Multiagentic Chatbot

Multiagentic Chatbot: A Document-Grounded AI Assistant

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

I built a Multiagentic Chatbot that helps users ask questions about their own documents and get context-aware, evidence-grounded answers.

Traditional document-based chatbots can struggle with ambiguous questions, multi-part queries, and retrieving the right information from large documents. I wanted to build a system that handles these challenges through a custom multi-agent workflow instead of relying entirely on a ready-made RAG pipeline.

The chatbot is designed to:

• Answer questions using uploaded documents.
• Retrieve relevant evidence using hybrid search.
• Handle summaries, comparisons, follow-up questions, and clarification.
• Generate answers with document-based citations.
• Validate citations before returning an answer.
• Maintain conversation context for follow-up questions.

Who is it for? Students, researchers, and professionals who need to extract useful information from PDFs and other supported documents without manually searching through every page.

The long-term goal is to make document-based research faster, more reliable, and easier to use.

Demo

Live demo: [Add your deployed application link]

Demo video: [Add a short walkthrough video, if available]

The application currently uses a Streamlit interface. Add a publicly accessible demo link once the deployment is ready.

Code

GitHub Repository: Multiagentic System

The project focuses on building a custom backend workflow with explicit routing, retrieval, evidence curation, answer generation, and validation.

How I Built It

I built the backend around a custom multi-agent architecture using Python and LangGraph, with Streamlit as the user interface.

Architecture

1. Document Ingestion: Extracts text and prepares uploaded documents for retrieval.
2. Orchestrator: Determines whether the query needs a direct response, clarification, a summary, or document retrieval.
3. Retrieval Workers: Search document content using vector retrieval and keyword-based search.
4. Evidence Curation: Combines relevant results, removes redundant evidence, and ranks passages.
5. Answer Generation: Produces an answer grounded in the retrieved evidence.
6. Citation Validation: Checks citation references against the available evidence before returning the response.

Tech Stack

• Language:Python
• Agent orchestration:LangGraph
• Interface:Streamlit
• Vector database:ChromaDB
• Keyword retrieval and local state:SQLite / FTS5
• Embeddings and reranking:Sentence Transformers
• LLM inference:Groq API with a configurable model

The central design decision is to make the workflow explicit. Rather than sending every question through the same sequence of LLM calls, the orchestrator routes each query according to its needs. This helps avoid unnecessary model calls for simpler questions while reserving additional verification for answers that need it.

Why Does Open Innovation Matter?

Open innovation made it possible to build and customize the individual parts of the system instead of treating document question-answering as a black box.

Frameworks such as LangGraph, ChromaDB, and Sentence Transformers provide building blocks that can be inspected, tested, and adapted. This gives me more control over how queries are routed, how evidence is retrieved, and how citations are checked.

Using open-source components also makes experimentation more accessible. I can improve retrieval strategies, replace models, evaluate failure cases, and modify the architecture without rebuilding the entire application from scratch.

For me, this project is also a way to learn how AI systems work beyond a single prompt and model call.

Built as part of my journey toward creating practical, reliable AI applications with open-source tools.