Building Knowledge-Grounded AI Agents: RAG Integration with Microsoft AutoGen

Building on code generation from Part 3, we now enhance our agents with knowledge retrieval capabilities.

RAG Architecture for Multi-Agent Systems

Traditional LLM agents rely solely on their training data, leading to hallucinations and outdated information. RAG addresses these limitations by retrieving relevant documents before generation, providing agents with factual context for their responses. In multi-agent systems, RAG enables specialized knowledge agents that access domain-specific document collections, enhancing the entire team’s capabilities.

AutoGen’s RetrieveAssistantAgent and RetrieveUserProxyAgent provide built-in RAG functionality. These agents automatically query vector databases, retrieve relevant documents, and incorporate retrieved context into conversations. Configure retrieval parameters including chunk size, overlap, similarity threshold, and maximum retrieved documents based on your knowledge base characteristics.

Vector database selection impacts retrieval quality and performance. ChromaDB offers simple local deployment for development. Pinecone provides managed infrastructure with excellent scaling. Weaviate combines vector search with structured filtering. Qdrant offers high-performance open-source options. Choose based on scale requirements, deployment preferences, and feature needs like hybrid search or metadata filtering.

flowchart TB
    DOC[Documents] --> CHUNK[Chunking]
    CHUNK --> EMBED[Embedding]
    EMBED --> VDB[(Vector DB)]
    QUERY[User Query] --> RET[Retriever]
    VDB --> RET
    RET --> CTX[Context]
    CTX --> AGENT[RAG Agent]
    AGENT --> RESP[Grounded Response]
    style VDB fill:#667eea,color:white
    style AGENT fill:#48bb78,color:white

Figure 1: RAG Pipeline Architecture

Document Processing and Embedding Pipeline

Effective RAG requires thoughtful document processing. Chunk documents into semantically meaningful segments—too small loses context, too large dilutes relevance. Overlap between chunks preserves context across boundaries. For technical documentation, respect code block and section boundaries. For conversational content, maintain dialogue coherence within chunks.

Embedding model selection affects retrieval accuracy. OpenAI’s text-embedding-ada-002 provides strong general-purpose embeddings. Sentence transformers offer open-source alternatives with domain-specific fine-tuning options. Cohere’s embeddings excel at multilingual content. Match embedding dimensions to your vector database configuration and consider embedding model updates’ impact on existing indexes.

Metadata enrichment improves retrieval precision. Tag documents with source, date, category, and custom attributes. Enable filtered retrieval—retrieve only documents from specific sources or time ranges. Implement hybrid search combining semantic similarity with keyword matching for queries requiring exact term matches.

Python Implementation: RAG-Enhanced Agent System

"""Microsoft AutoGen - RAG Integration"""
import autogen
from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
import chromadb
from typing import Dict, Any, List, Optional
from dataclasses import dataclass

@dataclass
class RAGConfig:
    docs_path: str
    collection_name: str = "knowledge_base"
    chunk_token_size: int = 500
    chunk_overlap: int = 50
    embedding_model: str = "text-embedding-ada-002"
    retrieve_top_k: int = 5

class RAGAgentSystem:
    """RAG-enhanced multi-agent system."""
    
    def __init__(self, llm_config: Dict[str, Any], rag_config: RAGConfig):
        self.llm_config = llm_config
        self.rag_config = rag_config
        self._setup_agents()
    
    def _setup_agents(self):
        """Initialize RAG-enabled agents."""
        
        # RAG Assistant with domain expertise
        self.rag_assistant = RetrieveAssistantAgent(
            name="RAG_Expert",
            system_message="""You are a knowledgeable assistant.
            
            Answer questions using the retrieved context.
            Always cite your sources with document references.
            If the context doesn't contain the answer, say so.
            Never make up information not in the documents.""",
            llm_config=self.llm_config,
        )
        
        # RAG User Proxy with retrieval capability
        self.rag_proxy = RetrieveUserProxyAgent(
            name="RAG_User",
            human_input_mode="NEVER",
            max_consecutive_auto_reply=5,
            retrieve_config={
                "task": "qa",
                "docs_path": self.rag_config.docs_path,
                "chunk_token_size": self.rag_config.chunk_token_size,
                "collection_name": self.rag_config.collection_name,
                "get_or_create": True,
                "embedding_model": self.rag_config.embedding_model,
            },
        )
        
        # Code generator with RAG context
        self.code_assistant = RetrieveAssistantAgent(
            name="Code_Generator",
            system_message="""You generate code based on documentation.
            
            Use retrieved API docs and examples as reference.
            Follow the patterns shown in documentation.
            Include error handling as documented.""",
            llm_config=self.llm_config,
        )
    
    def query(self, question: str) -> str:
        """Query the RAG system."""
        
        self.rag_proxy.initiate_chat(
            self.rag_assistant,
            problem=question
        )
        
        # Get last response
        messages = self.rag_proxy.chat_messages.get(self.rag_assistant, [])
        if messages:
            return messages[-1].get("content", "")
        return ""
    
    def generate_code_with_docs(self, requirement: str) -> str:
        """Generate code using documentation context."""
        
        self.rag_proxy.initiate_chat(
            self.code_assistant,
            problem=f"Generate code for: {requirement}"
        )
        
        messages = self.rag_proxy.chat_messages.get(self.code_assistant, [])
        if messages:
            return messages[-1].get("content", "")
        return ""

# Custom vector store integration
class CustomVectorStore:
    """Custom ChromaDB integration for RAG."""
    
    def __init__(self, collection_name: str = "docs"):
        self.client = chromadb.Client()
        self.collection = self.client.get_or_create_collection(
            name=collection_name
        )
    
    def add_documents(
        self,
        documents: List[str],
        ids: List[str],
        metadatas: Optional[List[Dict]] = None
    ):
        """Add documents to vector store."""
        self.collection.add(
            documents=documents,
            ids=ids,
            metadatas=metadatas or [{}] * len(documents)
        )
    
    def query(
        self,
        query: str,
        n_results: int = 5,
        where: Optional[Dict] = None
    ) -> List[str]:
        """Query vector store."""
        results = self.collection.query(
            query_texts=[query],
            n_results=n_results,
            where=where
        )
        return results.get("documents", [[]])[0]

# Example usage
def main():
    llm_config = {
        "config_list": [{"model": "gpt-4", "api_key": "your-key"}],
        "temperature": 0.3,
    }
    
    rag_config = RAGConfig(
        docs_path="./documentation",
        collection_name="api_docs",
        chunk_token_size=500,
    )
    
    rag_system = RAGAgentSystem(llm_config, rag_config)
    
    # Query documentation
    answer = rag_system.query("What are the HIPAA requirements for data encryption?")
    print(f"Answer: {answer}")

if __name__ == "__main__":
    main()

Conclusion

RAG transforms multi-agent systems from creative generators to knowledge-grounded assistants. By connecting agents to domain-specific document collections, we dramatically reduce hallucinations and enable accurate, verifiable responses.