mirror of https://github.com/langgenius/dify.git
feat: remove Vanna provider and associated assets from the project
Signed-off-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
parent
bbb080d5b2
commit
acd7fead87
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@ -77,5 +77,4 @@
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- onebot
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- regex
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- trello
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- vanna
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- fal
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from typing import Any, Union
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from vanna.remote import VannaDefault # type: ignore
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from core.tools.entities.tool_entities import ToolInvokeMessage
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from core.tools.errors import ToolProviderCredentialValidationError
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from core.tools.tool.builtin_tool import BuiltinTool
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class VannaTool(BuiltinTool):
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def _invoke(
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self, user_id: str, tool_parameters: dict[str, Any]
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) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
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"""
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invoke tools
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"""
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# Ensure runtime and credentials
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if not self.runtime or not self.runtime.credentials:
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raise ToolProviderCredentialValidationError("Tool runtime or credentials are missing")
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api_key = self.runtime.credentials.get("api_key", None)
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if not api_key:
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raise ToolProviderCredentialValidationError("Please input api key")
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model = tool_parameters.get("model", "")
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if not model:
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return self.create_text_message("Please input RAG model")
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prompt = tool_parameters.get("prompt", "")
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if not prompt:
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return self.create_text_message("Please input prompt")
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url = tool_parameters.get("url", "")
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if not url:
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return self.create_text_message("Please input URL/Host/DSN")
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db_name = tool_parameters.get("db_name", "")
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username = tool_parameters.get("username", "")
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password = tool_parameters.get("password", "")
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port = tool_parameters.get("port", 0)
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base_url = self.runtime.credentials.get("base_url", None)
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vn = VannaDefault(model=model, api_key=api_key, config={"endpoint": base_url})
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db_type = tool_parameters.get("db_type", "")
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if db_type in {"Postgres", "MySQL", "Hive", "ClickHouse"}:
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if not db_name:
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return self.create_text_message("Please input database name")
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if not username:
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return self.create_text_message("Please input username")
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if port < 1:
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return self.create_text_message("Please input port")
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schema_sql = "SELECT * FROM INFORMATION_SCHEMA.COLUMNS"
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match db_type:
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case "SQLite":
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schema_sql = "SELECT type, sql FROM sqlite_master WHERE sql is not null"
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vn.connect_to_sqlite(url)
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case "Postgres":
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vn.connect_to_postgres(host=url, dbname=db_name, user=username, password=password, port=port)
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case "DuckDB":
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vn.connect_to_duckdb(url=url)
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case "SQLServer":
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vn.connect_to_mssql(url)
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case "MySQL":
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vn.connect_to_mysql(host=url, dbname=db_name, user=username, password=password, port=port)
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case "Oracle":
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vn.connect_to_oracle(user=username, password=password, dsn=url)
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case "Hive":
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vn.connect_to_hive(host=url, dbname=db_name, user=username, password=password, port=port)
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case "ClickHouse":
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vn.connect_to_clickhouse(host=url, dbname=db_name, user=username, password=password, port=port)
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enable_training = tool_parameters.get("enable_training", False)
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reset_training_data = tool_parameters.get("reset_training_data", False)
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if enable_training:
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if reset_training_data:
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existing_training_data = vn.get_training_data()
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if len(existing_training_data) > 0:
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for _, training_data in existing_training_data.iterrows():
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vn.remove_training_data(training_data["id"])
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ddl = tool_parameters.get("ddl", "")
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question = tool_parameters.get("question", "")
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sql = tool_parameters.get("sql", "")
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memos = tool_parameters.get("memos", "")
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training_metadata = tool_parameters.get("training_metadata", False)
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if training_metadata:
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if db_type == "SQLite":
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df_ddl = vn.run_sql(schema_sql)
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for ddl in df_ddl["sql"].to_list():
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vn.train(ddl=ddl)
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else:
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df_information_schema = vn.run_sql(schema_sql)
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plan = vn.get_training_plan_generic(df_information_schema)
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vn.train(plan=plan)
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if ddl:
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vn.train(ddl=ddl)
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if sql:
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if question:
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vn.train(question=question, sql=sql)
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else:
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vn.train(sql=sql)
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if memos:
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vn.train(documentation=memos)
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#########################################################################################
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# Due to CVE-2024-5565, we have to disable the chart generation feature
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# The Vanna library uses a prompt function to present the user with visualized results,
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# it is possible to alter the prompt using prompt injection and run arbitrary Python code
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# instead of the intended visualization code.
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# Specifically - allowing external input to the library’s “ask” method
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# with "visualize" set to True (default behavior) leads to remote code execution.
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# Affected versions: <= 0.5.5
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#########################################################################################
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allow_llm_to_see_data = tool_parameters.get("allow_llm_to_see_data", False)
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res = vn.ask(
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prompt, print_results=False, auto_train=True, visualize=False, allow_llm_to_see_data=allow_llm_to_see_data
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)
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result = []
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if res is not None:
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result.append(self.create_text_message(res[0]))
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if len(res) > 1 and res[1] is not None:
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result.append(self.create_text_message(res[1].to_markdown()))
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if len(res) > 2 and res[2] is not None:
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result.append(
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self.create_blob_message(blob=res[2].to_image(format="svg"), meta={"mime_type": "image/svg+xml"})
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)
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return result
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@ -1,213 +0,0 @@
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identity:
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name: vanna
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author: QCTC
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label:
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en_US: Vanna.AI
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zh_Hans: Vanna.AI
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description:
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human:
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en_US: The fastest way to get actionable insights from your database just by asking questions.
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zh_Hans: 一个基于大模型和RAG的Text2SQL工具。
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llm: A tool for converting text to SQL.
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parameters:
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- name: prompt
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type: string
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required: true
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label:
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en_US: Prompt
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zh_Hans: 提示词
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pt_BR: Prompt
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human_description:
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en_US: used for generating SQL
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zh_Hans: 用于生成SQL
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llm_description: key words for generating SQL
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form: llm
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- name: model
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type: string
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required: true
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label:
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en_US: RAG Model
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zh_Hans: RAG模型
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human_description:
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en_US: RAG Model for your database DDL
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zh_Hans: 存储数据库训练数据的RAG模型
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llm_description: RAG Model for generating SQL
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form: llm
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- name: db_type
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type: select
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required: true
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options:
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- value: SQLite
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label:
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en_US: SQLite
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zh_Hans: SQLite
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- value: Postgres
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label:
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en_US: Postgres
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zh_Hans: Postgres
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- value: DuckDB
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label:
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en_US: DuckDB
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zh_Hans: DuckDB
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- value: SQLServer
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label:
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en_US: Microsoft SQL Server
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zh_Hans: 微软 SQL Server
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- value: MySQL
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label:
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en_US: MySQL
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zh_Hans: MySQL
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- value: Oracle
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label:
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en_US: Oracle
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zh_Hans: Oracle
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- value: Hive
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label:
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en_US: Hive
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zh_Hans: Hive
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- value: ClickHouse
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label:
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en_US: ClickHouse
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zh_Hans: ClickHouse
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default: SQLite
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label:
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en_US: DB Type
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zh_Hans: 数据库类型
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human_description:
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en_US: Database type.
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zh_Hans: 选择要链接的数据库类型。
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form: form
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- name: url
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type: string
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required: true
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label:
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en_US: URL/Host/DSN
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zh_Hans: URL/Host/DSN
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human_description:
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en_US: Please input depending on DB type, visit https://vanna.ai/docs/ for more specification
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zh_Hans: 请根据数据库类型,填入对应值,详情参考https://vanna.ai/docs/
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form: form
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- name: db_name
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type: string
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required: false
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label:
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en_US: DB name
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zh_Hans: 数据库名
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human_description:
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en_US: Database name
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zh_Hans: 数据库名
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form: form
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- name: username
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type: string
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required: false
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label:
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en_US: Username
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zh_Hans: 用户名
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human_description:
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en_US: Username
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zh_Hans: 用户名
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form: form
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- name: password
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type: secret-input
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required: false
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label:
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en_US: Password
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zh_Hans: 密码
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human_description:
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en_US: Password
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zh_Hans: 密码
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form: form
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- name: port
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type: number
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required: false
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label:
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en_US: Port
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zh_Hans: 端口
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human_description:
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en_US: Port
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zh_Hans: 端口
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form: form
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- name: ddl
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type: string
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required: false
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label:
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en_US: Training DDL
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zh_Hans: 训练DDL
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human_description:
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en_US: DDL statements for training data
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zh_Hans: 用于训练RAG Model的建表语句
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form: llm
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- name: question
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type: string
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required: false
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label:
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en_US: Training Question
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zh_Hans: 训练问题
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human_description:
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en_US: Question-SQL Pairs
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zh_Hans: Question-SQL中的问题
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form: llm
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- name: sql
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type: string
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required: false
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label:
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en_US: Training SQL
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zh_Hans: 训练SQL
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human_description:
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en_US: SQL queries to your training data
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zh_Hans: 用于训练RAG Model的SQL语句
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form: llm
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- name: memos
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type: string
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required: false
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label:
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en_US: Training Memos
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zh_Hans: 训练说明
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human_description:
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en_US: Sometimes you may want to add documentation about your business terminology or definitions
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zh_Hans: 添加更多关于数据库的业务说明
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form: llm
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- name: enable_training
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type: boolean
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required: false
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default: false
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label:
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en_US: Training Data
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zh_Hans: 训练数据
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human_description:
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en_US: You only need to train once. Do not train again unless you want to add more training data
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zh_Hans: 训练数据无更新时,训练一次即可
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form: form
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- name: reset_training_data
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type: boolean
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required: false
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default: false
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label:
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en_US: Reset Training Data
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zh_Hans: 重置训练数据
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human_description:
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en_US: Remove all training data in the current RAG Model
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zh_Hans: 删除当前RAG Model中的所有训练数据
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form: form
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- name: training_metadata
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type: boolean
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required: false
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default: false
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label:
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en_US: Training Metadata
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zh_Hans: 训练元数据
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human_description:
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en_US: If enabled, it will attempt to train on the metadata of that database
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zh_Hans: 是否自动从数据库获取元数据来训练
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form: form
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- name: allow_llm_to_see_data
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type: boolean
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required: false
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default: false
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label:
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en_US: Whether to allow the LLM to see the data
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zh_Hans: 是否允许LLM查看数据
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human_description:
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en_US: Whether to allow the LLM to see the data
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zh_Hans: 是否允许LLM查看数据
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form: form
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@ -1,46 +0,0 @@
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import re
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from typing import Any
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from urllib.parse import urlparse
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from core.tools.errors import ToolProviderCredentialValidationError
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from core.tools.provider.builtin.vanna.tools.vanna import VannaTool
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
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class VannaProvider(BuiltinToolProviderController):
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def _get_protocol_and_main_domain(self, url):
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parsed_url = urlparse(url)
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protocol = parsed_url.scheme
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hostname = parsed_url.hostname
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port = f":{parsed_url.port}" if parsed_url.port else ""
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# Check if the hostname is an IP address
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is_ip = re.match(r"^\d{1,3}(\.\d{1,3}){3}$", hostname) is not None
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# Return the full hostname (with port if present) for IP addresses, otherwise return the main domain
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main_domain = f"{hostname}{port}" if is_ip else ".".join(hostname.split(".")[-2:]) + port
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return f"{protocol}://{main_domain}"
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def _validate_credentials(self, credentials: dict[str, Any]) -> None:
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base_url = credentials.get("base_url")
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if not base_url:
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base_url = "https://ask.vanna.ai/rpc"
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else:
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base_url = base_url.removesuffix("/")
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credentials["base_url"] = base_url
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try:
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VannaTool().fork_tool_runtime(
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runtime={
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"credentials": credentials,
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}
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).invoke(
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user_id="",
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tool_parameters={
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"model": "chinook",
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"db_type": "SQLite",
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"url": f"{self._get_protocol_and_main_domain(credentials['base_url'])}/Chinook.sqlite",
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"query": "What are the top 10 customers by sales?",
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},
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)
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except Exception as e:
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raise ToolProviderCredentialValidationError(str(e))
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@ -1,35 +0,0 @@
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identity:
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author: QCTC
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name: vanna
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label:
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en_US: Vanna.AI
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zh_Hans: Vanna.AI
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description:
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en_US: The fastest way to get actionable insights from your database just by asking questions.
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zh_Hans: 一个基于大模型和RAG的Text2SQL工具。
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icon: icon.png
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tags:
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- utilities
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- productivity
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credentials_for_provider:
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api_key:
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type: secret-input
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required: true
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label:
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en_US: API key
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zh_Hans: API key
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placeholder:
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en_US: Please input your API key
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zh_Hans: 请输入你的 API key
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pt_BR: Please input your API key
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help:
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en_US: Get your API key from Vanna.AI
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zh_Hans: 从 Vanna.AI 获取你的 API key
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url: https://vanna.ai/account/profile
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base_url:
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type: text-input
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required: false
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label:
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en_US: Vanna.AI Endpoint Base URL
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placeholder:
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en_US: https://ask.vanna.ai/rpc
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