diff --git a/docs/search.js b/docs/search.js index 6728f647..007ebf11 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oA module to interact with the Vanna.AI API, providing the functionality to generate SQL explanations.

\n\n
\n
import vanna as vn\n\nvn.api_key = 'vanna-key-...' # Set your API key\nvn.set_org('') # Set your organization name\n\nvn.store_sql(question="Who are the top 10 customers by Sales?", sql="SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10")\n\nmy_question = 'What are the top 10 ABC by XYZ?'\n\nsql = vn.generate_sql(question=my_question, error_msg=None) \n# SELECT * FROM table_name WHERE column_name = 'value'\n\nconn = snowflake.connector.connect(\n        user='my_user',\n        password='my_password',\n        account='my_account',\n        database='my_database',\n    )\n\ncs = conn.cursor()\n\ndf = vn.get_results(cs, my_default_db, sql)\n\nplotly_code = vn.generate_plotly_code(question="Who are the top 10 customers by Sales?", sql=sql, df=df)\n# px.bar(df, x='column_name', y='column_name')\n\nfig = vn.get_plotly_figure(plotly_code=plotly_code, df=df)\n
\n
\n"}, "vanna.api_key": {"fullname": "vanna.api_key", "modulename": "vanna", "qualname": "api_key", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]", "default_value": "None"}, "vanna.set_org": {"fullname": "vanna.set_org", "modulename": "vanna", "qualname": "set_org", "kind": "function", "doc": "

Set the organization name for the Vanna.AI API.

\n\n

Args:\n org (str): The organization name.

\n", "signature": "(org: str) -> None:", "funcdef": "def"}, "vanna.store_sql": {"fullname": "vanna.store_sql", "modulename": "vanna", "qualname": "store_sql", "kind": "function", "doc": "

Store a question and its corresponding SQL query in the Vanna.AI database.

\n\n

Args:\n question (str): The question to store.\n sql (str): The SQL query to store.

\n", "signature": "(question: str, sql: str) -> bool:", "funcdef": "def"}, "vanna.remove_sql": {"fullname": "vanna.remove_sql", "modulename": "vanna", "qualname": "remove_sql", "kind": "function", "doc": "

Remove a question and its corresponding SQL query from the Vanna.AI database.

\n\n

Args:\n question (str): The question to remove.

\n", "signature": "(question: str) -> bool:", "funcdef": "def"}, "vanna.generate_sql": {"fullname": "vanna.generate_sql", "modulename": "vanna", "qualname": "generate_sql", "kind": "function", "doc": "

Generate an SQL query using the Vanna.AI API.

\n\n

Args:\n question (str): The question to generate an SQL query for.

\n\n

Returns:\n str or None: The SQL query, or None if an error occurred.

\n", "signature": "(question: str) -> str | None:", "funcdef": "def"}, "vanna.generate_plotly_code": {"fullname": "vanna.generate_plotly_code", "modulename": "vanna", "qualname": "generate_plotly_code", "kind": "function", "doc": "

Generate Plotly code using the Vanna.AI API.

\n\n

Args:\n question (str): The question to generate Plotly code for.\n sql (str): The SQL query to generate Plotly code for.\n df (pd.DataFrame): The dataframe to generate Plotly code for.

\n\n

Returns:\n str or None: The Plotly code, or None if an error occurred.

\n", "signature": "(\tquestion: str | None,\tsql: str | None,\tdf: pandas.core.frame.DataFrame) -> str | None:", "funcdef": "def"}, "vanna.get_plotly_figure": {"fullname": "vanna.get_plotly_figure", "modulename": "vanna", "qualname": "get_plotly_figure", "kind": "function", "doc": "

Get a Plotly figure from a dataframe and Plotly code.

\n\n

Args:\n df (pd.DataFrame): The dataframe to use.\n plotly_code (str): The Plotly code to use.

\n\n

Returns:\n plotly.graph_objs.Figure: The Plotly figure.

\n", "signature": "(\tplotly_code: str,\tdf: pandas.core.frame.DataFrame,\tdark_mode: bool = True) -> plotly.graph_objs._figure.Figure | None:", "funcdef": "def"}, "vanna.get_results": {"fullname": "vanna.get_results", "modulename": "vanna", "qualname": "get_results", "kind": "function", "doc": "

Get the results of an SQL query using the Vanna.AI API.

\n\n
Parameters
\n\n
    \n
  • cs: The Snowflake cursor to use.
    • \n
  • default_database: The default database to use (executed as \"USE DATABASE {default_database};\")
    • \n
  • sql: The SQL query to run.
    • \n
\n\n
Returns
\n\n
\n

The results of the SQL query.

\n
\n", "signature": "(cs, default_database: str, sql: str) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "vanna.generate_explanation": {"fullname": "vanna.generate_explanation", "modulename": "vanna", "qualname": "generate_explanation", "kind": "function", "doc": "

Example

\n\n
\n
vn.generate_explanation(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'AI Response'\n
\n
\n\n

Generate an explanation of an SQL query using the Vanna.AI API.

\n\n
Parameters
\n\n
    \n
  • sql: The SQL query to explain.
    • \n
\n\n
Returns
\n\n
\n

The explanation of the SQL query, or None if an error occurred.

\n
\n", "signature": "(sql: str) -> str | None:", "funcdef": "def"}, "vanna.types": {"fullname": "vanna.types", "modulename": "vanna.types", "kind": "module", "doc": "

\n"}, "vanna.types.Status": {"fullname": "vanna.types.Status", "modulename": "vanna.types", "qualname": "Status", "kind": "class", "doc": "

\n"}, "vanna.types.Status.__init__": {"fullname": "vanna.types.Status.__init__", "modulename": "vanna.types", "qualname": "Status.__init__", "kind": "function", "doc": "

\n", "signature": "(success: bool, message: str)"}, "vanna.types.Status.success": {"fullname": "vanna.types.Status.success", "modulename": "vanna.types", "qualname": "Status.success", "kind": "variable", "doc": "

\n", "annotation": ": bool"}, "vanna.types.Status.message": {"fullname": "vanna.types.Status.message", "modulename": "vanna.types", "qualname": "Status.message", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.FullQuestionDocument": {"fullname": "vanna.types.FullQuestionDocument", "modulename": "vanna.types", "qualname": "FullQuestionDocument", "kind": "class", "doc": "

\n"}, "vanna.types.FullQuestionDocument.__init__": {"fullname": "vanna.types.FullQuestionDocument.__init__", "modulename": "vanna.types", "qualname": "FullQuestionDocument.__init__", "kind": "function", "doc": "

\n", "signature": "(\tid: vanna.types.QuestionId,\tquestion: vanna.types.Question,\tanswer: vanna.types.SQLAnswer | None,\tdata: vanna.types.DataResult | None,\tplotly: vanna.types.PlotlyResult | None)"}, "vanna.types.FullQuestionDocument.id": {"fullname": "vanna.types.FullQuestionDocument.id", "modulename": "vanna.types", "qualname": "FullQuestionDocument.id", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.QuestionId"}, "vanna.types.FullQuestionDocument.question": {"fullname": "vanna.types.FullQuestionDocument.question", "modulename": "vanna.types", "qualname": "FullQuestionDocument.question", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.Question"}, "vanna.types.FullQuestionDocument.answer": {"fullname": "vanna.types.FullQuestionDocument.answer", "modulename": "vanna.types", "qualname": "FullQuestionDocument.answer", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.SQLAnswer | None"}, "vanna.types.FullQuestionDocument.data": {"fullname": "vanna.types.FullQuestionDocument.data", "modulename": "vanna.types", "qualname": "FullQuestionDocument.data", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.DataResult | None"}, "vanna.types.FullQuestionDocument.plotly": {"fullname": "vanna.types.FullQuestionDocument.plotly", "modulename": "vanna.types", "qualname": "FullQuestionDocument.plotly", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.PlotlyResult | None"}, "vanna.types.QuestionSQLPair": {"fullname": "vanna.types.QuestionSQLPair", "modulename": "vanna.types", "qualname": "QuestionSQLPair", "kind": "class", "doc": "

\n"}, "vanna.types.QuestionSQLPair.__init__": {"fullname": "vanna.types.QuestionSQLPair.__init__", "modulename": "vanna.types", "qualname": "QuestionSQLPair.__init__", "kind": "function", "doc": "

\n", "signature": "(question: str, sql: str)"}, "vanna.types.QuestionSQLPair.question": {"fullname": "vanna.types.QuestionSQLPair.question", "modulename": "vanna.types", "qualname": "QuestionSQLPair.question", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.QuestionSQLPair.sql": {"fullname": "vanna.types.QuestionSQLPair.sql", "modulename": "vanna.types", "qualname": "QuestionSQLPair.sql", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.Organization": {"fullname": "vanna.types.Organization", "modulename": "vanna.types", "qualname": "Organization", "kind": "class", "doc": "

\n"}, "vanna.types.Organization.__init__": {"fullname": "vanna.types.Organization.__init__", "modulename": "vanna.types", "qualname": "Organization.__init__", "kind": "function", "doc": "

\n", "signature": "(\tname: str,\tuser: str | None,\tconnection: vanna.types.Connection | None)"}, "vanna.types.Organization.name": {"fullname": "vanna.types.Organization.name", "modulename": "vanna.types", "qualname": "Organization.name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.Organization.user": {"fullname": "vanna.types.Organization.user", "modulename": "vanna.types", "qualname": "Organization.user", "kind": "variable", "doc": "

\n", "annotation": ": str | None"}, "vanna.types.Organization.connection": {"fullname": "vanna.types.Organization.connection", "modulename": "vanna.types", "qualname": "Organization.connection", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.Connection | None"}, "vanna.types.QuestionId": {"fullname": "vanna.types.QuestionId", "modulename": "vanna.types", "qualname": "QuestionId", "kind": "class", "doc": "

\n"}, "vanna.types.QuestionId.__init__": {"fullname": "vanna.types.QuestionId.__init__", "modulename": "vanna.types", "qualname": "QuestionId.__init__", "kind": "function", "doc": "

\n", "signature": "(id: str)"}, "vanna.types.QuestionId.id": {"fullname": "vanna.types.QuestionId.id", "modulename": "vanna.types", "qualname": "QuestionId.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.Question": {"fullname": "vanna.types.Question", "modulename": "vanna.types", "qualname": "Question", "kind": "class", "doc": "

\n"}, "vanna.types.Question.__init__": {"fullname": "vanna.types.Question.__init__", "modulename": "vanna.types", "qualname": "Question.__init__", "kind": "function", "doc": "

\n", "signature": "(question: str)"}, "vanna.types.Question.question": {"fullname": "vanna.types.Question.question", "modulename": "vanna.types", "qualname": "Question.question", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.Followup": {"fullname": "vanna.types.Followup", "modulename": "vanna.types", "qualname": "Followup", "kind": "class", "doc": "

\n"}, "vanna.types.Followup.__init__": {"fullname": "vanna.types.Followup.__init__", "modulename": "vanna.types", "qualname": "Followup.__init__", "kind": "function", "doc": "

\n", "signature": "(followup: str)"}, "vanna.types.Followup.followup": {"fullname": "vanna.types.Followup.followup", "modulename": "vanna.types", "qualname": "Followup.followup", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.QuestionEmbedding": {"fullname": "vanna.types.QuestionEmbedding", "modulename": "vanna.types", "qualname": "QuestionEmbedding", "kind": "class", "doc": "

\n"}, "vanna.types.QuestionEmbedding.__init__": {"fullname": "vanna.types.QuestionEmbedding.__init__", "modulename": "vanna.types", "qualname": "QuestionEmbedding.__init__", "kind": "function", "doc": "

\n", "signature": "(question: vanna.types.Question, embedding: List[float])"}, "vanna.types.QuestionEmbedding.question": {"fullname": "vanna.types.QuestionEmbedding.question", "modulename": "vanna.types", "qualname": "QuestionEmbedding.question", "kind": "variable", "doc": "

\n", "annotation": ": vanna.types.Question"}, "vanna.types.QuestionEmbedding.embedding": {"fullname": "vanna.types.QuestionEmbedding.embedding", "modulename": "vanna.types", "qualname": "QuestionEmbedding.embedding", "kind": "variable", "doc": "

\n", "annotation": ": List[float]"}, "vanna.types.Connection": {"fullname": "vanna.types.Connection", "modulename": "vanna.types", "qualname": "Connection", "kind": "class", "doc": "

\n"}, "vanna.types.SQLAnswer": {"fullname": "vanna.types.SQLAnswer", "modulename": "vanna.types", "qualname": "SQLAnswer", "kind": "class", "doc": "

\n"}, "vanna.types.SQLAnswer.__init__": {"fullname": "vanna.types.SQLAnswer.__init__", "modulename": "vanna.types", "qualname": "SQLAnswer.__init__", "kind": "function", "doc": "

\n", "signature": "(raw_answer: str, prefix: str, postfix: str, sql: str)"}, "vanna.types.SQLAnswer.raw_answer": {"fullname": "vanna.types.SQLAnswer.raw_answer", "modulename": "vanna.types", "qualname": "SQLAnswer.raw_answer", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.types.SQLAnswer.prefix": {"fullname": "vanna.types.SQLAnswer.prefix", "modulename": "vanna.types", "qualname": "SQLAnswer.prefix", "kind": "variable", "doc": "

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A module to interact with the Vanna.AI API, providing the functionality to generate SQL explanations.

\n\n
\n
import vanna as vn\n\nvn.api_key = 'vanna-key-...' # Set your API key\nvn.set_org('') # Set your organization name\n\nvn.store_sql(question="Who are the top 10 customers by Sales?", sql="SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10")\n\nmy_question = 'What are the top 10 ABC by XYZ?'\n\nsql = vn.generate_sql(question=my_question, error_msg=None) \n# SELECT * FROM table_name WHERE column_name = 'value'\n\nconn = snowflake.connector.connect(\n        user='my_user',\n        password='my_password',\n        account='my_account',\n        database='my_database',\n    )\n\ncs = conn.cursor()\n\ndf = vn.get_results(cs, my_default_db, sql)\n\nplotly_code = vn.generate_plotly_code(question="Who are the top 10 customers by Sales?", sql=sql, df=df)\n# px.bar(df, x='column_name', y='column_name')\n\nfig = vn.get_plotly_figure(plotly_code=plotly_code, df=df)\n
\n
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Set the organization name for the Vanna.AI API.

\n\n

Args:\n org (str): The organization name.

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Store a question and its corresponding SQL query in the Vanna.AI database.

\n\n

Args:\n question (str): The question to store.\n sql (str): The SQL query to store.

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Remove a question and its corresponding SQL query from the Vanna.AI database.

\n\n

Args:\n question (str): The question to remove.

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Generate an SQL query using the Vanna.AI API.

\n\n

Args:\n question (str): The question to generate an SQL query for.

\n\n

Returns:\n str or None: The SQL query, or None if an error occurred.

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Generate Plotly code using the Vanna.AI API.

\n\n

Args:\n question (str): The question to generate Plotly code for.\n sql (str): The SQL query to generate Plotly code for.\n df (pd.DataFrame): The dataframe to generate Plotly code for.

\n\n

Returns:\n str or None: The Plotly code, or None if an error occurred.

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Get a Plotly figure from a dataframe and Plotly code.

\n\n

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\n\n

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Get the results of an SQL query using the Vanna.AI API.

\n\n

Args:\n cs (pyodbc.Cursor): The cursor to use.\n default_database (str): The default database to use.\n sql (str): The SQL query to execute.

\n\n

Returns:\n pd.DataFrame: The results of the SQL query.

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Example

\n\n
\n
vn.generate_explanation(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'AI Response'\n
\n
\n\n

Generate an explanation of an SQL query using the Vanna.AI API.

\n\n

Args:\n sql (str): The SQL query to generate an explanation for.

\n\n

Returns:\n str or None: The explanation, or None if an error occurred.

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Example

\n\n
\n
vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'AI Response'\n
\n
\n\n

Generate a question from an SQL query using the Vanna.AI API.

\n\n

Args:\n sql (str): The SQL query to generate a question for.

\n\n

Returns:\n str or None: The question, or None if an error occurred.

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a/docs/vanna.html b/docs/vanna.html index 98fcfe82..696970b5 100644 --- a/docs/vanna.html +++ b/docs/vanna.html @@ -57,6 +57,9 @@

API Documentation

  • generate_explanation
    • +
  • + generate_question +
    • @@ -182,7 +185,7 @@

    def - generate_sql(question: str) -> str | None: + generate_sql(question: str) -> str:
    @@ -203,7 +206,7 @@

    def - generate_plotly_code( question: str | None, sql: str | None, df: pandas.core.frame.DataFrame) -> str | None: + generate_plotly_code( question: Optional[str], sql: Optional[str], df: pandas.core.frame.DataFrame) -> str:
    @@ -226,7 +229,7 @@

    def - get_plotly_figure( plotly_code: str, df: pandas.core.frame.DataFrame, dark_mode: bool = True) -> plotly.graph_objs._figure.Figure | None: + get_plotly_figure( plotly_code: str, df: pandas.core.frame.DataFrame, dark_mode: bool = True) -> plotly.graph_objs._figure.Figure:
    @@ -256,19 +259,13 @@

    Get the results of an SQL query using the Vanna.AI API.

    -
    Parameters
    - -
      -
    • cs: The Snowflake cursor to use.
      • -
    • default_database: The default database to use (executed as "USE DATABASE {default_database};")
      • -
    • sql: The SQL query to run.
      • -
    - -
    Returns
    +

    Args: + cs (pyodbc.Cursor): The cursor to use. + default_database (str): The default database to use. + sql (str): The SQL query to execute.

    -
    -

    The results of the SQL query.

    -
    +

    Returns: + pd.DataFrame: The results of the SQL query.

    @@ -277,7 +274,7 @@

    Returns
    def - generate_explanation(sql: str) -> str | None: + generate_explanation(sql: str) -> str:
    @@ -293,17 +290,40 @@
    Returns

    Generate an explanation of an SQL query using the Vanna.AI API.

    -
    Parameters
    +

    Args: + sql (str): The SQL query to generate an explanation for.

    + +

    Returns: + str or None: The explanation, or None if an error occurred.

    + + + + +
    +
    + + def + generate_question(sql: str) -> str: + + +
    + + +

    Example

    + +
    +
    vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'")
+# 'AI Response'
+
    +
    -
      -
    • sql: The SQL query to explain.
      • -
    +

    Generate a question from an SQL query using the Vanna.AI API.

    -
    Returns
    +

    Args: + sql (str): The SQL query to generate a question for.

    -
    -

    The explanation of the SQL query, or None if an error occurred.

    -
    +

    Returns: + str or None: The question, or None if an error occurred.

    diff --git a/pyproject.toml b/pyproject.toml index 5743d651..9c887a9c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "vanna" -version = "0.0.2" +version = "0.0.3" authors = [ { name="Zain Hoda", email="zain@vanna.ai" }, ] diff --git a/src/vanna/__init__.py b/src/vanna/__init__.py index 7d1dc6a9..838f600b 100644 --- a/src/vanna/__init__.py +++ b/src/vanna/__init__.py @@ -143,7 +143,7 @@ def remove_sql(question: str) -> bool: return status.success -def generate_sql(question: str) -> str | None: +def generate_sql(question: str) -> str: """ Generate an SQL query using the Vanna.AI API. @@ -165,7 +165,7 @@ def generate_sql(question: str) -> str | None: return sql_answer.sql -def generate_plotly_code(question: str | None, sql: str | None, df: pd.DataFrame) -> str | None: +def generate_plotly_code(question: Union[str, None], sql: Union[str, None], df: pd.DataFrame) -> str: """ Generate Plotly code using the Vanna.AI API. @@ -195,7 +195,7 @@ def generate_plotly_code(question: str | None, sql: str | None, df: pd.DataFrame return plotly_code.plotly_code -def get_plotly_figure(plotly_code: str, df: pd.DataFrame, dark_mode: bool = True) -> plotly.graph_objs.Figure | None: +def get_plotly_figure(plotly_code: str, df: pd.DataFrame, dark_mode: bool = True) -> plotly.graph_objs.Figure: """ Get a Plotly figure from a dataframe and Plotly code. @@ -223,15 +223,13 @@ def get_results(cs, default_database: str, sql: str) -> pd.DataFrame: """ Get the results of an SQL query using the Vanna.AI API. - :param cs: The Snowflake cursor to use. - :type cs: snowflake.connector.cursor.SnowflakeCursor - :param default_database: The default database to use (executed as "USE DATABASE {default_database};") - :type default_database: str - :param sql: The SQL query to run. - :type sql: str - - :return: The results of the SQL query. - :rtype: pd.DataFrame + Args: + cs (pyodbc.Cursor): The cursor to use. + default_database (str): The default database to use. + sql (str): The SQL query to execute. + + Returns: + pd.DataFrame: The results of the SQL query. """ cs.execute(f"USE DATABASE {default_database}") @@ -245,7 +243,7 @@ def get_results(cs, default_database: str, sql: str) -> pd.DataFrame: return df -def generate_explanation(sql: str) -> str | None: +def generate_explanation(sql: str) -> str: """ ## Example @@ -256,11 +254,11 @@ def generate_explanation(sql: str) -> str | None: Generate an explanation of an SQL query using the Vanna.AI API. - :param sql: The SQL query to explain. - :type sql: str - - :return: The explanation of the SQL query, or None if an error occurred. - :rtype: str or None + Args: + sql (str): The SQL query to generate an explanation for. + + Returns: + str or None: The explanation, or None if an error occurred. """ params = [SQLAnswer( @@ -279,3 +277,38 @@ def generate_explanation(sql: str) -> str | None: explanation = Explanation(**d['result']) return explanation.explanation + +def generate_question(sql: str) -> str: + """ + + ## Example + ```python + vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'") + # 'AI Response' + ``` + + Generate a question from an SQL query using the Vanna.AI API. + + Args: + sql (str): The SQL query to generate a question for. + + Returns: + str or None: The question, or None if an error occurred. + + """ + params = [SQLAnswer( + raw_answer="", + prefix="", + postfix="", + sql=sql, + )] + + d = __rpc_call(method="generate_question", params=params) + + if 'result' not in d: + return None + + # Load the result into a dataclass + question = Question(**d['result']) + + return question.question \ No newline at end of file