RAG Overview

Retrieval Augmented Generation with PraisonAI

RAG Overview

Retrieval Augmented Generation (RAG) combines the power of large language models with your own data. Instead of relying solely on the model’s training data, RAG retrieves relevant information from your documents and uses it to generate accurate, grounded answers.

How It Works

Diagram

Index: Your documents are chunked and stored as embeddings
Retrieve: When you ask a question, relevant chunks are found
Generate: The LLM uses retrieved context to answer
Cite: Sources are tracked for transparency

Architecture

PraisonAI’s RAG is built on a simple principle:

Knowledge indexes; RAG answers with citations.

Knowledge: Handles document ingestion, chunking, embedding, and retrieval
RAG: Thin orchestrator that combines Knowledge retrieval with LLM generation

This separation keeps the core SDK lean while providing powerful RAG capabilities.

Quick Example

python

from praisonaiagents import Agent

# Simplest approach - just works
agent = Agent(
    name="Research Assistant",
    knowledge=["research_paper.pdf", "data/"],
)

response = agent.start("What are the key findings?")
print(response)

When to Use RAG

Use Case	RAG Helps?
Q&A over documents	✅ Yes
Summarizing reports	✅ Yes
Code documentation lookup	✅ Yes
General knowledge questions	❌ No (use base LLM)
Real-time data	❌ No (use tools/APIs)