Source code for langchain_community.document_transformers.openai_functions

"""Document transformers that use OpenAI Functions models"""
from typing import Any, Dict, Optional, Sequence, Type, Union

from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel


[docs]class OpenAIMetadataTagger(BaseDocumentTransformer, BaseModel): """Extract metadata tags from document contents using OpenAI functions. Example: .. code-block:: python from langchain_community.chat_models import ChatOpenAI from langchain_community.document_transformers import OpenAIMetadataTagger from langchain_core.documents import Document schema = { "properties": { "movie_title": { "type": "string" }, "critic": { "type": "string" }, "tone": { "type": "string", "enum": ["positive", "negative"] }, "rating": { "type": "integer", "description": "The number of stars the critic rated the movie" } }, "required": ["movie_title", "critic", "tone"] } # Must be an OpenAI model that supports functions llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") tagging_chain = create_tagging_chain(schema, llm) document_transformer = OpenAIMetadataTagger(tagging_chain=tagging_chain) original_documents = [ Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\nThis is the greatest movie ever made. 4 out of 5 stars."), Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable": False}), ] enhanced_documents = document_transformer.transform_documents(original_documents) """ # noqa: E501 tagging_chain: Any """The chain used to extract metadata from each document."""
[docs] def transform_documents( self, documents: Sequence[Document], **kwargs: Any ) -> Sequence[Document]: """Automatically extract and populate metadata for each document according to the provided schema.""" new_documents = [] for document in documents: extracted_metadata: Dict = self.tagging_chain.run(document.page_content) # type: ignore[assignment] new_document = Document( page_content=document.page_content, metadata={**extracted_metadata, **document.metadata}, ) new_documents.append(new_document) return new_documents
[docs] async def atransform_documents( self, documents: Sequence[Document], **kwargs: Any ) -> Sequence[Document]: raise NotImplementedError
[docs]def create_metadata_tagger( metadata_schema: Union[Dict[str, Any], Type[BaseModel]], llm: BaseLanguageModel, prompt: Optional[ChatPromptTemplate] = None, *, tagging_chain_kwargs: Optional[Dict] = None, ) -> OpenAIMetadataTagger: """Create a DocumentTransformer that uses an OpenAI function chain to automatically tag documents with metadata based on their content and an input schema. Args: metadata_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary is passed in, it's assumed to already be a valid JsonSchema. For best results, pydantic.BaseModels should have docstrings describing what the schema represents and descriptions for the parameters. llm: Language model to use, assumed to support the OpenAI function-calling API. Defaults to use "gpt-3.5-turbo-0613" prompt: BasePromptTemplate to pass to the model. Returns: An LLMChain that will pass the given function to the model. Example: .. code-block:: python from langchain_community.chat_models import ChatOpenAI from langchain_community.document_transformers import create_metadata_tagger from langchain_core.documents import Document schema = { "properties": { "movie_title": { "type": "string" }, "critic": { "type": "string" }, "tone": { "type": "string", "enum": ["positive", "negative"] }, "rating": { "type": "integer", "description": "The number of stars the critic rated the movie" } }, "required": ["movie_title", "critic", "tone"] } # Must be an OpenAI model that supports functions llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613") document_transformer = create_metadata_tagger(schema, llm) original_documents = [ Document(page_content="Review of The Bee Movie\nBy Roger Ebert\n\nThis is the greatest movie ever made. 4 out of 5 stars."), Document(page_content="Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata={"reliable": False}), ] enhanced_documents = document_transformer.transform_documents(original_documents) """ # noqa: E501 from langchain.chains.openai_functions import create_tagging_chain metadata_schema = ( metadata_schema if isinstance(metadata_schema, dict) else metadata_schema.schema() ) _tagging_chain_kwargs = tagging_chain_kwargs or {} tagging_chain = create_tagging_chain( metadata_schema, llm, prompt=prompt, **_tagging_chain_kwargs ) return OpenAIMetadataTagger(tagging_chain=tagging_chain)