DocuMind: Build Your Own RAG-Powered Chatbot for Project Knowledge
Part 2 of our Agentic AI Series
Ever wished you had your own Jarvis — a chatbot that could instantly understand your project files, code, and notes? With Retrieval-Augmented Generation (RAG), you can build just that. In this post, we’ll walk through how to create a CLI-based chatbot that can learn from your local Markdown, TXT, and CSV files using open-source tools like Hugging Face Transformers and ChromaDB.
🔍 What is RAG (Retrieval-Augmented Generation)?
Imagine a student trying to write an essay using just memory (LLM-only) vs. a student who Googles relevant material first and then writes the essay (RAG). RAG combines the reasoning power of an LLM with factual grounding from external knowledge sources.
Core Components:
- Retriever: Finds relevant data chunks from a knowledge base
- Reader/Generator: Generates responses based on retrieved context
💡 Use Case: A Chatbot That Knows Your Codebase
In large teams or solo projects, context is scattered across README files, design docs, logs, and code comments. A RAG-based chatbot helps answer natural language questions like “What does this module do?” or “Where is the data pipeline defined?” using your own documents.
🧰 Tech Stack
- LLM: Hugging Face Transformers (e.g., Mistral or Claude via API)
- Vector DB: Chroma for local storage and fast similarity search
- Embedding Model: SentenceTransformers
- Interface: Python CLI with optional shell script
🛠️ Hands-on: Building DocuMind in Python
This section walks through setting up the chatbot, loading documents, and starting your RAG agent.
Step 1: Install Dependencies
pip install chromadb sentence-transformers transformersStep 2: Ingest Local Files (.txt, .md, .csv)
import os
import chromadb
from sentence_transformers import SentenceTransformerclient = chromadb.Client() db = client.create_collection("project_docs")
embedder = SentenceTransformer("all-MiniLM-L6-v2")
for file in os.listdir("docs"): if file.endswith(".txt") or file.endswith(".md") or file.endswith(".csv"): with open(f"docs/{file}", "r") as f: content = f.read() embedding = embedder.encode(content).tolist() db.add(documents=[content], embeddings=[embedding], ids=[file]) Step 3: Query Interface with Hugging Face LLM
from transformers import pipelineqa_pipeline = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
query = "How does data flow through the pipeline?"
results = db.query(query_texts=[query], n_results=3) context = "\n".join([doc["document"] for doc in results["documents"]])
response = qa_pipeline(f"Answer based on the following context:\n{context}\n\n{query}", max_new_tokens=200) print(response[0]["generated_text"])Step 4: CLI Launcher Script
#!/bin/bash
while true; do
echo -n "Ask DocuMind: "
read input
python rag_chatbot.py "$input"
done
🏢 Enterprise Alternative: AWS Bedrock + Kendra
For production-grade needs, AWS Bedrock + Kendra offers a managed RAG stack. Kendra indexes your S3/docs, while Bedrock handles orchestration and LLM inference. It's a robust choice for scaling, compliance, and enterprise-grade security.
📌 Summary
- RAG gives your LLM superpowers by grounding it in your real data
- We used Hugging Face + Chroma to build a local context-aware chatbot
- For enterprises, Bedrock + Kendra is a scalable solution
📣 What’s Next?
In the final part of our Agentic AI blog series, we’ll cover how to chain RAG with LangChain workflows and add autonomous task execution. Stay tuned!
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