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Vector Searchvs
Full-Text Search
Vector Search vs Full-Text Search
A detailed look at how Vector Search compares to Full-Text Search.
Vector SearchFull-Text SearchKey Differentiators
Key Vector Search Advantages
- Works with any data type that can be embedded: text, images, audio, code.
- Finds semantically similar content without keyword overlap.
- Powers recommendation systems, duplicate detection, and anomaly detection.
- Foundation for RAG (Retrieval-Augmented Generation) pipelines.
Key Full-Text Search Advantages
- Exact and phrase matching with rich text analysis (stemming, synonyms, stopwords).
- Faceted search, aggregations, and analytics on structured fields.
- Sub-millisecond latency for keyword lookups on large corpora.
- Proven at scale: petabytes of data, billions of documents.
Vector search uses approximate nearest neighbor (ANN) algorithms on dense embeddings for semantic similarity. Full-text search uses inverted indexes with BM25 scoring for exact and fuzzy text matching. Each has distinct strengths; production systems increasingly combine both via hybrid search.
Vector Search vs. Full-Text Search
Data Structures & Algorithms
| Feature / Dimension | Vector Search | Full-Text Search |
|---|---|---|
| Index Type | HNSW graph, IVF clusters, PQ codebooks, DiskANN | Inverted index (term -> document postings lists) |
| Scoring | Cosine similarity, dot product, or Euclidean distance between vectors | BM25, TF-IDF, or custom relevance functions |
| Query Processing | Encode query to vector -> search nearest neighbors in index | Tokenize query -> look up terms in inverted index -> score and rank |
| Index Size | ~4-8 bytes/dimension/vector (e.g., 768d = 3KB per doc for float32) | Typically 20-50% of raw text size for inverted index |
| Memory Requirements | HNSW: entire graph in RAM (or use DiskANN for disk-based) | Posting lists on disk with term dictionary in memory; efficient caching |
Performance Characteristics
| Feature / Dimension | Vector Search | Full-Text Search |
|---|---|---|
| Search Latency | 5-50ms typical for ANN (depends on index type and recall target) | 1-10ms typical for keyword queries on warm cache |
| Indexing Throughput | Embedding generation: 100-10,000 docs/sec (bottleneck is ML model) | 10,000-100,000 docs/sec (bottleneck is tokenization and disk I/O) |
| Recall vs. Speed | Tunable trade-off: higher recall = slower search (ef, nprobe parameters) | Exact recall for keyword matches (no approximation) |
| Scale | Millions to low billions with careful tuning (memory-bound) | Billions of documents routinely (disk-friendly) |
| Filtering | Pre-filter or post-filter; complex filters can reduce recall in ANN | Efficient filtering via inverted index (bool queries, range, terms) |
Hybrid Search Approaches
| Feature / Dimension | Vector Search | Full-Text Search |
|---|---|---|
| RRF (Reciprocal Rank Fusion) | Combine vector and text result lists by rank position | Supported in Elasticsearch 8.x+ with kNN + BM25 |
| Sparse-Dense Hybrid | SPLADE or sparse vectors + dense vectors in same query (Pinecone, Qdrant, Weaviate) | BM25 inverted index + dense vector kNN in same query (Elasticsearch, Vespa) |
| Two-Stage Pipeline | Stage 1: fast retrieval (keyword or vector); Stage 2: reranker (cross-encoder, Cohere Rerank) | Stage 1: BM25 candidate generation; Stage 2: vector reranking or cross-encoder |
| Best Practice | Hybrid search with fusion or two-stage reranking outperforms either alone | Hybrid search with fusion or two-stage reranking outperforms either alone |
Technology Options
| Feature / Dimension | Vector Search | Full-Text Search |
|---|---|---|
| Dedicated Vector DBs | Pinecone, Qdrant, Milvus, Weaviate, Chroma, LanceDB | N/A |
| Dedicated Text Search | N/A | Elasticsearch, OpenSearch, Solr, Typesense, Meilisearch |
| Hybrid Systems | Elasticsearch (kNN), Weaviate (BM25+vector), Vespa, Qdrant (sparse+dense) | Elasticsearch (kNN), Weaviate (BM25+vector), Vespa, OpenSearch (kNN) |
| PostgreSQL | pgvector extension | Built-in tsvector/tsquery full-text search |
| All-in-One | Vespa, Weaviate, and Elasticsearch offer both in one system | Vespa, Weaviate, and Elasticsearch offer both in one system |
Bottom Line: Vector Search vs. Full-Text Search
| Feature / Dimension | Vector Search | Full-Text Search |
|---|---|---|
| Use Vector Search When | You need semantic similarity, multimodal search, RAG, or recommendation systems | Not ideal for exact keyword lookups or when you need transparent scoring |
| Use Full-Text When | Not ideal for concept matching, cross-modal search, or non-text data | You need exact matching, faceting, aggregations, or sub-ms latency on text |
| Best Practice | Combine both: hybrid search consistently outperforms either approach alone | Combine both: hybrid search consistently outperforms either approach alone |
| Cost Consideration | Higher cost (embedding model + vector storage) | Lower cost (no ML inference; efficient disk-based indexes) |
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