Bug Buster AI

Bug Buster AI is an MCP-powered retrieval-augmented generation assistant with two capabilities: it answers questions over ingested documents and reviews GitHub repositories for bugs, security issues, and code quality problems. It uses Hugging Face embeddings, ChromaDB, LangChain and LangGraph orchestration, and a Groq-hosted LLM to retrieve relevant evidence before answering.

Architecture

• Hugging Face embeddings: Sentence Transformers embed documents and source-code chunks for semantic retrieval.

• ChromaDB vector store: Local persistent storage keeps document chunks in docmind_documents and repository code in code_chunks.

• MCP server: mcp_server/docs_server.py exposes search_docs and search_code over stdio.

• LangGraph ReAct agent: Chooses the appropriate MCP search tool and grounds responses in retrieved content.

• Groq LLM: Provides the default tool-calling model, configured through GROQ_API_KEY and GROQ_MODEL.

• Streamlit UI: Provides chat, provider selection, repository ingestion, and tool-use visibility.

Related MCP server: Semantic Code Search MCP Server

Setup

Python 3.11 or newer is required.

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
Copy-Item .env.example .env

Open .env and fill in your Groq credentials:

DOCMIND_LLM_PROVIDER=groq
GROQ_API_KEY=your-groq-api-key
GROQ_MODEL=openai/gpt-oss-120b

The default embedding model is BAAI/bge-small-en-v1.5. Set DOCMIND_EMBEDDING_MODEL=BAAI/bge-large-en-v1.5 when higher retrieval quality is worth the additional memory use.

Usage

Ingest documents

Place PDF, Markdown, or text files in data/, then run:

python -m ingestion.embed_and_store

The document chunks are stored in the document collection used by search_docs.

Ingest a GitHub repository

Clone and index a repository into the separate code_chunks collection:

python -m ingestion.embed_and_store_repo --repo-url <url> --reset

The Streamlit sidebar also provides a GitHub repo URL field and Ingest Repository button that calls the same ingestion function directly. The ingestion process skips generated/vendor directories, unsupported extensions, and source files larger than 500 KB.

Launch the app

streamlit run ui/app.py

Ask questions about indexed documents or request a code review, for example:

• What does the documentation say about authentication?

• Review the repository for security vulnerabilities in the login flow.

• Find error-handling gaps in the API client.

Example

Question:

Review the repository for bugs in the authentication code.

A grounded response may look like:

Issue: User input is interpolated directly into the SQL query, allowing SQL injection.
File: src/auth.py
Chunk: 2
Recommendation: Use a parameterized query and validate the input before execution.

Bug Buster AI reports only issues visible in retrieved code and cites the relevant file_path and chunk_index.

Tech Stack

• Hugging Face Transformers and Sentence Transformers

• LangChain

• LangGraph

• Model Context Protocol (MCP)

• ChromaDB

• Groq

• Streamlit