The New Archon (Beta) - The Operating System for AI Coding Assistants!

original_archon/iterations/v1-single-agent/README.md

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+# Archon V1 - Basic Pydantic AI Agent to Build other Pydantic AI Agents
+
+This is the first iteration of the Archon project - no use of LangGraph and built with a single AI agent to keep things very simple and introductory.
+
+An intelligent documentation crawler and RAG (Retrieval-Augmented Generation) agent built using Pydantic AI and Supabase that is capable of building other Pydantic AI agents. The agent crawls the Pydantic AI documentation, stores content in a vector database, and provides Pydantic AI agent code by retrieving and analyzing relevant documentation chunks.
+
+## Features
+
+- Pydantic AI documentation crawling and chunking
+- Vector database storage with Supabase
+- Semantic search using OpenAI embeddings
+- RAG-based question answering
+- Support for code block preservation
+- Streamlit UI for interactive querying
+
+## Prerequisites
+
+- Python 3.11+
+- Supabase account and database
+- OpenAI API key
+- Streamlit (for web interface)
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/coleam00/archon.git
+cd archon/iterations/v1-single-agent
+```
+
+2. Install dependencies (recommended to use a Python virtual environment):
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+pip install -r requirements.txt
+```
+
+3. Set up environment variables:
+   - Rename `.env.example` to `.env`
+   - Edit `.env` with your API keys and preferences:
+   ```env
+   OPENAI_API_KEY=your_openai_api_key
+   SUPABASE_URL=your_supabase_url
+   SUPABASE_SERVICE_KEY=your_supabase_service_key
+   LLM_MODEL=gpt-4o-mini  # or your preferred OpenAI model
+   ```
+
+## Usage
+
+### Database Setup
+
+Execute the SQL commands in `site_pages.sql` to:
+1. Create the necessary tables
+2. Enable vector similarity search
+3. Set up Row Level Security policies
+
+In Supabase, do this by going to the "SQL Editor" tab and pasting in the SQL into the editor there. Then click "Run".
+
+### Crawl Documentation
+
+To crawl and store documentation in the vector database:
+
+```bash
+python crawl_pydantic_ai_docs.py
+```
+
+This will:
+1. Fetch URLs from the documentation sitemap
+2. Crawl each page and split into chunks
+3. Generate embeddings and store in Supabase
+
+### Streamlit Web Interface
+
+For an interactive web interface to query the documentation:
+
+```bash
+streamlit run streamlit_ui.py
+```
+
+The interface will be available at `http://localhost:8501`
+
+## Configuration
+
+### Database Schema
+
+The Supabase database uses the following schema:
+```sql
+CREATE TABLE site_pages (
+    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
+    url TEXT,
+    chunk_number INTEGER,
+    title TEXT,
+    summary TEXT,
+    content TEXT,
+    metadata JSONB,
+    embedding VECTOR(1536)
+);
+```
+
+### Chunking Configuration
+
+You can configure chunking parameters in `crawl_pydantic_ai_docs.py`:
+```python
+chunk_size = 5000  # Characters per chunk
+```
+
+The chunker intelligently preserves:
+- Code blocks
+- Paragraph boundaries
+- Sentence boundaries
+
+## Project Structure
+
+- `crawl_pydantic_ai_docs.py`: Documentation crawler and processor
+- `pydantic_ai_expert.py`: RAG agent implementation
+- `streamlit_ui.py`: Web interface
+- `site_pages.sql`: Database setup commands
+- `requirements.txt`: Project dependencies
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.