ohdoking / ollama-with-rag

Ollama with RAG and Chainlit is a chatbot project leveraging Ollama, RAG, and Chainlit. It uses Chromadb for vector storage, gpt4all for text embeddings, and includes a fine-tuning and evaluation module for language models.

Ollama with RAG and Chainlit

This project is designed to use Ollama locally, run it with RAG (Retrieval-Augmented Generation), and use Chainlit for a UI chatbot.

🛠️ Tech Stack

• Chromadb: Used as a Vectorstore.
• gpt4all: Utilized for text embeddings.
• langchain: A framework that facilitates application development using LLMs (Language Learning Models).
• chainlit: Used to build a ChatGPT-like interface.

📋 Prerequisites

Before you begin, ensure you have met the following requirements:

1. Install Ollama. You can download it from the official website.
2. Install the necessary Python dependencies by running the following command in your terminal:

  pip install -r requirements.txt

3. create folder for data and vector store:

  mkdir data
  mkdir vectorstores/db

🚀 How to Use the Project

Follow these steps to get the project up and running:

Upload Required Data and Load into VectorStore

Run the following command to load your data into the VectorStore:

  python3 load_data_vdb.py

Run the Chatbot

You can start the chatbot by running the following command:

  chainlit run bot.py -w

This will start the chatbot with a web interface.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.