Sequential Questioning MCP Server

A specialized server that enables LLMs (Large Language Models) to gather specific information through sequential questioning. This project implements the MCP (Model Control Protocol) standard for seamless integration with LLM clients.

Project Status

🎉 Version 1.0.0 Released 🎉

The Sequential Questioning MCP Server is now complete and ready for production deployment. All planned features have been implemented, tested, and documented.

Features

• Sequential Questioning Engine: Generates contextually appropriate follow-up questions based on previous responses

• MCP Protocol Support: Full implementation of the MCP specification for integration with LLMs

• Robust API: RESTful API with comprehensive validation and error handling

• Vector Database Integration: Efficient storage and retrieval of question patterns

• Comprehensive Monitoring: Performance metrics and observability with Prometheus and Grafana

• Production-Ready Deployment: Kubernetes deployment configuration with multi-environment support

• High Availability: Horizontal Pod Autoscaler and Pod Disruption Budget for production reliability

• Security: Network policies to restrict traffic and secure the application

Documentation

• API Reference

• Architecture

• Usage Examples

• Deployment Guide

• Operational Runbook

• Load Testing

• Deployment Verification

• Final Deployment Plan

• Release Notes

Getting Started

Prerequisites

• Python 3.10+

• Docker and Docker Compose (for local development)

• Kubernetes cluster (for production deployment)

• PostgreSQL 15.4+

• Access to a Qdrant instance

Quick Start

The easiest way to get started is to use our initialization script:

This script will:

1. Check if Docker is running

2. Start all necessary containers with Docker Compose

3. Run database migrations automatically

4. Provide information on how to access the application

The application will be available at http://localhost:8001

Local Development

1. Clone the repository

   git clone https://github.com/your-organization/sequential-questioning.git cd sequential-questioning

2. Install dependencies

   pip install -e ".[dev]"

3. Set up environment variables

   cp .env.example .env # Edit .env file with your configuration

4. Run the development server

   uvicorn app.main:app --reload

Docker Deployment

Database Setup

If you're starting the application manually, don't forget to run the database migrations:

Kubernetes Deployment

1. Development Environment

   kubectl apply -k k8s/overlays/dev

2. Staging Environment

   kubectl apply -k k8s/overlays/staging

3. Production Environment

   kubectl apply -k k8s/overlays/prod

See the Final Deployment Plan and Operational Runbook for detailed instructions.

Monitoring

Access Prometheus and Grafana dashboards for monitoring:

CI/CD Pipeline

Automated CI/CD pipeline with GitHub Actions:

• Continuous Integration: Linting, type checking, and testing

• Continuous Deployment: Automated deployments to dev, staging, and production

• Deployment Verification: Automated checks post-deployment

Testing

Run the test suite:

Run performance tests:

Troubleshooting

Database Tables Not Created

If the application is running but the database tables don't exist:

1. Make sure the database container is running

2. Run the database migrations manually:

   export DATABASE_URL="postgresql://postgres:postgres@localhost:5432/postgres" bash scripts/run_migrations.sh

Pydantic Version Compatibility

If you encounter the error pydantic.errors.PydanticImportError: BaseSettings has been moved to the pydantic-settings package, ensure that:

1. The pydantic-settings package is included in your dependencies

2. You're importing BaseSettings from pydantic_settings instead of directly from pydantic

This project uses Pydantic v2.x which moved BaseSettings to a separate package.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License

Contact

For support or inquiries, contact support@example.com