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Vector Search vs Semantic Search: What's the Difference?

by Ask0 Team

If you've explored AI-powered search tools, you've likely heard terms like "vector search" and "semantic search" used interchangeably. While they're related, understanding the distinction helps you make better decisions about implementing AI search in your documentation.

What is Vector Search?

Vector search is a technique for finding similar items by representing them as numerical vectors (arrays of numbers) in a high-dimensional space. Think of it as converting text, images, or other data into coordinates that capture their meaning.

How Vector Search Works

  1. Embedding: Content is transformed into vectors using AI models (like OpenAI's text-embedding models or open-source alternatives)
  2. Indexing: Vectors are stored in a specialized database optimized for similarity search
  3. Querying: User queries are also converted to vectors
  4. Similarity Matching: The system finds vectors closest to the query vector using distance metrics (cosine similarity, euclidean distance, etc.)
# Simplified example
query_vector = embed("How do I install Ask0?")
# query_vector = [0.234, -0.123, 0.567, ...]

# Find similar document vectors
results = vector_db.search(query_vector, top_k=5)

What is Semantic Search?

Semantic search is the broader concept of understanding the meaning behind a search query, not just matching keywords. It's the "what" while vector search is one of the "how" methods to achieve it.

Traditional Keyword Search vs Semantic Search

Keyword Search:

  • Query: "How to authenticate users"
  • Matches: Documents containing exact words "authenticate" and "users"
  • Misses: Documents about "login", "sign in", or "user verification"

Semantic Search:

  • Query: "How to authenticate users"
  • Matches: Documents about login, authentication, OAuth, JWT, user verification, etc.
  • Understands: "authenticate" and "login" are related concepts

Vector Search Enables Semantic Search

Vector search is the primary technique that makes semantic search possible at scale. Here's why:

1. Captures Contextual Meaning

Embeddings trained on massive text corpora understand that "authenticate", "login", and "sign in" are semantically related, even if the words differ.

2. Multilingual Understanding

Good embedding models can match queries across languages:

  • English query: "authentication"
  • Matches French docs: "authentification"
  • Matches Spanish docs: "autenticación"

3. Beyond Exact Matches

Vector search finds conceptually similar content:

  • Query: "reduce memory usage"
  • Matches: "optimize RAM consumption", "lower memory footprint", "memory leak fixes"

Other Approaches to Semantic Search

While vector search is dominant, other techniques exist:

1. Knowledge Graphs

Explicitly map relationships between concepts (e.g., "JWT" → "authentication" → "security")

2. Natural Language Processing (NLP)

Use linguistic analysis to understand query intent (entity recognition, dependency parsing)

3. Hybrid Search

Combine vector search with traditional keyword search for best results:

  • Vector search for semantic relevance
  • Keyword search for exact term matches
  • Rank fusion to merge results

Ask0's Approach

Ask0 uses a hybrid approach:

  1. Vector embeddings for understanding query meaning
  2. Metadata filtering for precise constraints (date, category, etc.)
  3. Reranking to optimize result ordering
  4. LLM generation to synthesize answers from retrieved content

This combination gives you the best of both worlds: semantic understanding with precise control.

Choosing Your Vector Database

When implementing vector search, you'll need a vector database. Popular options include:

  • Pinecone: Managed, easy to start
  • Weaviate: Open source, feature-rich
  • Qdrant: Fast, written in Rust
  • pgvector: PostgreSQL extension (great if you already use Postgres)
  • Milvus: Scalable, enterprise-grade

Ask0 supports multiple vector databases, so you can choose what fits your infrastructure.

Practical Considerations

Embedding Quality Matters

The quality of your semantic search depends heavily on the embedding model:

  • OpenAI ada-002: Great quality, API-based, cost per token
  • Cohere embeddings: Good for retrieval tasks
  • Open source models: E5, BGE, GTE (free, self-hostable)

Chunking Strategy

How you split your documents affects search quality:

  • Too large: Less precise retrieval
  • Too small: Loss of context
  • Just right: Usually 500-1000 tokens with overlap

Index Size and Cost

Vector indexes can be memory-intensive. A million documents with 1536-dimensional embeddings requires several GB of RAM.

Conclusion

Vector search is the technology that powers semantic search in modern AI applications. It converts text to numerical vectors, enabling computers to understand meaning and find conceptually similar content.

When building AI search for your documentation:

  1. Start with good embeddings (OpenAI or quality open-source models)
  2. Choose a vector database that fits your scale
  3. Consider hybrid search for best results
  4. Test and iterate on chunking strategies

Ask0 handles all this complexity for you, but understanding these concepts helps you make informed decisions about configuration and optimization.

Want to add semantic search to your docs? Get started with Ask0 or explore our open-source code.