Build RAG systems that actually work. Zero cost. Complete privacy. No bullshit.

A collection of 100% local RAG (Retrieval-Augmented Generation) implementations in multiple languages. No API keys, no cloud services, no monthly bills. Your data stays on your machine.

Because paying 500ドル+/month for API calls is insane when you can run everything locally for 0ドル. Because your private data shouldn't be sent to OpenAI/Anthropic/Google. Because you should own your AI stack.

Full-featured local RAG with:

• LLM: Ollama (TinyLlama/Mistral/Llama2)
• Embeddings: Ollama or SentenceTransformers
• Vector Store: LanceDB (embedded, no server)
• Status: ✅ Production Ready

See Python README

Modern TypeScript implementation with:

• LLM: Ollama (TinyLlama by default)
• Embeddings: Ollama (nomic-embed-text)
• Vector Store: In-memory (extensible to LanceDB)
• Status: ✅ Production Ready

cd python_example
python -m venv venv
.\venv\Scripts\activate # Windows
# source venv/bin/activate # Mac/Linux
pip install -r requirements.txt
python tests/run_all_tests.py

cd typescript_example
npm install
npm test
npm run dev

1. Zero Cost: Everything runs on your hardware
2. Complete Privacy: Data never leaves your machine
3. No Dependencies: No API keys, no cloud services
4. Actually Works: Not a toy - production ready
5. Simple: Minimal config, maximum results

• 4GB RAM
• 2GB disk space
• Any CPU from last 10 years
• Windows/Mac/Linux

• 8GB RAM
• 10GB disk space
• 4+ CPU cores

• 16GB+ RAM
• 20GB disk space
• Modern CPU/GPU

At 1000 queries/day, you save 300ドル-900/month.

• Knowledge Bases: Query your documents/notes/code
• Customer Support: Answer questions from your docs
• Research Assistant: Analyze papers and reports
• Code Assistant: Search and understand codebases
• Personal Assistant: Private AI for personal data
• Education Tools: Interactive learning systems

All running locally. All for free. All private.

# Windows
winget install Ollama.Ollama
# Mac
brew install ollama
# Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Minimum setup (< 1GB)
ollama pull tinyllama
ollama pull nomic-embed-text
# Better quality (needs 8GB+ RAM)
ollama pull mistral
ollama pull llama2

• Python: Production ready, full featured
• TypeScript: Modern, runs in browser (coming)
• More coming: Rust, Go, Java

Your App
 ↓
RAG Pipeline
 ├── Document Loader (PDFs, text, markdown)
 ├── Chunker (splits documents)
 ├── Embeddings (Ollama/Transformers)
 ├── Vector Store (LanceDB)
 └── LLM (Ollama - Llama/Mistral)
 
All Local → 0ドル Cost → 100% Private

Q: Is this really free?
A: Yes. 100% free after you have a computer. No hidden costs.

Q: Is it as good as GPT-4?
A: For RAG tasks? Often yes. For general chat? Depends on your model choice.

Q: Can it run offline?
A: Yes. Once models are downloaded, no internet needed.

Q: How much disk space?
A: 1-50GB depending on models. TinyLlama = 1GB, Mixtral = 26GB.

Q: Is it hard to set up?
A: No. Three commands and you're running.

Q: Can I use my GPU?
A: Yes. Ollama auto-detects and uses CUDA/Metal/ROCm.

Q: What about embeddings?
A: Included. Ollama or SentenceTransformers. Both free.

Q: Production ready?
A: Python version is. TypeScript coming soon.

We believe AI should be:

• Free: Not 500ドル/month
• Private: Your data is yours
• Local: No internet required
• Simple: It should just work
• Honest: No marketing BS

Want to help? Here's how:

1. Use it and report issues
2. Add examples and docs
3. Create implementations in new languages
4. Share with others who are tired of API bills

Rules:

• No cloud dependencies
• No API keys required
• Must run offline
• Keep it simple
• Keep it free

• Issues: Open a GitHub issue
• Questions: Start a discussion
• Philosophy: If it needs internet or costs money, we don't want it

MIT - Use it, modify it, sell it, whatever. Just keep it local and free.

Stop paying for AI. Run it yourself.

Built with 🖕 to cloud pricing and ❤️ for local compute.