Skip to content

Seamless-RAG

Vector Search & TOON Format for MariaDB

Turn any MariaDB table into a searchable vector store. Query results come back in TOON v3 tabular format — a compact wire format that saves 20-40% of tokens when feeding structured data to LLMs or agents.

Features

  • Auto-Embed — Point at any MariaDB table, embed single or multiple columns
  • Watch Mode — Polls for new inserts and auto-embeds them in real time
  • RAG Query — Vector search → TOON context → LLM answer in one call
  • Hybrid Search — Combine SQL filters (WHERE price < 50) with vector similarity
  • MMR Diversity — Maximal Marginal Relevance for diverse result sets
  • Token Savings — Every query reports JSON vs TOON token comparison
  • Model-Agnostic — Swap embedding/LLM providers via environment variables
  • Web UI — Gradio interface with 6 tabs for interactive exploration

Quick Example

from seamless_rag import SeamlessRAG

with SeamlessRAG(host="localhost", database="mydb") as rag:
    # Embed multiple columns for richer semantics
    rag.embed_table("products", text_column=["name", "category", "price"])

    # Hybrid search: semantic + SQL filter
    result = rag.ask("affordable tools", where="price < 50", mmr=True)
    print(result.answer)
    print(result.context_toon)
    print(f"Tokens saved: {result.savings_pct:.1f}%")

Real-World Token Savings

Measured on MovieLens (9,742 movies) and SF Restaurant Health Scores (53,973 inspections):

Dataset Rows JSON Tokens TOON Tokens Savings
MovieLens (7 cols) 100 6,540 5,019 23.3%
MovieLens metadata (4 cols) 100 2,258 1,364 39.6%
Restaurant violations (9 cols) 100 7,071 4,326 38.8%

See Benchmark Results for the full analysis.

Next Steps