Vector Database
Semantic search and RAG with vector databases.
See [[VectorDB-Module]] for full documentation.
Quick Start
from openstackai.vectordb import ChromaDB
# Create store
db = ChromaDB(collection="knowledge")
# Add documents
db.add("openstackai is a Python SDK for AI agents")
db.add_documents(["doc1.txt", "doc2.pdf"])
# Search
results = db.search("What is openstackai?", top_k=5)
Supported Databases
| Database | Description |
|---|---|
| ChromaDB | Local, embedded vector store |
| Pinecone | Cloud-native, scalable |
| Qdrant | Self-hosted, feature-rich |
| Weaviate | GraphQL-based |
Features
- Semantic similarity search
- Document ingestion
- Metadata filtering
- Hybrid search
- Multiple embedding models
Related Pages
- [[VectorDB-Module]] - Full module documentation
- [[ChromaDB]] - ChromaDB integration
- [[Pinecone]] - Pinecone integration
- [[Qdrant]] - Qdrant integration
- [[Weaviate]] - Weaviate integration
- [[rag]] - RAG system