How to Intermediate · 4 min read

How to add RAG to AutoGen

Q: How to add RAG to AutoGen

To add RAG to AutoGen, integrate a vector store retrieval step before generating responses. Use AutoGen to orchestrate conversation and query a vector database like FAISS or Chroma to fetch relevant documents, then pass those as context to the OpenAI or CrewAI model for generation.

Quick answer

To add RAG to AutoGen, integrate a vector store retrieval step before generating responses. Use AutoGen to orchestrate conversation and query a vector database like FAISS or Chroma to fetch relevant documents, then pass those as context to the OpenAI or CrewAI model for generation.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai crewai faiss-cpu chromadb

Setup

Install required packages and set your environment variables for API keys.

Install dependencies: pip install openai crewai faiss-cpu chromadb
Set OPENAI_API_KEY in your environment.

bash

pip install openai crewai faiss-cpu chromadb

Step by step

This example shows how to add RAG to AutoGen by embedding documents, storing them in a vector store, retrieving relevant context, and generating answers with CrewAI.

python

import os
from crewai import AutoGen
from openai import OpenAI
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

# Initialize OpenAI client
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Sample documents to embed and store
documents = [
    "AutoGen is a framework for orchestrating AI agents.",
    "RAG stands for Retrieval-Augmented Generation.",
    "FAISS is a popular vector store for similarity search.",
    "CrewAI simplifies multi-agent workflows with AI.",
]

# Create embeddings for documents
embedding_model = OpenAIEmbeddings(client=client)
embeddings = [embedding_model.embed_query(doc) for doc in documents]

# Build FAISS vector store
vector_store = FAISS.from_texts(documents, embedding_model)

# Function to retrieve relevant docs
def retrieve(query, k=2):
    query_embedding = embedding_model.embed_query(query)
    docs = vector_store.similarity_search_by_vector(query_embedding, k=k)
    return "\n".join(docs)

# AutoGen prompt with RAG context
def generate_answer(query):
    context = retrieve(query)
    prompt = f"Use the following context to answer the question:\n{context}\nQuestion: {query}\nAnswer:"
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Example usage
question = "What is RAG in AI?"
answer = generate_answer(question)
print("Q:", question)
print("A:", answer)

output

Q: What is RAG in AI?
A: RAG stands for Retrieval-Augmented Generation, a technique that combines document retrieval with AI generation to provide more accurate and context-aware answers.

Common variations

Use Chroma instead of FAISS for vector storage.
Switch to gpt-4o-mini or claude-3-5-sonnet-20241022 models for cost or performance tradeoffs.
Implement async retrieval and generation for higher throughput.

Troubleshooting

If retrieval returns no relevant documents, increase k or improve embedding quality.
For API rate limits, implement exponential backoff retries.
Ensure environment variables are correctly set to avoid authentication errors.

✅

Key Takeaways

Integrate a vector store retrieval step before generation to add RAG to AutoGen.
Use embeddings from OpenAI models and store them in FAISS or Chroma for fast similarity search.
Pass retrieved context as prompt input to the language model for accurate, context-aware answers.

Verified 2026-04 · gpt-4o, gpt-4o-mini, claude-3-5-sonnet-20241022

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