Viveckh's Notepad

It is reasonable to frame generative AI (Gen AI) as having four core components—embedding, vector search, retrieval-augmented generation (RAG), and large language models (LLMs)—especially in the context of building practical systems that leverage Gen AI. Here's how these components fit into the picture:

# 1. Embeddings

  • Definition: Embeddings are dense vector representations of text, images, or other data that encode their semantic meaning.
  • Purpose: Convert data into a numerical form that models (e.g., LLMs) can understand and compare.
  • Role in Gen AI:
    • Used for semantic similarity comparisons (e.g., finding related documents).
    • Essential in powering vector search systems.

Example Tools:

  • OpenAI’s text-embedding-ada-002
  • Sentence Transformers
  • Hugging Face models for embeddings

# 2. Vector Search

  • Definition: A technique to efficiently search and retrieve data from a collection of vectors based on their similarity in high-dimensional space.
  • Purpose: Enable fast and accurate retrieval of relevant data from large datasets using similarity metrics like cosine similarity.
  • Role in Gen AI:
    • Supports retrieval-based tasks by locating relevant information from large knowledge bases.
    • Integrates with embeddings to provide context for LLMs.

Example Tools:

  • Pinecone, Weaviate, Vespa, Milvus
  • Elasticsearch with dense vector support
  • FAISS (Facebook AI Similarity Search)

# 3. Retrieval-Augmented Generation (RAG)

  • Definition: A framework that combines information retrieval with generative models to produce contextually informed outputs.
  • Purpose: Enhance generative AI by grounding responses in retrieved, relevant documents or knowledge.
  • Role in Gen AI:
    • Resolves the knowledge cutoff issue in LLMs by augmenting them with real-time, domain-specific knowledge.
    • Reduces hallucination by anchoring answers in factual references.

Typical Workflow:

  1. User query → Embed the query.
  2. Search embeddings in a vector database (vector search).
  3. Retrieve relevant documents.
  4. Feed the retrieved context to the LLM to generate a grounded response.

Example Applications:

  • Customer support systems
  • Document Q&A
  • Knowledge management tools

# 4. Large Language Models (LLMs)

  • Definition: Foundation models trained on massive corpora to generate and understand human-like text.
  • Purpose: Generate coherent, context-aware, and creative responses.
  • Role in Gen AI:
    • Serve as the generative backbone.
    • Can work standalone or in tandem with embeddings and vector search for RAG workflows.

Popular Models:

  • GPT (OpenAI)
  • PaLM (Google)
  • LLaMA (Meta)
  • Falcon, Cohere, etc.

# Relationship Between the Components

These components often work together as part of a pipeline:

  1. Embeddings: Represent queries and data semantically.
  2. Vector Search: Retrieve the most relevant documents using embeddings.
  3. RAG Framework: Incorporate retrieved information into LLM inputs for context.
  4. LLMs: Generate human-like responses enriched by retrieved knowledge.

# Why This Categorization Makes Sense

  • Embeddings and vector search provide the semantic understanding and retrieval capabilities.
  • RAG acts as the contextual glue that integrates search and generation.
  • LLMs provide the core generative functionality.

Embedding →