招聘AI代理

graph TB
    subgraph "User Interface"
        CLI[Command Line Interface]
        API[Future REST API]
    end

    subgraph "AI Agent Core"
        LC[LangChain ReAct Agent]
        GEM[Google Gemini 2.0 Flash]
    end

    subgraph "Tool Ecosystem"
        JD[Job Description Tool]
        RANK[Resume Ranking Tool]
        REPORT[Report Generation Tool]
    end

    subgraph "MCP Integration"
        HTTP[MCP Streamable HTTP Transport]
        SERVER[MCP Server<br/>resume-tailor-mcp]
    end

    subgraph "Data Layer"
        FILES[File System<br/>TXT, PDF, DOCX]
        CACHE[Tool Cache<br/>Performance Optimization]
    end

    CLI --> LC
    LC --> GEM
    GEM --> JD
    GEM --> RANK
    GEM --> REPORT

    JD --> HTTP
    RANK --> HTTP
    REPORT --> FILES

    HTTP --> SERVER
    SERVER --> CACHE

    style LC fill:#e1f5fe
    style GEM fill:#f3e5f5
    style HTTP fill:#e8f5e8
    style SERVER fill:#fff3e0

# Clone the repository
git clone https://github.com/maruthut/recruiter-agent.git
cd recruiter-agent

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your Google API key and MCP server URL

# Google Gemini API Configuration
GOOGLE_API_KEY=your_google_api_key_here

# MCP Server Configuration (Streamable HTTP)
MCP_HTTP_URL=http://localhost:5002/mcp

{
  "tools": [
    {
      "name": "rank_resumes_mcp",
      "description": "AI-powered resume ranking against job descriptions",
      "parameters": {
        "job_description": "string",
        "resume_texts": "array",
        "resume_filenames": "array"
      }
    },
    {
      "name": "fetch_job_description_mcp",
      "description": "Fetch job descriptions from URLs",
      "parameters": {
        "url": "string"
      }
    },
    {
      "name": "analyze_resume_mcp",
      "description": "Detailed single resume analysis",
      "parameters": {
        "resume_text": "string",
        "job_requirements": "string"
      }
    }
  ]
}

# Basic usage with default job description
python hr_agent.py

# Specify custom job description file
python hr_agent.py "senior_developer.txt"

# Process job description from URL
python hr_agent.py "https://company.com/jobs/senior-engineer"

# View help and options
python hr_agent.py --help

============================================================
HR Recruiter AI Agent - Starting Analysis
============================================================

> Entering new AgentExecutor chain...

Action: get_job_description_content
Action Input: software_engineer.txt

Job Title: Software Engineer
Required Skills: Python, Flask, Django, ML, Databases...

Action: match_resumes
Action Input: [Job description content]

Found 4 resume(s) to analyze
✅ MCP session initialized (Streamable HTTP) - Session ID: abc123
✅ Discovered MCP tools (HTTP): ['rank_resumes_mcp', 'fetch_job_description_mcp', 'analyze_resume_mcp']
✅ Successfully analyzed 4 resume(s)

Action: generate_report
Action Input: [Analysis results]

============================================================
Analysis Complete!
============================================================

results/analysis_report_Software_Engineer.md

recruiter-agent/
├── 🧠 hr_agent.py              # Main AI agent implementation
├── 🧪 test_mcp_http.py         # MCP HTTP transport testing
├── 📦 requirements.txt         # Python dependencies
├── ⚙️ .env                     # Environment configuration
├── 📚 README.md               # This documentation
├── 🔧 HTTP_MODE_SETUP.md      # MCP Streamable HTTP guide
├── 📋 CODE_REVIEW.md          # Code quality assessment
│
├── 💼 job_descriptions/       # Job description files
│   └── software_engineer.txt
├── 👥 resumes/                # Candidate resume files
│   ├── alice_johnson.pdf
│   └── bob_smith.docx
└── 📊 results/                # Generated analysis reports
    └── analysis_report_Software_Engineer.md

# Run tests
python test_mcp_http.py

# Development mode with verbose logging
LOG_LEVEL=DEBUG python hr_agent.py

# Lint and format code
black .
flake8 .

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["python", "hr_agent.py"]

apiVersion: apps/v1
kind: Deployment
metadata:
  name: hr-recruiter-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: hr-agent
  template:
    metadata:
      labels:
        app: hr-agent
    spec:
      containers:
      - name: hr-agent
        image: hr-recruiter-agent:latest
        env:
        - name: GOOGLE_API_KEY
          valueFrom:
            secretKeyRef:
              name: gemini-api-key
              key: api-key
        - name: MCP_HTTP_URL
          value: "http://mcp-server:5002/mcp"

# Fork and clone
git clone https://github.com/your-username/recruiter-agent.git
cd recruiter-agent

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

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest