# Vanna — AI SQL Agent That Learns Your Database

> Open-source AI SQL agent that trains on your schema and queries. Vanna converts natural language to accurate SQL using RAG over your database documentation and history.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install vanna
```

```python
from vanna.openai import OpenAI_Chat
from vanna.chromadb import ChromaDB_VectorStore

class MyVanna(ChromaDB_VectorStore, OpenAI_Chat):
    def __init__(self, config=None):
        ChromaDB_VectorStore.__init__(self, config=config)
        OpenAI_Chat.__init__(self, config=config)

vn = MyVanna(config={"api_key": "sk-...", "model": "gpt-4o"})

# Train on your schema
vn.train(ddl="CREATE TABLE orders (id INT, customer_id INT, total DECIMAL, created_at DATETIME)")

# Ask questions in natural language
sql = vn.generate_sql("How many orders were placed last month?")
print(sql)  # SELECT COUNT(*) FROM orders WHERE created_at >= DATE_SUB(NOW(), INTERVAL 1 MONTH)
```

## What is Vanna?

Vanna is an open-source AI SQL agent that learns your specific database. It uses RAG (Retrieval-Augmented Generation) to train on your DDL schemas, documentation, and example queries — then generates accurate SQL from natural language questions. Unlike generic text-to-SQL tools, Vanna improves over time as you add more training data specific to your database.

**Answer-Ready**: Vanna is an AI SQL agent using RAG to generate accurate SQL from natural language. Trains on your schema, docs, and query history. Supports PostgreSQL, MySQL, BigQuery, Snowflake, and 15+ databases. Built-in Streamlit UI for non-technical users. 13k+ GitHub stars.

**Best for**: Data teams wanting natural language database access. **Works with**: Any SQL database, OpenAI, Anthropic Claude, Ollama. **Setup time**: Under 5 minutes.

## Core Features

### 1. Train on Your Data

```python
# Train on DDL
vn.train(ddl="CREATE TABLE users (id INT, name VARCHAR, email VARCHAR)")

# Train on documentation
vn.train(documentation="The users table stores all registered users. The email field is unique.")

# Train on example queries
vn.train(sql="SELECT name, COUNT(*) as order_count FROM users JOIN orders ON users.id = orders.customer_id GROUP BY name")
```

### 2. Multi-Database Support

| Database | Connector |
|----------|-----------|
| PostgreSQL | psycopg2 |
| MySQL | pymysql |
| BigQuery | google-cloud-bigquery |
| Snowflake | snowflake-connector |
| DuckDB | duckdb |
| SQLite | sqlite3 |
| Databricks | databricks-sql |

### 3. Built-in UI

```python
from vanna.flask import VannaFlaskApp
app = VannaFlaskApp(vn)
app.run()
# Opens web UI at localhost:8084
```

### 4. Auto-Visualization

```python
# Generate SQL + run it + create chart
vn.ask("Show monthly revenue trend as a line chart")
# Returns: SQL query, DataFrame result, and Plotly chart
```

## FAQ

**Q: How accurate is it?**
A: Accuracy depends on training data quality. With good schema docs and example queries, 85-95% accuracy on typical business questions.

**Q: Does it support Claude?**
A: Yes, use `vanna.anthropic.Anthropic_Chat` as the LLM backend with any Anthropic model.

**Q: Is my data safe?**
A: Vanna sends schema metadata and questions to the LLM, not raw data. Use local models via Ollama for full privacy.

## Source & Thanks

> Created by [Vanna AI](https://github.com/vanna-ai). Licensed under MIT.
>
> [vanna-ai/vanna](https://github.com/vanna-ai/vanna) — 13k+ stars

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## 快速使用

```bash
pip install vanna
```

用自然语言查询数据库，AI 自动生成准确 SQL。

## 什么是 Vanna？

Vanna 是开源 AI SQL Agent，通过 RAG 学习你的数据库 schema 和文档，将自然语言转为准确 SQL。越用越准。

**一句话总结**：AI SQL Agent，RAG 学习你的 schema 后自然语言生成 SQL，支持 15+ 数据库，内置 Web UI，13k+ stars。

**适合人群**：需要自然语言查询数据库的数据团队。

## 核心功能

### 1. 训练
用 DDL、文档和示例查询训练，越用越准。

### 2. 多数据库
PostgreSQL、MySQL、BigQuery、Snowflake 等 15+。

### 3. 内置 UI
Flask Web 界面，非技术用户也能用。

## 常见问题

**Q: 准确率？**
A: 训练数据好的情况下 85-95%。

**Q: 数据安全？**
A: 只发送 schema 和问题，不发原始数据。可用本地模型。

## 来源与致谢

> [vanna-ai/vanna](https://github.com/vanna-ai/vanna) — 13k+ stars, MIT

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Source: https://tokrepo.com/en/workflows/0f1fba74-ae4f-4d9c-981c-9661dfbdc88e
Author: Script Depot