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General

Introduction to Vector Databases

Vector databases are specialized storage systems designed to store and efficiently search high-dimensional vector embeddings, which are numerical representations of data useful in AI/ML tasks.

Why Vector Databases?

Traditional relational databases are great for structured data but fall short when it comes to similarity searches across text, images, or audio.

Core Features

  • Indexing: Algorithms like HNSW (Hierarchical Navigable Small Worlds) for fast nearest-neighbor searches.
  • Filtering: Meta-data filtering for combining keyword searches with semantic similarity.
  • Scaling: Distributed architectures for handling millions of embeddings.
  1. Qdrant: Highly performant with advanced filtering capabilities.
  2. Pinecone: Serverless, cloud-native vector search service.
  3. Milvus: Open-source, highly scalable vector database.
  4. Chroma: Lightweight and easy to set up for local development.

Use Cases

  • Retrieval-Augmented Generation (RAG): Providing context to LLMs.
  • Recommendation Systems: Finding similar products or users.
  • Semantic Search: Understanding the intent behind a user’s query.