RAG with LangChain and Ollama

This project is an implementation of Retrieval-Augmented Generation (RAG) using LangChain, ChromaDB, and Ollama to enhance answer accuracy in an LLM-based (Large Language Model) system. The system performs document-based retrieval and answers user questions using data stored in the vector database.

Features

• Integration with Ollama as the LLM model.
• ChromaDB as a vector storage for documents.
• RAG pipeline for document retrieval and processing.
• PDF ingestion support to add documents to the vector database.
• Interactive system for answering user queries.

Installation

1. Requirements

Ensure Python is installed (recommended version 3.8+).

2. Clone the Repository

git clone https://github.com/username/rag-langchain-ollama.git
cd rag-langchain-ollama

3. Install Dependencies

pip install -r requirements.txt

4. Run Ollama

Ensure the Ollama server is running locally (example with WSL):

ollama serve

You can change model by modified code at models.py

# Model LLM Ollama
        self.model_ollama = ChatOllama(model="put your model LLM here", temperature=0, base_url=ollama_host)

Usage

1. Run the Ingestor to Add Documents

python ingest.py

PDF files in the data/ folder will be processed and stored in ChromaDB.

2. Run the Interactive Chatbot

python main.py

Use this chatbot to ask questions based on indexed documents.

Project Structure

├── data/                  # Folder for PDF documents
├── db/                    # ChromaDB storage folder
├── models.py              # Ollama model used (can be customized)
├── ingest.py              # Script for processing documents
├── chat.py                # Interactive chatbot
├── requirements.txt       # List of dependencies
└── README.md              # Project documentation

Documentation