RAG Setup

Directory: launch-agent-skills/skills/rag-setup/skill.md

name: FastAPI Project Setup description: Clone and set up launch-rag or launch-agentic-rag - production-ready FastAPI backends with RAG capabilities triggers: - fastapi - api setup - project structure - python api - backend setup - launch-rag - rag backend - agentic rag - agent backend

FastAPI Project Setup Skill

Purpose

Clone and set up a production-ready FastAPI backend for RAG (Retrieval-Augmented Generation):

Available Options:

1. launch-rag - Basic RAG

• Vector similarity search with Supabase
• Question answering with citations
• Simple, focused implementation
• Perfect for learning RAG fundamentals

2. launch-agentic-rag - Agentic RAG

• Everything in launch-rag PLUS:
• Agent reasoning (Retrieve → Reason → Decide → Act)
• Tool calling (schedule meetings, send emails)
• Multi-turn conversations
• Google Calendar/Gmail integration (optional)

Both include:

• Multiple AI provider support (OpenAI, Anthropic)
• Clean folder structure (core, services, models)
• Environment configuration with validation
• Health checks and API documentation
• Docker support
• Type hints and Pydantic validation

Prerequisites

• Python 3.11+
• Supabase account
• OpenAI API key
• Anthropic API key (optional)
• Google Cloud credentials (optional, for agentic-rag tools)

Instructions

Step 0: Ask User Which Repository

IMPORTANT: Ask the user which version they want to set up:

Prompt the user:

Which FastAPI backend would you like to set up?

1. launch-rag (Basic RAG - recommended for learning)
   - Simple vector search + Q&A
   - Faster setup, fewer dependencies

2. launch-agentic-rag (Agentic RAG - advanced features)
   - Agent reasoning with tools
   - Calendar/email integration
   - Multi-turn conversations

Choose: [1/2]

Decision Logic:

• If user says "basic", "simple", "learning", or "1" → use launch-rag
• If user says "agentic", "agent", "tools", or "2" → use launch-agentic-rag
• If unclear, recommend launch-rag for first-time users

Set variables:

# Based on user choice
REPO_NAME="launch-rag"  # or "launch-agentic-rag"
REPO_URL="https://github.com/ShenSeanChen/$REPO_NAME.git"

Step 1: Clone the Repository

IMPORTANT: Ask the user where they want to clone the repo (which directory), or use the current working directory.

# Clone the chosen repo
git clone $REPO_URL [project-name]
cd [project-name]

Replace [project-name] with the desired project folder name.

Example:

• git clone https://github.com/ShenSeanChen/launch-rag.git my-rag-api
• git clone https://github.com/ShenSeanChen/launch-agentic-rag.git my-agent-api

Step 2: Create Virtual Environment

python3.11 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Set Up Environment Variables

Create a .env file from the template:

cp .env.example .env

IMPORTANT: The .env.example has placeholder values that MUST be replaced.

Then edit .env with your actual credentials:

# Required - Supabase (will be set by rag-database skill)
SUPABASE_URL=https://your-project-ref.supabase.co
SUPABASE_ANON_KEY=your_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

# Required - OpenAI (needed for embeddings regardless of AI provider)
OPENAI_API_KEY=sk-your_openai_key
OPENAI_EMBED_MODEL=text-embedding-3-small
OPENAI_CHAT_MODEL=gpt-4o

# AI Provider Configuration
# IMPORTANT: Set to "openai" if you only have OpenAI key
# Set to "anthropic" only if you have BOTH OpenAI (for embeddings) AND Anthropic keys
AI_PROVIDER=openai

# Optional - Anthropic (only needed if AI_PROVIDER=anthropic)
ANTHROPIC_API_KEY=your_anthropic_key
ANTHROPIC_CHAT_MODEL=claude-3-5-sonnet-20241022

Key Points:

• ✅ OpenAI key is ALWAYS required (for embeddings via text-embedding-3-small)
• ✅ Set AI_PROVIDER=openai by default (uses GPT-4o for chat)
• ✅ Only set AI_PROVIDER=anthropic if you have a valid Anthropic key
• ⚠️ Don't leave placeholder values like your_anthropic_api_key_here - the app will fail at runtime

Step 5: Set Up Supabase Database

Run the SQL initialization script in your Supabase SQL editor:

# The sql/init.sql file contains the database schema
# Copy and run it in Supabase SQL Editor at:
# https://supabase.com/dashboard/project/[your-project]/editor

Step 6: Run the Server

uvicorn main:app --reload --port 8000

The API will be available at:

• API: http://localhost:8000
• Docs: http://localhost:8000/docs
• Health: http://localhost:8000/healthz
• Chat UI: http://localhost:8000/chat (if available)

Step 7: Test the Setup (Optional)

python test_setup.py

Step 8: Next Steps Based on Repository

If launch-rag:

• ✅ Basic setup complete!
• Run rag-database skill next to configure database
• Start using the RAG endpoints

If launch-agentic-rag:

• ✅ Basic setup complete!
• Run rag-database skill next to configure database
• (Optional) Run rag-tools skill to configure Google Calendar/Gmail tools
• Explore agent reasoning and tool calling features

Checklist

Common steps (both repos):

• Ask user: launch-rag or launch-agentic-rag?
• Clone the chosen repository to desired location
• Create Python 3.11+ virtual environment
• Install all dependencies from requirements.txt
• Copy .env.example to .env
• Add OpenAI API key to .env (REQUIRED)
• Set AI_PROVIDER=openai by default
• (Optional) Add Anthropic API key if using Claude

For rag-database (next step):

• Add Supabase credentials to .env
• Run SQL initialization script in Supabase
• Verify database connection

For launch-agentic-rag only:

• (Optional) Set up Google Cloud service account
• (Optional) Configure Calendar/Gmail tools

What You Get

launch-rag includes:

launch-rag/
├── app/
│   ├── core/              # Configuration & database
│   ├── models/            # Pydantic schemas
│   ├── services/          # RAG logic, embeddings, AI
│   └── main.py            # FastAPI app
├── sql/
│   └── init_supabase.sql  # Database setup
├── static/                # Chat UI
├── test_setup.py
└── requirements.txt

launch-agentic-rag includes (everything above PLUS):

launch-agentic-rag/
├── app/
│   ├── core/              # Same as launch-rag
│   ├── models/            # Same as launch-rag
│   ├── services/
│   │   ├── rag.py         # Enhanced with agent reasoning
│   │   ├── agent.py       # Agent decision-making logic
│   │   └── tools/         # 🆕 Tool implementations
│   │       ├── calendar.py   # Google Calendar integration
│   │       └── email.py      # Gmail integration
│   └── main.py            # FastAPI app with agent endpoints
├── credentials/           # 🆕 Google Cloud credentials
└── requirements.txt       # Additional dependencies for tools

Example Usage

User: "Set up a FastAPI backend for my new project"

Claude should:

1. Ask which repo: "Do you want basic RAG or agentic RAG?"
2. Ask where to clone: Current directory or specific path?
3. Clone chosen repo with user-specified project name
4. Create virtual environment
5. Install dependencies
6. Set up .env file (with AI_PROVIDER=openai by default)
7. Remind user about next steps:
   • Add OpenAI API key to .env
   • Run rag-database skill for database
   • (If agentic-rag) Optionally run rag-tools for Google integrations

Important Notes

• Always ask where to clone the repo before running git clone
• The repo is production-ready with RAG capabilities built-in
• Requires Supabase for vector storage (pgvector extension)
• Supports both OpenAI and Anthropic AI providers
• Includes Docker support for easy deployment

Related Skills

• rag-database - Configure Supabase database and pgvector