vanna.base.base

class VannaBase(abc.ABC)

提供使用继承创建 ABC 的标准方法的辅助类。

config

run_sql_is_set

def generate_sql(self, question: str, **kwargs) -> str:

def generate_followup_questions(self, question: str, **kwargs) -> str:

def generate_questions(self, **kwargs) -> list[str]:

示例

vn.generate_questions()

生成可以向 Vanna.AI 提出的问题列表。

@abstractmethod

def generate_embedding(self, data: str, **kwargs) -> list[float]:

@abstractmethod

def get_similar_question_sql(self, question: str, **kwargs) -> list:

@abstractmethod

def get_related_ddl(self, question: str, **kwargs) -> list:

@abstractmethod

def get_related_documentation(self, question: str, **kwargs) -> list:

@abstractmethod

def add_question_sql(self, question: str, sql: str, **kwargs) -> str:

@abstractmethod

def add_ddl(self, ddl: str, **kwargs) -> str:

@abstractmethod

def add_documentation(self, doc: str, **kwargs) -> str:

@abstractmethod

def get_training_data(self, **kwargs) -> pandas.core.frame.DataFrame:

@abstractmethod

def remove_training_data(id: str, **kwargs) -> bool:

@abstractmethod

def get_sql_prompt( self, question: str, question_sql_list: list, ddl_list: list, doc_list: list, **kwargs):

@abstractmethod

def get_followup_questions_prompt( self, question: str, question_sql_list: list, ddl_list: list, doc_list: list, **kwargs):

@abstractmethod

def submit_prompt(self, prompt, **kwargs) -> str:

@abstractmethod

def generate_question(self, sql: str, **kwargs) -> str:

@abstractmethod

def generate_plotly_code( self, question: str = None, sql: str = None, df_metadata: str = None, **kwargs) -> str:

def connect_to_snowflake( self, account: str, username: str, password: str, database: str, role: Optional[str] = None, warehouse: Optional[str] = None):

def connect_to_sqlite(self, url: str):

连接到 SQLite 数据库。这只是一个设置 [vn.run_sql][vanna.run_sql] 的辅助函数。

参数

url (str): 要连接的数据库 URL。

None

def connect_to_postgres( self, host: str = None, dbname: str = None, user: str = None, password: str = None, port: int = None):

使用 psycopg2 连接器连接到 postgres。这只是一个设置 [vn.run_sql][vanna.run_sql] 的辅助函数。示例:

vn.connect_to_postgres(
    host="myhost",
    dbname="mydatabase",
    user="myuser",
    password="mypassword",
    port=5432
)

参数

host (str): postgres 主机。
dbname (str): postgres 数据库名称。
user (str): postgres 用户。
password (str): postgres 密码。
port (int): postgres 端口。

def connect_to_bigquery(self, cred_file_path: str = None, project_id: str = None):

使用 bigquery 连接器连接到 gcs。这只是一个设置 [vn.run_sql][vanna.run_sql] 的辅助函数。示例:

vn.connect_to_bigquery(
    project_id="myprojectid",
    cred_file_path="path/to/credentials.json",
)

参数

project_id (str): gcs 项目 ID。
cred_file_path (str): gcs 凭据文件路径

def run_sql(sql: str, **kwargs) -> pandas.core.frame.DataFrame:

def ask( self, question: Optional[str] = None, print_results: bool = True, auto_train: bool = True) -> Optional[Tuple[Optional[str], Optional[pandas.core.frame.DataFrame], Optional[plotly.graph_objs._figure.Figure]]]:

def train( self, question: str = None, sql: str = None, ddl: str = None, documentation: str = None, plan: vanna.types.TrainingPlan = None) -> str:

示例

vn.train()

使用问题及其相应的 SQL 查询训练 Vanna.AI。如果您不带参数调用它，它将检查您是否连接到数据库，并尝试训练该数据库的元数据。如果您使用 sql 参数调用它，则等同于 [add_sql()][vanna.add_sql]。如果您使用 ddl 参数调用它，则等同于 [add_ddl()][vanna.add_ddl]。如果您使用 documentation 参数调用它，则等同于 [add_documentation()][vanna.add_documentation]。此外，您可以传递一个 [TrainingPlan][vanna.TrainingPlan] 对象。使用 [vn.get_training_plan_experimental()][vanna.get_training_plan_experimental] 获取训练计划。

参数

question (str): 用于训练的问题。
sql (str): 用于训练的 SQL 查询。
ddl (str): DDL 语句。
documentation (str): 用于训练的文档。
plan (TrainingPlan): 用于训练的训练计划。

def get_training_plan_generic(self, df) -> vanna.types.TrainingPlan:

def get_training_plan_snowflake( self, filter_databases: Optional[List[str]] = None, filter_schemas: Optional[List[str]] = None, include_information_schema: bool = False, use_historical_queries: bool = True) -> vanna.types.TrainingPlan:

def get_plotly_figure( self, plotly_code: str, df: pandas.core.frame.DataFrame, dark_mode: bool = True) -> plotly.graph_objs._figure.Figure:

示例

fig = vn.get_plotly_figure(
    plotly_code="fig = px.bar(df, x='name', y='salary')",
    df=df
)
fig.show()

从数据框和 Plotly 代码获取 Plotly 图形。

参数

df (pd.DataFrame): 要使用的数据框。
plotly_code (str): 要使用的 Plotly 代码。

plotly.graph_objs.Figure: Plotly 图形。

class SplitStorage(VannaBase)

提供使用继承创建 ABC 的标准方法的辅助类。

def get_similar_question_sql(self, embedding: str, **kwargs) -> list:

def get_related_ddl(self, embedding: str, **kwargs) -> list:

def get_related_documentation(self, embedding: str, **kwargs) -> list:

@abstractmethod

def store_question_sql_embedding(self, embedding: str, **kwargs) -> str:

@abstractmethod

def store_ddl_embedding(self, embedding: str, **kwargs) -> str:

@abstractmethod

def store_documentation_embedding(self, embedding: str, **kwargs) -> str:

@abstractmethod

def get_similar_question_sql_ids(self, embedding: str, **kwargs) -> list:

@abstractmethod

def get_related_ddl_ids(self, embedding: str, **kwargs) -> list:

@abstractmethod

def get_related_documentation_ids(self, embedding: str, **kwargs) -> list:

@abstractmethod

def get_question_sql(self, question_sql_ids: list, **kwargs) -> list:

@abstractmethod

def get_documentation(self, doc_ids: list, **kwargs) -> list:

@abstractmethod

def get_ddl(self, ddl_ids: list, **kwargs) -> list:

继承的成员

VannaBase: config; run_sql_is_set; generate_sql; generate_followup_questions; generate_questions; generate_embedding; add_question_sql; add_ddl; add_documentation; get_training_data; remove_training_data; get_sql_prompt; get_followup_questions_prompt; submit_prompt; generate_question; generate_plotly_code; connect_to_snowflake; connect_to_sqlite; connect_to_postgres; connect_to_bigquery; run_sql; ask; train; get_training_plan_generic; get_training_plan_snowflake; get_plotly_figure

vanna.base.base

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继承的成员