RAGVenture

RAGVenture is an intelligent startup idea generator powered by Retrieval-Augmented Generation (RAG). It helps entrepreneurs generate innovative startup ideas by learning from successful companies, combining the power of large language models with real-world startup data.

Why RAGVenture?

Traditional startup ideation tools either rely on expensive API calls or generate ideas without real-world context. RAGVenture solves this by:

• Data-Driven: Learns from real startup data to ground suggestions in reality
• Context-Aware: Understands patterns from successful startups
• Intelligent: Uses RAG to combine LLM capabilities with precise information retrieval
• Fast & Local: Runs entirely on your machine with no API costs (except optional Hugging Face)
• Production-Ready: Comprehensive test suite, error handling, and monitoring

System Requirements

• Python 3.11 or higher
• 8GB RAM minimum (16GB recommended)
• 2GB disk space for models and data
• Operating Systems:
  • Linux (recommended)
  • macOS
  • Windows (with WSL for best performance)

Quick Start

1. Installation:

# Clone the repository
git clone https://github.com/yourusername/RAGVenture.git
cd RAGVenture

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install spaCy language model for market analysis
python -m spacy download en_core_web_sm

2. Environment Setup:

# Required for text generation
export HUGGINGFACE_TOKEN="your-token-here"  # Get from huggingface.co

# Optional for LangChain tracing (debugging)
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
export LANGCHAIN_API_KEY="your-langsmith-api-key"
export LANGCHAIN_PROJECT="your-project-name"

3. Generate Ideas:

python rag_startup_ideas.py --topic "AI" --num-ideas 1

Features & Capabilities

Core Features

• Intelligent Idea Generation:
  • Uses RAG to combine LLM knowledge with real startup data
  • Generates contextually relevant and grounded ideas
  • Provides structured output with problem, solution, and market analysis

Command-Line Interface

• --topic: Target domain or area (required)
• --num-ideas: Number of ideas to generate (default: 3)
• --file: Custom startup data file (default: yc_startups.json)
• --max-lines: Limit data processing (optional)

Technical Features

• Smart Analysis:
  • Semantic search for relevant examples
  • Automatic metadata extraction
  • Pattern recognition from successful startups
• Performance Optimized:
  • One-time embedding generation (~22s)
  • Fast idea generation (~0.5s per idea)
  • Efficient data processing (~0.1s load time)
• Production Quality:
  • 31 comprehensive unit tests
  • Automated code formatting
  • Extensive error handling

Performance

Typical processing times on a standard machine:

• Initial Setup: ~22s (one-time embedding generation)
• Data Loading: ~0.1s
• Idea Generation: ~0.5s per idea

Docker Support

For containerized deployment, we provide both CPU and GPU support.

Prerequisites

• Docker and Docker Compose
• For GPU support:
  • NVIDIA GPU with CUDA
  • NVIDIA Container Toolkit
  • nvidia-docker2

Quick Start with Docker

# CPU Version
docker-compose up app-cpu

# GPU Version (with NVIDIA support)
docker-compose up app-gpu

Development Setup

1. Clone and setup:

git clone https://github.com/yourusername/RAGVenture.git
cd RAGVenture
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install development dependencies:

pip install -r requirements.txt
pre-commit install  # Sets up automatic code formatting

3. Run tests:

pytest tests/  # Should show 31 passing tests

Data Requirements

RAGVenture works with startup data in JSON format. Two options:

1. Use YC Data (Recommended):

   • Download from Y Combinator
   • Convert CSV to JSON:

     python -m rag_startups.data.convert_yc_data input.csv -o startups.json

2. Use Custom Data:

   • Prepare JSON file with required fields
   • See docs/data_format.md for schema

Troubleshooting

1. Embedding Generation Time:

   • First run takes ~22s to generate embeddings
   • Subsequent runs use cached embeddings
   • GPU can significantly speed up this process

2. Common Issues:

   • Missing HUGGINGFACE_TOKEN: Sign up at huggingface.co
   • Memory errors: Reduce batch size with --max-lines
   • GPU errors: Ensure CUDA toolkit is properly installed

Documentation

• docs/api.md: API documentation
• docs/examples.md: Usage examples
• docs/data_format.md: Data schema
• CONTRIBUTING.md: Development guidelines

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

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