`langchain.document_transformers.openai_functions`.OpenAIMetadataTagger¶

class langchain.document_transformers.openai_functions.OpenAIMetadataTagger[source]¶

Bases: BaseDocumentTransformer, BaseModel

Extract metadata tags from document contents using OpenAI functions.

Example:

from langchain.chat_models import ChatOpenAI
from langchain.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

By Roger Ebert

This is the greatest movie ever made. 4 out of 5 stars.”),: Document(page_content=”Review of The Godfather

By Anonymous

This movie was super boring. 1 out of 5 stars.”, metadata={“reliable”: False}),

]

enhanced_documents = document_transformer.transform_documents(original_documents)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param tagging_chain: langchain.chains.llm.LLMChain [Required]¶: The chain used to extract metadata from each document.

async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶

Asynchronously transform a list of documents.

Parameters: documents – A sequence of Documents to be transformed.
Returns: A list of transformed Documents.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶: Automatically extract and populate metadata for each document according to the provided schema.

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

langchain.document_transformers.openai_functions.OpenAIMetadataTagger¶

`langchain.document_transformers.openai_functions`.OpenAIMetadataTagger¶