Home/LLM Frameworks/repochat

repochat

Enrichment pending

pnkvalavala/repochat

Chatbot assistant enabling GitHub repository interaction using LLMs with Retrieval Augmented Generation

GitHub Website

GraphCanon updated today · GitHub synced today

316

Stars

Forks

Open issues

Watchers

Last push

Python Apache-2.0Created Jul 3, 2023

Trust & integrity

Full report

Maintenance: Dormant (681d since push); As of today · Source: github_public_v1
Provenance: Not a fork · Personal account; As of today · Source: github_public_v1
Security (OSV): 27 low (27 low); As of today · Source: osv@v1

Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.

Overview

Chatbot assistant enabling GitHub repository interaction using LLMs with Retrieval Augmented Generation

Capability facts

Languages: python
Source: github.language · Jul 11, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

python -m venv repochat-env

Source link

README

Installation

To get started with Repochat, you'll need to follow these installation steps:

Create a virtual environment and activate on your local machine to isolate the project's dependencies.
```
python -m venv repochat-env
source repochat-env/bin/activate
```
Clone the Repochat repository and navigate to the project directory.
```
git clone https://github.com/pnkvalavala/repochat.git
cd repochat
```
Install the required Python packages using pip.
```
pip install -r requirements.txt
```
Install the "llama-cpp-python" library.

Installation without Hardware Acceleration
```
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:
```
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:
```
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```
To install with CLBlast, set the LLAMA_CLBLAST=1 environment variable before installing:
```
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
```
To install with Metal (MPS), set the LLAMA_METAL=on environment variable before installing:
```
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:
```
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
Create a folder named models in the project directory.
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. If you want to quantize a model available on Hugging Face, follow the instructions from llama.cpp
Copy the downloaded model file to the "models" folder.

Open the models.py file located in the "repochat" folder and set the model file location in the code_llama() function as follows:

def code_llama():
    callbackmanager = CallbackManager([StreamingStdOutCallbackHandler()])
    llm = LlamaCpp(
        model_path="./models/codellama-7b.Q4_K_M.gguf",
        n_ctx=2048,
        max_tokens=200,
        n_gpu_layers=1,
        f16_kv=True,
        callback_manager=callbackmanager,
        verbose=True,
        use_mlock=True
    )
    return llm

License

This project is licensed under the Apache License 2.0. For details, see the LICENSE file. Please note that this is a change from the previous license, and it's important to review the terms and conditions of the new license.