使用其他LLM、Qdrant为SQLite生成SQL

本notebook演示了如何使用 vanna Python包，通过AI（RAG + LLMs）生成SQL，包括连接数据库和进行训练。如果您还没准备好在自己的数据库上训练，仍然可以使用示例SQLite数据库尝试。

在Colab中运行 在GitHub中打开

您想使用哪个LLM？

• OpenAI

  使用您自己的API密钥连接OpenAI
• Azure OpenAI

  如果您在Azure上部署了OpenAI模型
• Anthropic

  使用您的Anthropic API密钥连接Anthropics Claude
• Ollama

  免费在本地使用Ollama。需要额外设置。
• Google Gemini

  使用您的Gemini或Vertex API密钥连接Google Gemini
• 通过Mistral API连接Mistral

  如果您有Mistral API密钥
• [已选中] 其他LLM

  如果您有不同的LLM模型

您想将“训练”数据存储在哪里？

• ChromaDB

  免费在本地使用ChromaDB的开源向量数据库。无需额外设置——所有数据库文件将在本地创建和存储。
• [已选中] 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[qdrant]'

from vanna.base import VannaBase
from vanna.qdrant import Qdrant_VectorStore
from qdrant_client import QdrantClient

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(Qdrant_VectorStore, MyCustomLLM):
    def __init__(self, config=None):
        Qdrant_VectorStore.__init__(self, config=config)
        MyCustomLLM.__init__(self, config=config)

vn = MyVanna(config={'client': 'QdrantClient(...)'})

您想查询哪个数据库？

• Postgres
• Microsoft SQL Server
• MySQL
• DuckDB
• Snowflake
• BigQuery
• [已选中] SQLite
• Oracle
• 其他数据库

  使用Vanna为任何SQL数据库生成查询

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

训练

您只需要训练一次。除非想添加更多训练数据，否则无需再次训练。

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

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