# Semantic Search

> A search approach that understands the meaning and intent of queries rather than just matching keywords. Using vector embeddings and similarity measures, semantic search finds conceptually relevant results even when exact terms don't match, enabling natural language queries and cross-lingual retrieval.

- **URL:** https://en.wikipedia.org/wiki/Semantic_search
- **Category:** Concepts & Definitions
- **Tags:** search, NLP, Embeddings
- **Updated:** 2026-03-22 13:28
- **Canonical page:** https://vectordb.ever.works/items/semantic-search

## Details

## Overview

Semantic search finds results based on meaning and context rather than exact keyword matching. By representing queries and documents as vector embeddings, semantic search captures conceptual similarity and enables more intuitive, natural language search experiences.

## How It Works

1. **Encode Query**: Convert search query to vector embedding
2. **Encode Documents**: Pre-compute embeddings for all documents
3. **Similarity Search**: Find documents with closest embeddings (cosine similarity, etc.)
4. **Rank Results**: Return top-k most similar documents

## Key Advantages

- **Conceptual Matching**: Finds semantically similar content, not just keywords
- **Synonym Handling**: Automatically understands different ways to express ideas
- **Natural Language**: Works with conversational queries
- **Cross-Lingual**: Can match across languages with multilingual models
- **Context Aware**: Considers word sense and context

## Example

Traditional keyword search:
- Query: "best laptop for programming"
- Matches: Documents with exact words "laptop" and "programming"

Semantic search:
- Query: "best laptop for programming"
- Matches: Documents about "developer machines," "coding computers," "software engineering workstations"

## Use Cases

- **Enterprise Search**: Finding relevant documents in knowledge bases
- **E-commerce**: Product discovery beyond keyword matching
- **Customer Support**: Matching questions to similar resolved tickets
- **Academic Research**: Finding conceptually related papers
- **Content Recommendation**: Suggesting similar articles or videos
- **Legal/Medical**: Finding precedents or similar cases

## Implementation Components

### Embedding Models
- Sentence Transformers
- OpenAI embeddings
- Cohere embed
- BGE, GTE, E5 models

### Vector Databases
- Pinecone, Weaviate, Qdrant
- Elasticsearch, OpenSearch
- PostgreSQL with pgvector

### RAG Integration
- LangChain
- LlamaIndex
- Haystack

## Hybrid Approach

Best practice combines semantic + keyword search:
- BM25 for exact term matching
- Vector search for semantic similarity
- Reciprocal Rank Fusion to merge results

## Limitations

- Requires good embedding model
- Computational overhead for embeddings
- May miss exact matches if not hybrid
- Quality depends on training data
- Less effective for rare/technical terms

## Performance Considerations

- Pre-compute document embeddings offline
- Use ANN indexes for fast retrieval
- Cache frequently used query embeddings
- Consider quantization for storage

## Pricing

Varies by embedding API and vector database platform.

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