How to Build a Local AI Agent With Python (Ollama, LangChain & RAG)

Transcribed Jun 15, 2026 Watch on YouTube ↗

Intermediate 8 min read For: Python developers interested in building local AI applications with RAG.

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AI Summary

This tutorial demonstrates how to build a local AI agent using Python, Ollama, LangChain, and Chroma DB for retrieval-augmented generation (RAG). The agent can query a CSV file of restaurant reviews to answer questions, all running locally without any cloud APIs.

Chapters

1 Introduction and Demo 00:00 2 Setup: Dependencies and Ollama 02:02 3 Coding the Basic LLM Chain 07:03 4 Building the Vector Store with Chroma DB 14:52 5 Integration and Final Testing 24:58

[00:00]

Project Overview

Build a local AI agent in minutes using Python, Ollama, LangChain, and Chroma DB for RAG, enabling retrieval from CSV/PDF files.

[00:34]

Demo: Querying Reviews

Agent queries a CSV of fake pizza restaurant reviews to answer questions like 'how is the quality of the pizza?' and 'are there vegan options?'.

[02:02]

Setup: Dependencies

Create a virtual environment, install langchain, langchain-ollama, langchain-chroma, and pandas.

[04:34]

Setup: Ollama Models

Install Ollama, pull llama3.2 for the LLM and mxbai-embed-large for embeddings.

[07:03]

Coding: Basic LLM Chain

Import Ollama LLM, create a chat prompt template, build a chain, and test with a simple question.

[14:52]

Coding: Vector Store Setup

Create a separate file to load CSV, define embeddings, initialize Chroma DB, and create a retriever.

[24:58]

Integration and Testing

Import retriever into main.py, use it to fetch relevant reviews before invoking the LLM chain, and run the interactive loop.

This tutorial shows how to build a fully local AI agent with RAG using Python, Ollama, LangChain, and Chroma DB. The approach can be adapted to any CSV or document data, enabling private, offline question-answering.

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Mentioned in this Video

Ollama

tool

LangChain

tool

Chroma DB

tool

GitHub Copilot

tool

Pandas

tool

llama3.2

model

mxbai-embed-large

model

Tutorial Checklist

1 02:02 Create a new folder and add your CSV file (e.g., realistic_restaurant_reviews.csv).

2 02:43 Create a virtual environment: python -m venv venv, then activate it (Windows: .\venv\Scripts\activate; Mac/Linux: source venv/bin/activate).

3 03:56 Install dependencies: pip install langchain langchain-ollama langchain-chroma pandas (or use requirements.txt).

4 04:34 Download and install Ollama from ollama.com, then pull models: ollama pull llama3.2 and ollama pull mxbai-embed-large.

5 07:03 Create main.py: import Ollama LLM, create ChatPromptTemplate, build chain, and test with a simple invoke.

6 14:52 Create vector.py: load CSV with pandas, define OllamaEmbeddings, initialize Chroma DB, create documents with page_content (title + review) and metadata (rating, date), add to vector store, and create retriever with search_kwargs={'k': 5}.

7 24:58 In main.py, import retriever from vector, before invoking chain call retriever.invoke(question) to get relevant reviews, then pass reviews to chain.invoke().

8 13:37 Wrap the invoke logic in a while True loop to allow multiple questions; break on 'q'.

Study Flashcards (8)

What is the purpose of the embedding model in this project?

medium Click to reveal answer

To convert text into vectors for efficient similarity search in the vector database.