生成 SQLite SQL，使用其他 LLM、Vanna 托管向量数据库（推荐）

本 notebook 演示了如何使用 vanna Python 包通过 AI（RAG + LLMs）生成 SQL，包括连接数据库和训练。如果您还未准备好在自己的数据库上进行训练，仍然可以使用示例 SQLite 数据库进行尝试。

您想使用哪个 LLM？

您想将“训练”数据存储在哪里？

[Selected] Vanna 托管向量数据库（推荐）
免费使用 Vanna.AI 的托管向量数据库（pgvector）。这可以在不同的机器上使用，无需额外设置。
ChromaDB
免费在本地使用 ChromaDB 的开源向量数据库。无需额外设置——所有数据库文件都将在本地创建和存储。
Qdrant
使用 Qdrant 的开源向量数据库
Marqo
免费在本地使用 Marqo。需要额外设置。或者使用他们的托管选项。
其他向量数据库
使用任何其他向量数据库。需要额外设置。

设置

%pip install vanna

from vanna.vannadb import VannaDB_VectorStore
from vanna.base import VannaBase

class MyCustomLLM(VannaBase):
  def __init__(self, config=None):
    # Implement here
    pass

  def submit_prompt(self, prompt, **kwargs) -> str:
    # Implement here
    # See an example implementation here: https://github.com/vanna-ai/vanna/blob/main/src/vanna/mistral/mistral.py


class MyVanna(VannaDB_VectorStore, MyCustomLLM):
    def __init__(self, config=None):
        MY_VANNA_MODEL = # Your model name from https://vanna.org.cn/account/profile
        VannaDB_VectorStore.__init__(self, vanna_model=MY_VANNA_MODEL, vanna_api_key=MY_VANNA_API_KEY, config=config)
        MyCustomLLM.__init__(self, config=config)

vn = MyVanna()

您想查询哪个数据库？

vn.connect_to_sqlite('my-database.sqlite')

训练

您只需要训练一次。除非要添加更多训练数据，否则不要再次训练。

df_ddl = vn.run_sql("SELECT type, sql FROM sqlite_master WHERE sql is not null")

for ddl in df_ddl['sql'].to_list():
  vn.train(ddl=ddl)

# The following are methods for adding training data. Make sure you modify the examples to match your database.

# DDL statements are powerful because they specify table names, colume names, types, and potentially relationships
vn.train(ddl="""
    CREATE TABLE IF NOT EXISTS my-table (
        id INT PRIMARY KEY,
        name VARCHAR(100),
        age INT
    )
""")

# Sometimes you may want to add documentation about your business terminology or definitions.
vn.train(documentation="Our business defines OTIF score as the percentage of orders that are delivered on time and in full")

# You can also add SQL queries to your training data. This is useful if you have some queries already laying around. You can just copy and paste those from your editor to begin generating new SQL.
vn.train(sql="SELECT * FROM my-table WHERE name = 'John Doe'")

# At any time you can inspect what training data the package is able to reference
training_data = vn.get_training_data()
training_data

# You can remove training data if there's obsolete/incorrect information. 
vn.remove_training_data(id='1-ddl')

```## Asking the AI
Whenever you ask a new question, it will find the 10 most relevant pieces of training data and use it as part of the LLM prompt to generate the SQL.
```python
vn.ask(question=...)

启动用户界面

vanna-flask

from vanna.flask import VannaFlaskApp
app = VannaFlaskApp(vn)
app.run()