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Synthadoc benchmarks search, fuses keyword and vector search for improved wiki queries

By PulseAugur Editorial · [1 sources] ·

Synthadoc has benchmarked its search functionality to improve how it handles user queries in wikis. The tool found that while BM25 keyword search is efficient for smaller wikis, its performance degrades on larger ones, particularly with paraphrased questions. By fusing BM25 with semantic search and implementing a rule to prioritize BM25's top hits when it has a clear winner, Synthadoc significantly improved recall for paraphrased questions while maintaining accuracy for exact matches. Additionally, caching the BM25 index dramatically sped up keyword searches, and optimizations were made to semantic search and knowledge graph loading times. AI

IMPACT Improves LLM-powered wiki search by optimizing retrieval methods for better user query handling.

RANK_REASON Product update and feature benchmarking for an open-source tool.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Synthadoc benchmarks search, fuses keyword and vector search for improved wiki queries

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18 / 100
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Product update and feature benchmarking for an open-source tool.
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product, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Peter W. ·

    Should our LLM wiki use vector search? We benchmarked it before deciding

    <p>Every "add vector search" proposal comes with the same promise: users phrase questions their own way, keyword search misses, and embeddings fix it. A feature request on <a href="https://github.com/axoviq-ai/synthadoc" rel="noopener noreferrer">Synthadoc</a> made that case for …

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