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- **Description:** This PR adds a new document compressor called `ListRerank`. It's derived from `BaseDocumentCompressor`. It's a near exact implementation of introduced by this paper: [Zero-Shot Listwise Document Reranking with a Large Language Model](https://arxiv.org/pdf/2305.02156.pdf) which it finds to outperform pointwise reranking, which is somewhat implemented in LangChain as [LLMChainFilter](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py). - **Issue:** None - **Dependencies:** None - **Tag maintainer:** @hwchase17 @izzymsft - **Twitter handle:** @HarrisEMitchell Notes: 1. I didn't add anything to `docs`. I wasn't exactly sure which patterns to follow as [cohere reranker is under Retrievers](https://python.langchain.com/docs/integrations/retrievers/cohere-reranker) with other external document retrieval integrations, but other contextual compression is [here](https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/). Happy to contribute to either with some direction. 2. I followed syntax, docstrings, implementation patterns, etc. as well as I could looking at nearby modules. One thing I didn't do was put the default prompt in a separate `.py` file like [Chain Filter](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_filter_prompt.py) and [Chain Extract](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_extract_prompt.py). Happy to follow that pattern if it would be preferred. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Chester Curme <chester.curme@gmail.com> |
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