Agentic RAG: Complete A-to-Z Production Project
This guide walks you through building a complete Agentic RAG system — an AI agent that can reason about when to retrieve information, what to search for, and how to combine multiple sources to answer complex questions.
What is Agentic RAG?
Traditional RAG is a simple pipeline: retrieve → generate. Agentic RAG adds intelligence:
- Reasoning: The agent decides whether to retrieve at all
- Planning: It decomposes complex questions into sub-queries
- Tool Use: It can search databases, APIs, web, and internal knowledge bases
- Self-Correction: It evaluates its own answers and retries if needed
- Multi-Hop: It can chain multiple retrievals to answer complex questions
Architecture Diagram
Traditional RAG: Query → Retrieve → Generate → Answer
Agentic RAG: Query → Reason → Plan → Retrieve → Evaluate →
(if needed) Refine → Retrieve Again → Generate → Answer
Project Overview
We'll build an Intelligent Research Agent that:
- Ingests documents from multiple sources (PDF, markdown, web)
- Builds a searchable knowledge base
- Answers complex multi-hop questions
- Cites its sources
- Knows when it doesn't have enough information
- Can search the web as a fallback
Architecture
Architecture Diagram
┌─────────────────────────────────────────────────┐
│ User Interface │
│ (FastAPI) │
└──────────────────────┬──────────────────────────┘
│
┌──────────────────────▼──────────────────────────┐
│ Agent Orchestrator │
│ (LangGraph State Machine) │
│ │
│ ┌─────────┐ ┌──────────┐ ┌───────────────┐ │
│ │ Reason │→ │ Retrieve │→ │ Evaluate │ │
│ │ Step │ │ Step │ │ Step │ │
│ └─────────┘ └──────────┘ └───────────────┘ │
│ │ │ │
│ │ ┌──────────┐ ┌─────▼──────┐ │
│ └──→ │ Web │←── │ Reflect │ │
│ │ Search │ │ & Retry │ │
│ └──────────┘ └────────────┘ │
└──────────────────────┬──────────────────────────┘
│
┌──────────────────────▼──────────────────────────┐
│ Tool Layer │
│ ┌──────────┐ ┌──────────┐ ┌────────────────┐ │
│ │ Vector │ │ BM25 │ │ Web Search │ │
│ │ Search │ │ Search │ │ (Tavily) │ │
│ └──────────┘ └──────────┘ └────────────────┘ │
└──────────────────────┬──────────────────────────┘
│
┌──────────────────────▼──────────────────────────┐
│ Data Layer │
│ ┌──────────┐ ┌──────────┐ ┌────────────────┐ │
│ │ ChromaDB │ │ Redis │ │ Document │ │
│ │ Vectors │ │ Cache │ │ Store │ │
│ └──────────┘ └──────────┘ └────────────────┘ │
└─────────────────────────────────────────────────┘
Step 1: Project Setup
mkdir agentic-rag && cd agentic-rag
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install langchain langchain-openai langchain-community
pip install langgraph chromadb tavily-python
pip install fastapi uvicorn python-dotenv
pip install sentence-transformers tiktoken
Architecture Diagram
# .env
OPENAI_API_KEY=sk-your-key-here
TAVILY_API_KEY=tvly-your-key-here
Step 2: Document Ingestion Pipeline
Step 3: Tool Definitions
Step 4: Agent State Machine (LangGraph)
Step 5: API Layer
Step 6: Ingest Your Documents
Step 7: Test It
Expected Output
{
"question": "How does the transformer architecture relate to attention mechanisms?",
"answer": "The transformer architecture [1] is built entirely on attention mechanisms [2], "
"specifically self-attention. Unlike RNNs that process sequences sequentially, "
"transformers use multi-head attention to process all positions in parallel [1][3]. "
"This enabled the scaling that led to GPT models [2].",
"citations": [
{"source_id": "1", "content": "Transformer architecture uses self-attention..."},
{"source_id": "2", "content": "Attention mechanism computes relevance..."},
{"source_id": "3", "content": "Multi-head attention parallelizes..."}
],
"iterations": 2,
"sub_questions": [
"How does the transformer architecture use attention mechanisms?",
"How did transformers influence GPT model development?"
]
}
Production Deployment
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
# docker-compose.yml
version: '3.8'
services:
agentic-rag:
build: .
ports:
- "8000:8000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- TAVILY_API_KEY=${TAVILY_API_KEY}
volumes:
- ./chroma_db:/app/chroma_db
restart: unless-stopped
Key Takeaways
- Agentic RAG is a state machine, not a simple pipeline — use LangGraph or similar
- Query decomposition is critical for complex questions
- Self-evaluation prevents bad answers from reaching users
- Web search fallback handles knowledge gaps
- Citations build trust and enable verification
- Iteration limits prevent infinite loops
- Start simple — add complexity only when needed