prometheous 0.1.0

AI-powered documentation generation and vector indexing tool

Prometheous

AI-powered documentation generation and vector indexing tool for codebases.

Overview

Prometheous is a comprehensive tool that combines automated code documentation generation with advanced vector indexing capabilities for Retrieval-Augmented Generation (RAG). It helps developers create beautiful, searchable documentation and enables intelligent code exploration through semantic search.

Features

• 🤖 AI-Powered Documentation: Generate comprehensive documentation from your codebase using advanced language models
• 🔍 Vector Indexing: Create semantic search indexes for intelligent code exploration
• 💬 RAG Chat Interface: Interactive chat with your codebase using natural language queries
• 🌐 Beautiful Web UI: Modern, responsive documentation websites with search functionality
• 🔧 Flexible Configuration: Support for multiple AI providers (OpenAI, Ollama, etc.)
• 📦 Easy Integration: Simple CLI interface with sensible defaults

Installation

pip install prometheous

Quick Start

Generate Documentation

# Generate documentation for current directory
prom doc

# Generate documentation with custom paths
prom doc --project-root ./src --doc-root ./docs --project-url https://github.com/user/repo

Create Vector Index

# Create vector index for documentation
prom vec --source-path ./docs

# Create index with interactive chat
prom vec --source-path ./docs --interactive

Configuration

Environment Variables

# OpenAI Configuration
export OPENAI_API_KEY="your-api-key"
export OPENAI_API_BASE="https://api.openai.com/v1"  # or your custom endpoint

# Model Configuration
export PROMETHEOUS_MODEL_NAME="gpt-3.5-turbo"
export PROMETHEOUS_MAX_TOKENS=4096

# Embedding Configuration
export PROMETHEOUS_EMBEDDING_MODEL="text-embedding-ada-002"
export EMBEDDING_DIMENSION=1536

# Ollama Configuration (for local models)
export OLLAMA_BASE_URL="http://localhost:11434"

CLI Reference

prom doc - Generate Documentation

Option	Description	Default
--project-root, -p	Project root directory	.
--doc-root, -d	Documentation output directory	./docs
--project-url, -u	Project URL for links	https://github.com/user/project
--model-name	AI model to use	gpt-3.5-turbo
--max-tokens	Maximum tokens per request	4096
--headless	Run without browser preview	false

prom vec - Vector Indexing

Option	Description	Default
--source-path, -s	Source directory to index	Required
--cache-dir, -c	Cache directory for index	./cache
--embedding-model	Embedding model to use	text-embedding-ada-002
--embedding-dimension	Embedding vector dimension	1536
--interactive, -i	Start interactive chat	false

Examples

Complete Documentation Workflow

# 1. Generate documentation
prom doc --project-root ./my-project --doc-root ./documentation

# 2. Create vector index
prom vec --source-path ./documentation --cache-dir ./vector-cache

# 3. Start interactive chat
prom vec --source-path ./documentation --interactive

Using with Local Models (Ollama)

# Set up Ollama environment
export OLLAMA_BASE_URL="http://localhost:11434"
export OPENAI_API_BASE="http://localhost:11434/v1"
export PROMETHEOUS_MODEL_NAME="llama2"
export PROMETHEOUS_EMBEDDING_MODEL="llama2"

# Generate documentation
prom doc --project-root ./src

Development

For development setup and advanced usage, see:

• Usage Guide
• Project Understanding
• Legacy Documentation

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.