Skip to content

haz3141/ai-dev-lab

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

22 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AI-Enhanced Dev Lab v0.6.4

AI Development Lab with MCP Server for secure, auditable AI tool interactions and RAG evaluation gates.

Quick Start

  1. Install dependencies:

    pip install -r requirements.txt
  2. Start the MCP server:

    .venv/bin/python -m mcp_server.simple_server
  3. Run tests:

    pytest

RAG Evaluation Gates

Run evaluation locally:

# Run full evaluation
python eval/run.py --dataset eval/data/lab/lab_dev.jsonl --output eval/runs/$(date +%Y%m%d-%H%M%S)

# Check gates
python scripts/ci/parse_metrics.py eval/runs/*/metrics.json

# Start MCP server
.venv/bin/python -m mcp_server.simple_server

MCP Tools Available

The MCP server provides the following tools:

Terminal Operations

  • run_command: Execute terminal commands safely with timeout
  • check_file: Check if files exist and get metadata
  • read_file: Safely read files with line limits
  • list_directory: List directory contents with limits

Evaluation Operations

  • run_eval: Run RAG evaluation safely
  • check_gates: Check if evaluation gates pass

Usage Examples

# Test MCP server
curl -X POST http://localhost:8000/tools/run_command \
  -H "Content-Type: application/json" \
  -d '{"command": "ls -la", "timeout": 10}'

# Check file existence
curl -X POST http://localhost:8000/tools/check_file \
  -H "Content-Type: application/json" \
  -d '{"filepath": "eval/run.py"}'

# Run evaluation
curl -X POST http://localhost:8000/tools/run_eval \
  -H "Content-Type: application/json" \
  -d '{"dataset": "eval/data/lab/lab_dev.jsonl", "output_dir": "eval/runs/test"}'

Architecture

  • MCP Server: FastAPI-based server providing AI tools via MCP protocol
  • Security: Guardian-based access control and PII redaction
  • Audit: Comprehensive logging of all tool interactions
  • Evaluation: Automated testing and metrics for AI models
  • RAG Gates: Comprehensive evaluation framework with automated CI integration

Project Structure

  • lab/ - Research and development experiments
  • eval/ - Evaluation framework and gates
  • mcp_server/ - MCP server implementation
  • evidence/ - Evaluation evidence and reports

Development

See docs/cursor-usage.md for Cursor IDE setup and usage.

About

AI-Enhanced Dev Lab v3.0 — lab/app split, MCP, DSPy, RAG

Topics

Resources

Stars

Watchers

Forks

Packages

No packages published

Languages