ariasql 1.0.1

AriaSQL — Agentic SQL Agent. Natural language to SQL with ReAct reasoning, semantic cache, and a bundled React UI.

AriaSQL

Agentic SQL Agent — Natural Language to SQL with Multi-step Reasoning

GitHub · SQLAS Eval Framework

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AriaSQL transforms natural language into SQL using a ReAct agentic loop — the agent inspects your schema, reasons step-by-step, and runs multiple queries before answering. Works with any SQL database, any LLM, and scales to 100+ tables with zero configuration.

Author: Pradip Tivhale

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Quick Start

git clone https://github.com/thepradip/AriaSQL.git && cd AriaSQL
cd backend && pip install -r requirements.txt && cp .env.example .env
python ingest.py && uvicorn main:app --reload
# frontend: cd frontend && npm install && npm run dev

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Agentic Reasoning

• ReAct loop — Reason → call tool → observe result → repeat until confident
• 4 tools: list_tables, describe_table, execute_sql, final_answer
• Auto-routing: complex queries use ReAct; simple queries use fast pipeline

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Intelligent Schema Retrieval (100+ tables)

BM25 + dense embedding hybrid search with RRF. FK-graph-aware. Token budget control.

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Semantic Query Cache

L1 exact → L2 semantic → L4 result TTL. Learning loop from user feedback.

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Any LLM, Any Database

Azure OpenAI · OpenAI · Anthropic · Ollama · any compatible endpoint. SQLite · PostgreSQL · MySQL · any SQLAlchemy async URL.

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Production Safety

AST-based read-only (sqlglot) · query timeout · fetchmany OOM protection · persistent conversations.

API Reference

Method	Endpoint	Description
GET	/health	DB status, table count, MLflow experiment
GET	/schema	Full auto-discovered schema context
POST	/query	NL → SQL → Execute → Narrate
POST	/feedback	Thumbs up/down — teaches the few-shot cache
POST	/feedback/detailed	Multi-dimension rating
POST	/export/csv	Download results as CSV
GET	/cache/stats	Hit rates, tokens saved, cost savings
DELETE	/cache/results	Flush result cache after data updates
POST	/evaluate	Run SQLAS evaluation suite
DELETE	/conversations/{id}	Clear conversation history

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Evaluation — SQLAS v2.0

python backend/eval_runner.py --quick                                    # 5 test cases
python backend/eval_runner.py                                            # 28 test cases
python backend/eval_runner.py --provider anthropic:claude-opus-4-7      # test a specific LLM
python backend/eval_runner.py --compare azure,anthropic:claude-opus-4-7,ollama:sqlcoder --quick

45 metrics across 9 dimensions — correctness, agentic quality, cache ROI, safety, faithfulness:

Dimension	Weight
Execution Accuracy	25%
Semantic Correctness	10%
Agentic Quality (planning, grounding, steps)	10%
Context Quality (RAGAS-mapped)	8%
Cost Efficiency (VES, scan, SQL quality)	10%
Task Success (faithfulness, relevance)	8%
Result + Visualization	7%
Guardrails (read-only, injection, PII)	15%
Execution Quality	7%

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Tech Stack

Layer	Technology
Agent	Custom ReAct loop — tool calling, MLflow tracing
Schema retrieval	BM25 + dense embeddings + Reciprocal Rank Fusion
Semantic cache	SQLite-backed L1/L2/L4, verified few-shot learning
Evaluation	SQLAS v2.0 — 45 metrics, 9 categories
LLM	Azure OpenAI · OpenAI · Anthropic · Ollama · any compatible
Backend	FastAPI + SQLAlchemy async
Frontend	React 18 + Vite + Tailwind CSS
Observability	MLflow — traces, spans, feedback
Database	SQLite · PostgreSQL · MySQL (any SQLAlchemy async URL)

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License

MIT — Pradip Tivhale