语义检索助手

Documents (PDF/DOCX/HTML/MD)
    ↓
Document Processor → Text Chunker → Embeddings
    ↓
ChromaDB Vector Store
    ↓
├─→ MCP Server (FastMCP) → Claude Desktop
└─→ Streamlit UI → Monitoring/Testing

# Clone repository
git clone https://github.com/yourusername/research-assistant-mcp.git
cd research-assistant-mcp

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

# Install dependencies
pip install -r requirements.txt

# Install local embeddings
pip install sentence-transformers

# Configure environment
cp .env.example .env
# Edit .env - add OPENAI_API_KEY if using OpenAI embeddings

# Download 25 AI/ML papers from arXiv
python scripts/download_sample_papers.py --count 25

# Index sample papers
python scripts/index_docs.py --folder ./sample_papers

# Or index your own documents
python scripts/index_docs.py --folder /path/to/your/papers --recursive

python mcp_server/server.py

{
  "mcpServers": {
    "research-assistant": {
      "command": "python",
      "args": ["/full/path/to/research-assistant-mcp/mcp_server/server.py"],
      "env": {}
    }
  }
}

streamlit run ui/app.py

Query: "What are the challenges in RAG systems?"
Returns: Top-k results with sources, scores, and metadata

Input: Document path or title
Returns: Title, author, keywords, preview

Query: "prompt engineering techniques"
Returns: Related papers with relevance scores

research-assistant-mcp/
├── mcp_server/          # MCP server implementation
│   └── server.py
├── rag_pipeline/        # RAG components
│   ├── config.py
│   ├── document_processor.py
│   ├── chunker.py
│   ├── vector_store.py
│   ├── retriever.py
│   └── metadata_extractor.py
├── ui/                  # Streamlit dashboard
│   ├── app.py
│   └── pages/
├── scripts/             # CLI utilities
│   ├── index_docs.py
│   └── download_sample_papers.py
├── tests/              # Testing & benchmarks
│   ├── sample_queries.json
│   └── benchmark_performance.py
├── data/               # Data storage
│   ├── chroma_db/
│   └── query_logs/
└── docs/               # Documentation
    └── METRICS.md

# Run performance benchmarks
python tests/benchmark_performance.py

# Output: Accuracy, latency, scale metrics

# Build and run
docker-compose up -d

# Access UI at http://localhost:8501
# MCP server runs on localhost:8000

# OpenAI (paid, best quality)
EMBEDDING_MODEL=text-embedding-3-small

# Or use local (free) by default - already configured

CHUNK_SIZE=1000        # Characters per chunk
CHUNK_OVERLAP=200      # Overlap between chunks