Rohan Paul (@rohanpaul_ai)
2024-10-07 | โค๏ธ 507 | ๐ 83
For a collection of advanced Retrieval-Augmented Generation (RAG) techniques this is a very resourceful repo.
Many topics are covered like
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Metadata Filtering: Apply filters based on attributes like date, source, author, or document type.
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Similarity Thresholds: Set thresholds for relevance scores to keep only the most pertinent results.
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Content Filtering: Remove results that donโt match specific content criteria or essential keywords.
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Diversity Filtering: Ensure result diversity by filtering out near-duplicate entries.
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LLM-based Scoring: Use a language model to score the relevance of each retrieved chunk.
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Cross-Encoder Models: Re-encode both the query and retrieved documents jointly for similarity scoring.
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Metadata-enhanced Ranking: Incorporate metadata into the scoring process for more nuanced ranking.
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