Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Chatbot assistant enabling GitHub repository interaction using LLMs with Retrieval Augmented Generation
| Date | Stars |
|---|---|
| 2026-07-24 | 317 |
| 2026-07-25 | 317 |
| 2026-07-28 | 317 |
| 2026-07-30 | 317 |
| 2026-07-31 | 317 |
| 2026-08-06 | 317 |
Today
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Momentum
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growth rate 0.00%/day
# Repochat - GitHub Repository Interactive Chatbot
Repochat is an interactive chatbot project designed to engage in conversations about GitHub repositories using a Large Language Model (LLM). It allows users to have meaningful discussions, ask questions, and retrieve relevant information from a GitHub repository. This README provides step-by-step instructions for setting up and using Repochat on your local machine.
https://github.com/pnkvalavala/repochat/assets/108526358/91819459-ddbf-4985-ab84-81c8c67da0e6
## Table of Contents
- [Branches](#branches)
- [Installation](#installation)
- [Usage](#usage)
- [Chatbot Functionality](#chatbot-functionality)
- [Issues](#raising-issues)
- [License](#license)
## Branches
Repochat offers 2 branches with distinct functionalities:
### Main Branch
The [main](https://github.com/pnkvalavala/repochat) branch of Repochat is designed to run entirely on your local machine. This version of Repochat doesn't rely on external API calls and offers greater control over your data. If you're looking for a self-contained solution, the `main` branch is the way to go.
### Cloud Branch
The [cloud](https://github.com/pnkvalavala/repochat/tree/cloud) branch of Repochat primarily relies on API calls to external services for model inference and storage. It's well-suited for those who prefer a cloud-based solution and don't want to set up a local environment.
## Installation
To get started with Repochat, you'll need to follow these installation steps:
1. Create a virtual environment and activate on your local machine to isolate the project's dependencies.
```bash
python -m venv repochat-env
source repochat-env/bin/activate
```
2. Clone the Repochat repository and navigate to the project directory.
```bash
git clone https://github.com/pnkvalavala/repochat.git
cd repochat
```
3. Install the required Python packages using `pip`.
```bash
pip install -r requirements.txt
```
4. Install the "llama-cpp-python" library.
### Installation without Hardware Acceleration
```bash
pip install llama-cpp-python
```
### Installation with Hardware Acceleration
`llama.cpp` supports multiple BLAS backends for faster processing.
To install with OpenBLAS, set the `LLAMA_BLAS and LLAMA_BLAS_VENDOR` environment variables before installing:
```bash
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
```
To install with cuBLAS, set the `LLAMA_CUBLAS=1` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```
To install with CLBlast, set the `LLAMA_CLBLAST=1` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
```
To install with Metal (MPS), set the `LLAMA_METAL=on` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
```
To install with hipBLAS / ROCm support for AMD cards, set the `LLAMA_HIPBLAS=on` environment variable before installing:
```bash
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
```
To get to know more about Hardware Acceleration, refer to official README from [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
5. Create a folder named `models` in the project directory.
6. Download a Language Model from the Hugging Face Model Hub based on your computer's capabilities. It is recommended using the following model as a starting point: [TheBloke/CodeLlama-7B-GGUF](https://huggingface.co/TheBloke/CodeLlama-7B-GGUF/blob/main/codellama-7b.Q4_K_M.gguf). If you want to quantize a model available on Hugging Face, follow the instructions from [llama.cpp](https://github.com/ggerganov/llama.cpp)
7. Copy the downloaded model file to the "models" folder.
8. Open the `models.py` file located in the "repochat" folder and set the model file location in Excerpt of 6,278 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:568e682b39988118, topic:retrieval-augmented-generation, desc:retrieval-augmented generation, desc:retrieval augmented
matched fp:568e682b39988118, topic:streamlit, desc:chatbot, readme:chatbot