My RAG pipeline couldn't find the CEO — here's how I fixed it with hybrid retrieval
The article discusses improvements made to a RAG pipeline that initially struggled to retrieve specific information such as the CEO's name. The author identified that the issue stemmed from a dense table of information that muddled the semantic search results. By implementing a hybrid retrieval approach combining semantic and keyword searches, the pipeline successfully retrieved accurate answers.
- ▪The initial RAG pipeline scored 17 out of 19 on the test set but failed to answer specific questions about the CEO and employee count.
- ▪The problem was due to a dense 'Company snapshot' table that averaged multiple topics, weakening the search results.
- ▪The author implemented a hybrid retrieval method using FAISS for semantic similarity and BM25 for keyword matching, merging results with Reciprocal Rank Fusion.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3959064) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Santanu Mohanta Posted on Jun 3 My RAG pipeline couldn't find the CEO — here's how I fixed it with hybrid retrieval #rag #python #ai #fastapi In my last post, I built a RAG pipeline from scratch — no LangChain, just FastAPI + FAISS. It scored 17/19 on my test set. But two questions failed: "Who is the CEO?" — couldn't find it "How many employees does Zentara have?" — couldn't find it Both answers were right there on page 1.
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