Vector Database

Vector Database

Semantic search and RAG with vector databases.

See VectorDB-Module for full documentation.

Quick Start

from pyai.vectordb import ChromaDB

# Create store
db = ChromaDB(collection="knowledge")

# Add documents
db.add("PYAI is a Python SDK for AI agents")
db.add_documents(["doc1.txt", "doc2.pdf"])

# Search
results = db.search("What is PYAI?", 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