tiny-rag-ai 0.1.2

Complete In-Device RAG chatbot library, no API keys needed.With 2 lines of implementation in applications.

Tiny RAG AI

A fully local RAG chatbot library. No API keys, no external servers, just 2 lines of code.
Runs entirely on-device in ~500MB of memory using the Qwen2.5-0.5B model.

What it is

• Wraps the Qwen2.5-0.5B-Instruct-GGUF model.
• Runs inference in about 330 MB of memory on-device.
• Avoids heavy RAG pipelines by accepting documents directly through its parameters.

Why use it

• Minimal setup and small footprint.
• Focus on your app logic instead of infrastructure.

Installation

Step 1 — Install llama-cpp-python (pre-built, no compilation needed)

Pick the version that matches your hardware:

# CPU only
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
 
# CUDA 12.1 (NVIDIA GPU)
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
 
# Metal (macOS Apple Silicon)
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal

Step 2 — Install tiny-rag-ai

pip install tiny-rag-ai

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Quick Start

import tiny_rag_ai
 
tiny_rag_ai.index("./docs")
 
answer = tiny_rag_ai.chat("What is your return policy?", use_case="customer support bot")
print(answer)

index() only needs to run once. After that, the FAISS index is saved to disk and reloaded automatically on the next run.

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Framework Examples

Flask

import tiny_rag_ai
tiny_rag_ai.index("./docs")
 
@app.route("/chat", methods=["POST"])
def chat():
    data = request.get_json()
    return jsonify({"answer": tiny_rag_ai.chat(data["message"], use_case="support bot")})

FastAPI

import tiny_rag_ai
tiny_rag_ai.index("./docs")
 
@app.post("/chat")
def chat(req: ChatRequest):
    return {"answer": tiny_rag_ai.chat(req.message, use_case="support bot")}

Django

# Call index() in AppConfig.ready(), then use tiny_rag_ai.chat() in your view as normal.
import tiny_rag_ai
answer = tiny_rag_ai.chat(request.POST["message"], use_case="support bot")

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Deploying to Render (or any cloud server)

Set this environment variable to persist downloaded models across deploys:

TINY_AI_CACHE_DIR=/data/models

Mount a persistent disk at /data with a minimum of 1GB.

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API Reference

tiny_rag_ai.index(folder_path, save_path, n_ctx, threads)

Parameter	Default	Description
folder_path	required	Path to your documents folder (PDF/TXT)
save_path	./tiny_ai_data	Where to save the FAISS index and chunks
n_ctx	2048	Context window size for the LLM
threads	8	Number of CPU threads for inference

tiny_rag_ai.chat(query, use_case)

Parameter	Default	Description
query	required	The user's question
use_case	required	Describes the bot's role e.g. "customer support bot"

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Stack

• LLM: Qwen2.5 0.5B via llama-cpp-python
• Embeddings: sentence-transformers (all-MiniLM-L6-v2)
• Vector store: FAISS
• PDF loading: PyMuPDF

License

MIT