable.

    Mwith_alisteners
    —

    Bind async lifecycle listeners to a Runnable.

    Mwith_types
    —

    Bind input and output types to a Runnable, returning a new Runnable.

    Mwith_retry
    —

    Create a new Runnable that retries the original Runnable on exceptions.

    Mmap
    —

    Map a function to multiple iterables.

    Mwith_fallbacks
    —

    Add fallbacks to a Runnable, returning a new Runnable.

    Mas_tool
    —

    Create a BaseTool from a Runnable.

    View source on GitHub