pip install vanna
+import vanna as vn
+vn.api_key = 'vanna-key-...'
+vn.set_org('my_org')
+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'
+import vanna as vn
+
+vn.api_key = 'vanna-key-...' # Set your API key
+vn.set_org('') # Set your organization name
+
+# Train Vanna.AI on your data
+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"
+)
+
+# Ask questions about your data
+my_question = 'What are the top 10 ABC by XYZ?'
+
+# Generate SQL
+sql = vn.generate_sql(question=my_question, error_msg=None)
+
+# Connect to your database
+conn = snowflake.connector.connect(
+ user='my_user',
+ password='my_password',
+ account='my_account',
+ database='my_database',
+ )
+
+cs = conn.cursor()
+
+# Get results
+df = vn.get_results(
+ cs=cs,
+ default_db=my_default_db,
+ sql=sql
+ )
+
+# Generate Plotly code
+plotly_code = vn.generate_plotly_code(
+ question=my_question,
+ sql=sql,
+ df=df
+ )
+
+# Get Plotly figure
+fig = vn.get_plotly_figure(
+ plotly_code=plotly_code,
+ df=df
+ )
+Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
+Each organization has its own isolated training set. This means that Vanna.AI can understand the unique language of your organization and answer questions about your data.
+After every question you can tell Vanna.AI whether the results were correct. This allows Vanna.AI to learn from the questions that are asked and become smarter immediately.
+Vanna provides additional functionality to manage your training data to maintain the highest level of accuracy for your organization.
+It can support apps that allow you to:
+ + +/askvanna [question]Vanna.AI can be used in a Jupyter Notebook to generate SQL from natural language questions.
+%pip install vanna
+import vanna as vn
+vn.api_key = 'vanna-key-...'
+vn.set_org('my_org')
+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?'
+
+# Generate SQL
+sql = vn.generate_sql(question=my_question, error_msg=None)
+# SELECT * FROM table_name WHERE column_name = 'value'
+Run your SQL as you normally would.
+ + + + + + +Vanna.AI uses a combination of documentation and historical question and SQL pairs to generate SQL from natural language.
+flowchart LR
+ Generate[vn.generate_question]
+ Question[Question]
+ Verify{Is the question correct?}
+ SQL --> Generate
+ Generate --> Question
+ Question --> Verify
+ Verify -- Yes --> Store[vn.store_sql]
+ Verify -- No --> Update[Update the Question]
+ Update --> Store
+flowchart LR
+ Question[Question]
+ Generate[vn.generate_sql]
+ SQL[SQL]
+ Question --> Generate
+ Generate --> SQL | + | Free Tier | +Paid Tier | +
|---|---|---|
| Price | +Free | +$500/month | +
| Documentation Storage | +100 chunks | +10,000 chunks | +
| Question Storage | +1,000 questions | +10,000 questions | +
| Multi-User | +No | +Yes | +
| Support | +Discord | +Email, Slack, Phone | +
Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
+flag_sql_for_review(question, sql=None, error_msg=None)
+
+vn.flag_sql_for_review(question="What is the average salary of employees?")
+get_flagged_questions().
+
+ Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
question |
+
+ str
+ |
+
+
+
+ The question to flag. + |
+ + required + | +
sql |
+
+ str
+ |
+
+
+
+ The SQL query to flag. + |
+
+ None
+ |
+
error_msg |
+
+ str
+ |
+
+
+
+ The error message to flag. + |
+
+ None
+ |
+
Returns:
+| Name | Type | +Description | +
|---|---|---|
bool |
+ bool
+ |
+
+
+
+ True if the question and SQL query were flagged successfully, False otherwise. + |
+
generate_explanation(sql)
+
+vn.generate_explanation(sql="SELECT * FROM students WHERE name = 'John Doe'")
+# 'This query selects all columns from the students table where the name is John Doe.'
+Generate an explanation of an SQL query using the Vanna.AI API.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
sql |
+
+ str
+ |
+
+
+
+ The SQL query to generate an explanation for. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ str
+ |
+
+
+
+ str or None: The explanation, or None if an error occurred. + |
+
generate_plotly_code(question, sql, df)
+
+vn.generate_plotly_code(
+ question="What is the average salary of employees?",
+ sql="SELECT AVG(salary) FROM employees",
+ df=df
+)
+# fig = px.bar(df, x="name", y="salary")
+Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
question |
+
+ str
+ |
+
+
+
+ The question to generate Plotly code for. + |
+ + required + | +
sql |
+
+ str
+ |
+
+
+
+ The SQL query to generate Plotly code for. + |
+ + required + | +
df |
+
+ pd.DataFrame
+ |
+
+
+
+ The dataframe to generate Plotly code for. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ str
+ |
+
+
+
+ str or None: The Plotly code, or None if an error occurred. + |
+
generate_question(sql)
+
+vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'")
+# 'What is the name of the student?'
+Generate a question from an SQL query using the Vanna.AI API.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
sql |
+
+ str
+ |
+
+
+
+ The SQL query to generate a question for. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ str
+ |
+
+
+
+ str or None: The question, or None if an error occurred. + |
+
generate_sql(question)
+
+vn.generate_sql(question="What is the average salary of employees?")
+# SELECT AVG(salary) FROM employees
+Generate an SQL query using the Vanna.AI API.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
question |
+
+ str
+ |
+
+
+
+ The question to generate an SQL query for. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ str
+ |
+
+
+
+ str or None: The SQL query, or None if an error occurred. + |
+
get_accuracy_stats()
+
+vn.get_accuracy_stats()
+Get the accuracy statistics from the Vanna.AI API.
+ +Returns:
+| Type | +Description | +
|---|---|
+ AccuracyStats
+ |
+
+
+
+ dict or None: The accuracy statistics, or None if an error occurred. + |
+
get_flagged_questions()
+
+questions = vn.get_flagged_questions()
+Get a list of flagged questions from the Vanna.AI API.
+ +Returns:
+| Type | +Description | +
|---|---|
+ QuestionList
+ |
+
+
+
+ List[FullQuestionDocument] or None: The list of flagged questions, or None if an error occurred. + |
+
get_plotly_figure(plotly_code, df, dark_mode=True)
+
+fig = vn.get_plotly_figure(
+ plotly_code="fig = px.bar(df, x='name', y='salary')",
+ df=df
+)
+fig.show()
+Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+
+ pd.DataFrame
+ |
+
+
+
+ The dataframe to use. + |
+ + required + | +
plotly_code |
+
+ str
+ |
+
+
+
+ The Plotly code to use. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ plotly.graph_objs.Figure
+ |
+
+
+
+ plotly.graph_objs.Figure: The Plotly figure. + |
+
get_results(cs, default_database, sql)
+
+df = vn.get_results(cs=cs, default_database="PUBLIC", sql="SELECT * FROM students")
+Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
cs |
+ + | +
+
+
+ Snowflake connection cursor. + |
+ + required + | +
default_database |
+
+ str
+ |
+
+
+
+ The default database to use. + |
+ + required + | +
sql |
+
+ str
+ |
+
+
+
+ The SQL query to execute. + |
+ + required + | +
Returns:
+| Type | +Description | +
|---|---|
+ pd.DataFrame
+ |
+
+
+
+ pd.DataFrame: The results of the SQL query. + |
+
list_orgs()
+
+orgs = vn.list_orgs()
+List the organizations that the user is a member of.
+ +Returns:
+| Type | +Description | +
|---|---|
+ List[str]
+ |
+
+
+
+ List[str]: A list of organization names. + |
+
login(email, otp_code=None)
+
+vn.login(email="username@example.com")
+Login to the Vanna.AI API.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
email |
+
+ str
+ |
+
+
+
+ The email address to login with. + |
+ + required + | +
otp_code |
+
+ Union[str, None]
+ |
+
+
+
+ The OTP code to login with. If None, an OTP code will be sent to the email address. + |
+
+ None
+ |
+
Returns:
+| Name | Type | +Description | +
|---|---|---|
bool |
+ bool
+ |
+
+
+
+ True if the login was successful, False otherwise. + |
+
remove_sql(question)
+
+vn.remove_sql(question="What is the average salary of employees?")
+Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
question |
+
+ str
+ |
+
+
+
+ The question to remove. + |
+ + required + | +
set_org(org)
+
+vn.set_org("my-org")
+Set the organization name for the Vanna.AI API.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
org |
+
+ str
+ |
+
+
+
+ The organization name. + |
+ + required + | +
store_sql(question, sql)
+
+vn.store_sql(
+ question="What is the average salary of employees?",
+ sql="SELECT AVG(salary) FROM employees"
+)
+Store a question and its corresponding SQL query in the Vanna.AI database.
+ +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
question |
+
+ str
+ |
+
+
+
+ The question to store. + |
+ + required + | +
sql |
+
+ str
+ |
+
+
+
+ The SQL query to store. + |
+ + required + | +
Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
\n\n\n\nflowchart TD\n DB[(Known Correct Question-SQL)]\n Try[Try to Use DDL/Documentation]\n SQL(SQL)\n Check{Is the SQL correct?}\n Generate[fa:fa-circle-question Use Examples to Generate]\n DB --> Find\n Question[fa:fa-circle-question Question] --> Find{fa:fa-magnifying-glass Do we have similar questions?}\n Find -- Yes --> Generate\n Find -- No --> Try\n Generate --> SQL\n Try --> SQL\n SQL --> Check\n Check -- Yes --> DB\n Check -- No --> Analyst[fa:fa-glasses Analyst Writes the SQL]\n Analyst -- Adds --> DB\n
import vanna as vn\nvn.api_key = 'vanna-key-...'\nvn.set_org('my_org')\nvn.store_sql(\n question="Who are the top 10 customers by Sales?", \n 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'\nimport vanna as vn\n\nvn.api_key = 'vanna-key-...' # Set your API key\nvn.set_org('') # Set your organization name\n\n# Train Vanna.AI on your data\nvn.store_sql(\n question="Who are the top 10 customers by Sales?", \n sql="SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10"\n)\n\n# Ask questions about your data\nmy_question = 'What are the top 10 ABC by XYZ?'\n\n# Generate SQL\nsql = vn.generate_sql(question=my_question, error_msg=None) \n\n# Connect to your database\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\n# Get results\ndf = vn.get_results(\n cs=cs, \n default_db=my_default_db, \n sql=sql\n )\n\n# Generate Plotly code\nplotly_code = vn.generate_plotly_code(\n question=my_question, \n sql=sql, \n df=df\n )\n\n# Get Plotly figure\nfig = vn.get_plotly_figure(\n plotly_code=plotly_code, \n df=df\n )\nSet the organization name for the Vanna.AI API.
\n\nStore a question and its corresponding SQL query in the Vanna.AI database.
\n\nFlag a question and its corresponding SQL query for review by the Vanna.AI team.
\n\n\n\n", "signature": "(\tquestion: str,\tsql: Optional[str] = None,\terror_msg: Optional[str] = None) -> bool:", "funcdef": "def"}, "vanna.remove_sql": {"fullname": "vanna.remove_sql", "modulename": "vanna", "qualname": "remove_sql", "kind": "function", "doc": "bool: True if the question and SQL query were flagged successfully, False otherwise.
\n
Remove a question and its corresponding SQL query from the Vanna.AI database.
\n\nGenerate an SQL query using the Vanna.AI API.
\n\n\n\n", "signature": "(question: str) -> str:", "funcdef": "def"}, "vanna.generate_plotly_code": {"fullname": "vanna.generate_plotly_code", "modulename": "vanna", "qualname": "generate_plotly_code", "kind": "function", "doc": "str or None: The SQL query, or None if an error occurred.
\n
Generate Plotly code using the Vanna.AI API.
\n\n\n\n", "signature": "(\tquestion: Optional[str],\tsql: Optional[str],\tdf: pandas.core.frame.DataFrame) -> str:", "funcdef": "def"}, "vanna.get_plotly_figure": {"fullname": "vanna.get_plotly_figure", "modulename": "vanna", "qualname": "get_plotly_figure", "kind": "function", "doc": "str or None: The Plotly code, or None if an error occurred.
\n
Get a Plotly figure from a dataframe and Plotly code.
\n\n\n\n", "signature": "(\tplotly_code: str,\tdf: pandas.core.frame.DataFrame,\tdark_mode: bool = True) -> plotly.graph_objs._figure.Figure:", "funcdef": "def"}, "vanna.get_results": {"fullname": "vanna.get_results", "modulename": "vanna", "qualname": "get_results", "kind": "function", "doc": "plotly.graph_objs.Figure: The Plotly figure.
\n
Run the SQL query and return the results as a pandas dataframe.
\n\n\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": "pd.DataFrame: The results of the SQL query.
\n
vn.generate_explanation(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'AI Response'\nGenerate an explanation of an SQL query using the Vanna.AI API.
\n\n\n\n", "signature": "(sql: str) -> str:", "funcdef": "def"}, "vanna.generate_question": {"fullname": "vanna.generate_question", "modulename": "vanna", "qualname": "generate_question", "kind": "function", "doc": "str or None: The explanation, or None if an error occurred.
\n
vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'AI Response'\nGenerate a question from an SQL query using the Vanna.AI API.
\n\n\n\n", "signature": "(sql: str) -> str:", "funcdef": "def"}, "vanna.get_flagged_questions": {"fullname": "vanna.get_flagged_questions", "modulename": "vanna", "qualname": "get_flagged_questions", "kind": "function", "doc": "str or None: The question, or None if an error occurred.
\n
vn.get_flagged_questions()\n# [FullQuestionDocument(...), ...]\nGet a list of flagged questions from the Vanna.AI API.
\n\n\n\n", "signature": "() -> vanna.types.QuestionList:", "funcdef": "def"}, "vanna.get_accuracy_stats": {"fullname": "vanna.get_accuracy_stats", "modulename": "vanna", "qualname": "get_accuracy_stats", "kind": "function", "doc": "List[FullQuestionDocument] or None: The list of flagged questions, or None if an error occurred.
\n
vn.get_accuracy_stats()\n# {'accuracy': 0.0, 'total': 0, 'correct': 0}\nGet the accuracy statistics from the Vanna.AI API.
\n\n\n\n", "signature": "() -> vanna.types.AccuracyStats:", "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.QuestionList": {"fullname": "vanna.types.QuestionList", "modulename": "vanna.types", "qualname": "QuestionList", "kind": "class", "doc": "\n"}, "vanna.types.QuestionList.__init__": {"fullname": "vanna.types.QuestionList.__init__", "modulename": "vanna.types", "qualname": "QuestionList.__init__", "kind": "function", "doc": "\n", "signature": "(questions: List[vanna.types.FullQuestionDocument])"}, "vanna.types.QuestionList.questions": {"fullname": "vanna.types.QuestionList.questions", "modulename": "vanna.types", "qualname": "QuestionList.questions", "kind": "variable", "doc": "\n", "annotation": ": List[vanna.types.FullQuestionDocument]"}, "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": 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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/search/search_index.json b/search/search_index.json new file mode 100644 index 000000000..eacb5bade --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"What is Vanna.AI?","text":""},{"location":"#what-is-vannaai","title":"What is Vanna.AI?","text":"dict or None: The accuracy statistics, or None if an error occurred.
\n
Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
Each organization has its own isolated training set. This means that Vanna.AI can understand the unique language of your organization and answer questions about your data.
After every question you can tell Vanna.AI whether the results were correct. This allows Vanna.AI to learn from the questions that are asked and become smarter immediately.
Vanna provides additional functionality to manage your training data to maintain the highest level of accuracy for your organization.
"},{"location":"#what-can-it-do","title":"What can it do?","text":"It can support apps that allow you to:
"},{"location":"#where-can-i-use-vannaai","title":"Where can I use Vanna.AI?","text":"/askvanna [question]pip install vanna\nimport vanna as vn\nvn.api_key = 'vanna-key-...'\nvn.set_org
vn.set_org('my_org')\nvn.store_sql
vn.store_sql(\n question=\"Who are the top 10 customers by Sales?\", \n sql=\"SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10\"\n)\nvn.generate_sql
my_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'\nimport vanna as vn\n\nvn.api_key = 'vanna-key-...' # Set your API key\nvn.set_org('') # Set your organization name\n\n# Train Vanna.AI on your data\nvn.store_sql(\n question=\"Who are the top 10 customers by Sales?\", \n sql=\"SELECT customer_name, sales FROM customers ORDER BY sales DESC LIMIT 10\"\n)\n\n# Ask questions about your data\nmy_question = 'What are the top 10 ABC by XYZ?'\n\n# Generate SQL\nsql = vn.generate_sql(question=my_question, error_msg=None) \n\n# Connect to your database\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\n# Get results\ndf = vn.get_results(\n cs=cs, \n default_db=my_default_db, \n sql=sql\n )\n\n# Generate Plotly code\nplotly_code = vn.generate_plotly_code(\n question=my_question, \n sql=sql, \n df=df\n )\n\n# Get Plotly figure\nfig = vn.get_plotly_figure(\n plotly_code=plotly_code, \n df=df\n )\nVanna.AI can be used in a Jupyter Notebook to generate SQL from natural language questions.
"},{"location":"jupyter/#installation","title":"Installation","text":"%pip install vanna\nimport vanna as vn\nvn.api_key = 'vanna-key-...'\nvn.set_org('my_org')\nvn.store_sql(\n question=\"Who are the top 10 customers by Sales?\", \n 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\n# Generate SQL\nsql = vn.generate_sql(question=my_question, error_msg=None)\n# SELECT * FROM table_name WHERE column_name = 'value'\nRun your SQL as you normally would.
"},{"location":"onboarding/","title":"Onboarding","text":""},{"location":"onboarding/#what-do-i-need-to-do-to-use-vannaai","title":"What do I need to do to use Vanna.AI?","text":"Vanna.AI uses a combination of documentation and historical question and SQL pairs to generate SQL from natural language.
"},{"location":"onboarding/#step-1-train-vannaai","title":"Step 1: Train Vanna.AI","text":"flowchart LR\n Generate[vn.generate_question]\n Question[Question]\n Verify{Is the question correct?}\n SQL --> Generate\n Generate --> Question\n Question --> Verify\n Verify -- Yes --> Store[vn.store_sql]\n Verify -- No --> Update[Update the Question]\n Update --> Store\nflowchart LR\n Question[Question]\n Generate[vn.generate_sql]\n SQL[SQL]\n Question --> Generate\n Generate --> SQL Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
"},{"location":"reference/#vanna--api-reference","title":"API Reference","text":""},{"location":"reference/#vanna.flag_sql_for_review","title":"flag_sql_for_review(question, sql=None, error_msg=None)","text":""},{"location":"reference/#vanna.flag_sql_for_review--example","title":"Example","text":"vn.flag_sql_for_review(question=\"What is the average salary of employees?\")\nget_flagged_questions(). Parameters:
Name Type Description Defaultquestion str The question to flag.
requiredsql str The SQL query to flag.
None error_msg str The error message to flag.
None Returns:
Name Type Descriptionbool bool True if the question and SQL query were flagged successfully, False otherwise.
"},{"location":"reference/#vanna.generate_explanation","title":"generate_explanation(sql)","text":""},{"location":"reference/#vanna.generate_explanation--example","title":"Example","text":"vn.generate_explanation(sql=\"SELECT * FROM students WHERE name = 'John Doe'\")\n# 'This query selects all columns from the students table where the name is John Doe.'\nGenerate an explanation of an SQL query using the Vanna.AI API.
Parameters:
Name Type Description Defaultsql str The SQL query to generate an explanation for.
requiredReturns:
Type Descriptionstr str or None: The explanation, or None if an error occurred.
"},{"location":"reference/#vanna.generate_plotly_code","title":"generate_plotly_code(question, sql, df)","text":""},{"location":"reference/#vanna.generate_plotly_code--example","title":"Example","text":"vn.generate_plotly_code(\n question=\"What is the average salary of employees?\",\n sql=\"SELECT AVG(salary) FROM employees\",\n df=df\n)\n# fig = px.bar(df, x=\"name\", y=\"salary\")\nParameters:
Name Type Description Defaultquestion str The question to generate Plotly code for.
requiredsql str The SQL query to generate Plotly code for.
requireddf pd.DataFrame The dataframe to generate Plotly code for.
requiredReturns:
Type Descriptionstr str or None: The Plotly code, or None if an error occurred.
"},{"location":"reference/#vanna.generate_question","title":"generate_question(sql)","text":""},{"location":"reference/#vanna.generate_question--example","title":"Example","text":"vn.generate_question(sql=\"SELECT * FROM students WHERE name = 'John Doe'\")\n# 'What is the name of the student?'\nGenerate a question from an SQL query using the Vanna.AI API.
Parameters:
Name Type Description Defaultsql str The SQL query to generate a question for.
requiredReturns:
Type Descriptionstr str or None: The question, or None if an error occurred.
"},{"location":"reference/#vanna.generate_sql","title":"generate_sql(question)","text":""},{"location":"reference/#vanna.generate_sql--example","title":"Example","text":"vn.generate_sql(question=\"What is the average salary of employees?\")\n# SELECT AVG(salary) FROM employees\nGenerate an SQL query using the Vanna.AI API.
Parameters:
Name Type Description Defaultquestion str The question to generate an SQL query for.
requiredReturns:
Type Descriptionstr str or None: The SQL query, or None if an error occurred.
"},{"location":"reference/#vanna.get_accuracy_stats","title":"get_accuracy_stats()","text":""},{"location":"reference/#vanna.get_accuracy_stats--example","title":"Example","text":"vn.get_accuracy_stats()\nGet the accuracy statistics from the Vanna.AI API.
Returns:
Type DescriptionAccuracyStats dict or None: The accuracy statistics, or None if an error occurred.
"},{"location":"reference/#vanna.get_flagged_questions","title":"get_flagged_questions()","text":""},{"location":"reference/#vanna.get_flagged_questions--example","title":"Example","text":"questions = vn.get_flagged_questions()\nGet a list of flagged questions from the Vanna.AI API.
Returns:
Type DescriptionQuestionList List[FullQuestionDocument] or None: The list of flagged questions, or None if an error occurred.
"},{"location":"reference/#vanna.get_plotly_figure","title":"get_plotly_figure(plotly_code, df, dark_mode=True)","text":""},{"location":"reference/#vanna.get_plotly_figure--example","title":"Example","text":"fig = vn.get_plotly_figure(\n plotly_code=\"fig = px.bar(df, x='name', y='salary')\",\n df=df\n)\nfig.show()\nParameters:
Name Type Description Defaultdf pd.DataFrame The dataframe to use.
requiredplotly_code str The Plotly code to use.
requiredReturns:
Type Descriptionplotly.graph_objs.Figure plotly.graph_objs.Figure: The Plotly figure.
"},{"location":"reference/#vanna.get_results","title":"get_results(cs, default_database, sql)","text":""},{"location":"reference/#vanna.get_results--example","title":"Example","text":"df = vn.get_results(cs=cs, default_database=\"PUBLIC\", sql=\"SELECT * FROM students\")\nParameters:
Name Type Description Defaultcs Snowflake connection cursor.
requireddefault_database str The default database to use.
requiredsql str The SQL query to execute.
requiredReturns:
Type Descriptionpd.DataFrame pd.DataFrame: The results of the SQL query.
"},{"location":"reference/#vanna.list_orgs","title":"list_orgs()","text":""},{"location":"reference/#vanna.list_orgs--example","title":"Example","text":"orgs = vn.list_orgs()\nList the organizations that the user is a member of.
Returns:
Type DescriptionList[str] List[str]: A list of organization names.
"},{"location":"reference/#vanna.login","title":"login(email, otp_code=None)","text":""},{"location":"reference/#vanna.login--example","title":"Example","text":"vn.login(email=\"username@example.com\")\nLogin to the Vanna.AI API.
Parameters:
Name Type Description Defaultemail str The email address to login with.
requiredotp_code Union[str, None] The OTP code to login with. If None, an OTP code will be sent to the email address.
None Returns:
Name Type Descriptionbool bool True if the login was successful, False otherwise.
"},{"location":"reference/#vanna.remove_sql","title":"remove_sql(question)","text":""},{"location":"reference/#vanna.remove_sql--example","title":"Example","text":"vn.remove_sql(question=\"What is the average salary of employees?\")\nParameters:
Name Type Description Defaultquestion str The question to remove.
required"},{"location":"reference/#vanna.set_org","title":"set_org(org)","text":""},{"location":"reference/#vanna.set_org--example","title":"Example","text":"vn.set_org(\"my-org\")\nSet the organization name for the Vanna.AI API.
Parameters:
Name Type Description Defaultorg str The organization name.
required"},{"location":"reference/#vanna.store_sql","title":"store_sql(question, sql)","text":""},{"location":"reference/#vanna.store_sql--example","title":"Example","text":"vn.store_sql(\n question=\"What is the average salary of employees?\", \n sql=\"SELECT AVG(salary) FROM employees\"\n)\nStore a question and its corresponding SQL query in the Vanna.AI database.
Parameters:
Name Type Description Defaultquestion str The question to store.
requiredsql str The SQL query to store.
required"},{"location":"slack/","title":"Vanna.AI Slack App","text":"Coming Soon
"},{"location":"streamlit/","title":"Use Vanna.AI with Streamlit","text":""},{"location":"streamlit/#app","title":"App","text":""},{"location":"streamlit/#code","title":"Code","text":"https://github.com/vanna-ai/vanna-streamlit
"},{"location":"support/","title":"Getting Support","text":"E-mail us at support@vanna.ai
Join our Slack
"},{"location":"vanna-py-overview/","title":"Vanna py overview","text":""},{"location":"vanna-py-overview/#vannaai","title":"Vanna.AI","text":""},{"location":"vanna-py-overview/#python-package","title":"Python Package","text":"For Natural Language to SQL (and associated functionality)
Full Documentation Reference
Slack
support@vanna.ai
"},{"location":"vanna-py-overview/#what-can-you-do-with-vannaai","title":"What can you do with Vanna.AI?","text":"Vanna.AI has a Python package that allows you to convert natural language to SQL.
import vanna as vn\n\nvn.api_key = 'vanna-key-...' # Set your API key\nvn.set_org('') # Set your organization name\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\n(my_df, error_msg) = vn.run_sql(cs: snowflake.Cursor, sql=sql)\n\nvn.generate_plotly_code(question=my_question, df=my_df)\n# fig = px.bar(df, x='column_name', y='column_name')\n\nvn.run_plotly_code(plotly_code=fig, df=my_df)\npip install vanna\npip3 install vanna\npython3 -m venv venv\nsource venv/bin/activate\npip install vanna\nflowchart TD\n DB[(Known Correct Question-SQL)]\n Try[Try to Use DDL/Documentation]\n SQL(SQL)\n Check{Is the SQL correct?}\n Generate[fa:fa-circle-question Use Examples to Generate]\n DB --> Find\n Question[fa:fa-circle-question Question] --> Find{fa:fa-magnifying-glass Do we have similar questions?}\n Find -- Yes --> Generate\n Find -- No --> Try\n Generate --> SQL\n Try --> SQL\n SQL --> Check\n Check -- Yes --> DB\n Check -- No --> Analyst[fa:fa-glasses Analyst Writes the SQL]\n Analyst -- Adds --> DBComing Soon
+ + + + + + ++
+E-mail us at support@vanna.ai
+ + + + + + + +
For Natural Language to SQL +(and associated functionality)
+ + +support@vanna.ai
+Vanna.AI has a Python package that allows you to convert natural language to SQL.
+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)
+pip install vanna
+pip3 install vanna
+python3 -m venv venv
+source venv/bin/activate
+pip install vanna
+Vanna.AI is a platform that allows you to ask questions about your data in plain English. It is an AI-powered data analyst that can answer questions about your data, generate SQL, and create visualizations.
+ ++ +flowchart TD + DB[(Known Correct Question-SQL)] + Try[Try to Use DDL/Documentation] + SQL(SQL) + Check{Is the SQL correct?} + Generate[fa:fa-circle-question Use Examples to Generate] + DB --> Find + Question[fa:fa-circle-question Question] --> Find{fa:fa-magnifying-glass Do we have similar questions?} + Find -- Yes --> Generate + Find -- No --> Try + Generate --> SQL + Try --> SQL + SQL --> Check + Check -- Yes --> DB + Check -- No --> Analyst[fa:fa-glasses Analyst Writes the SQL] + Analyst -- Adds --> DB +
import vanna as vn
+vn.api_key = 'vanna-key-...'
+vn.set_org('my_org')
+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'
+import vanna as vn
+
+vn.api_key = 'vanna-key-...' # Set your API key
+vn.set_org('') # Set your organization name
+
+# Train Vanna.AI on your data
+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"
+)
+
+# Ask questions about your data
+my_question = 'What are the top 10 ABC by XYZ?'
+
+# Generate SQL
+sql = vn.generate_sql(question=my_question, error_msg=None)
+
+# Connect to your database
+conn = snowflake.connector.connect(
+ user='my_user',
+ password='my_password',
+ account='my_account',
+ database='my_database',
+ )
+
+cs = conn.cursor()
+
+# Get results
+df = vn.get_results(
+ cs=cs,
+ default_db=my_default_db,
+ sql=sql
+ )
+
+# Generate Plotly code
+plotly_code = vn.generate_plotly_code(
+ question=my_question,
+ sql=sql,
+ df=df
+ )
+
+# Get Plotly figure
+fig = vn.get_plotly_figure(
+ plotly_code=plotly_code,
+ df=df
+ )
+Set the organization name for the Vanna.AI API.
+ +Store a question and its corresponding SQL query in the Vanna.AI database.
+ +Flag a question and its corresponding SQL query for review by the Vanna.AI team.
+ +++bool: True if the question and SQL query were flagged successfully, False otherwise.
+
Remove a question and its corresponding SQL query from the Vanna.AI database.
+ +Generate an SQL query using the Vanna.AI API.
+ +++str or None: The SQL query, or None if an error occurred.
+
Generate Plotly code using the Vanna.AI API.
+ +++str or None: The Plotly code, or None if an error occurred.
+
Get a Plotly figure from a dataframe and Plotly code.
+ +++plotly.graph_objs.Figure: The Plotly figure.
+
Run the SQL query and return the results as a pandas dataframe.
+ +++pd.DataFrame: The results of the SQL query.
+
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.
+ +++str or None: The explanation, or None if an error occurred.
+
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.
+ +++str or None: The question, or None if an error occurred.
+
vn.get_flagged_questions()
+# [FullQuestionDocument(...), ...]
+Get a list of flagged questions from the Vanna.AI API.
+ +++List[FullQuestionDocument] or None: The list of flagged questions, or None if an error occurred.
+
vn.get_accuracy_stats()
+# {'accuracy': 0.0, 'total': 0, 'correct': 0}
+Get the accuracy statistics from the Vanna.AI API.
+ +++dict or None: The accuracy statistics, or None if an error occurred.
+
flowchart TD
+ DB[(Known Correct Question-SQL)]
+ Try[Try to Use DDL/Documentation]
+ SQL(SQL)
+ Check{Is the SQL correct?}
+ Generate[fa:fa-circle-question Use Examples to Generate]
+ DB --> Find
+ Question[fa:fa-circle-question Question] --> Find{fa:fa-magnifying-glass Do we have similar questions?}
+ Find -- Yes --> Generate
+ Find -- No --> Try
+ Generate --> SQL
+ Try --> SQL
+ SQL --> Check
+ Check -- Yes --> DB
+ Check -- No --> Analyst[fa:fa-glasses Analyst Writes the SQL]
+ Analyst -- Adds --> DB