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
\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\nArgs:\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\nArgs:\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\nArgs:\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\nArgs:\n question (str): The question to generate an SQL query for.
\n\nReturns:\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\nArgs:\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\nReturns:\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\nArgs:\n df (pd.DataFrame): The dataframe to use.\n plotly_code (str): The Plotly code to use.
\n\nReturns:\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\nParameters \n\n\ncs : The Snowflake cursor to use. \ndefault_database : The default database to use (executed as \"USE DATABASE {default_database};\") \nsql : The SQL query to run. \n \n\nReturns \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\nGenerate an explanation of an SQL query using the Vanna.AI API.
\n\nParameters \n\n\nsql : The SQL query to explain. \n \n\nReturns \n\n\n The explanation of the SQL query, or None if an error occurred.
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0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 1}, "vanna.store_sql": {"tf": 1.7320508075688772}}, "df": 2}}}, "r": {"docs": {"vanna.set_org": {"tf": 1}, "vanna.store_sql": {"tf": 1.4142135623730951}, "vanna.remove_sql": {"tf": 1}, "vanna.generate_sql": {"tf": 1.4142135623730951}, "vanna.generate_plotly_code": {"tf": 1.7320508075688772}, "vanna.get_plotly_figure": {"tf": 1}}, "df": 6}, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"vanna.generate_explanation": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"vanna": {"tf": 2}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 1}, 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"vanna.set_org": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"vanna": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"vanna.generate_sql": {"tf": 1}, "vanna.generate_plotly_code": {"tf": 1}, "vanna.generate_explanation": {"tf": 1}}, "df": 3}}}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {"vanna.get_plotly_figure": {"tf": 1}}, "df": 1}}}, "f": {"docs": {"vanna.get_results": {"tf": 1.4142135623730951}, "vanna.generate_explanation": {"tf": 1.4142135623730951}}, "df": 2}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 2.449489742783178}, "vanna.set_org": {"tf": 1.4142135623730951}, "vanna.generate_explanation": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 1}, "vanna.generate_sql": {"tf": 1.4142135623730951}, "vanna.generate_plotly_code": {"tf": 1.4142135623730951}, "vanna.generate_explanation": {"tf": 1}}, "df": 4}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"vanna": {"tf": 2.23606797749979}, "vanna.store_sql": {"tf": 1.7320508075688772}, "vanna.remove_sql": {"tf": 1.7320508075688772}, "vanna.generate_sql": {"tf": 1.4142135623730951}, "vanna.generate_plotly_code": {"tf": 1.4142135623730951}}, "df": 5}}}}}, "r": {"docs": {}, "df": 0, "y": {"docs": {"vanna.store_sql": {"tf": 1.4142135623730951}, "vanna.remove_sql": {"tf": 1}, "vanna.generate_sql": {"tf": 1.7320508075688772}, "vanna.generate_plotly_code": {"tf": 1}, "vanna.get_results": {"tf": 1.7320508075688772}, "vanna.generate_explanation": {"tf": 1.7320508075688772}}, "df": 6}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"vanna": {"tf": 2.449489742783178}, "vanna.generate_explanation": {"tf": 1.4142135623730951}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"vanna": {"tf": 1}}, "df": 1, "s": {"docs": {"vanna": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"vanna": {"tf": 1}, "vanna.get_results": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"vanna": {"tf": 1.7320508075688772}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "n": {"docs": {"vanna": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"vanna": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"vanna": {"tf": 1}}, "df": 1}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 2}, "vanna.generate_plotly_code": {"tf": 2.23606797749979}, "vanna.get_plotly_figure": {"tf": 1.7320508075688772}}, "df": 3}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"vanna.store_sql": {"tf": 1}, "vanna.remove_sql": {"tf": 1}}, "df": 2}}}}}}}}}}}}, "s": {"docs": {"vanna": {"tf": 1.4142135623730951}, "vanna.get_results": {"tf": 1}}, "df": 2}}, "b": {"docs": {}, "df": 0, "y": {"docs": {"vanna": {"tf": 2}}, "df": 1}, "a": {"docs": {}, "df": 0, "r": {"docs": {"vanna": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {"vanna": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"vanna": {"tf": 1}, "vanna.get_results": {"tf": 1.7320508075688772}}, "df": 2}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"vanna": {"tf": 1.4142135623730951}, "vanna.store_sql": {"tf": 1}, "vanna.remove_sql": {"tf": 1}, "vanna.get_results": {"tf": 2}}, "df": 4}}}}, "f": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"vanna.generate_plotly_code": {"tf": 1.4142135623730951}, "vanna.get_plotly_figure": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}}, "f": {"docs": {"vanna": {"tf": 2.449489742783178}, "vanna.generate_plotly_code": {"tf": 1}, "vanna.get_plotly_figure": {"tf": 1}}, "df": 3}, "b": {"docs": {"vanna": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "e": {"docs": {"vanna.generate_explanation": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"vanna": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {"vanna": {"tf": 1}}, "df": 1, "y": {"docs": {}, "df": 0, "z": {"docs": {"vanna": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"vanna.get_plotly_figure": {"tf": 1.4142135623730951}, "vanna.get_results": {"tf": 1.7320508075688772}}, "df": 2, "r": {"docs": {"vanna": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"vanna.generate_sql": {"tf": 1}, "vanna.generate_plotly_code": {"tf": 1}, "vanna.get_results": {"tf": 1}, "vanna.generate_explanation": {"tf": 1}}, "df": 4}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"vanna": {"tf": 1}, "vanna.get_results": {"tf": 1.4142135623730951}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"vanna.generate_explanation": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"vanna.remove_sql": {"tf": 1.4142135623730951}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"vanna.generate_sql": {"tf": 1}, "vanna.generate_plotly_code": {"tf": 1}, "vanna.get_plotly_figure": {"tf": 1}, "vanna.get_results": {"tf": 1}, "vanna.generate_explanation": {"tf": 1}}, "df": 5}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"vanna.get_results": {"tf": 1}}, "df": 1}}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "n": {"docs": {"vanna.generate_explanation": {"tf": 1}}, "df": 1}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
+
+ // mirrored in build-search-index.js (part 1)
+ // Also split on html tags. this is a cheap heuristic, but good enough.
+ elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/);
+
+ let searchIndex;
+ if (docs._isPrebuiltIndex) {
+ console.info("using precompiled search index");
+ searchIndex = elasticlunr.Index.load(docs);
+ } else {
+ console.time("building search index");
+ // mirrored in build-search-index.js (part 2)
+ searchIndex = elasticlunr(function () {
+ this.pipeline.remove(elasticlunr.stemmer);
+ this.pipeline.remove(elasticlunr.stopWordFilter);
+ this.addField("qualname");
+ this.addField("fullname");
+ this.addField("annotation");
+ this.addField("default_value");
+ this.addField("signature");
+ this.addField("bases");
+ this.addField("doc");
+ this.setRef("fullname");
+ });
+ for (let doc of docs) {
+ searchIndex.addDoc(doc);
+ }
+ console.timeEnd("building search index");
+ }
+
+ return (term) => searchIndex.search(term, {
+ fields: {
+ qualname: {boost: 4},
+ fullname: {boost: 2},
+ annotation: {boost: 2},
+ default_value: {boost: 2},
+ signature: {boost: 2},
+ bases: {boost: 2},
+ doc: {boost: 1},
+ },
+ expand: true
+ });
+})();
\ No newline at end of file
diff --git a/docs/vanna-py-overview.md b/docs/vanna-py-overview.md
new file mode 100644
index 00000000..e7771d62
--- /dev/null
+++ b/docs/vanna-py-overview.md
@@ -0,0 +1,81 @@
+---
+marp: true
+theme: gaia
+_class: lead
+paginate: true
+backgroundColor: #111827
+color: #fff
+header: 'Updated: 2023-05-22'
+---
+
+
+![bg left:40% 80%](https://ask.vanna.ai/static/img/vanna.svg)
+
+# **Vanna.AI**
+## Python Package
+
+For Natural Language to SQL
+(and associated functionality)
+
+support@vanna.ai
+
+---
+# What can you do with **Vanna.AI**?
+
+**Vanna.AI** has a Python package that allows you to convert natural language to SQL.
+
+```python
+import vanna as vn
+
+vn.api_key = 'vanna-key-...' # Set your API key
+vn.set_org('') # Set your organization name
+
+my_question = 'What are the top 10 ABC by XYZ?'
+
+sql = vn.generate_sql(question=my_question, error_msg=None)
+# SELECT * FROM table_name WHERE column_name = 'value'
+
+(my_df, error_msg) = vn.run_sql(cs: snowflake.Cursor, sql=sql)
+
+vn.generate_plotly_code(question=my_question, df=my_df)
+# fig = px.bar(df, x='column_name', y='column_name')
+
+vn.run_plotly_code(plotly_code=fig, df=my_df)
+
+```
+
+---
+
+# Installation
+
+## Global Installation
+```bash
+pip install vanna
+```
+or
+```bash
+pip3 install vanna
+```
+
+## Use a Virtual Environment
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install vanna
+```
+
+---
\ No newline at end of file
diff --git a/docs/vanna.html b/docs/vanna.html
new file mode 100644
index 00000000..98fcfe82
--- /dev/null
+++ b/docs/vanna.html
@@ -0,0 +1,494 @@
+
+
+
+
+
+
+ vanna API documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+vanna
+
+ A module to interact with the Vanna.AI API, providing the functionality to generate SQL explanations.
+
+
+
import vanna as vn
+
+vn . api_key = 'vanna-key-...' # Set your API key
+vn . set_org ( '' ) # Set your organization name
+
+vn . store_sql ( question = "Who are the top 10 customers by Sales?" , sql = "SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10" )
+
+my_question = 'What are the top 10 ABC by XYZ?'
+
+sql = vn . generate_sql ( question = my_question , error_msg = None )
+# SELECT * FROM table_name WHERE column_name = 'value'
+
+conn = snowflake . connector . connect (
+ user = 'my_user' ,
+ password = 'my_password' ,
+ account = 'my_account' ,
+ database = 'my_database' ,
+ )
+
+cs = conn . cursor ()
+
+df = vn . get_results ( cs , my_default_db , sql )
+
+plotly_code = vn . generate_plotly_code ( question = "Who are the top 10 customers by Sales?" , sql = sql , df = df )
+# px.bar(df, x='column_name', y='column_name')
+
+fig = vn . get_plotly_figure ( plotly_code = plotly_code , df = df )
+
+
+
+
+
+
+
+
+
+
+ api_key : Optional[str] =
+None
+
+
+
+
+
+
+
+
+
+
+
+ def
+ set_org (org : str ) -> None :
+
+
+
+
+
+ Set the organization name for the Vanna.AI API.
+
+
Args:
+ org (str): The organization name.
+
+
+
+
+
+
+
+ def
+ store_sql (question : str , sql : str ) -> bool :
+
+
+
+
+
+ Store a question and its corresponding SQL query in the Vanna.AI database.
+
+
Args:
+ question (str): The question to store.
+ sql (str): The SQL query to store.
+
+
+
+
+
+
+
+ def
+ remove_sql (question : str ) -> bool :
+
+
+
+
+
+ Remove a question and its corresponding SQL query from the Vanna.AI database.
+
+
Args:
+ question (str): The question to remove.
+
+
+
+
+
+
+
+ def
+ generate_sql (question : str ) -> str | None :
+
+
+
+
+
+ Generate an SQL query using the Vanna.AI API.
+
+
Args:
+ question (str): The question to generate an SQL query for.
+
+
Returns:
+ str or None: The SQL query, or None if an error occurred.
+
+
+
+
+
+
+
+ def
+ generate_plotly_code ( question : str | None , sql : str | None , df : pandas . core . frame . DataFrame ) -> str | None :
+
+
+
+
+
+ Generate Plotly code using the Vanna.AI API.
+
+
Args:
+ question (str): The question to generate Plotly code for.
+ sql (str): The SQL query to generate Plotly code for.
+ df (pd.DataFrame): The dataframe to generate Plotly code for.
+
+
Returns:
+ str or None: The Plotly code, or None if an error occurred.
+
+
+
+
+
+
+
+
+ def
+ get_results (cs , default_database : str , sql : str ) -> pandas . core . frame . DataFrame :
+
+
+
+
+
+ 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
+
+
+ The results of the SQL query.
+
+
+
+
+
+
+
+
+ def
+ generate_explanation (sql : str ) -> str | None :
+
+
+
+
+
+ Example
+
+
+
vn . generate_explanation ( sql = "SELECT * FROM students WHERE name = 'John Doe'" )
+# 'AI Response'
+
+
+
+
Generate an explanation of an SQL query using the Vanna.AI API.
+
+
Parameters
+
+
+sql : The SQL query to explain.
+
+
+
Returns
+
+
+ The explanation of the SQL query, or None if an error occurred.
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/vanna/types.html b/docs/vanna/types.html
new file mode 100644
index 00000000..aacf7c70
--- /dev/null
+++ b/docs/vanna/types.html
@@ -0,0 +1,1406 @@
+
+
+
+
+
+
+ vanna.types API documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Status :
+
+
+
+
+
+
+
+
+
+
+ Status (success : bool , message : str )
+
+
+
+
+
+
+
+
+
+
+ success : bool
+
+
+
+
+
+
+
+
+
+
+ message : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
FullQuestionDocument :
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
QuestionSQLPair :
+
+
+
+
+
+
+
+
+
+
+ QuestionSQLPair (question : str , sql : str )
+
+
+
+
+
+
+
+
+
+
+ question : str
+
+
+
+
+
+
+
+
+
+
+ sql : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Organization :
+
+
+
+
+
+
+
+
+
+
+ name : str
+
+
+
+
+
+
+
+
+
+
+ user : str | None
+
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
QuestionId :
+
+
+
+
+
+
+
+
+
+
+ QuestionId (id : str )
+
+
+
+
+
+
+
+
+
+
+ id : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Question :
+
+
+
+
+
+
+
+
+
+
+ Question (question : str )
+
+
+
+
+
+
+
+
+
+
+ question : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Followup :
+
+
+
+
+
+
+
+
+
+
+ Followup (followup : str )
+
+
+
+
+
+
+
+
+
+
+ followup : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
QuestionEmbedding :
+
+
+
+
+
+
+
+
+
+
+
+ embedding : List[float]
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Connection :
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
SQLAnswer :
+
+
+
+
+
+
+
+
+
+
+ SQLAnswer (raw_answer : str , prefix : str , postfix : str , sql : str )
+
+
+
+
+
+
+
+
+
+
+ raw_answer : str
+
+
+
+
+
+
+
+
+
+
+ prefix : str
+
+
+
+
+
+
+
+
+
+
+ postfix : str
+
+
+
+
+
+
+
+
+
+
+ sql : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Explanation :
+
+
+
+
+
+
+
+
+
+
+ Explanation (explanation : str )
+
+
+
+
+
+
+
+
+
+
+ explanation : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
DataResult :
+
+
+
+
+
+
+
+
+
+
+ DataResult ( question : str | None , sql : str | None , table_markdown : str , error : str | None , correction_attempts : int )
+
+
+
+
+
+
+
+
+
+
+ question : str | None
+
+
+
+
+
+
+
+
+
+
+ sql : str | None
+
+
+
+
+
+
+
+
+
+
+ table_markdown : str
+
+
+
+
+
+
+
+
+
+
+ error : str | None
+
+
+
+
+
+
+
+
+
+
+ correction_attempts : int
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
PlotlyResult :
+
+
+
+
+
+
+
+
+
+
+ PlotlyResult (plotly_code : str )
+
+
+
+
+
+
+
+
+
+
+ plotly_code : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
WarehouseDefinition :
+
+
+
+
+
+
+
+
+
+
+ name : str
+
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
TableDefinition :
+
+
+
+
+
+
+
+
+
+
+ schema_name : str
+
+
+
+
+
+
+
+
+
+
+ table_name : str
+
+
+
+
+
+
+
+
+
+
+ ddl : str | None
+
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
ColumnDefinition :
+
+
+
+
+
+
+
+
+
+
+ ColumnDefinition ( name : str , type : str , is_primary_key : bool , is_foreign_key : bool , foreign_key_table : str , foreign_key_column : str )
+
+
+
+
+
+
+
+
+
+
+ name : str
+
+
+
+
+
+
+
+
+
+
+ type : str
+
+
+
+
+
+
+
+
+
+
+ is_primary_key : bool
+
+
+
+
+
+
+
+
+
+
+ is_foreign_key : bool
+
+
+
+
+
+
+
+
+
+
+ foreign_key_table : str
+
+
+
+
+
+
+
+
+
+
+ foreign_key_column : str
+
+
+
+
+
+
+
+
+
+
+
+
@dataclass
+
+
class
+
Diagram :
+
+
+
+
+
+
+
+
+
+
+ Diagram (raw : str , mermaid_code : str )
+
+
+
+
+
+
+
+
+
+
+ raw : str
+
+
+
+
+
+
+
+
+
+
+ mermaid_code : str
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 95214500..5743d651 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "vanna"
-version = "0.0.1"
+version = "0.0.2"
authors = [
{ name="Zain Hoda", email="zain@vanna.ai" },
]
@@ -12,6 +12,9 @@ classifiers = [
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
]
+dependencies = [
+ "requests", "tabulate", "plotly"
+]
[project.urls]
"Homepage" = "https://github.com/vanna-ai/vanna-py"
diff --git a/src/vanna/__init__.py b/src/vanna/__init__.py
index 05a00516..7d1dc6a9 100644
--- a/src/vanna/__init__.py
+++ b/src/vanna/__init__.py
@@ -1 +1,281 @@
-print("Vanna.AI Imported")
\ No newline at end of file
+r'''
+A module to interact with the Vanna.AI API, providing the functionality to generate SQL explanations.
+
+```python
+import vanna as vn
+
+vn.api_key = 'vanna-key-...' # Set your API key
+vn.set_org('') # Set your organization name
+
+vn.store_sql(question="Who are the top 10 customers by Sales?", sql="SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10")
+
+my_question = 'What are the top 10 ABC by XYZ?'
+
+sql = vn.generate_sql(question=my_question, error_msg=None)
+# SELECT * FROM table_name WHERE column_name = 'value'
+
+conn = snowflake.connector.connect(
+ user='my_user',
+ password='my_password',
+ account='my_account',
+ database='my_database',
+ )
+
+cs = conn.cursor()
+
+df = vn.get_results(cs, my_default_db, sql)
+
+plotly_code = vn.generate_plotly_code(question="Who are the top 10 customers by Sales?", sql=sql, df=df)
+# px.bar(df, x='column_name', y='column_name')
+
+fig = vn.get_plotly_figure(plotly_code=plotly_code, df=df)
+
+```
+'''
+print("Vanna.AI Imported")
+
+import requests
+import pandas as pd
+import json
+import dataclasses
+import plotly
+import plotly.express as px
+import plotly.graph_objects as go
+from .types import SQLAnswer, Explanation, QuestionSQLPair, Question, QuestionId, DataResult, PlotlyResult, Status
+from typing import List, Dict, Any, Union, Optional
+
+api_key: Union[str, None] = None # API key for Vanna.AI
+__org: Union[str, None] = None # Organization name for Vanna.AI
+_endpoint = "https://ask.vanna.ai/rpc"
+
+def __rpc_call(method, params):
+ """
+ Make a RPC call to the Vanna.AI API.
+
+ Args:
+ method (str): The name of the method to call.
+ params (list): A list of parameters for the method.
+
+ Returns:
+ dict: The JSON response from the API converted into a dictionary.
+ """
+ global api_key
+ global __org
+
+ if api_key is None:
+ raise Exception("API key not set")
+
+ if __org is None:
+ raise Exception("Organization name not set")
+
+ headers = {
+ 'Content-Type': 'application/json',
+ 'Vanna-Key': api_key,
+ 'Vanna-Org': __org
+ }
+ data = {
+ "method": method,
+ "params": [__dataclass_to_dict(obj) for obj in params]
+ }
+
+ response = requests.post(_endpoint, headers=headers, data=json.dumps(data))
+ return response.json()
+
+def __dataclass_to_dict(obj):
+ """
+ Converts a dataclass object to a dictionary.
+
+ Args:
+ obj (object): The dataclass object to convert.
+
+ Returns:
+ dict: The dataclass object as a dictionary.
+ """
+ return dataclasses.asdict(obj)
+
+def set_org(org: str) -> None:
+ """
+ Set the organization name for the Vanna.AI API.
+
+ Args:
+ org (str): The organization name.
+ """
+ global __org
+ __org = org
+
+def store_sql(question: str, sql: str) -> bool:
+ """
+ Store a question and its corresponding SQL query in the Vanna.AI database.
+
+ Args:
+ question (str): The question to store.
+ sql (str): The SQL query to store.
+ """
+ params = [QuestionSQLPair(
+ question=question,
+ sql=sql,
+ )]
+
+ d = __rpc_call(method="store_sql", params=params)
+
+ if 'result' not in d:
+ return False
+
+ status = Status(**d['result'])
+
+ return status.success
+
+def remove_sql(question: str) -> bool:
+ """
+ Remove a question and its corresponding SQL query from the Vanna.AI database.
+
+ Args:
+ question (str): The question to remove.
+ """
+ params = [Question(question=question)]
+
+ d = __rpc_call(method="remove_sql", params=params)
+
+ if 'result' not in d:
+ return False
+
+ status = Status(**d['result'])
+
+ return status.success
+
+def generate_sql(question: str) -> str | None:
+ """
+ Generate an SQL query using the Vanna.AI API.
+
+ Args:
+ question (str): The question to generate an SQL query for.
+
+ Returns:
+ str or None: The SQL query, or None if an error occurred.
+ """
+ params = [Question(question=question)]
+
+ d = __rpc_call(method="generate_sql_from_question", params=params)
+
+ if 'result' not in d:
+ return None
+
+ # Load the result into a dataclass
+ sql_answer = SQLAnswer(**d['result'])
+
+ return sql_answer.sql
+
+def generate_plotly_code(question: str | None, sql: str | None, df: pd.DataFrame) -> str | None:
+ """
+ Generate Plotly code using the Vanna.AI API.
+
+ Args:
+ question (str): The question to generate Plotly code for.
+ sql (str): The SQL query to generate Plotly code for.
+ df (pd.DataFrame): The dataframe to generate Plotly code for.
+
+ Returns:
+ str or None: The Plotly code, or None if an error occurred.
+ """
+ params = [DataResult(
+ question=question,
+ sql=sql,
+ table_markdown=df.head().to_markdown(),
+ error=None,
+ correction_attempts=0,
+ )]
+
+ d = __rpc_call(method="generate_plotly_code", params=params)
+
+ if 'result' not in d:
+ return None
+
+ # Load the result into a dataclass
+ plotly_code = PlotlyResult(**d['result'])
+
+ return plotly_code.plotly_code
+
+def get_plotly_figure(plotly_code: str, df: pd.DataFrame, dark_mode: bool = True) -> plotly.graph_objs.Figure | None:
+ """
+ Get a Plotly figure from a dataframe and Plotly code.
+
+ Args:
+ df (pd.DataFrame): The dataframe to use.
+ plotly_code (str): The Plotly code to use.
+
+ Returns:
+ plotly.graph_objs.Figure: The Plotly figure.
+ """
+ ldict = {'df': df, 'px': px, 'go': go}
+ exec(plotly_code, globals(), ldict)
+
+ fig = ldict.get('fig', None)
+
+ if fig is None:
+ return None
+
+ if dark_mode:
+ fig.update_layout(template="plotly_dark")
+
+ return fig
+
+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
+ """
+ cs.execute(f"USE DATABASE {default_database}")
+
+ cur = cs.execute(sql)
+
+ results = cur.fetchall()
+
+ # Create a pandas dataframe from the results
+ df = pd.DataFrame(results, columns=[desc[0] for desc in cur.description])
+
+ return df
+
+
+def generate_explanation(sql: str) -> str | None:
+ """
+
+ ## Example
+ ```python
+ vn.generate_explanation(sql="SELECT * FROM students WHERE name = 'John Doe'")
+ # 'AI Response'
+ ```
+
+ 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
+
+ """
+ params = [SQLAnswer(
+ raw_answer="",
+ prefix="",
+ postfix="",
+ sql=sql,
+ )]
+
+ d = __rpc_call(method="generate_explanation", params=params)
+
+ if 'result' not in d:
+ return None
+
+ # Load the result into a dataclass
+ explanation = Explanation(**d['result'])
+
+ return explanation.explanation
diff --git a/src/vanna/types.py b/src/vanna/types.py
new file mode 100644
index 00000000..59f3f9fb
--- /dev/null
+++ b/src/vanna/types.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+from typing import List
+from dataclasses import dataclass
+
+@dataclass
+class Status:
+ success: bool
+ message: str
+
+@dataclass
+class FullQuestionDocument:
+ id: QuestionId
+ question: Question
+ answer: SQLAnswer | None
+ data: DataResult | None
+ plotly: PlotlyResult | None
+
+@dataclass
+class QuestionSQLPair:
+ question: str
+ sql: str
+
+@dataclass
+class Organization:
+ name: str
+ user: str | None
+ connection: Connection | None
+
+@dataclass
+class QuestionId:
+ id: str
+
+@dataclass
+class Question:
+ question: str
+
+@dataclass
+class Followup:
+ followup: str
+
+@dataclass
+class QuestionEmbedding:
+ question: Question
+ embedding: List[float]
+
+@dataclass
+class Connection:
+ #TODO: implement
+ pass
+
+@dataclass
+class SQLAnswer:
+ raw_answer: str
+ prefix: str
+ postfix: str
+ sql: str
+
+@dataclass
+class Explanation:
+ explanation: str
+
+@dataclass
+class DataResult:
+ question: str | None
+ sql: str | None
+ table_markdown: str
+ error: str | None
+ correction_attempts: int
+
+@dataclass
+class PlotlyResult:
+ plotly_code: str
+
+@dataclass
+class WarehouseDefinition:
+ name: str
+ tables: List[TableDefinition]
+
+@dataclass
+class TableDefinition:
+ schema_name: str
+ table_name: str
+ ddl: str | None
+ columns: List[ColumnDefinition]
+
+@dataclass
+class ColumnDefinition:
+ name: str
+ type: str
+ is_primary_key: bool
+ is_foreign_key: bool
+ foreign_key_table: str
+ foreign_key_column: str
+
+@dataclass
+class Diagram:
+ raw: str
+ mermaid_code: str
\ No newline at end of file