Data & Research · PyPI

seamless-rag

TOON-Native Auto-Embedding & RAG Toolkit for MariaDB — VECTOR(N), HNSW, VEC_DISTANCE_COSINE, with TOON tabular output that saves 10-55% LLM tokens vs JSON.

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Details

Author
LiuWei
GitHub profile
@MariaDB-Hackathon-MY-2026
Category
Data & Research
Platform
PyPI
GitHub
https://github.com/MariaDB-Hackathon-MY-2026/seamless-rag
Framework
unknown
Language
python
Stars
0
First indexed
2026-05-15
Last active
—
Directory sync
2026-05-15
@MariaDB-Hackathon-MY-2026

Overview

TOON-Native Auto-Embedding & RAG Toolkit for MariaDB — VECTOR(N), HNSW, VEC_DISTANCE_COSINE, with TOON tabular output that saves 10-55% LLM tokens vs JSON.

Quick start

pip

pip install seamless-rag

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What seamless-rag can do

llmragembeddingretrieval
  • Llm — llm task automation.
  • Rag — Retrieves grounded context before answering.
  • Embedding — Computes vector embeddings for semantic search.
  • Retrieval — retrieval task automation.

Frequently asked questions

What is seamless-rag?
TOON-Native Auto-Embedding & RAG Toolkit for MariaDB — VECTOR(N), HNSW, VEC_DISTANCE_COSINE, with TOON tabular output that saves 10-55% LLM tokens vs JSON.
How do I install seamless-rag?
Use pip: `pip install seamless-rag`. Full setup details on the source page linked above.
Is seamless-rag open source?
seamless-rag is published on PyPI.
What are alternatives to seamless-rag?
Comparable agents include ragflow, autoresearch, OpenBB. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for seamless-rag is sourced from PyPI, published by LiuWei.

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