HR问答代理

%% Monochrome (Black & White) Architecture Diagram
flowchart LR
    subgraph UI
        ST["Streamlit Chat UI"]
    end

    subgraph MCP["MCP Server & Router"]
        Router["Router / Health"]
    end

    subgraph TOOLS["Tools"]
        subgraph RAG_GROUP["Policy RAG"]
            PS["PolicySearchTool"]
            RAG["RAGEngine"]
            CONV["ConversationManager"]
            CACHE[("Redis / In-Memory Cache")]
        end
        RS["ResumeScreeningTool"]
        ONB["OnboardingTool"]
    end

    subgraph DATA["Data Stores"]
        CHROMA[("Chroma Vector DB")]
        DOCS[("HR Documents")]
        RESUMES[("Resumes Store")]
        ONBDATA[("Onboarding JSON")]
    end

    subgraph LLM["Providers"]
        OPENAI["OpenAI"]
        GEMINI["Gemini"]
    end

    ST --> Router
    Router --> PS
    Router --> RAG
    Router --> RS
    Router --> ONB
    PS --> CHROMA
    PS --> DOCS
    RS --> RESUMES
    ONB --> ONBDATA
    RAG --> OPENAI
    RAG --> GEMINI
    CONV --> CACHE

    %% Grayscale styles
    classDef ui fill:#ffffff,stroke:#000000,stroke-width:1px
    classDef mcp fill:#f5f5f5,stroke:#000000,stroke-width:1px
    classDef tool fill:#ffffff,stroke:#000000,stroke-dasharray: 3 2
    classDef data fill:#f5f5f5,stroke:#000000,stroke-width:1px
    classDef prov fill:#ffffff,stroke:#000000,stroke-width:1px
    class ST ui
    class Router mcp
    class PS,RAG,CONV,CACHE,RS,ONB tool
    class CHROMA,DOCS,RESUMES,ONBDATA data
    class OPENAI,GEMINI prov

# Clone and run the quick start script
git clone https://github.com/sanjanb/mcp-agent.git
cd mcp-agent
.\quick_start.bat

# 1) Create and activate virtual environment
python -m venv .venv
.\.venv\Scripts\activate

# 2) Install dependencies
pip install -r requirements.txt

# 3) Configure API keys (optional for AI mode)
$env:OPENAI_API_KEY = "your-openai-key"
# OR
$env:GEMINI_API_KEY = "your-gemini-key"

# 4) Setup database and ingest sample documents
python setup.py

# 5) Warm up the system (reduces first response latency)
python scripts/warmup.py

# 6) Launch the application
streamlit run ui/streamlit_app.py

# OpenAI Configuration
OPENAI_API_KEY=sk-...                    # Your OpenAI API key
OPENAI_MODEL=gpt-4                       # Default model (gpt-3.5-turbo, gpt-4)

# Google Gemini Configuration
GEMINI_API_KEY=...                       # Your Gemini API key
GEMINI_MODEL=gemini-1.5-pro             # Default model

# Provider Selection
LLM_PROVIDER=auto                        # auto, openai, gemini

VECTOR_DB_PATH=./data/vector_db          # Chroma database location
VECTOR_DB_COLLECTION_NAME=hr_policies    # Collection name
HR_DOCUMENTS_PATH=./data/hr_documents    # Source documents folder
EMBEDDING_MODEL=all-MiniLM-L6-v2        # Sentence transformer model

# Redis Cache (optional - falls back to in-memory)
REDIS_URL=redis://localhost:6379/0
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_DB=0

# Low-Latency Mode Settings
FAST_OPENAI_MODEL=gpt-3.5-turbo         # Fast model for low-latency
FAST_GEMINI_MODEL=gemini-1.5-flash      # Fast Gemini model
LOW_LATENCY_MAX_TOKENS=350               # Token limit for fast responses
LOW_LATENCY_TEMPERATURE=0.0              # Lower temperature for consistency
LOW_LATENCY_BASIC_ONLY=false             # Force Basic mode in low-latency

RESUME_EMBEDDING_MODEL=all-MiniLM-L6-v2 # Model for resume embeddings
RESUME_STORAGE_PATH=./data/resumes       # Resume files location

mcp-agent/
├── 📱 ui/
│   └── streamlit_app.py             # Chat interface with streaming responses
├── 🖥️ mcp_server/
│   └── server.py                    # MCP server orchestration and routing
├── 🔧 tools/
│   ├── policy_rag/
│   │   ├── document_processor.py    # PDF/text ingestion and chunking
│   │   ├── vector_database.py       # Chroma vector DB operations
│   │   ├── mcp_tool.py              # Policy search MCP tool
│   │   └── rag_engine.py            # RAG logic and conversation management
│   ├── resume_screening/
│   │   └── mcp_tool.py              # Resume ranking and screening
│   └── cache/
│       └── redis_cache.py           # Redis/in-memory caching layer
├── 🚀 scripts/
│   └── warmup.py                    # System warm-up for reduced latency
├── 📊 data/
│   ├── hr_documents/                # Source policy documents (PDFs/text)
│   ├── vector_db/                   # Chroma embeddings database
│   └── resumes/                     # Candidate resume files
├── 📖 docs/
│   ├── HOW_IT_WORKS.md              # Detailed architecture guide
│   ├── FEATURE_HR_POLICY_RAG.md     # HR RAG feature documentation
│   └── plan.md                      # Complete development roadmap
├── 🖼️ assets/
│   ├── Screenshot_29-11-2025_9451_localhost.jpeg   # Main UI demo
│   ├── Screenshot_30-11-2025_94541_localhost.jpeg  # Low-latency mode
│   └── Screenshot_30-11-2025_9461_localhost.jpeg   # Resume screening
├── ⚙️ setup.py                      # Database setup and document ingestion
├── 📝 requirements.txt              # Python dependencies
├── 🚀 quick_start.bat               # Windows automation script
├── 🚀 quick_start.sh                # Linux/Mac automation script
└── 📋 README.md                     # This comprehensive guide

# ✅ Solution: Set API keys
$env:OPENAI_API_KEY = "your-openai-key"
# OR
$env:GEMINI_API_KEY = "your-gemini-key"

# ✅ Solution: Add documents and re-run setup
# 1. Add PDF/text files to data/hr_documents/
# 2. Re-run ingestion
python setup.py

# ✅ Solution: Run warm-up script
python scripts/warmup.py
# Then enable low-latency mode in sidebar

# ✅ Solution: Install and start Redis, or ignore for in-memory fallback
# Option 1: Use in-memory cache (automatic fallback)
# Option 2: Install Redis
# Windows: Download from https://redis.io/download
# Linux: sudo apt-get install redis-server

# ✅ Solution: Reinstall dependencies
pip install --upgrade -r requirements.txt

{
  "engineering": [
    { "id": 1, "task": "Set up email", "completed": false },
    { "id": 2, "task": "Read engineering handbook", "completed": false }
  ],
  "hr": [{ "id": 1, "task": "Access HRIS portal", "completed": true }]
}

$env:LOG_LEVEL = "DEBUG"
streamlit run ui/streamlit_app.py