┌─────────────┐
│   papers/   │  Raw PDFs (downloaded from arXiv)
└──────┬──────┘
       │  ingest.py: load → chunk → embed
       ▼
┌─────────────┐
│  chroma_db/ │  Vector store (291 chunks, all-MiniLM-L6-v2 embeddings)
└──────┬──────┘
       │
       ├─────────────────┬─────────────────────┐
       ▼                 ▼                      ▼
┌─────────────┐  ┌──────────────┐   ┌────────────────────┐
│ rag_chain.py│  │  agent.py    │   │  evaluation.py      │
│ Simple LCEL │  │  LangGraph:  │   │  RAGAS metrics:      │
│ retrieve→   │  │  plan→       │   │  faithfulness,        │
│ generate    │  │  retrieve→   │   │  answer relevancy,    │
│             │  │  synthesize  │   │  context precision    │
└──────┬──────┘  └──────┬───────┘   └─────────────────────┘
       │                │
       └───────┬────────┘
               ▼
      ┌─────────────────┐
      │  mcp_server.py   │  Exposes both as MCP tools
      │  + app.py (CLI)  │  for external LLM clients
      └─────────────────┘

ai-research-paper-assistant/
├── src/
│   ├── config.py       # Single source of truth: embeddings, vectorstore, LLM setup
│   ├── ingest.py        # PDF loading, chunking, embedding into Chroma
│   ├── rag_chain.py     # Simple retrieve-then-generate LCEL chain
│   ├── agent.py          # LangGraph agent: plan → retrieve → synthesize
│   └── evaluation.py    # RAGAS evaluation harness
├── mcp_server.py         # MCP tool wrapper around rag_chain and agent
├── app.py                 # CLI entry point (ingest / ask / agent / evaluate)
├── download_papers.py     # One-time script to pull papers from arXiv
├── requirements.txt
└── eval_results.txt        # Saved RAGAS output

# 1. Clone and enter the project
git clone <your-repo-url>
cd ai-research-paper-assistant

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Add your Groq API key (free at console.groq.com)
echo "GROQ_API_KEY=your_key_here" > .env

# 5. Download the papers and build the vector store
python download_papers.py
python app.py ingest

python app.py ask         # Simple RAG: ask direct questions, one retrieval pass
python app.py agent       # Agentic RAG: handles multi-part/comparison questions
python app.py evaluate    # Re-run RAGAS scoring, saves to eval_results.txt
python mcp_server.py      # Start as an MCP server for external clients (e.g. Claude Desktop)