Vector Database Toolkit
Vector Database Toolkit Vector databases are the backbone of every RAG pipeline, semantic search engine, and recommendation system — but each one has different APIs, indexing strategies, and operat...
Source: DEV Community
Vector Database Toolkit Vector databases are the backbone of every RAG pipeline, semantic search engine, and recommendation system — but each one has different APIs, indexing strategies, and operational quirks. This toolkit gives you unified setup guides, working code examples, and benchmarking scripts for ChromaDB, Pinecone, Weaviate, and pgvector. Plus hybrid search patterns, indexing strategies, and production operational guides. Key Features Multi-Database Support — Unified Python client abstraction for ChromaDB, Pinecone, Weaviate, and pgvector with consistent CRUD operations Setup & Migration Guides — Step-by-step setup for each database, including Docker configs, cloud provisioning, and schema migration scripts Indexing Strategies — HNSW, IVF, and PQ index configuration with tuning guides for recall vs. speed tradeoffs Hybrid Search — Combine dense vector search with sparse keyword search across all supported backends Benchmarking Scripts — Measure query latency, throughput,
