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repochat

pnkvalavala/repochat

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

GitHub Website

GraphCanon updated 6d · GitHub synced 6d

317 stars53 forksLast push 1y Python Apache-2.0

Decision brief

Repochat is a chatbot assistant built for interacting with GitHub repositories leveraging Large Language Models and retrieval-augmented generation techniques in Python under the Apache License.

Good fit when

When you need to interact directly through chat-based queries with your GitHub repository content using advanced language models like CodeLlama-7B-GGUF.
For scenarios where integration of both LLM capabilities and direct retrieval from a codebase are required for enhanced contextual responses.

Avoid when

If the hardware acceleration setup is too complex or not supported by your system, as Repochat's setup involves configuring environment variables and possibly installing BLAS backends.
In environments where continuous network access to Hugging Face model hub and GitHub repositories is restricted or unreliable, given that this tool relies on these services for operation.

Requirements:: Requires creation of a virtual environment.; Installation of llama-cpp-python with various optional backends for hardware acceleration is supported.

Observed Jul 14, 2026 · Source: enrich:decision_facts

repochat alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Dormant (716d since push); As of 6d
Provenance: Not a fork · Personal account; As of 6d
Security (OSV): 27 low (27 low); As of 1mo

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

Install

pip install repochat

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A chat-based application that enables interactions with software repositories via Large Language Models (LLMs) enhanced by retrieval-augmented generation techniques.

Capability facts

Languages: python
Source: github.language · Aug 15, 2026

Compatibility

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

Python runtimePython

Source: README excerpt (regex_v1, Aug 15, 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.

For agents

This page has a .md twin and JSON over the API.

/tools/pnkvalavala-repochat.md GET /api/graphcanon/tools/pnkvalavala-repochat GET /api/graphcanon/graph?tool=pnkvalavala-repochat