Offered By: IND
Multi-Agent RAG Smart Document QA with Docling & LangGraph
Build a multi-agent RAG document question-answering system using LangGraph workflows and Docling for document processing. Learn to extract content from PDFs with Docling, implement hybrid retrieval combining BM25 and vector search, and create specialized agents for relevance checking, research, and verification. Integrate with IBM WatsonX AI for embeddings and language models to generate accurate answers from documents. Master techniques for document chunking, caching, fact-checking responses, and handling complex questions through coordinated agent interactions.
Continue readingGuided Project
Artificial Intelligence
At a Glance
Build a multi-agent RAG document question-answering system using LangGraph workflows and Docling for document processing. Learn to extract content from PDFs with Docling, implement hybrid retrieval combining BM25 and vector search, and create specialized agents for relevance checking, research, and verification. Integrate with IBM WatsonX AI for embeddings and language models to generate accurate answers from documents. Master techniques for document chunking, caching, fact-checking responses, and handling complex questions through coordinated agent interactions.
In this hands-on lab, you'll build a sophisticated question-answering system that makes document comprehension accessible to everyone in your organization—from researchers analyzing technical papers to legal teams extracting insights from contracts, all without the tedious manual review.
Project Overview
2️⃣ Hybrid Retrieval - Combine keyword-based BM25 and semantic vector search for optimal document retrieval
3️⃣ Multi-Agent Verification - Use specialized agents for relevance checking, research, and fact verification
4️⃣ LangGraph Orchestration - Coordinate agent interactions with conditional workflows and feedback loops
By connecting specialized agents through LangGraph, you'll create a seamless experience where users can upload documents and get verified, accurate answers using IBM's Granite AI's powerful models.
What You'll Learn
- Design effective document processing pipelines with caching and deduplication
- Build hybrid retrieval systems that balance keyword precision with semantic understanding
- Create specialized AI agents for different stages of the question-answering process
- Implement verification mechanisms to ensure factual accuracy
- Orchestrate complex workflows with conditional branching and feedback loops
Who Should Do This Lab
- Developers looking to build practical document intelligence applications
- Data scientists wanting to make document insights accessible to non-technical colleagues
- AI enthusiasts interested in creating trustworthy information retrieval systems
What You Need
✅ Basic Python knowledge (understanding functions and data structures)
✅ Basic LangChain and LLM knowledge
✅ Sample documents (we provide examples, or bring your own PDFs)
By the end of this project, you'll have built an AI document assistant that transforms how people interact with information—enabling anyone to ask questions about complex documents and receive verified, accurate answers in seconds.
Estimated Effort
60 Minutes
Level
Intermediate
Skills You Will Learn
RAG, AI Agent, LLM, Generative AI, LangGraph, Docling
Language
English
Course Code
GPXX05RWEN
Released
May 05, 2025