Source code for langchain.chains.openai_functions.extraction

from typing import Any, List, Optional

from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_functions import (
    JsonKeyOutputFunctionsParser,
    PydanticAttrOutputFunctionsParser,
)
from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
from pydantic import BaseModel

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import (
    _convert_schema,
    _resolve_schema_references,
    get_llm_kwargs,
)


def _get_extraction_function(entity_schema: dict) -> dict:
    return {
        "name": "information_extraction",
        "description": "Extracts the relevant information from the passage.",
        "parameters": {
            "type": "object",
            "properties": {
                "info": {"type": "array", "items": _convert_schema(entity_schema)}
            },
            "required": ["info"],
        },
    }


_EXTRACTION_TEMPLATE = """Extract and save the relevant entities mentioned \
in the following passage together with their properties.

Only extract the properties mentioned in the 'information_extraction' function.

If a property is not present and is not required in the function parameters, do not include it in the output.

Passage:
{input}
"""  # noqa: E501


[docs] @deprecated( since="0.1.14", message=( "LangChain has introduced a method called `with_structured_output` that" "is available on ChatModels capable of tool calling." "You can read more about the method here: " "<https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. " "Please follow our extraction use case documentation for more guidelines" "on how to do information extraction with LLMs." "<https://python.langchain.com/docs/use_cases/extraction/>. " "If you notice other issues, please provide " "feedback here:" "<https://github.com/langchain-ai/langchain/discussions/18154>" ), removal="1.0", alternative=( """ from pydantic import BaseModel, Field from langchain_anthropic import ChatAnthropic class Joke(BaseModel): setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") # Or any other chat model that supports tools. # Please reference to to the documentation of structured_output # to see an up to date list of which models support # with_structured_output. model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0) structured_llm = model.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats. Make sure to call the Joke function.") """ ), ) def create_extraction_chain( schema: dict, llm: BaseLanguageModel, prompt: Optional[BasePromptTemplate] = None, tags: Optional[List[str]] = None, verbose: bool = False, ) -> Chain: """Creates a chain that extracts information from a passage. Args: schema: The schema of the entities to extract. llm: The language model to use. prompt: The prompt to use for extraction. verbose: Whether to run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to the global `verbose` value, accessible via `langchain.globals.get_verbose()`. Returns: Chain that can be used to extract information from a passage. """ function = _get_extraction_function(schema) extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) output_parser = JsonKeyOutputFunctionsParser(key_name="info") llm_kwargs = get_llm_kwargs(function) chain = LLMChain( llm=llm, prompt=extraction_prompt, llm_kwargs=llm_kwargs, output_parser=output_parser, tags=tags, verbose=verbose, ) return chain
[docs] @deprecated( since="0.1.14", message=( "LangChain has introduced a method called `with_structured_output` that" "is available on ChatModels capable of tool calling." "You can read more about the method here: " "<https://python.langchain.com/docs/modules/model_io/chat/structured_output/>. " "Please follow our extraction use case documentation for more guidelines" "on how to do information extraction with LLMs." "<https://python.langchain.com/docs/use_cases/extraction/>. " "If you notice other issues, please provide " "feedback here:" "<https://github.com/langchain-ai/langchain/discussions/18154>" ), removal="1.0", alternative=( """ from pydantic import BaseModel, Field from langchain_anthropic import ChatAnthropic class Joke(BaseModel): setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") # Or any other chat model that supports tools. # Please reference to to the documentation of structured_output # to see an up to date list of which models support # with_structured_output. model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0) structured_llm = model.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats. Make sure to call the Joke function.") """ ), ) def create_extraction_chain_pydantic( pydantic_schema: Any, llm: BaseLanguageModel, prompt: Optional[BasePromptTemplate] = None, verbose: bool = False, ) -> Chain: """Creates a chain that extracts information from a passage using pydantic schema. Args: pydantic_schema: The pydantic schema of the entities to extract. llm: The language model to use. prompt: The prompt to use for extraction. verbose: Whether to run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to the global `verbose` value, accessible via `langchain.globals.get_verbose()` Returns: Chain that can be used to extract information from a passage. """ class PydanticSchema(BaseModel): info: List[pydantic_schema] # type: ignore if hasattr(pydantic_schema, "model_json_schema"): openai_schema = pydantic_schema.model_json_schema() else: openai_schema = pydantic_schema.schema() openai_schema = _resolve_schema_references( openai_schema, openai_schema.get("definitions", {}) ) function = _get_extraction_function(openai_schema) extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) output_parser = PydanticAttrOutputFunctionsParser( pydantic_schema=PydanticSchema, attr_name="info" ) llm_kwargs = get_llm_kwargs(function) chain = LLMChain( llm=llm, prompt=extraction_prompt, llm_kwargs=llm_kwargs, output_parser=output_parser, verbose=verbose, ) return chain