Skip to content

README.md

Latest commit

 

History

History
196 lines (147 loc) · 5.79 KB

README.md

File metadata and controls

196 lines (147 loc) · 5.79 KB

🧠 Serotonin Script

Python FastAPI Taskiq RAG License

AI-driven medical content engine using RAG (LlamaIndex/Qdrant), FastAPI, and Taskiq for automated multi-platform publishing with physician style preservation.

🎯 Overview

Serotonin Script is an autonomous system for generating and distributing medically-accurate content across social platforms. It leverages RAG (Retrieval-Augmented Generation) to ensure medical precision while preserving the unique authorial voice of healthcare professionals.

Key Capabilities

  • Style Preservation: Vector-based retrieval of physician's writing patterns
  • Medical Accuracy: Fact-checking against clinical guidelines (PubMed, protocols)
  • Multi-Platform Publishing: Automated distribution to Telegram, X (Twitter), Threads
  • Async-First Architecture: High-performance task processing via Taskiq + Redis
  • Slack-Native UX: Draft approval workflow with interactive Block Kit UI

🛠 Tech Stack

Layer Technology Purpose
API Framework FastAPI Async-native REST API
Task Queue Taskiq + Redis Background job processing
AI Engine Claude 3.5 Sonnet / GPT-4o Content generation with fallback
Vector Store Qdrant Semantic search for style/knowledge
RAG Framework LlamaIndex Retrieval-augmented generation pipeline
Orchestration n8n Workflow automation & scheduling
Database PostgreSQL + Alembic Relational data with migrations
Monitoring Prometheus + Grafana + Loki Metrics, dashboards, logs

📁 Project Structure

seratonin_script/
├── backend/
│   ├── api/                    # FastAPI routes & middleware
│   ├── services/               # Business logic orchestration
│   ├── agents/                 # LangChain agents + tools
│   ├── rag/                    # LlamaIndex indexing & retrieval
│   ├── integrations/           # External APIs (LLMs, social platforms, PubMed)
│   ├── workers/                # Taskiq async workers
│   └── tests/                  # Unit, integration, E2E tests
├── knowledge_base/             # Training data for RAG
│   ├── doctor_style/           # Physician's articles & posts
│   └── medical_guidelines/     # Clinical protocols (PDFs)
├── slack_app/                  # Slack Block Kit UI & handlers
├── orchestration/n8n/          # Workflow definitions
├── database/migrations/        # Alembic schema versions
├── infra/docker/               # Docker Compose + monitoring configs
└── docs/                       # Architecture & ADRs

🚀 Quick Start

Prerequisites

  • Docker & Docker Compose
  • Python 3.11+ (for local development)
  • Slack workspace (for approval workflow)
  • API keys: Anthropic/OpenAI, Telegram, X, Threads

Installation

# Clone repository
git clone https://github.com/PyDevDeep/serotonin-script.git
cd serotonin-script

# Configure environment
cp .env.example .env
# Edit .env with your API keys and credentials

# Start all services
docker-compose up --build

Service URLs

📖 Usage

1. Index Knowledge Base

# Ingest physician's writing samples + medical guidelines into Qdrant
python scripts/index_knowledge_base.py

2. Generate Draft (via Slack)

/draft anxiety management tips

Workflow:

  1. Slack command triggers n8n webhook
  2. n8n calls POST /draft with topic
  3. Backend enqueues Taskiq task generate_draft
  4. Worker:
    • Retrieves similar physician posts (style matching)
    • Fetches relevant medical facts (knowledge retrieval)
    • Generates draft via Claude 3.5 Sonnet
  5. Slack receives Block Kit card with draft + action buttons

3. Approve & Publish

Click "Publish to Telegram" button in Slack:

  • Taskiq task publish_post executes
  • Content posted to configured channels
  • Slack notification confirms success

🔧 Development

Run Tests

# Unit tests
make test-unit

# Integration tests (requires running containers)
make test-integration

# Full test suite
make test

Local Backend Development

# Install dependencies
poetry install

# Run API server
poetry run uvicorn backend.api.main:app --reload

# Run Taskiq worker
poetry run taskiq worker backend.workers.broker:broker

Database Migrations

# Generate migration
alembic revision --autogenerate -m "description"

# Apply migrations
alembic upgrade head

📊 Monitoring

🤝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open Pull Request

See CONTRIBUTING.md for detailed guidelines.

📄 License

This project is licensed under the MIT License - see LICENSE file for details.

🙏 Acknowledgments

  • LlamaIndex for RAG framework
  • Taskiq for modern async task processing
  • Qdrant for vector search capabilities

Created by PyDevDeep