Laravel RAG

A Retrieval-Augmented Generation (RAG) system for querying Laravel documentation using Ollama and ChromaDB.

Overview

This project allows you to chat with the Laravel documentation. It ingests markdown files from the documentation, stores them in a vector database (ChromaDB), and uses a local LLM (via Ollama) to answer questions based on the documentation context.

Prerequisites

• Python 3.8+
• Ollama installed and running.

Required Ollama Models

You need to pull the following models:

ollama pull nomic-embed-text
ollama pull llama3.2

Installation

1. Clone the repository.
2. Install the dependencies:

pip install -r requirements.txt

Usage

1. Ingest Documentation

The project expects Laravel documentation markdown files in the laravel-docs/ directory.

Run the ingestion script to process the documents and create the vector database:

python ingest.py

This script will:

• Read markdown files from laravel-docs/.
• Chunk the text.
• Generate embeddings using nomic-embed-text.
• Store the embeddings in a local ChromaDB instance (./chroma_db).

2. Query the Documentation

Start the interactive CLI to ask questions:

python query.py

Type your question when prompted. The system will:

• Search for relevant documentation chunks.
• Use llama3.2 to generate an answer based on the retrieved context.
• Display the answer and the source files used.

Project Structure

• ingest.py: Script to process documentation and populate the vector database.
• query.py: Script to run the interactive query interface.
• requirements.txt: Python dependencies.
• laravel-docs/: Directory containing the Laravel documentation markdown files.
• chroma_db/: (Created after ingestion) Directory storing the vector database.