Compress page content of raw documents.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: Optional[langchain.prompts.prompt.PromptTemplate] = None, get_input: Optional[Callable[[str, langchain.schema.Document], str]] = None, llm_chain_kwargs: Optional[dict] = None) → langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor[source]#
Initialize from LLM.
pydantic model langchain.retrievers.document_compressors.LLMChainFilter[source]#
Filter that drops documents that aren’t relevant to the query.
field get_input: Callable[[str, langchain.schema.Document], dict] = <function default_get_input>#
Callable for constructing the chain input from the query and a Document.
field llm_chain: langchain.chains.llm.LLMChain [Required]#
LLM wrapper to use for filtering documents.
The chain prompt is expected to have a BooleanOutputParser.
async acompress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Filter down documents.
compress_documents(documents: Sequence[langchain.schema.Document], query: str) → Sequence[langchain.schema.Document][source]#
Filter down documents based on their relevance to the query.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, prompt: Optional[langchain.prompts.base.BasePromptTemplate] = None, **kwargs: Any) → langchain.retrievers.document_compressors.chain_filter.LLMChainFilter[source]#
previous
Retrievers
next
Document Transformers
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/document_compressors.html