README.md

metadata

title: Code Knowledge Graph Explorer — 🤗 Transformers Library
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
tags:
  - building-mcp-track-enterprise
short_description: MCP server for big code — explore Transformers

🎓 Code Knowledge Graph MCP Server

Helping LLM-based agents navigate and understand large codebases

📚 What is this project?

This project provides a Model Context Protocol (MCP) server that transforms code repositories into navigable knowledge graphs. It enables Large Language Model (LLM) based agents to efficiently explore, understand, and reason about complex codebases — a critical capability for modern software engineering education and practice.

🔬 Use Case: EPITA Coding Courses

This project was developed with educational applications in mind, specifically to support EPITA coding courses:

🔍 Enhanced Code Discovery for Agents

LLM-based coding agents can use this tool to better discover and navigate large repositories. Instead of blindly searching through files, agents can:

• Query the knowledge graph to understand the overall architecture
• Follow relationships between modules, classes, and functions
• Identify entry points and critical code paths
• Understand how different parts of the codebase interact

📈 Detecting Areas for Code Improvement

For EPITA courses, this tool helps agents identify areas where student code can be improved:

• Dead Code Detection: Find unused functions, classes, or variables
• Circular Dependencies: Detect problematic import cycles between modules
• Code Coupling Analysis: Identify tightly coupled components that should be refactored
• Missing Documentation: Find undocumented public APIs and complex functions
• Complexity Hotspots: Locate chunks with many outgoing calls (high coupling)
• Orphan Code: Detect code that is declared but never called

🎓 EPITA Course Integration

• Project Reviews: Quickly understand student project architectures before grading
• Automated Feedback: Integrate with LLM tutors to provide targeted improvement suggestions
• Code Quality Assessment: Consistent evaluation criteria across student submissions
• Learning Tool: Help students navigate and understand unfamiliar codebases (e.g., open-source projects)
• Research: Study code organization patterns across student projects

The MCP interface makes it easy to integrate with any LLM-based tutoring or code review system used in EPITA courses.

---

🎯 The Problem We Solve

At EPITA (École pour l'informatique et les techniques avancées), students work on increasingly complex software projects throughout their curriculum. Understanding large codebases — whether their own, their teammates', or open-source libraries — is a fundamental skill for any computer science engineer.

However, LLM-based coding assistants face significant challenges when working with large repositories:

• Context window limitations: LLMs cannot process entire codebases at once
• Lack of structural awareness: Without understanding how code is organized, LLMs struggle to locate relevant files
• Missing relationships: Function calls, class inheritance, and module dependencies are not immediately visible
• Inefficient search: Simple keyword search fails to capture semantic meaning

💡 Our Solution: Knowledge Graphs + MCP

This project addresses these challenges by:

1. Parsing repositories into a structured knowledge graph (files → chunks → entities)
2. Extracting relationships between code elements (calls, contains, declares, imports)
3. Indexing content with hybrid search (semantic embeddings + keyword matching)
4. Exposing tools via MCP that allow LLM agents to navigate the codebase intelligently

┌─────────────────────────────────────────────────────────────────┐
│                     CODE REPOSITORY                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐         │
│  │  File A  │  │  File B  │  │  File C  │  │  File D  │   ...   │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘         │
└───────┼─────────────┼─────────────┼─────────────┼───────────────┘
        ▼             ▼             ▼             ▼
┌─────────────────────────────────────────────────────────────────┐
│               KNOWLEDGE GRAPH CONSTRUCTION                       │
│  • AST Parsing (Python, C/C++, Java, JavaScript, Rust, HTML)    │
│  • Entity Extraction (classes, functions, variables, methods)   │
│  • Relationship Detection (calls, inheritance, imports)         │
│  • Code Chunking & Embedding (semantic vectors)                 │
└───────────────────────────────┬─────────────────────────────────┘
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    MCP SERVER (Gradio)                           │
│  ┌─────────────┐ ┌────────────┐ ┌──────────────┐ ┌────────────┐ │
│  │search_nodes │ │go_to_def   │ │find_usages   │ │get_neighbors│ │
│  └─────────────┘ └────────────┘ └──────────────┘ └────────────┘ │
│  ┌─────────────┐ ┌────────────┐ ┌──────────────┐ ┌────────────┐ │
│  │get_file_    │ │get_related │ │find_path     │ │print_tree  │ │
│  │structure    │ │_chunks     │ │              │ │            │ │
│  └─────────────┘ └────────────┘ └──────────────┘ └────────────┘ │
└───────────────────────────────┬─────────────────────────────────┘
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    LLM-BASED AGENT                               │
│  • Can search for relevant code using natural language          │
│  • Navigate from function calls to their definitions            │
│  • Understand the structure of files and directories            │
│  • Trace dependencies and relationships across the codebase     │
└─────────────────────────────────────────────────────────────────┘

🛠️ MCP Tools Available

The MCP server exposes the following tools for LLM agents:

Tool	Description
search_nodes	Semantic + keyword search for code chunks
get_node_info	Detailed information about any node (file, chunk, entity)
get_node_edges	Incoming and outgoing relationships of a node
go_to_definition	Find where a function/class/variable is declared
find_usages	Find all places where an entity is called/used
get_neighbors	Get all directly connected nodes
get_file_structure	Overview of a file's chunks and entities
get_related_chunks	Find chunks related by a specific relationship type
list_all_entities	List all tracked entities in the codebase
get_graph_stats	Statistics about the knowledge graph
find_path	Find shortest path between two nodes
get_subgraph	Extract a subgraph around a node
print_tree	Display repository structure as a tree
diff_chunks	Compare content between two code chunks
search_by_type_and_name	Search entities by type (class, function, etc.) and name
get_chunk_context	Get a chunk with its surrounding context

🌐 Supported Languages

The knowledge graph builder uses AST-based entity extraction for accurate parsing:

Language	Parser	Entity Types
Python	ast module	classes, functions, methods, variables, imports
C	libclang	functions, structs, typedefs, variables
C++	libclang	classes, namespaces, methods, templates
Java	javalang	classes, interfaces, methods, fields
JavaScript/TypeScript	esprima	classes, functions, variables, imports
Rust	tree-sitter	structs, enums, traits, functions, modules
HTML	BeautifulSoup	DOM elements, inline JS extraction

The system also detects API endpoints for web frameworks (FastAPI, Flask, Spring Boot, Actix-web, etc.).

🚀 Getting Started

Prerequisites

• Docker & Docker Compose
• Python 3.10+ (for local development)
• CUDA-capable GPU (optional, for faster embeddings)

Quick Start with Docker

# Start the MCP server with a sample knowledge graph
docker-compose up

Building a Knowledge Graph from Your Repository

from RepoKnowledgeGraphLib.RepoKnowledgeGraph import RepoKnowledgeGraph

# From a local path
kg = RepoKnowledgeGraph.from_path(
    "/path/to/your/repo",
    skip_dirs=["node_modules", ".git", "__pycache__"],
    extract_entities=True,
    index_nodes=True
)

# Save for later use
kg.save_graph_to_file("my_knowledge_graph.json")

Running the MCP using Gradio

python gradio_mcp.py --graph-file my_knowledge_graph.json --host 0.0.0.0 --port 7860

📊 Interactive Explorer (Gradio UI)

The project includes a Gradio-based web interface for exploring knowledge graphs interactively:

• Search: Use natural language or keywords to find relevant code
• Navigate: Click through nodes to explore relationships
• Analyze: Get statistics about code structure and dependencies
• Visualize: View the repository tree and entity relationships

📁 Data Sources

The application supports loading knowledge graphs from multiple sources:

1. HuggingFace Hub Dataset (Recommended for Sharing)

Load directly from a HuggingFace dataset created by the library (cf. Publishing to Huggingface Hub):

python gradio_mcp.py --host 0.0.0.0 --port 7860 --hf-dataset "username/dataset-name"

2. Local JSON File

Use a local JSON file (e.g., multihop_knowledge_graph_with_embeddings.json):

python gradio_mcp.py --host 0.0.0.0 --port 7860 --graph-file data/multihop_knowledge_graph_with_embeddings.json

3. Direct from Git Repository

Clone and analyze a repository on-the-fly:

python gradio_mcp.py --host 0.0.0.0 --port 7860 --repo-url "https://github.com/user/repo.git"

Publishing to HuggingFace Hub

You can save an existing knowledge graph to HuggingFace Hub for sharing:

from RepoKnowledgeGraphLib import RepoKnowledgeGraph

# Load from local file
kg = RepoKnowledgeGraph.load("path/to/graph.json")

# Push to HuggingFace Hub (without embeddings to reduce size)
kg.to_hf_dataset("username/my-knowledge-graph", save_embeddings=False, private=False)

# Or with embeddings (larger dataset)
kg.to_hf_dataset("username/my-knowledge-graph-with-embeddings", save_embeddings=True)

🏗️ Architecture Overview

root/
├── Dockerfile                  # Docker configuration
├── requirements.txt            # Python dependencies
├── RepoKnowledgeGraphLib/  # Knowledge graph implementation
│   ├── RepoKnowledgeGraph.py    # Main graph class
│   ├── KnowledgeGraphMCPServer.py # MCP server implementation
│   ├── EntityExtractor.py       # AST-based entity extraction
│   ├── CodeParser.py            # Code chunking
│   ├── CodeIndex.py             # Hybrid search (LanceDB/Weaviate)
│   ├── ModelService.py          # Embedding generation
│   └── Node.py                  # Graph node types
└── gradio_mcp_space.py              # Main Gradio web interface

👥 Team

Team Name: CEPIA Ionis Team

Team Members:

• Laila ELKOUSSY - @lailaelkoussy - Research Engineer, Data Scientist
• Julien PEREZ - @jnm38 - Research Director

---

📄 License

This project is developed as part of research at EPITA / Ionis Group.

🔗 Related Resources

• Model Context Protocol (MCP) - The protocol standard
• Gradio - Python web interface framework with MCP support
• LanceDB - Vector database for code indexing
• Salesforce SFR-Embedding-Code - Code embedding model

🆚 VS Code Integration

To use this MCP server with GitHub Copilot in VS Code, you need to configure an mcp.json file.

Configuration File Location

Create or edit the file at .vscode/mcp.json in your workspace root:

your-workspace/
├── .vscode/
│   └── mcp.json    ← Place the configuration here
├── src/
└── ...

Configuration Content

Add the following content to .vscode/mcp.json:

{
    "servers": {
        "transformers-code-graph": {
            "url": "https://lailaelkoussy-transformers-library-knowledge-graph.hf.space/gradio_api/mcp/",
            "type": "http"
        }
    },
    "inputs": []
}

What This Does

• servers: Defines the MCP servers available to VS Code
• transformers-code-graph: A custom name for this server connection
• url: The endpoint of the hosted MCP server (here pointing to the HuggingFace Space)
• type: Set to "http" for remote HTTP-based MCP servers

Using with Your Own Server

If you're running your own MCP server locally, update the URL accordingly:

{
    "servers": {
        "my-code-graph": {
            "url": "http://localhost:7860/gradio_api/mcp/",
            "type": "http"
        }
    },
    "inputs": []
}

Once configured, GitHub Copilot in VS Code will have access to all the knowledge graph tools (search_nodes, go_to_definition, find_usages, etc.) to help navigate and understand your codebase.