Types of RAG (Retrieval-Augmented Generation) architectures with Lang Graph.

1. Standard RAG (Basic Retrieval + Generation)

How it works:

• The query goes to a retriever (vector database).
• Retrieved documents are sent to an LLM for response generation.
• A simple, single-step process.

Diagram:

?? User Query → ?? Retriever → ?? LLM → ?? Response

?? Use case: Basic Q&A over knowledge bases.

2. Conversational RAG (Memory-Augmented)

How it works:

• Stores conversation history to improve context.
• The retriever considers both the new query and past interactions.

Diagram:

?? User Query + Memory → ?? Retriever → ?? LLM → ?? Response

?? Use case: Chatbots, virtual assistants with context-awareness.

3. Hybrid RAG (Keyword + Vector Retrieval)

How it works:

• Uses two retrieval methods (dense vector + keyword search).
• Merges the results before sending them to the LLM.

Diagram:

?? Query → ?? (BM25 Search + Vector Search) → ?? Merge Results → ?? LLM → ?? Response

?? Use case: Improves accuracy by leveraging different retrieval techniques.

4. Multi-Hop RAG (Chain of Thought for Retrieval)

How it works:

• Breaks down a complex query into multiple sub-queries.
• Retrieves information iteratively and refines the response.

Diagram:

?? Query → ?? Retriever (Step 1) → ?? Retriever (Step 2) → ?? LLM → ?? Response

?? Use case: Multi-step reasoning tasks, research assistants.

5. Agentic RAG (LLM as an Agent with Tools)

How it works:

• The LLM decides when to retrieve, summarize, or use external tools.
• Can access APIs, web search, and calculators.

Diagram:

?? Query → ?? LLM Agent → ?? (Retriever / API / Calculator) → ?? Response

? Use case: AI agents that autonomously retrieve and process data.

6. Personalized RAG (User-Adaptive)

How it works:

• Retrieves documents based on user profile or past interactions.

Diagram:

?? Query + User Profile → ?? Retriever → ?? LLM → ?? Response

?? Use case: Personalized customer support, recommendations.

Krish Naik