🔬 PaperGraph

GraphRAG over arXiv — explore, ingest, and query scientific research through a structured knowledge graph.

PaperGraph is a high-performance GraphRAG (Retrieval-Augmented Generation over Knowledge Graphs) system. It transforms flat research papers into a multi-dimensional knowledge graph of Entities and Relationships, enabling deep reasoning that standard vector-based RAG cannot achieve.

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🏗️ System Architecture

PaperGraph follows a two-stage architecture: Automated Ingestion and Hybrid Retrieval.

Stage 1 — Ingestion Pipeline

  +-------------+       +-------------------+       +-----------------------+
  |  arXiv API  | ----> | Python Ingestion  | ----> | LLM Extraction (GPT)  |
  +-------------+       +---------+---------+       +-----------+-----------+
                                  |                             |
                                  v                             v
                        +---------+---------+       +-----------+-----------+
                        |  Neo4j AuraDB     | <---- |   Knowledge Graph     |
                        +---------+---------+       +-----------------------+
                                  |
                                  v
                        +---------+---------+
                        |  Vector Index     |
                        +-------------------+

Stage 2 — Retrieval Pipeline (Hybrid GraphRAG)

      [ User Question ]
              |
              v
     +-----------------+
     |  Query Router   |
     +--------+--------+
              |
      /-------+-------\
      |               |
  [ GLOBAL ]      [ LOCAL ]
      |               |
  [ Cypher ]    [ Hybrid Search ]
      |               |
  [ Stats  ]    [ Graph Expand ]
      |               |
      \-------+-------/
              |
              v
     +-----------------+
     |  GPT-4o Answer  |
     +--------+--------+
              |
              v
         [ UI App ]

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📂 Project Structure

The project is split into a robust Python backend and a premium React frontend.

papergraph/
├── backend/                # FastAPI Application
│   ├── main.py             # API Entry Point
│   ├── ingestion/          # arXiv fetching & processing
│   ├── graph/              # Neo4j connections & LLM extraction
│   └── retrieval/          # Hybrid GraphRAG logic
├── frontend/               # Next.js Application
│   ├── src/app/            # Pages & UI Components
│   ├── package.json        # Node.js dependencies
│   └── public/             # Static Assets
├── .env                    # Environment variables (Azure, Neo4j)
├── requirements.txt        # Python dependencies
└── README.md

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## 🚀 Getting Started

### 1. Backend Setup
1. Create a `.env` file (see `.env.example`).
2. Install dependencies: `pip install -r requirements.txt`
3. Start the API: `uvicorn backend.main:app --reload`

### 2. Frontend Setup
1. Navigate to `/frontend`.
2. Install dependencies: `npm install`
3. Start the UI: `npm run dev`

### 3. Data Ingestion
PaperGraph is built for scale. 
*   **Current State:** Successfully ingested **50 documents** as a baseline.
*   **Scalability:** You can easily ingest **500, 5,000, or more** documents by adjusting the `--max` parameter in the ingestion script.
```bash
python -m backend.ingestion.pipeline --max 100 --category cs.AI

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💡 Key Considerations

• Knowledge Density: GraphRAG performs best when papers are within the same domain (e.g., cs.AI), allowing for rich link extraction between authors and methods.
• Cost Efficiency: Entity extraction uses one LLM call per abstract. Ingesting 50 documents costs ~$0.10 with GPT-4o.
• Neo4j Aura: Using the free tier of Neo4j Aura provides plenty of space for ~1,000 research papers and their associated entities. Note: Free tier instances may expire after 14 days of inactivity.

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📊 Graph Schema

Node	Description	Relationships
Paper	Research Articles	AUTHORED_BY, CITES, PROPOSES
Author	Researchers	AFFILIATED_WITH
Method	Algorithms/Models	USED_BY, PROPOSED_IN
Institution	Universities/Labs	AFFILIATES

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Built with Neo4j Aura · OpenAI GPT-4o · FastAPI · Next.js

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🔗 Demo Video: You can see the application walkthrough here