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 pydantic 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)