raptor-rag

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BlueBash / RAG-Raptor-RE-Ranker-demo

The method of re-ranking involves a two-stage retrieval system, with re-rankers playing a crucial role in evaluating the relevance of each document to the query. RAG systems can be optimized to mitigate hallucinations and ensure dependable search outcomes by selecting the optimal reranking model.

embeddings openai summarization reranker langchain retrieval-augmented-generation genrative-ai raptor-rag

Updated
Python

BlueBash / RAG-Raptor-demo

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RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models.

openai summarization langchain retrieval-augmented-generation raptor-rag

Updated
Python

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