🧠 Build a Local RAG Chatbot with LangChain, Ollama, and Chroma

In today’s AI-powered world, LLMs (Large Language Models) like GPT, LLaMA, or Mistral can generate human-like responses—but they often forget facts or can’t access your private data. That’s where RAG (Retrieval-Augmented Generation) comes in: it combines the creativity of LLMs with the accuracy of your own data sources, like PDFs, websites, or CSVs. Think of it as giving your AI a brain and a library. Using tools like LangChain for chaining logic, Chroma for vector storage, and Ollama for running models locally, you can build powerful chatbots that are smart, private, and fully offline. No cloud. Just pure, local AI magic.

In this post, we’ll walk through how to build a local retrieval-augmented generation (RAG) chatbot using:

• 🦜 LangChain for orchestration

• 🐙 Ollama with LLaMA3.2 for local language model inference

• 🧠 ChromaDB for vector search

• 📁 Your own Q&A knowledge stored in a simple CSV file

• ⚙️ Embedding with mxbai-embed-large

Whether you’re just getting started with LangChain or you want to run AI locally without sending anything to the cloud, this step-by-step guide is for you.

The chatbot is designed to answer questions based on your custom CSV data — perfect for:

• Internal knowledge bases

• Offline assistants

• Personal tools that protect data privacy

• Quick AI-powered lookups from structured documents

🏗️ Architecture

Architecture of local llm rag

🧰 Requirements

Make sure you have:

• Python 3.11+

• Conda

• Ollama installed locally

• VS Code or any IDE

• Your CSV file (x.csv) with columns: Question, Answer

⚙️ Environment Setup with Conda

1. Install Miniconda (if not installed)

Download Miniconda

Check it's installed:

conda --version

2. Create and activate the environment

conda create --name myqnaenv python=3.11
conda activate myqnaenv

3. Add your requirements.txt

langchain
langchain-ollama
langchain-chroma
langchain-core
pandas

Then install:

pip install -r requirements.txt

🗂️ Folder Structure

my-rag-app/
├── main.py
├── vector.py
├── x.csv
├── requirements.txt
└── README.md

🧑‍💻 Step-by-Step Code Implementation

✅ Step 1: Main Chatbot Script (main.py)

✅ Step 1: CSV to Vector Store (vector.py)

🧪 Run the App

Make sure x.csv is in your folder, and then just:

python main.py     

Try asking things like:

Question: What is the capital of France?

✅ You Did It!

You're now running a local, private, and extendable RAG chatbot using open-source tools and your own data. No cloud, no limits, just local power.

🌟 What’s Next?

Here are a few ideas to level it up:

• 🔁 Add streaming outputs (like ChatGPT)

• 🖼️ Build a web UI with Gradio or Streamlit

• 🗂️ Load data from multiple files, not just CSV

• 🧠 Try different LLMs with Ollama (e.g. gemma, mistral, etc.)

With just a few Python scripts and open-source tools, you've built a fully local RAG-powered chatbot that understands your custom data and responds intelligently—without ever sending a single byte to the cloud. You've explored how LangChain simplifies orchestration, how Ollama lets you run powerful LLMs like LLaMA3.2 locally, and how Chroma stores and retrieves knowledge from your CSV. This is just the beginning. You now have a flexible foundation to build smarter, more secure AI tools tailored to your needs—whether that’s internal documentation search, private research assistants, or domain-specific copilots.

Ready to take it further? Try adding a web UI, plug in different models, or scale to multiple data sources. The tools are in your hands. Happy building! 💡🚀