使用 Mistral API 通过 Mistral 生成 SQLite SQL，以及其他 VectorDB¶
本笔记本将介绍如何使用 vanna Python 包通过 AI (RAG + LLMs) 生成 SQL，包括连接数据库和训练。如果您尚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？
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通过 Vanna.AI 使用 OpenAI（推荐）免费使用 Vanna.AI 生成您的查询
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OpenAI使用您自己的 API 密钥使用 OpenAI
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Azure OpenAI如果您在 Azure 上部署了 OpenAI 模型
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[已选] 通过 Mistral API 使用 Mistral如果您有 Mistral API 密钥
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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？
设置¶
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%pip install 'vanna[mistralai]'
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from vanna.base import VannaBase
from vanna.mistral.mistral import Mistral
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class MyCustomVectorDB(VannaBase):
def add_ddl(self, ddl: str, **kwargs) -> str:
# Implement here
def add_documentation(self, doc: str, **kwargs) -> str:
# Implement here
def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
# Implement here
def get_related_ddl(self, question: str, **kwargs) -> list:
# Implement here
def get_related_documentation(self, question: str, **kwargs) -> list:
# Implement here
def get_similar_question_sql(self, question: str, **kwargs) -> list:
# Implement here
def get_training_data(self, **kwargs) -> pd.DataFrame:
# Implement here
def remove_training_data(id: str, **kwargs) -> bool:
# Implement here
class MyVanna(MyCustomVectorDB, Mistral):
def __init__(self, config=None):
MyCustomVectorDB.__init__(self, config=config)
Mistral.__init__(self, config={'api_key': MISTRAL_API_KEY, 'model': 'mistral-tiny'})
vn = MyVanna()
您想查询哪个数据库？
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Postgres
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Snowflake
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BigQuery
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[已选] SQLite
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其他数据库使用 Vanna 为任何 SQL 数据库生成查询
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vn.connect_to_sqlite('my-database.sqlite')
训练¶
您只需要训练一次。除非您想添加更多训练数据，否则不要再次训练。
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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)
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# 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'")
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# At any time you can inspect what training data the package is able to reference
training_data = vn.get_training_data()
training_data
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# You can remove training data if there's obsolete/incorrect information.
vn.remove_training_data(id='1-ddl')
询问 AI¶
每当您提出新问题时，它都会找到 10 个最相关的训练数据片段，并将其用作 LLM 提示的一部分来生成 SQL。
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vn.ask(question=...)
启动用户界面¶
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from vanna.flask import VannaFlaskApp
app = VannaFlaskApp(vn)
app.run()
