Core Concepts
Vector Search
Semantic search that understands meaning, not just keywords.
Vector search uses AI embeddings to find content based on semantic similarity. This means searching for "pricing" will also find content about "costs," "fees," and "subscription plans."
How It Works
- 1
Text to Vector
Your content is converted into high-dimensional vectors (embeddings)
- 2
Query Embedding
User questions are also converted to the same vector space
- 3
Similarity Search
Find content vectors closest to the query vector
Technology Stack
ChromaDB
Purpose-built vector database for fast similarity search
OpenAI Embeddings
State-of-the-art text embeddings for semantic understanding