aaizaql 0.2.2

Natural Language to SQL — open-source library with RAG, self-correction, and federation

AaizaQL — Natural Language to SQL

Query any database in plain English. AaizaQL is an open-source Python library that converts natural language questions into SQL, executes them, and returns results with charts and insights. It fixes the key limitations of Vanna AI: better security, context memory, self-correction, and a plugin architecture.

from aaizaql import QueryEngine

engine = QueryEngine(llm="groq", database="sqlite", dsn="sqlite:///sales.db")
engine.ingest_schema()

result = engine.query("Show top 5 customers by revenue last quarter")
print(result.sql)      # Generated SQL
print(result.data)     # pandas DataFrame
result.chart.show()    # Interactive Plotly chart
print(result.summary)  # "The top customer was Acme Corp with $1.2M revenue..."

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Why AaizaQL over Vanna AI?

Feature	AaizaQL	Vanna AI
SQL security layer (whitelist + injection detection)	✅	⚠️ Partial
Self-correction loop (auto-fix broken SQL)	✅	⚠️ Partial
Context memory (multi-turn conversations)	✅	⚠️ Limited
Enum/code mapping (always injected, no miss)	✅	❌
Per-user credential delegation	✅	❌ (CVE-2024-5565)
Plugin architecture (zero core changes)	✅	❌
Groq support (free, fast LLM)	✅	❌
Local LLM via Ollama	✅	✅

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Installation

pip install aaizaql

Install with your LLM provider and database driver:

# Groq (free, fast — recommended for getting started)
pip install "aaizaql[groq]"

# Anthropic Claude
pip install "aaizaql[claude]"

# OpenAI
pip install "aaizaql[openai]"

# DeepSeek
pip install "aaizaql[deepseek]"

# Google Gemini
pip install "aaizaql[gemini]"

# Mistral
pip install "aaizaql[mistral]"

# Perplexity
pip install "aaizaql[perplexity]"

# PostgreSQL
pip install "aaizaql[postgres]"

# Microsoft SQL Server
pip install "aaizaql[mssql]"

# Oracle
pip install "aaizaql[oracle]"

# MongoDB
pip install "aaizaql[mongodb]"

# BigQuery
pip install "aaizaql[bigquery]"

# Everything
pip install "aaizaql[all]"

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Quick Start

1. Get a free Groq API key

Sign up at console.groq.com — it is free.

export AAIZAQL_GROQ_API_KEY="gsk_your_key_here"

2. Query your database

from aaizaql import QueryEngine

engine = QueryEngine(
    llm="groq",
    database="sqlite",
    dsn="sqlite:///mydata.db",
)
engine.ingest_schema()

result = engine.query("How many orders were placed last month?")
print(result.sql)
print(result.data)

3. CLI usage

# Interactive REPL
aaizaql query --db sqlite:///mydata.db --llm groq

# Single question
aaizaql query --db sqlite:///mydata.db --llm groq -q "Total revenue by region"

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Core Features

Multi-turn memory

engine.query("Show me the top 10 customers by revenue")
engine.query("Now filter those to only US customers")   # remembers context
engine.query("Which of those signed up in 2024?")       # still remembers

Train with business knowledge

# Free-text business context (retrieved via RAG)
engine.train(documentation="""
    employees.status: 1=Active, 2=On Leave, 3=Resigned, 4=Terminated
    Use strftime('%Y-%m', created_at) for SQLite month grouping.
    business_unit_id: 4=ACCL, 8=APFIL, 12=IBOS
""")

# Enum mappings — ALWAYS injected, never missed by RAG
engine.define_enum("employees", "status", {
    1: "Active", 2: "On Leave", 3: "Resigned", 4: "Terminated"
})

# Sample Q→SQL pairs for few-shot learning
engine.train(
    question="Top 5 employees by total sales",
    sql="SELECT e.name, SUM(s.total) FROM employees e JOIN sales s ON e.id = s.emp_id GROUP BY e.name ORDER BY 2 DESC LIMIT 5",
)

Self-correction loop

When the generated SQL fails, AaizaQL automatically sends the error back to the LLM and retries (up to 3 times by default):

attempt 1: SELECT * FROM employes   → DatabaseError: no such table
attempt 2: SELECT * FROM employees  → ✅ success

SQL security layer

Every SQL passes through a security gate before execution:

• Whitelist enforcement — only SELECT and WITH are allowed
• Prompt injection detection — scans user questions for manipulation attempts
• Structural parsing — uses sqlglot to catch disguised dangerous statements
• Multi-statement blocking — SELECT 1; DROP TABLE x is rejected

---

Configuration

All settings can be set via environment variables (prefixed AAIZAQL_) or passed directly to QueryEngine:

Setting	Env var	Default	Description
LLM provider	AAIZAQL_LLM_PROVIDER	groq	See supported providers below
Groq API key	AAIZAQL_GROQ_API_KEY	—	Free at console.groq.com
Groq model	AAIZAQL_GROQ_MODEL	llama-3.3-70b-versatile	Any Groq-supported model
Anthropic key	AAIZAQL_ANTHROPIC_API_KEY	—	For llm="claude"
OpenAI key	AAIZAQL_OPENAI_API_KEY	—	For llm="openai"
DeepSeek key	AAIZAQL_DEEPSEEK_API_KEY	—	For llm="deepseek"
DeepSeek model	AAIZAQL_DEEPSEEK_MODEL	deepseek-chat	deepseek-chat, deepseek-reasoner
Perplexity key	AAIZAQL_PERPLEXITY_API_KEY	—	For llm="perplexity"
Perplexity model	AAIZAQL_PERPLEXITY_MODEL	sonar	sonar, sonar-pro, sonar-reasoning
Gemini key	AAIZAQL_GEMINI_API_KEY	—	For llm="gemini"
Gemini model	AAIZAQL_GEMINI_MODEL	gemini-2.5-flash	gemini-2.5-flash, gemini-2.5-pro
Mistral key	AAIZAQL_MISTRAL_API_KEY	—	For llm="mistral"
Mistral model	AAIZAQL_MISTRAL_MODEL	mistral-large-latest	mistral-large-latest, codestral-latest
Ollama URL	AAIZAQL_OLLAMA_BASE_URL	http://localhost:11434	For local models
Vector store	AAIZAQL_VECTOR_STORE	chroma	chroma or qdrant
Max retries	AAIZAQL_MAX_SELF_CORRECTION_RETRIES	3	Self-correction attempts
Session history	AAIZAQL_SESSION_HISTORY_LIMIT	10	Turns kept in context

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Supported LLM Providers

Provider	Key	Default Model	Install	Notes
Groq	groq	llama-3.3-70b-versatile	pip install "aaizaql[groq]"	Free tier. Fastest inference. Recommended.
Anthropic Claude	claude	claude-sonnet-4-20250514	pip install "aaizaql[claude]"	Best accuracy on complex schemas.
OpenAI	openai	gpt-4o	pip install "aaizaql[openai]"	GPT-4o and others.
DeepSeek	deepseek	deepseek-chat	pip install "aaizaql[deepseek]"	High quality at very low cost.
Perplexity	perplexity	sonar	pip install "aaizaql[perplexity]"	Fast Sonar models.
Google Gemini	gemini	gemini-2.5-flash	pip install "aaizaql[gemini]"	1M token context window.
Mistral	mistral	mistral-large-latest	pip install "aaizaql[mistral]"	codestral-latest great for SQL.
Ollama	ollama	llama3	Built-in	Local, private, no API key.

Provider usage examples

# DeepSeek — high quality, very affordable
engine = QueryEngine(
    llm="deepseek",
    database="sqlite",
    dsn="sqlite:///mydata.db",
    deepseek_api_key="sk-...",
)

# Google Gemini
engine = QueryEngine(
    llm="gemini",
    database="postgresql",
    dsn="postgresql://user:pass@localhost:5432/mydb",
    gemini_api_key="AIza...",
)

# Mistral — codestral is specialized for code/SQL
engine = QueryEngine(
    llm="mistral",
    database="mysql",
    dsn="mysql+pymysql://user:pass@localhost/mydb",
    mistral_api_key="...",
    mistral_model="codestral-latest",
)

# Perplexity
engine = QueryEngine(
    llm="perplexity",
    database="sqlite",
    dsn="sqlite:///mydata.db",
    perplexity_api_key="pplx-...",
)

---

Supported Databases

Database	Connector name	Install
SQLite	sqlite	Built-in (no install needed)
PostgreSQL	postgresql / postgres	pip install "aaizaql[postgres]"
MySQL	mysql	pip install pymysql
Snowflake	snowflake	pip install "aaizaql[snowflake]"
DuckDB	duckdb	pip install "aaizaql[duckdb]"
Microsoft SQL Server	mssql / sqlserver	pip install "aaizaql[mssql]"
Oracle	oracle	pip install "aaizaql[oracle]"
MongoDB	mongodb / mongo	pip install "aaizaql[mongodb]"
Google BigQuery	bigquery	pip install "aaizaql[bigquery]"

Database usage examples

# Microsoft SQL Server
engine = QueryEngine(
    llm="groq",
    database="mssql",
    dsn="mssql://user:password@localhost:1433/mydb",
)

# Oracle (thin mode — no Oracle Client needed)
engine = QueryEngine(
    llm="groq",
    database="oracle",
    dsn="oracle://hr:password@localhost:1521/XEPDB1",
)

# MongoDB
engine = QueryEngine(
    llm="groq",
    database="mongodb",
    dsn="mongodb://user:password@localhost:27017/mydb",
)

# Google BigQuery
engine = QueryEngine(
    llm="gemini",
    database="bigquery",
    dsn="bigquery://my-project/my_dataset",
)

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Roadmap

• Phase 1: Core library (RAG, self-correction, security, memory, connectors)
• Phase 2: Extended LLM providers (DeepSeek, Perplexity, Gemini, Mistral)
• Phase 2: Extended DB connectors (MSSQL, Oracle, MongoDB, BigQuery)
• Phase 3: SaaS web UI (FastAPI + Next.js)
• Phase 4: Federated cross-database queries (DuckDB workspace)
• Phase 5: Enterprise (SSO, RBAC, audit log, SOC2)

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Contributing

Contributions are welcome. See CONTRIBUTING.md for guidelines.

git clone https://github.com/ibrahimkhalilCorp/aaizaql
cd aaizaql
pip install -e ".[dev]"
pytest tests/

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License

MIT — see LICENSE.