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Introduction to RAG

  • RAG(Retrieval-Augmented Generation) combines retrieval and generation to enhance AI model responses
  • Retrieves relevant knowledge from external data sources and feeds it into the model

  • Provides more accurate, grounded, up-to-date answers than standalone LLMs

  • Core use cases:

    • enterprise knowledge assistants
    • chatbots with factual grounding
    • document Q&A

  • RAG workflow:

    • Query → Vector DB finds closest vectors
    • Matched text fed into LLM

NVIDIA UI RAG and VectorDB

Introduction to Vector Databases

  • Vector databases store and search high-dimensional vectors generated by AI models
  • Their goal is to provide fast, accurate, scalable similarity search
  • Core use cases:
    • RAG
    • semantic search
    • recommendation systems
    • anomaly detection

What Are Vectors?

  • A vector is a list of numbers representing meaning or features
  • AI models convert text, images, audio, or video into embeddings
  • Similar items → vectors close in space
  • Example similarities/differences:
    • “cat” ↔ “kitten” (similar)
    • “car” ↔ “banana” (different)

Why Vector Databases?

  • Traditional databases cannot efficiently search high-dimensional vectors
  • Vector DBs support Approximate Nearest Neighbor (ANN) search
  • ANN enables millisecond-level retrieval at billion scale
  • Benefits:
    • Fast search
    • High scalability
    • Built-in indexing structures
    • Real-time updates

Key Operations

  • Store vectors
  • Index vectors
  • Search using similarity metrics (cosine, dot product, Euclidean)

Vector Similarity Search

  • The output will produce a value ranging from -1 to 1, indicating similarity where -1 is non-similar, 0 is orthogonal (perpendicular), and 1 represents total similarity.

Vector Similarity Search

  • Filter using metadata (tags, categories, user IDs)

Popular Vector Databases

  • Milvus – open-source, cloud-native
  • Pinecone – fully managed
  • Weaviate – plugin-based, modular
  • Qdrant – Rust-based, lightweight
  • FAISS – library for vector search (not a full DB)

When to Use a Vector Database

  • You need semantic search
  • You handle multimodal data
  • You expect large-scale datasets
  • You need low-latency retrieval
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