> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/avnlp/vectordb/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Production-ready RAG pipelines and vector database toolkit for Haystack and LangChain # VectorDB ## What is VectorDB? VectorDB provides a unified, production-oriented toolkit for Semantic Search and Retrieval-Augmented Generation across five vector databases, with feature parity between Haystack and LangChain. It ships ready-to-run pipelines for Dense, Sparse, and Hybrid Retrieval, plus advanced RAG capabilities like Reranking, Query Enhancement, Contextual Compression, Parent-Child Retrieval, and Agentic Retrieval Loops. The design is configuration-driven, environment-variable friendly, and built for consistent benchmarking across databases and datasets. Use VectorDB to build, compare, and deploy retrieval systems without re-implementing logic per backend. ## Supported vector databases Managed vector database with namespaces and native sparse-dense hybrid retrieval Open-source vector search with BM25 hybrid retrieval, collections, and multi-tenancy High-performance search with payload filtering and scalar or binary quantization Scalable vector database with partition-key isolation and hybrid retrieval Lightweight vector store for local development and rapid prototyping ## Key features Dense vector retrieval with metadata filters and optional answer generation Dense + sparse retrieval fused with RRF or weighted scoring Two-stage retrieval using cross-encoders for higher precision Multi-query, HyDE, and step-back prompting to improve recall Reduce retrieved context via reranking or LLM extraction Index chunks but return parent documents or context windows Tenant isolation using database-specific strategies at scale Iterative retrieval loop with search, reflect, and refine steps ## Built-in datasets and evaluation VectorDB includes dataset loaders and standardized evaluation utilities so you can benchmark retrieval quality across databases and frameworks. **Supported datasets:** * **TriviaQA** - Open-domain question-answer pairs for general knowledge retrieval * **ARC** - Science reasoning questions requiring multi-hop inference * **PopQA** - Factoid questions about popular entities * **FactScore** - Atomic facts for verification and hallucination detection * **Earnings Calls** - Financial transcript Q\&A for domain-specific RAG **Built-in evaluation metrics:** * Recall\@k * Precision\@k * MRR (Mean Reciprocal Rank) * NDCG\@k * Hit rate ## Get started Install VectorDB using uv package manager Build your first RAG pipeline in minutes