tinyrag

Lightweight RAG implementation for all your LLM endpoints

About

tinyrag is a "tiny" RAG implementation that aims to give developers a plug and play experience while writing RAG applications. It aims to be compatible with major LLM providers and be as tiny (as the name suggests) as possible while doing so.

Usage

Before you run tinyrag make sure you have pulled the following models from the ollama repo:

• Nomic Text Embeddings
• Any large language model of your choice from the ollama library

Install using pip:

pip install tinyrag

Example usage:

from tinyrag.tinyrag import TinyRAG_Ollama

rag = TinyRAG_Ollama(llm_model='llama3.2:1b')

sample_docs = [ # made up info, pass any text here
    "Zephyr Quantum was founded in 2023 by Dr. Voss in Maple Ridge, BC.",
    "QuantumShield 3.0 launched January 2025 with 512-bit encryption.",
    "Board: Dr. Voss (CEO), Chen (CTO), Karim (CFO), plus 3 non-execs.",
    "Series B: $42M in October 2024 from Horizon, Quantum Capital, TechFusion.",
    "78 employees across Maple Ridge, Singapore, Zurich. 120 planned by end of 2025.",
    "Competitors: QuantumWall (US), SecureFuture (Germany), NexGen (Israel).",
    "Q4 2024: $8.7M revenue, up 34%. 2025 projection: $45-50M.",
    "Project Aurora: quantum-resistant IoT protocol in development.",
    "Clients: 2 Fortune 500 banks, 3 govt agencies, healthcare providers. Largest: GlobalBank.",
    "17 patents filed, 9 granted as of January 2025."
]

rag.add_documents(sample_docs) # add custom instructions to the LLM here if required
result = rag.query("What's Zephyr Quantums largest client? and give me more info about who their other clients are, be super verbose about it (do not makeup info and stick to the context)")
print(result)

Current features:

• Uses nomic text embeddings as the embedding model via ollama
• Is currently compatible with any model available on Ollama

Planned features/Roadmap

• Add support to more embedding models
• Currently only plaintext documents are supported, so support for database files, spreadsheets, documents, ppts, etc
• Support more endpoints such as Open AI, Claude, Deepseek, etc
• Multimodal embedding support
• Chroma DB/Custom Vector DBs support
• Implement FAISS from scratch

Contribution | Issues/Bug reports

Contributions are always welcome! Make sure to test your forks before making a PR.
Open an issue for Bug reports or to report other issues

License

This project is licensed under the MIT license