https://github.com/amscotti/local-llm-with-rag

Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)
https://github.com/amscotti/local-llm-with-rag

chatbot embeddings langchain llm mistral ollama python rag retrieval-augmented-generation streamlit

Last synced: 5 days ago
JSON representation

Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)

• Host: GitHub
• URL: https://github.com/amscotti/local-llm-with-rag
• Owner: amscotti
• License: mit
• Created: 2023-11-05T21:35:34.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-02T20:20:46.000Z (8 months ago)
• Last Synced: 2025-01-09T06:09:44.490Z (12 days ago)
• Topics: chatbot, embeddings, langchain, llm, mistral, ollama, python, rag, retrieval-augmented-generation, streamlit
• Language: Python
• Homepage:
• Size: 18.1 MB
• Stars: 192
• Watchers: 5
• Forks: 36
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Local LLM with RAG



    



This project is an experimental sandbox for testing out ideas related to running local Large Language Models (LLMs) with [Ollama](https://ollama.ai/) to perform Retrieval-Augmented Generation (RAG) for answering questions based on sample PDFs. In this project, we are also using Ollama to create embeddings with the [nomic-embed-text](https://ollama.com/library/nomic-embed-text) to use with [Chroma](https://docs.trychroma.com/). Please note that the embeddings are reloaded each time the application runs, which is not efficient and is only done here for testing purposes.

[![asciicast](https://asciinema.org/a/fepTvXf1UiDpRUhhNiswL8isu.svg)](https://asciinema.org/a/fepTvXf1UiDpRUhhNiswL8isu)

There is also a web UI created using [Streamlit](https://streamlit.io/) to provide a different way to interact with Ollama.



    



## Requirements

- [Ollama](https://ollama.ai/) verson 0.1.26 or higher.

## Setup

1. Clone this repository to your local machine.

2. Create a Python virtual environment by running `python3 -m venv .venv`.

3. Activate the virtual environment by running `source .venv/bin/activate` on Unix or MacOS, or `.\.venv\Scripts\activate` on Windows.

4. Install the required Python packages by running `pip install -r requirements.txt`.

## Running the Project

**Note:** The first time you run the project, it will download the necessary models from Ollama for the LLM and embeddings. This is a one-time setup process and may take some time depending on your internet connection.

1. Ensure your virtual environment is activated.

2. Run the main script with `python app.py -m  -p ` to specify a model and the path to documents. If no model is specified, it defaults to [mistral](https://ollama.com/library/mistral). If no path is specified, it defaults to `Research` located in the repository for example purposes.

3. Optionally, you can specify the embedding model to use with `-e `. If not specified, it defaults to [nomic-embed-text](https://ollama.com/library/nomic-embed-text).

This will load the PDFs and Markdown files, generate embeddings, query the collection, and answer the question defined in `app.py`.

## Running the Streamlit UI

1. Ensure your virtual environment is activated.

2. Navigate to the directory containing the `ui.py` script.

3. Run the Streamlit application by executing `streamlit run ui.py` in your terminal.

This will start a local web server and open a new tab in your default web browser where you can interact with the application. The Streamlit UI allows you to select models, select a folder, providing an easier and more intuitive way to interact with the RAG chatbot system compared to the command-line interface. The application will handle the loading of documents, generating embeddings, querying the collection, and displaying the results interactively.

## Technologies Used

- [Langchain](https://github.com/langchain/langchain): A Python library for working with Large Language Model

- [Ollama](https://ollama.ai/): A platform for running Large Language models locally.

- [Chroma](https://docs.trychroma.com/): A vector database for storing and retrieving embeddings.

- [PyPDF](https://pypi.org/project/PyPDF2/): A Python library for reading and manipulating PDF files.

- [Streamlit](https://streamlit.io/): A web framework for creating interactive applications for machine learning and data science projects.