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Introduction to Vector Stores

Vector stores (or vector databases) are specialized types of databases that store data as high-dimensional vectors (or embeddings). These are essential for building Retrieval Augmented Generation (RAG) systems.

What is a Vector Database?

Unlike traditional databases that search for exact text matches, a vector database searches for data by understanding semantic meaning.

Key Components

  1. Embeddings: Numerical representations of data (text, images, audio) that capture their meaning.
  2. Indexing: Efficiently organizing vectors for fast searching and retrieval.
  3. Similarity Search: Finding the closest vectors to a query, often using methods like Cosine Similarity or Euclidean Distance.

Why Use Vector Databases?

Most Large Language Models (LLMs) can’t search for information across vast amounts of data in real-time. Vector databases act as an “external memory” for LLMs.

  • Pinecone: A managed vector database for fast and scalable AI search.
  • Weaviate: Open-source vector database with powerful search and classification features.
  • Chroma: A powerful and lightweight vector database for AI and LLM projects.
  • Milvus: An open-source vector database for enterprise-grade AI applications.

Use Cases

  • Retrieval Augmented Generation (RAG): Enhancing LLM responses with information retrieved from a vector database.
  • Semantic Search: Building search engines that understand the intent and meaning of the user query.
  • Similarity Search: Finding similar items, مانند similar images or similar products.