> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Vector Quering Methods > Overview of seven vector database query methods and trade-offs, explaining semantic, KNN, hybrid, range, multi-vector, ANN, and two-stage retrieval with re-ranking. Welcome back. We’ve already seen how vector databases store embeddings — now let’s cover how you search them. There’s no single “best” approach: seven common query methods address different trade-offs between accuracy, latency, and functionality. Below is a concise overview of each method with guidance on when to use it. Later lessons can dive into implementation details and example code. 1. Semantic search 2. K-nearest neighbor (KNN) search 3. Hybrid search (vector + filters) 4. Range / threshold search 5. Multi-vector (multimodal) search 6. Approximate nearest neighbor (ANN) search 7. Two-stage retrieval with re-ranking Quick summary table | Method | Primary use case | When to choose | | -------------------------------- | ----------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | Semantic search | Natural language queries matched by meaning | When users search with free text and you want meaning-based ranking (e.g., QA, conversational search) | | KNN search | Exact similarity ranking by distance metrics | Small to medium datasets or when you require exhaustive nearest neighbors | | Hybrid search | Semantic + structured attribute filtering | E-commerce, catalogs, and scenarios requiring both semantic relevance and exact metadata filters | | Range / threshold search | Return only highly similar items above a cutoff | When precision matters more than recall (e.g., deduplication, near-duplicate detection) | | Multi-vector search | Multimodal queries (text + image, etc.) | When matching multiple modalities or combining complementary signals | | ANN search | Fast retrieval at large scale | Billion-scale collections where exhaustive KNN is infeasible | | Two-stage retrieval + re-ranking | Balance latency and final ranking quality | Production systems needing high precision without prohibitive latency | Semantic search * What it is: Convert a natural language query into an embedding, then retrieve items whose embeddings are semantically close. Results prioritize meaning over literal keyword overlap. * When to use: Conversational assistants, semantic product search, FAQ matching, code search. * Example: Query “find products similar to a gaming laptop” matches products that share the same intent or attributes even if they don’t share keywords. K-nearest neighbor (KNN) search * What it is: Compute exact distances (cosine, dot product, Euclidean) between the query vector and every stored vector, then return the top-k closest items. * When to use: Small-to-moderate datasets or workflows that require exact nearest neighbors (no approximation). * Trade-offs: Precise but computationally heavy at scale. Exhaustive KNN is exact but does not scale well to millions or billions of vectors — expect high CPU and memory costs as dataset size grows. The image illustrates "Vector Database Query Methods," focusing on K-Nearest Neighbors (KNN) and distance-based retrieval, highlighting techniques like Cosine, Euclidean, and Dot Product for finding similar results. Hybrid search * What it is: Combine vector similarity with structured filters (metadata, numeric ranges, boolean flags). For example: “Find items semantically similar to ‘cozy home decor’ AND price \< 100 AND in stock = true.” * When to use: E-commerce catalogs, inventory-aware recommendations, business search where both semantics and exact attributes matter. * Benefit: Precision from filters + recall and relevance from semantic matching. The image is an infographic titled "Vector Database Query Methods," depicting the concept of a Hybrid Search using vector and keyword filtering in a vector database. It illustrates how semantic queries and metadata filtering are combined for efficient searches. Range / threshold search * What it is: Instead of asking for top-k, return all items whose similarity exceeds a given threshold (e.g., similarity > 0.9). * When to use: High-precision needs like deduplication, exact matches by meaning, or flagging near-duplicates for moderation. * Trade-offs: You may get variable result counts (including zero); tune thresholds based on validation data. The image illustrates vector database query methods, highlighting the "Range/Threshold Search" as one of the methods, with details on setting similarity thresholds for filtering results. Multi-vector (multimodal) search * What it is: Submit multiple query vectors (for example, text + image). The database combines similarity scores (often weighted) to find results that satisfy multiple modalities simultaneously. * When to use: Product search that mixes example images and descriptive text, multimodal retrieval, or any use case where signals from different encoders should be combined. * Implementation note: You can weight each modality differently depending on importance (e.g., 0.7*image\_score + 0.3*text\_score). The image is a diagram titled "Vector Database Query Methods" showing a central "Vector Database" surrounded by steps, with step 5 highlighting "Multi-Vector Search" which involves multiple query vectors, combined similarity scoring, and multi-modal search capability. Approximate nearest neighbor (ANN) search * What it is: Use index structures and heuristics (e.g., HNSW, IVF, PQ) to find vectors likely to be near the query without exhaustively scanning every item. * When to use: Large-scale systems (millions to billions of vectors) where latency and cost are primary concerns. * Trade-offs: Slight loss in recall/accuracy in exchange for major gains in speed and reduced memory usage. * Practical note: ANN index parameters (e.g., ef, PQ code size, number of probes) let you tune the accuracy/latency trade-off. Two-stage retrieval with re-ranking * Pattern: A fast first stage retrieves a candidate set (often via ANN or a lightweight model). A slower, higher-precision model then re-scores those candidates to produce the final ranking. * Stage 1: Fast retriever returns top-N candidates (e.g., 100). * Stage 2: Re-ranker (cross-encoder or LLM-based scoring) refines ranking and filters results for quality. * When to use: Production search systems that need both low latency and high ranking quality (e.g., RAG, QA systems, enterprise search). * Benefit: Best of both worlds — you retain speed from ANN while achieving final precision with an expensive model. ANN + re-ranking is a common production pattern: use ANN to quickly get a compact candidate set, then apply a more expensive but precise model to re-rank for final quality. Wrap-up * The seven query methods above cover the core retrieval strategies used in modern vector search systems. Choose based on your dataset size, latency constraints, precision/recall needs, and whether you must combine structured filters or multiple modalities. * Next steps: explore index architectures (HNSW, IVF, PQ), tuning ANN parameters, and building re-rankers (cross-encoders or LLM-based scorers) for production-grade pipelines. Links and references * [Understanding KNN and distance metrics](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) * HNSW, IVF, PQ overviews: check vendor docs (e.g., Faiss, Milvus, Pinecone) and academic references * Two-stage retrieval / RAG patterns: various online guides and research papers That’s it for this lesson — see you in the next one.