LLMChainExtractor#

class langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor[source]#

Document compressor that uses an LLM chain to extract the relevant parts of documents.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param get_input: Callable[[str, Document], dict] = <function default_get_input>#: Callable for constructing the chain input from the query and a Document.

param llm_chain: Runnable [Required]#: LLM wrapper to use for compressing documents.

async acompress_documents(documents: Sequence[Document], query: str, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None) → Sequence[Document][source]#

Compress page content of raw documents asynchronously.

Parameters:

Return type:

Sequence[Document]

compress_documents(documents: Sequence[Document], query: str, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None) → Sequence[Document][source]#

Compress page content of raw documents.

Parameters:

Return type:

Sequence[Document]

classmethod from_llm(llm: BaseLanguageModel, prompt: PromptTemplate | None = None, get_input: Callable[[str, Document], str] | None = None, llm_chain_kwargs: dict | None = None) → LLMChainExtractor[source]#

Initialize from LLM.

Parameters:

Return type:

LLMChainExtractor

Examples using LLMChainExtractor