turborag-ahx47 0.1.1

My own RAG library built on turbovec

TurboRag-ahx47

TurboRag is a fully offline, low‑CPU, low‑RAM RAG (Retrieval Augmented Generation) engine.
This package (turborag-ahx47) is a custom build that leverages:

• TurboVec – quantized (Q4) vector index (8× smaller than float32, faster than FAISS)
• llama-cpp-python – runs all models as Q4_K_M GGUF files on CPU
• Optional REST API – via FastAPI (not included in this core library)
• Multi‑language SDKs – Python only for now

Note: The original project name turborag was already taken on PyPI. This is the official library under the name turborag-ahx47.

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Features

• No GPU required, no internet at runtime – everything runs offline on CPU.
• Tiny memory footprint – Gemma Embedding 300M (≈150 MB) + Qwen 0.5B (300 MB).
• TurboVec Q4 index – 8× compression, fast brute‑force search.
• Built‑in SQLite document store – metadata and chunk storage.
• Easy‑to‑use Python API – add documents, ask questions, get answers with sources.

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Installation

Prerequisites

• Python 3.10 or higher
• Rust (only if you want to build TurboVec from source – not required for normal use)

Install from PyPI

pip install turborag-ahx47

This will automatically install the required dependencies, including turbovec, numpy, and llama-cpp-python.

Optional: Build TurboVec from source (advanced)

If you need a custom version of TurboVec, you can build it manually:

git clone https://github.com/RyanCodrai/turbovec.git
cd turbovec/turbovec-python
pip install maturin
maturin develop --release

But for most users, the pre‑built turbovec wheel is sufficient.

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Quick Start

1. Download required models

You need two GGUF models:

• Embedding model: embeddinggemma-300m-q4_k_m.gguf (≈150 MB)
  Download from: Hugging Face
• LLM model (e.g., Qwen 0.5B): qwen-0.5b-q4_k_m.gguf (≈300 MB)
  Download from your preferred source.

Place them in a folder, e.g., models/.

2. Use the library

from turborag import TurboRag

# Create RAG instance
rag = TurboRag.create(
    embed_model="models/embeddinggemma-300m-q4_k_m.gguf",
    llm_model="models/qwen-0.5b-q4_k_m.gguf",
)

# Add a document
rag.add_document("Paris is the capital of France.")

# Ask a question
answer, sources = rag.ask("What is the capital of France?")
print(answer)  # "Paris"
print(sources)  # List of source chunks

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API Reference

TurboRag.create(embed_model, llm_model, **kwargs)

Class method to instantiate the RAG engine.

Parameter	Type	Description
embed_model	str	Path to the embedding GGUF file (Gemma 300M).
llm_model	str	Path to the LLM GGUF file (e.g., Qwen 0.5B).
chunk_size	int	(optional) Chunk size for splitting documents, default 512.
chunk_overlap	int	(optional) Overlap between chunks, default 50.

Returns: TurboRag instance.

rag.add_document(text, metadata=None)

Adds a document to the index.

Parameter	Type	Description
text	str	Document content.
metadata	dict	(optional) Additional metadata.

rag.ask(question, k=5)

Asks a question and retrieves an answer.

Parameter	Type	Description
question	str	User query.
k	int	Number of chunks to retrieve (default 5).

Returns: (answer, sources) where answer is a string and sources is a list of chunk texts.

rag.search(query, k=5)

Performs a pure vector search without generation.

Parameter	Type	Description
query	str	Search query.
k	int	Number of results.

Returns: List of tuples (chunk_text, score, metadata).

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Advanced Usage

Using a custom document store

from turborag import TurboRag
from turborag.store import SQLiteDocStore

store = SQLiteDocStore("my_docs.db")
rag = TurboRag.create(
    embed_model="models/embeddinggemma-300m-q4_k_m.gguf",
    llm_model="models/qwen-0.5b-q4_k_m.gguf",
    doc_store=store,
)

Batching documents

docs = [
    "Paris is the capital of France.",
    "Berlin is the capital of Germany.",
    "Madrid is the capital of Spain.",
]
rag.add_documents(docs)  # list of strings

Changing the LLM at runtime

rag.set_llm_model("models/deepseek-1.3b-q4_k_m.gguf")

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Dependencies

• turbovec (quantized vector index)
• llama-cpp-python (GGUF inference)
• numpy (vector operations)
• sqlite3 (built‑in, for docstore)

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Troubleshooting

Issue	Solution
ImportError: cannot import name 'TurboRag'	Make sure you have installed the package correctly.
OSError: Llama model not found	Provide the correct absolute or relative path to the GGUF file.
turbovec.IdMapIndex not found	Reinstall turbovec with pip install --upgrade turbovec.
High RAM usage	Reduce chunk_size or use a smaller LLM.

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License

This project is licensed under the MIT License.

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Links

• PyPI package: turborag-ahx47
• Source code: GitHub (replace with your actual repo URL)
• Report issues: Issue tracker

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Acknowledgements

• TurboVec – efficient quantized vector search
• llama.cpp – GGUF model inference
• Gemma embedding model