RankLLMRerank

class langchain_community.document_compressors.rankllm_rerank.RankLLMRerank

Bases: BaseDocumentCompressor

Document compressor using Flashrank interface.

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 client: Any = None

RankLLM client to use for compressing documents

param gpt_model: str = 'gpt-3.5-turbo'

OpenAI model name.

param model: str = 'zephyr'

Name of model to use for reranking.

param step_size: int = 10

Step size for moving sliding window.

param top_n: int = 3

Top N documents to return.

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

Async compress retrieved documents given the query context.

Parameters:

• documents (Sequence[Document]) – The retrieved documents.

• query (str) – The query context.

• callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Optional callbacks to run during compression.

Returns:

The compressed documents.

Return type:

Sequence[Document]

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

Compress retrieved documents given the query context.

Parameters:

• documents (Sequence[Document]) – The retrieved documents.

• query (str) – The query context.

• callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Optional callbacks to run during compression.

Returns:

The compressed documents.

Return type:

Sequence[Document]

Examples using RankLLMRerank

• RankLLM Reranker