Vector Indexing
Vector indexing is the process of organizing high-dimensional vectors in data structures optimized for fast approximate nearest neighbor (ANN) search. Algorithms like HNSW, IVF, and Product Quantization enable sub-millisecond similarity searches across millions of vectors. The choice of index type directly affects search speed, memory usage, and recall accuracy.
What Is Vector Indexing?
When you store millions of document embeddings in a vector database, brute-force comparison of the query vector against every stored vector is computationally prohibitive. A query against 10 million 1,536-dimension vectors using exact search would take seconds, far too slow for interactive applications. Vector indexing solves this by building data structures that enable approximate nearest neighbor (ANN) search, finding the closest vectors in milliseconds by examining only a fraction of the dataset.
The most popular indexing algorithm is HNSW (Hierarchical Navigable Small World), which builds a multi-layered graph where each node is a vector and edges connect similar vectors. Search starts at a coarse top layer and refines through increasingly dense lower layers, typically examining only 200-500 vectors to find the top matches among millions. HNSW offers excellent recall (95-99%+), fast query times (under 5ms for 1M vectors), and good support for incremental insertions. Pinecone, Weaviate, Qdrant, and pgvector all use HNSW as their primary index type.
Alternative index types serve specific needs. IVF (Inverted File Index) partitions vectors into clusters and only searches relevant clusters at query time. It uses less memory than HNSW but requires a training step. Product Quantization compresses vectors to reduce memory by 4-16x, enabling larger collections to fit in RAM at the cost of some recall. ScaNN (Scalable Nearest Neighbors) from Google combines quantization with smart pruning for excellent speed/recall tradeoffs on very large datasets.
Index configuration requires balancing speed, recall, and memory. Higher HNSW parameters (ef_construction, M) improve recall but increase index build time and memory. For most production deployments with 100K to 10M vectors, default HNSW parameters achieve 95%+ recall with sub-10ms latency. Salt Technologies AI benchmarks index configurations against client query patterns and SLA requirements, optimizing for the specific recall/speed/cost tradeoff each project demands.
Real-World Use Cases
Real-Time Recommendation Engine
A streaming platform indexes 50 million content embeddings using HNSW with product quantization. The system retrieves personalized recommendations in under 5ms per request, serving 100,000 concurrent users with 97% recall accuracy.
Large-Scale Document Retrieval
A government agency indexes 20 million document chunks across 5 million regulatory documents. IVF indexing with 4,096 clusters enables searches across the entire corpus in under 50ms while keeping infrastructure costs manageable through memory-efficient quantization.
Image Similarity Search
An e-commerce company indexes CLIP embeddings for 10 million product images. Customers upload a photo and the system finds visually similar products in under 100ms using HNSW indexing on GPU-accelerated infrastructure.
Common Misconceptions
Vector indexing gives exact nearest neighbor results.
Most vector indexes use approximate nearest neighbor (ANN) algorithms that trade a small amount of accuracy for massive speed improvements. HNSW typically achieves 95-99% recall, meaning it finds 95-99 of the true 100 nearest neighbors. For RAG applications, this level of approximation is more than sufficient.
You need to choose between speed and accuracy.
Modern index algorithms offer tunable parameters that let you adjust the speed/accuracy tradeoff. HNSW's ef_search parameter controls how many candidates to evaluate at query time: higher values improve recall at the cost of latency. You can optimize this for your specific requirements.
All vector databases use the same indexing approach.
Different databases optimize for different scenarios. Pinecone uses a proprietary index optimized for serverless scaling. Weaviate and Qdrant use HNSW with different memory management strategies. pgvector supports both IVFFlat and HNSW. Choosing the right database depends on your scale, latency, and cost requirements.
Why Vector Indexing Matters for Your Business
Vector indexing determines whether your AI search system can handle production-scale traffic with acceptable latency. Without proper indexing, search latency grows linearly with dataset size, making large-scale AI applications impractical. As organizations build AI systems over growing document collections, understanding index types and their tradeoffs becomes essential for maintaining performance and controlling infrastructure costs.
How Salt Technologies AI Uses Vector Indexing
Salt Technologies AI selects and configures vector indexes based on each project's scale, latency requirements, and budget constraints. For most RAG deployments (under 5M vectors), we use HNSW with default parameters on managed vector databases like Pinecone or Weaviate. For larger deployments, we evaluate quantization and sharding strategies to balance cost and performance. We publish benchmark results in our Vector Database Performance Benchmark dataset to help teams make informed decisions.
Further Reading
- Vector Database Performance Benchmark 2026
Salt Technologies AI Datasets
- RAG vs Fine-Tuning: When to Use Each
Salt Technologies AI Blog
Related Terms
Vector Database
A vector database is a specialized data store designed to index, store, and query high-dimensional vector embeddings at scale. Unlike traditional databases that search by exact keyword matches, vector databases perform similarity search to find the most semantically relevant results. They are the critical infrastructure component in RAG systems, semantic search engines, and recommendation systems.
Embeddings
Embeddings are numerical vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space. Similar concepts produce similar vectors, enabling machines to measure meaning-based similarity between documents, sentences, or words. Embeddings are the mathematical backbone of semantic search, RAG systems, recommendation engines, and clustering applications.
Semantic Search
Semantic search uses vector embeddings to find documents based on meaning rather than keyword matching. It converts queries and documents into high-dimensional vectors, then finds the closest matches using distance metrics like cosine similarity. This approach understands synonyms, paraphrases, and conceptual relationships that keyword search completely misses.
Hybrid Search
Hybrid search combines vector (semantic) search with keyword (BM25/sparse) search to retrieve documents that match both the meaning and specific terms of a query. By fusing results from both approaches, hybrid search captures conceptual relevance and exact keyword matches that either method alone would miss. It is the recommended retrieval strategy for production RAG systems.
Retrieval Pipeline
A retrieval pipeline is the sequence of steps that finds and ranks the most relevant documents or data chunks in response to a user query. It typically includes query processing, embedding generation, vector search, optional keyword search, reranking, and filtering. The quality of your retrieval pipeline directly determines the quality of your RAG system's answers.
RAG Pipeline
A RAG pipeline is an architecture that augments large language model responses by retrieving relevant documents from an external knowledge base before generating answers. It combines retrieval (typically vector search) with generation, grounding LLM output in verified, up-to-date information. This pattern dramatically reduces hallucinations and enables domain-specific accuracy without retraining the model.