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Keyword search and semantic (vector) search both break in different ways. Keyword search is great for exact terms, IDs, acronyms, and codes — but it misses paraphrases and synonyms. Semantic search understands meaning and intent — but fails on things like *OAuth acronyms, PKCE flows, and error codes*. In this video, I break down hybrid search*: combining keyword + semantic retrieval to get *high precision *and high recall*. What you’ll learn: Why keyword search = high precision, low recall Why semantic search = high recall, lower precision Where each approach fails in real systems The most common *hybrid score fusion strategies*: Reciprocal Rank Fusion (RRF) Distribution-based score fusion Weighted combinations Max score Learned fusion using click signals How hybrid search catches real-world edge cases most systems miss Real demo: Searching developer docs for *“OAuth2 setup problems”*: Keyword-only → finds exact matches like OAuth2*, *PKCE (high precision, low recall) Semantic-only → finds conceptual matches like authentication issues (high recall, noisy) Hybrid search → catches both Acronyms like PKCE Concepts like secure login flow Achieves 91% relevance with the best overall coverage How this works in Mixpeek: Studio: Build a retriever with `attribute_filter` (keyword search) `feature_filter` (vector search) API: Multi-stage retrievers combining dense + sparse indexes in one request Supports text, image, and multimodal queries across collections If you’re building search, RAG, or retrieval systems that need to work in the real world — hybrid search is the default you should be using.