From 4d3b25fc13bc28a05fb87976f275da5abc3f994f Mon Sep 17 00:00:00 2001 From: <> Date: Thu, 31 Aug 2023 04:08:16 +0000 Subject: [PATCH] Update documentation --- .nojekyll | 0 CNAME | 1 + databases.html | 7685 ++++++++++++++++++++++++++++++ databases.md | 142 + getting-started.html | 7989 +++++++++++++++++++++++++++++++ index.html | 7511 +++++++++++++++++++++++++++++ index.md | 239 + intro-to-vanna.md | 64 + local.html | 7871 +++++++++++++++++++++++++++++++ manual-train.html | 7840 +++++++++++++++++++++++++++++++ onboarding.md | 30 + reference.md | 4 + search.js | 46 + sidebar.py | 130 + sidebar.yaml | 77 + slack.html | 7492 +++++++++++++++++++++++++++++ streamlit.html | 7481 +++++++++++++++++++++++++++++ streamlit.md | 13 + support.md | 5 + vanna.html | 1894 ++++++++ vanna/base.html | 901 ++++ vanna/chromadb_vector.html | 480 ++ vanna/exceptions.html | 518 ++ vanna/local.html | 332 ++ vanna/mock.html | 341 ++ vanna/openai_chat.html | 415 ++ vanna/openai_embeddings.html | 319 ++ vanna/types.html | 2601 ++++++++++ vanna/utils.html | 286 ++ vn-ask.html | 8030 +++++++++++++++++++++++++++++++ vn-connect-to-bigquery.html | 7916 +++++++++++++++++++++++++++++++ vn-connect-to-postgres.html | 8585 ++++++++++++++++++++++++++++++++++ vn-train.html | 7848 +++++++++++++++++++++++++++++++ workflow.md | 19 + 34 files changed, 95105 insertions(+) create mode 100644 .nojekyll create mode 100644 CNAME create mode 100644 databases.html create mode 100644 databases.md create mode 100644 getting-started.html create mode 100644 index.html create mode 100644 index.md create mode 100644 intro-to-vanna.md create mode 100644 local.html create mode 100644 manual-train.html create mode 100644 onboarding.md create mode 100644 reference.md create mode 100644 search.js create mode 100644 sidebar.py create mode 100644 sidebar.yaml create mode 100644 slack.html create mode 100644 streamlit.html create mode 100644 streamlit.md create mode 100644 support.md create mode 100644 vanna.html create mode 100644 vanna/base.html create mode 100644 vanna/chromadb_vector.html create mode 100644 vanna/exceptions.html create mode 100644 vanna/local.html create mode 100644 vanna/mock.html create mode 100644 vanna/openai_chat.html create mode 100644 vanna/openai_embeddings.html create mode 100644 vanna/types.html create mode 100644 vanna/utils.html create mode 100644 vn-ask.html create mode 100644 vn-connect-to-bigquery.html create mode 100644 vn-connect-to-postgres.html create mode 100644 vn-train.html create mode 100644 workflow.md diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 000000000..e69de29bb diff --git a/CNAME b/CNAME new file mode 100644 index 000000000..ad1c2a237 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +docs.vanna.ai \ No newline at end of file diff --git a/databases.html b/databases.html new file mode 100644 index 000000000..16fcee1cf --- /dev/null +++ b/databases.html @@ -0,0 +1,7685 @@ + + + + + + + + + + + + + +Vanna Docs: Databases + + + + + + + + + + + + + + + + + + + + +
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+ + diff --git a/databases.md b/databases.md new file mode 100644 index 000000000..6924d34c2 --- /dev/null +++ b/databases.md @@ -0,0 +1,142 @@ +# How to use Vanna with various databases + +You can use Vanna with any database that you can connect to via Python. Here are some examples of how to connect to various databases. + +All you have to do is provide Vanna with a function that takes in a SQL query and returns a Pandas DataFrame. Here are some examples of how to do that. + +## **PostgreSQL** + +```python +import pandas as pd +import psycopg2 + +conn_details = {...} # fill this with your connection details +conn_postgres = psycopg2.connect(**conn_details) + +def run_sql_postgres(sql: str) -> pd.DataFrame: + df = pd.read_sql_query(sql, conn_postgres) + return df + +vn.run_sql = run_sql_postgres +``` + +## **Snowflake** + +We have a built-in function for Snowflake, so you don't need to write your own. + +```python +vn.connect_to_snowflake(account='my-account', username='my-username', password='my-password', database='my-database') +``` + +```python +import pandas as pd +from snowflake.connector.pandas_tools import pd_read_sql +from snowflake.connector import connect + +conn_details = {...} # fill this with your connection details +conn_snowflake = connect(**conn_details) + +def run_sql_snowflake(sql: str) -> pd.DataFrame: + df = pd_read_sql(sql, conn_snowflake) + return df + +vn.run_sql = run_sql_snowflake +``` + +## **Google BigQuery** + +```python +from google.cloud import bigquery +import pandas as pd + +project_id = 'your-project-id' # replace with your Project ID +client_bigquery = bigquery.Client(project=project_id) + +def run_sql_bigquery(sql: str) -> pd.DataFrame: + df = client_bigquery.query(sql).to_dataframe() + return df + +vn.run_sql = run_sql_bigquery +``` + +## **Amazon Athena** + +```python +import pandas as pd +from pyathena import connect + +conn_details = {...} # fill this with your connection details +conn_athena = connect(**conn_details) + +def run_sql_athena(sql: str) -> pd.DataFrame: + df = pd.read_sql(sql, conn_athena) + return df + +vn.run_sql = run_sql_athena +``` + +## **Amazon Redshift** + +```python +import pandas as pd +import psycopg2 + +conn_details = {...} # fill this with your connection details +conn_redshift = psycopg2.connect(**conn_details) + +def run_sql_redshift(sql: str) -> pd.DataFrame: + df = pd.read_sql_query(sql, conn_redshift) + return df + +vn.run_sql = run_sql_redshift +``` + +Sure, here is an example for Google Cloud SQL using the MySQL connector: + +## **Google Cloud SQL (MySQL)** + +```python +import pandas as pd +import mysql.connector + +conn_details = {...} # fill this with your connection details +conn_google_cloud_sql = mysql.connector.connect(**conn_details) + +def run_sql_google_cloud_sql(sql: str) -> pd.DataFrame: + df = pd.read_sql(sql, conn_google_cloud_sql) + return df +``` + +Note: Google Cloud SQL supports MySQL, PostgreSQL, and SQL Server. The above example uses MySQL. If you are using PostgreSQL or SQL Server, you should use the appropriate connector. + +## **SQLite** + +```python +import sqlite3 +import pandas as pd + +db_path = 'path_to_your_db' # replace with your SQLite DB path +conn_sqlite = sqlite3.connect(db_path) + +def run_sql_sqlite(sql: str) -> pd.DataFrame: + df = pd.read_sql_query(sql, conn_sqlite) + return df + +vn.run_sql = run_sql_sqlite +``` + +## **Microsoft SQL Server** + +```python +import pandas as pd +import pyodbc + +conn_details = {...} # fill this with your connection details +conn_sql_server = pyodbc.connect(**conn_details) + +def run_sql_sql_server(sql: str) -> pd.DataFrame: + df = pd.read_sql(sql, conn_sql_server) + return df + +vn.run_sql = run_sql_sql_server +``` diff --git a/getting-started.html b/getting-started.html new file mode 100644 index 000000000..bf2a27348 --- /dev/null +++ b/getting-started.html @@ -0,0 +1,7989 @@ + + + + + + + + + + + + + +Vanna Docs: Getting Started + + + + + + + + + + + + + + + + + + + + +
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+ + diff --git a/index.md b/index.md new file mode 100644 index 000000000..02d2fc636 --- /dev/null +++ b/index.md @@ -0,0 +1,239 @@ +# Vanna.AI - Personalized AI SQL Agent + +**Let Vanna.AI write your nasty SQL for you**. Vanna is a Python based AI SQL agent trained on your schema that writes complex SQL in seconds. `pip install vanna` to get started now. + + + +## An example + +A business user asks you **"who are the top 2 customers in each region?"**. Right in the middle of lunch. And they need it for a presentation this afternoon. 😡😡😡 + +### The old way 😡 😫 💩 +Simple question to ask, not so fun to answer. You spend over an hour a) finding the tables, b) figuring out out the joins, c) look up the syntax for ranking, d) putting this into a CTE, e) filtering by rank, and f) choosing the correct metrics. Finally, you come up with this ugly mess - + +```sql +with ranked_customers as (SELECT c.c_name as customer_name, + r.r_name as region_name, + row_number() OVER (PARTITION BY r.r_name + ORDER BY sum(l.l_quantity * l.l_extendedprice) desc) as rank + FROM snowflake_sample_data.tpch_sf1.customer c join snowflake_sample_data.tpch_sf1.orders o + ON c.c_custkey = o.o_custkey join snowflake_sample_data.tpch_sf1.lineitem l + ON o.o_orderkey = l.l_orderkey join snowflake_sample_data.tpch_sf1.nation n + ON c.c_nationkey = n.n_nationkey join snowflake_sample_data.tpch_sf1.region r + ON n.n_regionkey = r.r_regionkey + GROUP BY customer_name, region_name) +SELECT region_name, + customer_name +FROM ranked_customers +WHERE rank <= 2; +``` + +And you had to skip your lunch. **HANGRY!** + +### The Vanna way 😍 🌟 🚀 +With Vanna, you train up a custom model on your data warehouse, and simply enter this in your Jupyter Notebook - + +```python +import vanna as vn +vn.set_model('your-model') +vn.ask('who are the top 2 customers in each region?') +``` + +Vanna generates that nasty SQL above for you, runs it (locally & securely) and gives you back a Dataframe in seconds: + +| region_name | customer_name | total_sales | +| ----------- | ------------- | ----------- | +| ASIA | Customer#000000001 | 68127.72 | +| ASIA | Customer#000000002 | 65898.69 | +... + +And you ate your lunch in peace. **YUMMY!** + +## How Vanna works +Vanna works in two easy steps - train a model on your data, and then ask questions. + +1. **Train a model on your data**. +2. **Ask questions**. + +When you ask a question, we utilize a custom model for your dataset to generate SQL, as seen below. Your model performance and accuracy depends on the quality and quantity of training data you use to train your model. +how-vanna-works + + + +## Why Vanna? + +1. **High accuracy on complex datasets.** + - Vanna’s capabilities are tied to the training data you give it + - More training data means better accuracy for large and complex datasets +2. **Secure and private.** + - Your database contents are never sent to Vanna’s servers + - We only see the bare minimum - schemas & queries. +3. **Isolated, custom model.** + - You train a custom model specific to your database and your schema. + - Nobody else can use your model or view your model’s training data unless you choose to add members to your model or make it public + - We use a combination of third-party foundational models (OpenAI, Google) and our own LLM. +4. **Self learning.** + - As you use Vanna more, your model continuously improves as we augment your training data +5. **Supports many databases.** + - We have out-of-the-box support Snowflake, BigQuery, Postgres + - You can easily make a connector for any [database](https://docs.vanna.ai/databases/) +6. **Pretrained models.** + - If you’re a data provider you can publish your models for anyone to use + - As part of our roadmap, we are in the process of pre-training models for common datasets (Google Ads, Facebook ads, etc) +7. **Choose your front end.** + - Start in a Jupyter Notebook. + - Expose to business users via Slackbot, web app, Streamlit app, or Excel plugin. + - Even integrate in your web app for customers. + +## Getting started +You can start by [automatically training Vanna (currently works for Snowflake)](https://docs.vanna.ai/notebooks/vn-train/) or add manual training data. + +### Train with DDL Statements +If you prefer to manually train, you do not need to connect to a database. You can use the train function with other parmaeters like ddl + + +```python +vn.train(ddl=""" + CREATE TABLE IF NOT EXISTS my-table ( + id INT PRIMARY KEY, + name VARCHAR(100), + age INT + ) +""") +``` + +### Train with Documentation +Sometimes you may want to add documentation about your business terminology or definitions. + +```python +vn.train(documentation="Our business defines OTIF score as the percentage of orders that are delivered on time and in full") +``` + +### Train with SQL +You can also add SQL queries to your training data. This is useful if you have some queries already laying around. You can just copy and paste those from your editor to begin generating new SQL. + +```python +vn.train(sql="SELECT * FROM my-table WHERE name = 'John Doe'") +``` + + + +## Asking questions +```python +vn.ask("What are the top 10 customers by sales?") +``` + + SELECT c.c_name as customer_name, + sum(l.l_extendedprice * (1 - l.l_discount)) as total_sales + FROM snowflake_sample_data.tpch_sf1.lineitem l join snowflake_sample_data.tpch_sf1.orders o + ON l.l_orderkey = o.o_orderkey join snowflake_sample_data.tpch_sf1.customer c + ON o.o_custkey = c.c_custkey + GROUP BY customer_name + ORDER BY total_sales desc limit 10; + + + +
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6Customer#0000696826057779.0348
7Customer#0001020226039653.6335
8Customer#0000985876027021.5855
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+ + + + +![png](notebooks/vn-ask_files/vn-ask_10_2.png) + + + + +AI-generated follow-up questions: + +* What is the country name for each of the top 10 customers by sales? +* How many orders does each of the top 10 customers by sales have? +* What is the total revenue for each of the top 10 customers by sales? +* What are the customer names and total sales for customers in the United States? +* Which customers in Africa have returned the most parts with a gross value? +* What are the total sales for the top 3 customers? +* What are the customer names and total sales for the top 5 customers? +* What are the total sales for customers in Europe? +* How many customers are there in each country? + +## More resources + - [Full Documentation](https://docs.vanna.ai) + - [Website](https://vanna.ai) + - [Slack channel for support](https://join.slack.com/t/vanna-ai/shared_invite/zt-1unu0ipog-iE33QCoimQiBDxf2o7h97w) + - [LinkedIn](https://www.linkedin.com/company/vanna-ai/) diff --git a/intro-to-vanna.md b/intro-to-vanna.md new file mode 100644 index 000000000..d935082e0 --- /dev/null +++ b/intro-to-vanna.md @@ -0,0 +1,64 @@ +# Intro to Vanna: A Python-based AI SQL co-pilot + +**TLDR**: We help data people that know Python write SQL faster using AI. [See our starter notebook here](notebooks/vn-ask.md). + +## The deluge of data + +We are bathing in an ocean of data, sitting in Snowflake or BigQuery, that is brimming with potential insights. Yet only a small fraction of people in an enterprise have the two skills required to harness the data — + +1. A solid comprehension of advanced SQL, and +2. A comprehensive knowledge of the data structure & schema + +## The burden of being data-savvy + +Since you are reading this, chances are you are one of those fortunate few (data analysts, data scientists, data engineers, etc) with those abilities. It’s an invaluable skill, but you also get hit tons requests requiring you to write complex SQL queries. Annoying! + +## Introducing Vanna, the SQL co-pilot + +Vanna, at its core, is a co-pilot to Python & SQL savvy data people to to streamline the process of writing custom SQL on your company’s data warehouse using AI and LLMs. Most of our users use our Python package directly via Jupyter Notebooks ([starter notebook here](notebooks/vn-ask.md)) — + +```python +sql = vn.generate_sql(question='What are the top 10 customers by Sales?') +print(sql) +``` + +And here are the results — + +```sql +SELECT customer_name, + total_sales +FROM (SELECT c.c_name as customer_name, + sum(l.l_extendedprice * (1 - l.l_discount)) as total_sales, + row_number() OVER (ORDER BY sum(l.l_extendedprice * (1 - l.l_discount)) desc) as rank + FROM snowflake_sample_data.tpch_sf1.lineitem l join snowflake_sample_data.tpch_sf1.orders o + ON l.l_orderkey = o.o_orderkey join snowflake_sample_data.tpch_sf1.customer c + ON o.o_custkey = c.c_custkey + GROUP BY customer_name) +WHERE rank <= 10; +``` + +## Getting started with Vanna in a Notebook + +Vanna is super easy to get started with — + +1. **Grab an API key** directly through the notebook +2. **Train a custom model** on some past queries from your data warehouse +3. **Ask questions in plain English** and get back SQL that you can run in your workflow + +Check out the full starter notebook here. + +Vanna is built with a privacy-first and security-first design — **your data never leaves your environment**. + +## Using Vanna with a Streamlit front end + +[Streamlit](https://streamlit.io/) is an open source pure Python front end. We have built an UI for Vanna on top of Streamlit, that you can either use directly (eg our hosted version), and that you can clone, download, optionally modify, and self host. + +If you choose to self host it, you can run Vanna with a UI without any data leaving your environment. + +![Image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*PmScp647UWIaxUatib_4SQ.png) + +[Check out the Streamlit UI here](https://github.com/vanna-ai/vanna-streamlit). + +## Conclusion + +Vanna is a powerful tool for data people that know Python to write SQL faster using AI. It's easy to get started with, and you can even use it with a Streamlit front end for a more interactive experience. Best of all, it's built with a privacy-first and security-first design, so your data never leaves your environment. Give it a try and see how it can streamline your SQL writing process. \ No newline at end of file diff --git a/local.html b/local.html new file mode 100644 index 000000000..a42fe437f --- /dev/null +++ b/local.html @@ -0,0 +1,7871 @@ + + + + + + + + + + + + + +Vanna Docs: Running Locally + + + + + + + + + + + + + + + + + + + + +
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+ + diff --git a/onboarding.md b/onboarding.md new file mode 100644 index 000000000..8f15aa78b --- /dev/null +++ b/onboarding.md @@ -0,0 +1,30 @@ +## What do I need to do to use **Vanna.AI**? +Vanna.AI uses a combination of documentation and historical question and SQL pairs to generate SQL from natural language. + +### Step 1: Train **Vanna.AI** +- Give **Vanna.AI** sample SQL +- **Vanna.AI** will try to guess the question +- Verify the question is correct +```mermaid +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 + +``` + +### Step 2: Ask **Vanna.AI** a Question +```mermaid +flowchart LR + Question[Question] + Generate[vn.generate_sql] + SQL[SQL] + Question --> Generate + Generate --> SQL +``` diff --git a/reference.md b/reference.md new file mode 100644 index 000000000..fc4459af9 --- /dev/null +++ b/reference.md @@ -0,0 +1,4 @@ +# Vanna Package Full Reference +::: vanna + options: + show_source: false \ No newline at end of file diff --git a/search.js b/search.js new file mode 100644 index 000000000..b7b644047 --- /dev/null +++ b/search.js @@ -0,0 +1,46 @@ +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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importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return 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();oBasic Usage\n\n

Getting an API key

\n\n
\n
import vanna as vn\napi_key = vn.get_api_key('my-email@example.com')\nvn.set_api_key(api_key)\n
\n
\n\n

Setting the model

\n\n
\n
vn.set_model('demo-tpc-h')\n
\n
\n\n

Asking a question

\n\n
\n
sql, df, fig, followup_questions = vn.ask(question='What are the top 10 customers by sales?')\n
\n
\n\n

For a more comprehensive starting guide see the Starter Notebook.

\n\n

Nomenclature

\n\n\n\n\n \n \n \n\n\n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n
PrefixDefinitionExamples
vn.set_Sets the variable for the current session[vn.set_model(...)][vanna.set_model]
[vn.set_api_key(...)][vanna.set_api_key]
vn.get_Performs a read-only operation[vn.get_model()][vanna.get_models]
vn.add_Adds something to the model[vn.add_sql(...)][vanna.add_sql]
[vn.add_ddl(...)][vanna.add_ddl]
vn.generate_Generates something using AI based on the information in the model[vn.generate_sql(...)][vanna.generate_sql]
[vn.generate_explanation()][vanna.generate_explanation]
vn.run_Runs code (SQL or Plotly)[vn.run_sql][vanna.run_sql]
vn.remove_Removes something from the model[vn.remove_training_data][vanna.remove_training_data]
vn.update_Updates something in the model[vn.update_model_visibility(...)][vanna.update_model_visibility]
vn.connect_Connects to a database[vn.connect_to_snowflake(...)][vanna.connect_to_snowflake]
\n\n

Permissions

\n\n

By default when you create a model it is private. You can add members or admins to your model or make it public.

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
User RolePublic ModelPrivate Model
UseTrainUseTrain
Non-Member\u2705\u274c\u274c\u274c
Member\u2705\u274c\u2705\u274c
Admin\u2705\u2705\u2705\u2705
\n\n

API Reference

\n"}, "vanna.api_key": {"fullname": "vanna.api_key", "modulename": "vanna", "qualname": "api_key", "kind": "variable", "doc": "

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

Example

\n\n
\n
vn.run_sql = lambda sql: pd.read_sql(sql, engine)\n
\n
\n\n

Set the SQL to DataFrame function for Vanna.AI. This is used in the [vn.ask(...)][vanna.ask] function.\nInstead of setting this directly you can also use [vn.connect_to_snowflake(...)][vanna.connect_to_snowflake] to set this.

\n", "annotation": ": Optional[Callable[[str], pandas.core.frame.DataFrame]]", "default_value": "None"}, "vanna.get_api_key": {"fullname": "vanna.get_api_key", "modulename": "vanna", "qualname": "get_api_key", "kind": "function", "doc": "

Example:

\n\n
\n
vn.get_api_key(email="my-email@example.com")\n
\n
\n\n

Login to the Vanna.AI API.

\n\n
Arguments:
\n\n
    \n
  • email (str): The email address to login with.
    • \n
  • otp_code (Union[str, None]): The OTP code to login with. If None, an OTP code will be sent to the email address.
    • \n
\n\n
Returns:
\n\n
\n

str: The API key.

\n
\n", "signature": "(email: str, otp_code: Optional[str] = None) -> str:", "funcdef": "def"}, "vanna.set_api_key": {"fullname": "vanna.set_api_key", "modulename": "vanna", "qualname": "set_api_key", "kind": "function", "doc": "

Sets the API key for Vanna.AI.

\n\n

Example:

\n\n
\n
api_key = vn.get_api_key(email="my-email@example.com")\nvn.set_api_key(api_key)\n
\n
\n\n
Arguments:
\n\n
    \n
  • key (str): The API key.
    • \n
\n", "signature": "(key: str) -> None:", "funcdef": "def"}, "vanna.get_models": {"fullname": "vanna.get_models", "modulename": "vanna", "qualname": "get_models", "kind": "function", "doc": "

Example:

\n\n
\n
models = vn.get_models()\n
\n
\n\n

List the models that the user is a member of.

\n\n
Returns:
\n\n
\n

List[str]: A list of model names.

\n
\n", "signature": "() -> List[str]:", "funcdef": "def"}, "vanna.create_model": {"fullname": "vanna.create_model", "modulename": "vanna", "qualname": "create_model", "kind": "function", "doc": "

Example:

\n\n
\n
vn.create_model(model="my-model", db_type="postgres")\n
\n
\n\n

Create a new model.

\n\n
Arguments:
\n\n
    \n
  • model (str): The name of the model to create.
    • \n
  • db_type (str): The type of database to use for the model. This can be \"Snowflake\", \"BigQuery\", \"Postgres\", or anything else.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the model was created successfully, False otherwise.

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

Example:

\n\n
\n
vn.add_user_to_model(model="my-model", email="user@example.com")\n
\n
\n\n

Add a user to an model.

\n\n
Arguments:
\n\n
    \n
  • model (str): The name of the model to add the user to.
    • \n
  • email (str): The email address of the user to add.
    • \n
  • is_admin (bool): Whether or not the user should be an admin.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the user was added successfully, False otherwise.

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

Example:

\n\n
\n
vn.update_model_visibility(public=True)\n
\n
\n\n

Set the visibility of the current model. If a model is visible, anyone can see it. If it is not visible, only members of the model can see it.

\n\n
Arguments:
\n\n
    \n
  • public (bool): Whether or not the model should be publicly visible.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the model visibility was set successfully, False otherwise.

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

Set the models to use for the Vanna.AI API.

\n\n

Example:

\n\n
\n
vn.set_model("my-model")\n
\n
\n\n
Arguments:
\n\n
    \n
  • model (str): The name of the model to use.
    • \n
\n", "signature": "(model: str):", "funcdef": "def"}, "vanna.add_sql": {"fullname": "vanna.add_sql", "modulename": "vanna", "qualname": "add_sql", "kind": "function", "doc": "

Adds a question and its corresponding SQL query to the model's training data

\n\n

Example:

\n\n
\n
vn.add_sql(\n    question="What is the average salary of employees?",\n    sql="SELECT AVG(salary) FROM employees"\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • question (str): The question to store.
    • \n
  • sql (str): The SQL query to store.
    • \n
  • tag (Union[str, None]): A tag to associate with the question and SQL query.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the question and SQL query were stored successfully, False otherwise.

\n
\n", "signature": "(question: str, sql: str, tag: Optional[str] = 'Manually Trained') -> bool:", "funcdef": "def"}, "vanna.add_ddl": {"fullname": "vanna.add_ddl", "modulename": "vanna", "qualname": "add_ddl", "kind": "function", "doc": "

Adds a DDL statement to the model's training data

\n\n

Example:

\n\n
\n
vn.add_ddl(\n    ddl="CREATE TABLE employees (id INT, name VARCHAR(255), salary INT)"\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • ddl (str): The DDL statement to store.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the DDL statement was stored successfully, False otherwise.

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

Adds documentation to the model's training data

\n\n

Example:

\n\n
\n
vn.add_documentation(\n    documentation="Our organization's definition of sales is the discount price of an item multiplied by the quantity sold."\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • documentation (str): The documentation string to store.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the documentation string was stored successfully, False otherwise.

\n
\n", "signature": "(documentation: str) -> bool:", "funcdef": "def"}, "vanna.TrainingPlanItem": {"fullname": "vanna.TrainingPlanItem", "modulename": "vanna", "qualname": "TrainingPlanItem", "kind": "class", "doc": "

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

\n", "signature": "(item_type: str, item_group: str, item_name: str, item_value: str)"}, "vanna.TrainingPlanItem.item_type": {"fullname": "vanna.TrainingPlanItem.item_type", "modulename": "vanna", "qualname": "TrainingPlanItem.item_type", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.TrainingPlanItem.item_group": {"fullname": "vanna.TrainingPlanItem.item_group", "modulename": "vanna", "qualname": "TrainingPlanItem.item_group", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.TrainingPlanItem.item_name": {"fullname": "vanna.TrainingPlanItem.item_name", "modulename": "vanna", "qualname": "TrainingPlanItem.item_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.TrainingPlanItem.item_value": {"fullname": "vanna.TrainingPlanItem.item_value", "modulename": "vanna", "qualname": "TrainingPlanItem.item_value", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "vanna.TrainingPlanItem.ITEM_TYPE_SQL": {"fullname": "vanna.TrainingPlanItem.ITEM_TYPE_SQL", "modulename": "vanna", "qualname": "TrainingPlanItem.ITEM_TYPE_SQL", "kind": "variable", "doc": "

\n", "default_value": "'sql'"}, "vanna.TrainingPlanItem.ITEM_TYPE_DDL": {"fullname": "vanna.TrainingPlanItem.ITEM_TYPE_DDL", "modulename": "vanna", "qualname": "TrainingPlanItem.ITEM_TYPE_DDL", "kind": "variable", "doc": "

\n", "default_value": "'ddl'"}, "vanna.TrainingPlanItem.ITEM_TYPE_IS": {"fullname": "vanna.TrainingPlanItem.ITEM_TYPE_IS", "modulename": "vanna", "qualname": "TrainingPlanItem.ITEM_TYPE_IS", "kind": "variable", "doc": "

\n", "default_value": "'is'"}, "vanna.TrainingPlan": {"fullname": "vanna.TrainingPlan", "modulename": "vanna", "qualname": "TrainingPlan", "kind": "class", "doc": "

A class representing a training plan. You can see what's in it, and remove items from it that you don't want trained.

\n\n

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.get_summary()\n
\n
\n"}, "vanna.TrainingPlan.__init__": {"fullname": "vanna.TrainingPlan.__init__", "modulename": "vanna", "qualname": "TrainingPlan.__init__", "kind": "function", "doc": "

\n", "signature": "(plan: List[vanna.TrainingPlanItem])"}, "vanna.TrainingPlan.get_summary": {"fullname": "vanna.TrainingPlan.get_summary", "modulename": "vanna", "qualname": "TrainingPlan.get_summary", "kind": "function", "doc": "

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.get_summary()\n
\n
\n\n

Get a summary of the training plan.

\n\n
Returns:
\n\n
\n

List[str]: A list of strings describing the training plan.

\n
\n", "signature": "(self) -> List[str]:", "funcdef": "def"}, "vanna.TrainingPlan.remove_item": {"fullname": "vanna.TrainingPlan.remove_item", "modulename": "vanna", "qualname": "TrainingPlan.remove_item", "kind": "function", "doc": "

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.remove_item("Train on SQL: What is the average salary of employees?")\n
\n
\n\n

Remove an item from the training plan.

\n\n
Arguments:
\n\n
    \n
  • item (str): The item to remove.
    • \n
\n", "signature": "(self, item: str):", "funcdef": "def"}, "vanna.get_training_plan_postgres": {"fullname": "vanna.get_training_plan_postgres", "modulename": "vanna", "qualname": "get_training_plan_postgres", "kind": "function", "doc": "

\n", "signature": "(\tfilter_databases: Optional[List[str]] = None,\tfilter_schemas: Optional[List[str]] = None,\tinclude_information_schema: bool = False,\tuse_historical_queries: bool = True) -> vanna.TrainingPlan:", "funcdef": "def"}, "vanna.get_training_plan_generic": {"fullname": "vanna.get_training_plan_generic", "modulename": "vanna", "qualname": "get_training_plan_generic", "kind": "function", "doc": "

\n", "signature": "(df) -> vanna.TrainingPlan:", "funcdef": "def"}, "vanna.get_training_plan_experimental": {"fullname": "vanna.get_training_plan_experimental", "modulename": "vanna", "qualname": "get_training_plan_experimental", "kind": "function", "doc": "

EXPERIMENTAL : This method is experimental and may change in future versions.

\n\n

Get a training plan based on the metadata in the database. Currently this only works for Snowflake.

\n\n

Example:

\n\n
\n
plan = vn.get_training_plan_experimental(filter_databases=["employees"], filter_schemas=["public"])\n\nvn.train(plan=plan)\n
\n
\n", "signature": "(\tfilter_databases: Optional[List[str]] = None,\tfilter_schemas: Optional[List[str]] = None,\tinclude_information_schema: bool = False,\tuse_historical_queries: bool = True) -> vanna.TrainingPlan:", "funcdef": "def"}, "vanna.train": {"fullname": "vanna.train", "modulename": "vanna", "qualname": "train", "kind": "function", "doc": "

Example:

\n\n
\n
vn.train()\n
\n
\n\n

Train Vanna.AI on a question and its corresponding SQL query. \nIf you call it with no arguments, it will check if you connected to a database and it will attempt to train on the metadata of that database.\nIf you call it with the sql argument, it's equivalent to [add_sql()][vanna.add_sql].\nIf you call it with the ddl argument, it's equivalent to [add_ddl()][vanna.add_ddl].\nIf you call it with the documentation argument, it's equivalent to [add_documentation()][vanna.add_documentation].\nIt can also accept a JSON file path or SQL file path to train on a batch of questions and SQL queries or a list of SQL queries respectively.\nAdditionally, you can pass a [TrainingPlan][vanna.TrainingPlan] object. Get a training plan with [vn.get_training_plan_experimental()][vanna.get_training_plan_experimental].

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to train on.
    • \n
  • sql (str): The SQL query to train on.
    • \n
  • sql_file (str): The SQL file path.
    • \n
  • json_file (str): The JSON file path.
    • \n
  • ddl (str): The DDL statement.
    • \n
  • documentation (str): The documentation to train on.
    • \n
  • plan (TrainingPlan): The training plan to train on.
    • \n
\n", "signature": "(\tquestion: str = None,\tsql: str = None,\tddl: str = None,\tdocumentation: str = None,\tjson_file: str = None,\tsql_file: str = None,\tplan: vanna.TrainingPlan = None) -> bool:", "funcdef": "def"}, "vanna.flag_sql_for_review": {"fullname": "vanna.flag_sql_for_review", "modulename": "vanna", "qualname": "flag_sql_for_review", "kind": "function", "doc": "

Example:

\n\n
\n
vn.flag_sql_for_review(question="What is the average salary of employees?")\n
\n
\n\n

Flag a question and its corresponding SQL query for review. You can see the tag show up in [vn.get_all_questions()][vanna.get_all_questions]

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to flag.
    • \n
  • sql (str): The SQL query to flag.
    • \n
  • error_msg (str): The error message to flag.
    • \n
\n\n
Returns:
\n\n
\n

bool: True if the question and SQL query were flagged successfully, False otherwise.

\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": "

Remove a question and its corresponding SQL query from the model's training data

\n\n

Example:

\n\n
\n
vn.remove_sql(question="What is the average salary of employees?")\n
\n
\n\n
Arguments:
\n\n
    \n
  • question (str): The question to remove.
    • \n
\n", "signature": "(question: str) -> bool:", "funcdef": "def"}, "vanna.remove_training_data": {"fullname": "vanna.remove_training_data", "modulename": "vanna", "qualname": "remove_training_data", "kind": "function", "doc": "

Remove training data from the model

\n\n

Example:

\n\n
\n
vn.remove_training_data(id="1-ddl")\n
\n
\n\n
Arguments:
\n\n
    \n
  • id (str): The ID of the training data to remove.
    • \n
\n", "signature": "(id: str) -> bool:", "funcdef": "def"}, "vanna.generate_sql": {"fullname": "vanna.generate_sql", "modulename": "vanna", "qualname": "generate_sql", "kind": "function", "doc": "

Example:

\n\n
\n
vn.generate_sql(question="What is the average salary of employees?")\n# SELECT AVG(salary) FROM employees\n
\n
\n\n

Generate an SQL query using the Vanna.AI API.

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to generate an SQL query for.
    • \n
\n\n
Returns:
\n\n
\n

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

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

Example:

\n\n
\n
training_data = vn.get_related_training_data(question="What is the average salary of employees?")\n
\n
\n\n

Get the training data related to a question.

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to get related training data for.
    • \n
\n\n
Returns:
\n\n
\n

TrainingData or None: The related training data, or None if an error occurred.

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

Example:

\n\n
\n
vn.generate_meta(question="What tables are in the database?")\n# Information about the tables in the database\n
\n
\n\n

Generate answers about the metadata of a database using the Vanna.AI API.

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to generate an answer for.
    • \n
\n\n
Returns:
\n\n
\n

str or None: The answer, or None if an error occurred.

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

Example:

\n\n
\n
vn.generate_followup_questions(question="What is the average salary of employees?", df=df)\n# ['What is the average salary of employees in the Sales department?', 'What is the average salary of employees in the Engineering department?', ...]\n
\n
\n\n

Generate follow-up questions using the Vanna.AI API.

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to generate follow-up questions for.
    • \n
  • df (pd.DataFrame): The DataFrame to generate follow-up questions for.
    • \n
\n\n
Returns:
\n\n
\n

List[str] or None: The follow-up questions, or None if an error occurred.

\n
\n", "signature": "(question: str, df: pandas.core.frame.DataFrame) -> List[str]:", "funcdef": "def"}, "vanna.generate_questions": {"fullname": "vanna.generate_questions", "modulename": "vanna", "qualname": "generate_questions", "kind": "function", "doc": "

Example:

\n\n
\n
vn.generate_questions()\n# ['What is the average salary of employees?', 'What is the total salary of employees?', ...]\n
\n
\n\n

Generate questions using the Vanna.AI API.

\n\n
Returns:
\n\n
\n

List[str] or None: The questions, or None if an error occurred.

\n
\n", "signature": "() -> List[str]:", "funcdef": "def"}, "vanna.ask": {"fullname": "vanna.ask", "modulename": "vanna", "qualname": "ask", "kind": "function", "doc": "

Example:

\n\n
\n
# RECOMMENDED IN A NOTEBOOK:\nsql, df, fig, followup_questions = vn.ask()\n\n\nsql, df, fig, followup_questions = vn.ask(question="What is the average salary of employees?")\n# SELECT AVG(salary) FROM employees\n
\n
\n\n

Ask a question using the Vanna.AI API. This generates an SQL query, runs it, and returns the results in a dataframe and a Plotly figure.\nIf you set print_results to True, the sql, dataframe, and figure will be output to the screen instead of returned.

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to ask. If None, you will be prompted to enter a question.
    • \n
  • print_results (bool): Whether to print the SQL query and results.
    • \n
  • auto_train (bool): Whether to automatically train the model if the SQL query is incorrect.
    • \n
  • generate_followups (bool): Whether to generate follow-up questions.
    • \n
\n\n
Returns:
\n\n
\n

str or None: The SQL query, or None if an error occurred.\n pd.DataFrame or None: The results of the SQL query, or None if an error occurred.\n plotly.graph_objs.Figure or None: The Plotly figure, or None if an error occurred.\n List[str] or None: The follow-up questions, or None if an error occurred.

\n
\n", "signature": "(\tquestion: Optional[str] = None,\tprint_results: bool = True,\tauto_train: bool = True,\tgenerate_followups: bool = True) -> Optional[Tuple[Optional[str], Optional[pandas.core.frame.DataFrame], Optional[plotly.graph_objs._figure.Figure], Optional[List[str]]]]:", "funcdef": "def"}, "vanna.generate_plotly_code": {"fullname": "vanna.generate_plotly_code", "modulename": "vanna", "qualname": "generate_plotly_code", "kind": "function", "doc": "

Example:

\n\n
\n
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")\n
\n
\n\n

Generate Plotly code using the Vanna.AI API.

\n\n
Arguments:
\n\n
    \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
  • chart_instructions (str): Optional instructions for how to plot the chart.
    • \n
\n\n
Returns:
\n\n
\n

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

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

Example:

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

Get a Plotly figure from a dataframe and Plotly code.

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

plotly.graph_objs.Figure: The Plotly figure.

\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": "

DEPRECATED. Use vn.run_sql instead.\nRun the SQL query and return the results as a pandas dataframe. This is just a helper function that does not use the Vanna.AI API.

\n\n
Arguments:
\n\n
    \n
  • cs: Snowflake connection cursor.
    • \n
  • default_database (str): The default database to use.
    • \n
  • sql (str): The SQL query to execute.
    • \n
\n\n
Returns:
\n\n
\n

pd.DataFrame: 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# 'This query selects all columns from the students table where the name is John Doe.'\n
\n
\n\n

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

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

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

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

Example:

\n\n
\n
vn.generate_question(sql="SELECT * FROM students WHERE name = 'John Doe'")\n# 'What is the name of the student?'\n
\n
\n\n

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

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

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

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

Get a list of questions from the Vanna.AI API.

\n\n

Example:

\n\n
\n
questions = vn.get_all_questions()\n
\n
\n\n
Returns:
\n\n
\n

pd.DataFrame or None: The list of questions, or None if an error occurred.

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

Get the training data for the current model

\n\n

Example:

\n\n
\n
training_data = vn.get_training_data()\n
\n
\n\n
Returns:
\n\n
\n

pd.DataFrame or None: The training data, or None if an error occurred.

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

Connect to a SQLite database. This is just a helper function to set [vn.run_sql][vanna.run_sql]

\n\n
Arguments:
\n\n
    \n
  • url (str): The URL of the database to connect to.
    • \n
\n\n
Returns:
\n\n
\n

None

\n
\n", "signature": "(url: str):", "funcdef": "def"}, "vanna.connect_to_snowflake": {"fullname": "vanna.connect_to_snowflake", "modulename": "vanna", "qualname": "connect_to_snowflake", "kind": "function", "doc": "

Connect to Snowflake using the Snowflake connector. This is just a helper function to set [vn.run_sql][vanna.run_sql]

\n\n

Example:

\n\n
\n
import snowflake.connector\n\nvn.connect_to_snowflake(\n    account="myaccount",\n    username="myusername",\n    password="mypassword",\n    database="mydatabase",\n    role="myrole",\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • account (str): The Snowflake account name.
    • \n
  • username (str): The Snowflake username.
    • \n
  • password (str): The Snowflake password.
    • \n
  • database (str): The default database to use.
    • \n
  • role (Union[str, None], optional): The role to use. Defaults to None.
    • \n
\n", "signature": "(\taccount: str,\tusername: str,\tpassword: str,\tdatabase: str,\trole: Optional[str] = None):", "funcdef": "def"}, "vanna.connect_to_postgres": {"fullname": "vanna.connect_to_postgres", "modulename": "vanna", "qualname": "connect_to_postgres", "kind": "function", "doc": "

Connect to postgres using the psycopg2 connector. This is just a helper function to set [vn.run_sql][vanna.run_sql]\nExample:

\n\n
\n
import psycopg2.connect\nvn.connect_to_bigquery(\n    host="myhost",\n    dbname="mydatabase",\n    user="myuser",\n    password="mypassword",\n    port=5432\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • host (str): The postgres host.
    • \n
  • dbname (str): The postgres database name.
    • \n
  • user (str): The postgres user.
    • \n
  • password (str): The postgres password.
    • \n
  • port (int): The postgres Port.
    • \n
\n", "signature": "(\thost: str = None,\tdbname: str = None,\tuser: str = None,\tpassword: str = None,\tport: int = None):", "funcdef": "def"}, "vanna.connect_to_bigquery": {"fullname": "vanna.connect_to_bigquery", "modulename": "vanna", "qualname": "connect_to_bigquery", "kind": "function", "doc": "

Connect to gcs using the bigquery connector. This is just a helper function to set [vn.run_sql][vanna.run_sql]\nExample:

\n\n
\n
import bigquery.Client\nvn.connect_to_bigquery(\n    project_id="myprojectid",\n    cred_file_path="path/to/credentials.json",\n)\n
\n
\n\n
Arguments:
\n\n
    \n
  • project_id (str): The gcs project id.
    • \n
  • cred_file_path (str): The gcs credential file path
    • \n
\n", "signature": "(cred_file_path: str = None, project_id: str = None):", "funcdef": "def"}, "vanna.base": {"fullname": "vanna.base", "modulename": "vanna.base", "kind": "module", "doc": "

\n"}, "vanna.base.VannaBase": {"fullname": "vanna.base.VannaBase", "modulename": "vanna.base", "qualname": "VannaBase", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "abc.ABC"}, "vanna.base.VannaBase.config": {"fullname": "vanna.base.VannaBase.config", "modulename": "vanna.base", "qualname": "VannaBase.config", "kind": "variable", "doc": "

\n"}, "vanna.base.VannaBase.run_sql_is_set": {"fullname": "vanna.base.VannaBase.run_sql_is_set", "modulename": "vanna.base", "qualname": "VannaBase.run_sql_is_set", "kind": "variable", "doc": "

\n"}, "vanna.base.VannaBase.generate_sql_from_question": {"fullname": "vanna.base.VannaBase.generate_sql_from_question", "modulename": "vanna.base", "qualname": "VannaBase.generate_sql_from_question", "kind": "function", "doc": "

\n", "signature": "(self, question: str, **kwargs) -> str:", "funcdef": "def"}, "vanna.base.VannaBase.generate_embedding": {"fullname": "vanna.base.VannaBase.generate_embedding", "modulename": "vanna.base", "qualname": "VannaBase.generate_embedding", "kind": "function", "doc": "

\n", "signature": "(self, data: str, **kwargs) -> list[float]:", "funcdef": "def"}, "vanna.base.VannaBase.get_similar_question_sql": {"fullname": "vanna.base.VannaBase.get_similar_question_sql", "modulename": "vanna.base", "qualname": "VannaBase.get_similar_question_sql", "kind": "function", "doc": "

\n", "signature": "(self, question: str, **kwargs) -> list:", "funcdef": "def"}, "vanna.base.VannaBase.get_related_ddl": {"fullname": "vanna.base.VannaBase.get_related_ddl", "modulename": "vanna.base", "qualname": "VannaBase.get_related_ddl", "kind": "function", "doc": "

\n", "signature": "(self, question: str, **kwargs) -> list:", "funcdef": "def"}, "vanna.base.VannaBase.get_related_documentation": {"fullname": "vanna.base.VannaBase.get_related_documentation", "modulename": "vanna.base", "qualname": "VannaBase.get_related_documentation", "kind": "function", "doc": "

\n", "signature": "(self, question: str, **kwargs) -> list:", "funcdef": "def"}, "vanna.base.VannaBase.add_question_sql": {"fullname": "vanna.base.VannaBase.add_question_sql", "modulename": "vanna.base", "qualname": "VannaBase.add_question_sql", "kind": "function", "doc": "

\n", "signature": "(self, question: str, sql: str, **kwargs):", "funcdef": "def"}, "vanna.base.VannaBase.add_ddl": {"fullname": "vanna.base.VannaBase.add_ddl", "modulename": "vanna.base", "qualname": "VannaBase.add_ddl", "kind": "function", "doc": "

\n", "signature": "(self, ddl: str, **kwargs):", "funcdef": "def"}, "vanna.base.VannaBase.add_documentation": {"fullname": "vanna.base.VannaBase.add_documentation", "modulename": "vanna.base", "qualname": "VannaBase.add_documentation", "kind": "function", "doc": "

\n", "signature": "(self, doc: str, **kwargs):", "funcdef": "def"}, "vanna.base.VannaBase.get_prompt": {"fullname": "vanna.base.VannaBase.get_prompt", "modulename": "vanna.base", "qualname": "VannaBase.get_prompt", "kind": "function", "doc": "

\n", "signature": "(\tself,\tquestion: str,\tquestion_sql_list: list,\tddl_list: list,\tdoc_list: list,\t**kwargs):", "funcdef": "def"}, "vanna.base.VannaBase.submit_prompt": {"fullname": "vanna.base.VannaBase.submit_prompt", "modulename": "vanna.base", "qualname": "VannaBase.submit_prompt", "kind": "function", "doc": "

\n", "signature": "(self, prompt, **kwargs) -> str:", "funcdef": "def"}, "vanna.base.VannaBase.generate_question": {"fullname": "vanna.base.VannaBase.generate_question", "modulename": "vanna.base", "qualname": "VannaBase.generate_question", "kind": "function", "doc": "

\n", "signature": "(self, sql: str, **kwargs) -> str:", "funcdef": "def"}, "vanna.base.VannaBase.generate_plotly_code": {"fullname": "vanna.base.VannaBase.generate_plotly_code", "modulename": "vanna.base", "qualname": "VannaBase.generate_plotly_code", "kind": "function", "doc": "

\n", "signature": "(\tself,\tquestion: str = None,\tsql: str = None,\tdf_metadata: str = None,\t**kwargs) -> str:", "funcdef": "def"}, "vanna.base.VannaBase.connect_to_snowflake": {"fullname": "vanna.base.VannaBase.connect_to_snowflake", "modulename": "vanna.base", "qualname": "VannaBase.connect_to_snowflake", "kind": "function", "doc": "

\n", "signature": "(\tself,\taccount: str,\tusername: str,\tpassword: str,\tdatabase: str,\trole: Optional[str] = None):", "funcdef": "def"}, "vanna.base.VannaBase.run_sql": {"fullname": "vanna.base.VannaBase.run_sql", "modulename": "vanna.base", "qualname": "VannaBase.run_sql", "kind": "function", "doc": "

\n", "signature": "(sql: str, **kwargs) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "vanna.base.VannaBase.ask": {"fullname": "vanna.base.VannaBase.ask", "modulename": "vanna.base", "qualname": "VannaBase.ask", "kind": "function", "doc": "

\n", "signature": "(\tself,\tquestion: Optional[str] = None,\tprint_results: bool = True,\tauto_train: bool = True) -> Optional[Tuple[Optional[str], Optional[pandas.core.frame.DataFrame], Optional[plotly.graph_objs._figure.Figure]]]:", "funcdef": "def"}, "vanna.base.VannaBase.train": {"fullname": "vanna.base.VannaBase.train", "modulename": "vanna.base", "qualname": "VannaBase.train", "kind": "function", "doc": "

Example:

\n\n
\n
vn.train()\n
\n
\n\n

Train Vanna.AI on a question and its corresponding SQL query.\nIf you call it with no arguments, it will check if you connected to a database and it will attempt to train on the metadata of that database.\nIf you call it with the sql argument, it's equivalent to [add_sql()][vanna.add_sql].\nIf you call it with the ddl argument, it's equivalent to [add_ddl()][vanna.add_ddl].\nIf you call it with the documentation argument, it's equivalent to [add_documentation()][vanna.add_documentation].\nAdditionally, you can pass a [TrainingPlan][vanna.TrainingPlan] object. Get a training plan with [vn.get_training_plan_experimental()][vanna.get_training_plan_experimental].

\n\n
Arguments:
\n\n
    \n
  • question (str): The question to train on.
    • \n
  • sql (str): The SQL query to train on.
    • \n
  • ddl (str): The DDL statement.
    • \n
  • documentation (str): The documentation to train on.
    • \n
  • plan (TrainingPlan): The training plan to train on.
    • \n
\n", "signature": "(\tself,\tquestion: str = None,\tsql: str = None,\tddl: str = None,\tdocumentation: str = None,\tplan: vanna.types.TrainingPlan = None) -> bool:", "funcdef": "def"}, "vanna.base.VannaBase.get_training_plan_snowflake": {"fullname": "vanna.base.VannaBase.get_training_plan_snowflake", "modulename": "vanna.base", "qualname": "VannaBase.get_training_plan_snowflake", "kind": "function", "doc": "

\n", "signature": "(\tself,\tfilter_databases: Optional[List[str]] = None,\tfilter_schemas: Optional[List[str]] = None,\tinclude_information_schema: bool = False,\tuse_historical_queries: bool = True) -> vanna.types.TrainingPlan:", "funcdef": "def"}, "vanna.base.VannaBase.get_plotly_figure": {"fullname": "vanna.base.VannaBase.get_plotly_figure", "modulename": "vanna.base", "qualname": "VannaBase.get_plotly_figure", "kind": "function", "doc": "

Example:

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

Get a Plotly figure from a dataframe and Plotly code.

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

plotly.graph_objs.Figure: The Plotly figure.

\n
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Helper class that provides a standard way to create an ABC using\ninheritance.

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Raise for incorrect configuration.

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Raise for missing dependencies.

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Raise for connection

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Raise for invalid otp or not able to send it

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Raise when not able to remove SQL

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Raise when not able to execute Code

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Raise for validations

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Raise for API errors

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\n"}, "vanna.local.LocalContext_OpenAI": {"fullname": "vanna.local.LocalContext_OpenAI", "modulename": "vanna.local", "qualname": "LocalContext_OpenAI", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

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\n"}, "vanna.mock.MockModel": {"fullname": "vanna.mock.MockModel", "modulename": "vanna.mock", "qualname": "MockModel", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

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\n"}, "vanna.openai_chat.OpenAI_Chat": {"fullname": "vanna.openai_chat.OpenAI_Chat", "modulename": "vanna.openai_chat", "qualname": "OpenAI_Chat", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "vanna.base.VannaBase"}, "vanna.openai_chat.OpenAI_Chat.system_message": {"fullname": "vanna.openai_chat.OpenAI_Chat.system_message", "modulename": "vanna.openai_chat", "qualname": "OpenAI_Chat.system_message", "kind": "function", "doc": "

\n", "signature": "(message: str) -> dict:", "funcdef": "def"}, "vanna.openai_chat.OpenAI_Chat.user_message": {"fullname": "vanna.openai_chat.OpenAI_Chat.user_message", "modulename": "vanna.openai_chat", "qualname": "OpenAI_Chat.user_message", "kind": "function", "doc": "

\n", "signature": "(message: str) -> dict:", "funcdef": "def"}, "vanna.openai_chat.OpenAI_Chat.assistant_message": {"fullname": "vanna.openai_chat.OpenAI_Chat.assistant_message", "modulename": "vanna.openai_chat", "qualname": "OpenAI_Chat.assistant_message", "kind": "function", "doc": "

\n", "signature": "(message: str) -> dict:", "funcdef": "def"}, "vanna.openai_chat.OpenAI_Chat.get_prompt": {"fullname": "vanna.openai_chat.OpenAI_Chat.get_prompt", "modulename": "vanna.openai_chat", "qualname": "OpenAI_Chat.get_prompt", "kind": "function", "doc": "

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Helper class that provides a standard way to create an ABC using\ninheritance.

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

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\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": "

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\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": "

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

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

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

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

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

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

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\n", "default_value": "'Bootstrap Training Query'"}, "vanna.types.QuestionCategory.SQL_RAN": {"fullname": "vanna.types.QuestionCategory.SQL_RAN", "modulename": "vanna.types", "qualname": "QuestionCategory.SQL_RAN", "kind": "variable", "doc": "

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\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": "

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

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

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

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

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\n", "signature": "(\tquestion: str | None,\tsql: str | None,\ttable_markdown: str,\terror: str | None,\tcorrection_attempts: int)"}, "vanna.types.DataResult.question": {"fullname": "vanna.types.DataResult.question", "modulename": "vanna.types", "qualname": "DataResult.question", "kind": "variable", "doc": "

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

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

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

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

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\n", "signature": "(name: str, tables: List[vanna.types.TableDefinition])"}, "vanna.types.WarehouseDefinition.name": {"fullname": "vanna.types.WarehouseDefinition.name", "modulename": "vanna.types", "qualname": "WarehouseDefinition.name", "kind": "variable", "doc": "

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

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

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

\n", "signature": "(\tschema_name: str,\ttable_name: str,\tddl: str | None,\tcolumns: List[vanna.types.ColumnDefinition])"}, "vanna.types.TableDefinition.schema_name": {"fullname": "vanna.types.TableDefinition.schema_name", "modulename": "vanna.types", "qualname": "TableDefinition.schema_name", "kind": "variable", "doc": "

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

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

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

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

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

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

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

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

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\n", "signature": "(questions: List[dict], ddl: List[str], documentation: List[str])"}, "vanna.types.TrainingData.questions": {"fullname": "vanna.types.TrainingData.questions", "modulename": "vanna.types", "qualname": "TrainingData.questions", "kind": "variable", "doc": "

\n", "annotation": ": List[dict]"}, "vanna.types.TrainingData.ddl": {"fullname": "vanna.types.TrainingData.ddl", "modulename": "vanna.types", "qualname": "TrainingData.ddl", "kind": "variable", "doc": "

\n", "annotation": ": List[str]"}, "vanna.types.TrainingData.documentation": {"fullname": "vanna.types.TrainingData.documentation", "modulename": "vanna.types", "qualname": "TrainingData.documentation", "kind": "variable", "doc": "

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

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

\n", "signature": "(item_type: str, item_group: str, item_name: str, item_value: str)"}, "vanna.types.TrainingPlanItem.item_type": {"fullname": "vanna.types.TrainingPlanItem.item_type", "modulename": "vanna.types", "qualname": "TrainingPlanItem.item_type", "kind": "variable", "doc": "

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

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

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

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

\n", "default_value": "'sql'"}, "vanna.types.TrainingPlanItem.ITEM_TYPE_DDL": {"fullname": "vanna.types.TrainingPlanItem.ITEM_TYPE_DDL", "modulename": "vanna.types", "qualname": "TrainingPlanItem.ITEM_TYPE_DDL", "kind": "variable", "doc": "

\n", "default_value": "'ddl'"}, "vanna.types.TrainingPlanItem.ITEM_TYPE_IS": {"fullname": "vanna.types.TrainingPlanItem.ITEM_TYPE_IS", "modulename": "vanna.types", "qualname": "TrainingPlanItem.ITEM_TYPE_IS", "kind": "variable", "doc": "

\n", "default_value": "'is'"}, "vanna.types.TrainingPlan": {"fullname": "vanna.types.TrainingPlan", "modulename": "vanna.types", "qualname": "TrainingPlan", "kind": "class", "doc": "

A class representing a training plan. You can see what's in it, and remove items from it that you don't want trained.

\n\n

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.get_summary()\n
\n
\n"}, "vanna.types.TrainingPlan.__init__": {"fullname": "vanna.types.TrainingPlan.__init__", "modulename": "vanna.types", "qualname": "TrainingPlan.__init__", "kind": "function", "doc": "

\n", "signature": "(plan: List[vanna.types.TrainingPlanItem])"}, "vanna.types.TrainingPlan.get_summary": {"fullname": "vanna.types.TrainingPlan.get_summary", "modulename": "vanna.types", "qualname": "TrainingPlan.get_summary", "kind": "function", "doc": "

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.get_summary()\n
\n
\n\n

Get a summary of the training plan.

\n\n
Returns:
\n\n
\n

List[str]: A list of strings describing the training plan.

\n
\n", "signature": "(self) -> List[str]:", "funcdef": "def"}, "vanna.types.TrainingPlan.remove_item": {"fullname": "vanna.types.TrainingPlan.remove_item", "modulename": "vanna.types", "qualname": "TrainingPlan.remove_item", "kind": "function", "doc": "

Example:

\n\n
\n
plan = vn.get_training_plan()\n\nplan.remove_item("Train on SQL: What is the average salary of employees?")\n
\n
\n\n

Remove an item from the training plan.

\n\n
Arguments:
\n\n
    \n
  • item (str): The item to remove.
    • \n
\n", "signature": "(self, item: str):", "funcdef": "def"}, "vanna.utils": {"fullname": "vanna.utils", "modulename": "vanna.utils", "kind": "module", "doc": "

\n"}, "vanna.utils.validate_config_path": {"fullname": "vanna.utils.validate_config_path", "modulename": "vanna.utils", "qualname": "validate_config_path", "kind": "function", "doc": "

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["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/sidebar.py b/sidebar.py new file mode 100644 index 000000000..f6923a386 --- /dev/null +++ b/sidebar.py @@ -0,0 +1,130 @@ +import yaml +import sys +import nbformat +from nbconvert import HTMLExporter + +# Get the yaml file path from the command line +file_path = sys.argv[1] + +# Get the directory to search for the .ipynb files from the command line +notebook_dir = sys.argv[2] + +# Get the output directory from the command line +output_dir = sys.argv[3] + +def generate_html(sidebar_data, current_path: str): + html = '
    \n' + for entry in sidebar_data: + html += '
  • \n' + if 'sub_entries' in entry: + # Dropdown menu with sub-entries + html += f'\n' + html += f'\n' + else: + # Regular sidebar entry without sub-entries + highlighted = 'bg-indigo-100 dark:bg-indigo-700' if entry['link'] == current_path else '' + html += f'\n' + html += f'{entry["svg_text"]}\n' + html += f'{entry["title"]}\n' + html += '\n' + html += '
    • \n' + html += '
' + return html + +def generate_header(notebook_name: str) -> str: + return f""" + + + Run Using Colab + + + + Open in GitHub + +""" + +# Read YAML data from a file +def read_yaml_file(file_path): + with open(file_path, 'r') as file: + yaml_data = file.read() + return yaml_data + +yaml_data = read_yaml_file(file_path) + +# Parse YAML data +sidebar_data = yaml.safe_load(yaml_data) + +# Get a list of all .ipynb files in the directory +import os +notebook_files = [file for file in os.listdir(notebook_dir) if file.endswith('.ipynb')] + +def is_runnable(notebook_name: str, sidebar_data: dict) -> bool: + # Check if the notebook is runnable + for entry in sidebar_data: + if 'link' in entry: + if entry['link'] == f'{notebook_name}.html': + return entry.get('runnable', 'true') == 'true' + + if 'sub_entries' in entry: + for sub_entry in entry['sub_entries']: + if sub_entry['link'] == f'{notebook_name}.html': + return sub_entry.get('runnable', 'true') == 'true' + + return False + +def get_title_from_sidebar_data(notebook_name: str, sidebar_data: dict) -> str: + # Check if the notebook is runnable + for entry in sidebar_data: + if 'link' in entry: + if entry['link'] == f'{notebook_name}.html': + return entry['title'] + + if 'sub_entries' in entry: + for sub_entry in entry['sub_entries']: + if sub_entry['link'] == f'{notebook_name}.html': + return sub_entry['title'] + + return '' + +for notebook_file in notebook_files: + # Get just the file name without the extension + notebook_name = os.path.splitext(notebook_file)[0] + + # Get the full path to the notebook + notebook_file_path = os.path.join(notebook_dir, notebook_file) + + # Generate HTML code + html_code = generate_html(sidebar_data, f'{notebook_name}.html') + + # Read notebook file + current_notebook = nbformat.read(notebook_file_path, as_version=4) + + html_exporter = HTMLExporter(template_name='nb-theme') + + (body, resources) = html_exporter.from_notebook_node(current_notebook) + + notebook_title = get_title_from_sidebar_data(notebook_name, sidebar_data) + + # Write body to file + with open(os.path.join(output_dir, f'{notebook_name}.html'), 'w') as file: + # From sidebar_data, see if there is a matching entry for the current notebook + if is_runnable(notebook_name, sidebar_data): + file.write(body.replace('nb_title', f"Vanna Docs: {notebook_title}").replace('', html_code).replace('', generate_header(notebook_name))) + else: + file.write(body.replace('nb_title', f"Vanna Docs: {notebook_title}").replace('', html_code)) + + diff --git a/sidebar.yaml b/sidebar.yaml new file mode 100644 index 000000000..5c0a68642 --- /dev/null +++ b/sidebar.yaml @@ -0,0 +1,77 @@ +- title: How It Works + link: index.html + runnable: false + svg_text: |- + + +- title: Getting Started + link: getting-started.html + svg_text: |- + + +- title: Train Vanna + svg_text: |- + + sub_entries: + - title: Snowflake + link: vn-train.html + svg_text: |- + + + - title: Other Databases + link: manual-train.html + svg_text: |- + + +- title: User Interfaces + svg_text: |- + + sub_entries: + - title: Streamlit + link: streamlit.html + runnable: false + svg_text: |- + + + - title: Slack + link: slack.html + runnable: false + svg_text: |- + + +- title: Databases + link: databases.html + svg_text: |- + + +- title: Running Locally + link: local.html + svg_text: |- + + +- title: API Reference + link: vanna.html + svg_text: |- + diff --git a/slack.html b/slack.html new file mode 100644 index 000000000..2c97e7d9b --- /dev/null +++ b/slack.html @@ -0,0 +1,7492 @@ + + + + + + + + + + + + + +Vanna Docs: Slack + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + diff --git a/streamlit.html b/streamlit.html new file mode 100644 index 000000000..100d11b47 --- /dev/null +++ b/streamlit.html @@ -0,0 +1,7481 @@ + + + + + + + + + + + + + +Vanna Docs: Streamlit + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + diff --git a/streamlit.md b/streamlit.md new file mode 100644 index 000000000..2b80b17a2 --- /dev/null +++ b/streamlit.md @@ -0,0 +1,13 @@ +# Use **Vanna.AI** with Streamlit + +## App + + +## Code +[https://github.com/vanna-ai/vanna-streamlit](https://github.com/vanna-ai/vanna-streamlit) + + \ No newline at end of file diff --git a/support.md b/support.md new file mode 100644 index 000000000..465992464 --- /dev/null +++ b/support.md @@ -0,0 +1,5 @@ +# Getting Support + +E-mail us at [support@vanna.ai](mailto:support@vanna.ai) + +[Join our Slack](https://join.slack.com/t/vanna-ai/shared_invite/zt-1unu0ipog-iE33QCoimQiBDxf2o7h97w) \ No newline at end of file diff --git a/vanna.html b/vanna.html new file mode 100644 index 000000000..f08b0d994 --- /dev/null +++ b/vanna.html @@ -0,0 +1,1894 @@ + + + + + + + vanna API documentation + + + + + + + + + + +
+
+

+vanna

+ +

Basic Usage

+ +

Getting an API key

+ +
+
import vanna as vn
+api_key = vn.get_api_key('my-email@example.com')
+vn.set_api_key(api_key)
+
+
+ +

Setting the model

+ +
+
vn.set_model('demo-tpc-h')
+
+
+ +

Asking a question

+ +
+
sql, df, fig, followup_questions = vn.ask(question='What are the top 10 customers by sales?')
+
+
+ +

For a more comprehensive starting guide see the Starter Notebook.

+ +

Nomenclature

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PrefixDefinitionExamples
vn.set_Sets the variable for the current session[vn.set_model(...)][vanna.set_model]
[vn.set_api_key(...)][vanna.set_api_key]
vn.get_Performs a read-only operation[vn.get_model()][vanna.get_models]
vn.add_Adds something to the model[vn.add_sql(...)][vanna.add_sql]
[vn.add_ddl(...)][vanna.add_ddl]
vn.generate_Generates something using AI based on the information in the model[vn.generate_sql(...)][vanna.generate_sql]
[vn.generate_explanation()][vanna.generate_explanation]
vn.run_Runs code (SQL or Plotly)[vn.run_sql][vanna.run_sql]
vn.remove_Removes something from the model[vn.remove_training_data][vanna.remove_training_data]
vn.update_Updates something in the model[vn.update_model_visibility(...)][vanna.update_model_visibility]
vn.connect_Connects to a database[vn.connect_to_snowflake(...)][vanna.connect_to_snowflake]
+ +

Permissions

+ +

By default when you create a model it is private. You can add members or admins to your model or make it public.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
User RolePublic ModelPrivate Model
UseTrainUseTrain
Non-Member✅❌❌❌
Member✅❌✅❌
Admin✅✅✅✅
+ +

API Reference

+
+ + + + +
+
+
+ api_key: Optional[str] = +None + + +
+ + + + +
+
+
+ run_sql: Optional[Callable[[str], pandas.core.frame.DataFrame]] = +None + + +
+ + +

Example

+ +
+
vn.run_sql = lambda sql: pd.read_sql(sql, engine)
+
+
+ +

Set the SQL to DataFrame function for Vanna.AI. This is used in the [vn.ask(...)][vanna.ask] function. +Instead of setting this directly you can also use [vn.connect_to_snowflake(...)][vanna.connect_to_snowflake] to set this.

+
+ + +
+
+
+ + def + get_api_key(email: str, otp_code: Optional[str] = None) -> str: + + +
+ + +

Example:

+ +
+
vn.get_api_key(email="my-email@example.com")
+
+
+ +

Login to the Vanna.AI API.

+ +
Arguments:
+ +
    +
  • email (str): The email address to login with.
    • +
  • otp_code (Union[str, None]): The OTP code to login with. If None, an OTP code will be sent to the email address.
    • +
+ +
Returns:
+ +
+

str: The API key.

+
+
+ + +
+
+
+ + def + set_api_key(key: str) -> None: + + +
+ + +

Sets the API key for Vanna.AI.

+ +

Example:

+ +
+
api_key = vn.get_api_key(email="my-email@example.com")
+vn.set_api_key(api_key)
+
+
+ +
Arguments:
+ +
    +
  • key (str): The API key.
    • +
+
+ + +
+
+
+ + def + get_models() -> List[str]: + + +
+ + +

Example:

+ +
+
models = vn.get_models()
+
+
+ +

List the models that the user is a member of.

+ +
Returns:
+ +
+

List[str]: A list of model names.

+
+
+ + +
+
+
+ + def + create_model(model: str, db_type: str) -> bool: + + +
+ + +

Example:

+ +
+
vn.create_model(model="my-model", db_type="postgres")
+
+
+ +

Create a new model.

+ +
Arguments:
+ +
    +
  • model (str): The name of the model to create.
    • +
  • db_type (str): The type of database to use for the model. This can be "Snowflake", "BigQuery", "Postgres", or anything else.
    • +
+ +
Returns:
+ +
+

bool: True if the model was created successfully, False otherwise.

+
+
+ + +
+
+
+ + def + add_user_to_model(model: str, email: str, is_admin: bool) -> bool: + + +
+ + +

Example:

+ +
+
vn.add_user_to_model(model="my-model", email="user@example.com")
+
+
+ +

Add a user to an model.

+ +
Arguments:
+ +
    +
  • model (str): The name of the model to add the user to.
    • +
  • email (str): The email address of the user to add.
    • +
  • is_admin (bool): Whether or not the user should be an admin.
    • +
+ +
Returns:
+ +
+

bool: True if the user was added successfully, False otherwise.

+
+
+ + +
+
+
+ + def + update_model_visibility(public: bool) -> bool: + + +
+ + +

Example:

+ +
+
vn.update_model_visibility(public=True)
+
+
+ +

Set the visibility of the current model. If a model is visible, anyone can see it. If it is not visible, only members of the model can see it.

+ +
Arguments:
+ +
    +
  • public (bool): Whether or not the model should be publicly visible.
    • +
+ +
Returns:
+ +
+

bool: True if the model visibility was set successfully, False otherwise.

+
+
+ + +
+
+
+ + def + set_model(model: str): + + +
+ + +

Set the models to use for the Vanna.AI API.

+ +

Example:

+ +
+
vn.set_model("my-model")
+
+
+ +
Arguments:
+ +
    +
  • model (str): The name of the model to use.
    • +
+
+ + +
+
+
+ + def + add_sql(question: str, sql: str, tag: Optional[str] = 'Manually Trained') -> bool: + + +
+ + +

Adds a question and its corresponding SQL query to the model's training data

+ +

Example:

+ +
+
vn.add_sql(
+    question="What is the average salary of employees?",
+    sql="SELECT AVG(salary) FROM employees"
+)
+
+
+ +
Arguments:
+ +
    +
  • question (str): The question to store.
    • +
  • sql (str): The SQL query to store.
    • +
  • tag (Union[str, None]): A tag to associate with the question and SQL query.
    • +
+ +
Returns:
+ +
+

bool: True if the question and SQL query were stored successfully, False otherwise.

+
+
+ + +
+
+
+ + def + add_ddl(ddl: str) -> bool: + + +
+ + +

Adds a DDL statement to the model's training data

+ +

Example:

+ +
+
vn.add_ddl(
+    ddl="CREATE TABLE employees (id INT, name VARCHAR(255), salary INT)"
+)
+
+
+ +
Arguments:
+ +
    +
  • ddl (str): The DDL statement to store.
    • +
+ +
Returns:
+ +
+

bool: True if the DDL statement was stored successfully, False otherwise.

+
+
+ + +
+
+
+ + def + add_documentation(documentation: str) -> bool: + + +
+ + +

Adds documentation to the model's training data

+ +

Example:

+ +
+
vn.add_documentation(
+    documentation="Our organization's definition of sales is the discount price of an item multiplied by the quantity sold."
+)
+
+
+ +
Arguments:
+ +
    +
  • documentation (str): The documentation string to store.
    • +
+ +
Returns:
+ +
+

bool: True if the documentation string was stored successfully, False otherwise.

+
+
+ + +
+
+
+
@dataclass
+ + class + TrainingPlanItem: + + +
+ + + + +
+
+ + TrainingPlanItem(item_type: str, item_group: str, item_name: str, item_value: str) + + +
+ + + + +
+
+
+ item_type: str + + +
+ + + + +
+
+
+ item_group: str + + +
+ + + + +
+
+
+ item_name: str + + +
+ + + + +
+
+
+ item_value: str + + +
+ + + + +
+
+
+ ITEM_TYPE_SQL = +'sql' + + +
+ + + + +
+
+
+ ITEM_TYPE_DDL = +'ddl' + + +
+ + + + +
+
+
+ ITEM_TYPE_IS = +'is' + + +
+ + + + +
+
+
+
+ + class + TrainingPlan: + + +
+ + +

A class representing a training plan. You can see what's in it, and remove items from it that you don't want trained.

+ +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.get_summary()
+
+
+
+ + +
+
+ + TrainingPlan(plan: List[vanna.TrainingPlanItem]) + + +
+ + + + +
+
+
+ + def + get_summary(self) -> List[str]: + + +
+ + +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.get_summary()
+
+
+ +

Get a summary of the training plan.

+ +
Returns:
+ +
+

List[str]: A list of strings describing the training plan.

+
+
+ + +
+
+
+ + def + remove_item(self, item: str): + + +
+ + +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.remove_item("Train on SQL: What is the average salary of employees?")
+
+
+ +

Remove an item from the training plan.

+ +
Arguments:
+ +
    +
  • item (str): The item to remove.
    • +
+
+ + +
+
+
+
+ + def + get_training_plan_postgres( filter_databases: Optional[List[str]] = None, filter_schemas: Optional[List[str]] = None, include_information_schema: bool = False, use_historical_queries: bool = True) -> vanna.TrainingPlan: + + +
+ + + + +
+
+
+ + def + get_training_plan_generic(df) -> vanna.TrainingPlan: + + +
+ + + + +
+
+
+ + def + get_training_plan_experimental( filter_databases: Optional[List[str]] = None, filter_schemas: Optional[List[str]] = None, include_information_schema: bool = False, use_historical_queries: bool = True) -> vanna.TrainingPlan: + + +
+ + +

EXPERIMENTAL : This method is experimental and may change in future versions.

+ +

Get a training plan based on the metadata in the database. Currently this only works for Snowflake.

+ +

Example:

+ +
+
plan = vn.get_training_plan_experimental(filter_databases=["employees"], filter_schemas=["public"])
+
+vn.train(plan=plan)
+
+
+
+ + +
+
+
+ + def + train( question: str = None, sql: str = None, ddl: str = None, documentation: str = None, json_file: str = None, sql_file: str = None, plan: vanna.TrainingPlan = None) -> bool: + + +
+ + +

Example:

+ +
+
vn.train()
+
+
+ +

Train Vanna.AI on a question and its corresponding SQL query. +If you call it with no arguments, it will check if you connected to a database and it will attempt to train on the metadata of that database. +If you call it with the sql argument, it's equivalent to [add_sql()][vanna.add_sql]. +If you call it with the ddl argument, it's equivalent to [add_ddl()][vanna.add_ddl]. +If you call it with the documentation argument, it's equivalent to [add_documentation()][vanna.add_documentation]. +It can also accept a JSON file path or SQL file path to train on a batch of questions and SQL queries or a list of SQL queries respectively. +Additionally, you can pass a [TrainingPlan][vanna.TrainingPlan] object. Get a training plan with [vn.get_training_plan_experimental()][vanna.get_training_plan_experimental].

+ +
Arguments:
+ +
    +
  • question (str): The question to train on.
    • +
  • sql (str): The SQL query to train on.
    • +
  • sql_file (str): The SQL file path.
    • +
  • json_file (str): The JSON file path.
    • +
  • ddl (str): The DDL statement.
    • +
  • documentation (str): The documentation to train on.
    • +
  • plan (TrainingPlan): The training plan to train on.
    • +
+
+ + +
+
+
+ + def + flag_sql_for_review( question: str, sql: Optional[str] = None, error_msg: Optional[str] = None) -> bool: + + +
+ + +

Example:

+ +
+
vn.flag_sql_for_review(question="What is the average salary of employees?")
+
+
+ +

Flag a question and its corresponding SQL query for review. You can see the tag show up in [vn.get_all_questions()][vanna.get_all_questions]

+ +
Arguments:
+ +
    +
  • question (str): The question to flag.
    • +
  • sql (str): The SQL query to flag.
    • +
  • error_msg (str): The error message to flag.
    • +
+ +
Returns:
+ +
+

bool: True if the question and SQL query were flagged successfully, False otherwise.

+
+
+ + +
+
+
+ + def + remove_sql(question: str) -> bool: + + +
+ + +

Remove a question and its corresponding SQL query from the model's training data

+ +

Example:

+ +
+
vn.remove_sql(question="What is the average salary of employees?")
+
+
+ +
Arguments:
+ +
    +
  • question (str): The question to remove.
    • +
+
+ + +
+
+
+ + def + remove_training_data(id: str) -> bool: + + +
+ + +

Remove training data from the model

+ +

Example:

+ +
+
vn.remove_training_data(id="1-ddl")
+
+
+ +
Arguments:
+ +
    +
  • id (str): The ID of the training data to remove.
    • +
+
+ + +
+
+
+ + def + generate_sql(question: str) -> str: + + +
+ + +

Example:

+ +
+
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.

+ +
Arguments:
+ +
    +
  • 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_meta(question: str) -> str: + + +
+ + +

Example:

+ +
+
vn.generate_meta(question="What tables are in the database?")
+# Information about the tables in the database
+
+
+ +

Generate answers about the metadata of a database using the Vanna.AI API.

+ +
Arguments:
+ +
    +
  • question (str): The question to generate an answer for.
    • +
+ +
Returns:
+ +
+

str or None: The answer, or None if an error occurred.

+
+
+ + +
+
+
+ + def + generate_followup_questions(question: str, df: pandas.core.frame.DataFrame) -> List[str]: + + +
+ + +

Example:

+ +
+
vn.generate_followup_questions(question="What is the average salary of employees?", df=df)
+# ['What is the average salary of employees in the Sales department?', 'What is the average salary of employees in the Engineering department?', ...]
+
+
+ +

Generate follow-up questions using the Vanna.AI API.

+ +
Arguments:
+ +
    +
  • question (str): The question to generate follow-up questions for.
    • +
  • df (pd.DataFrame): The DataFrame to generate follow-up questions for.
    • +
+ +
Returns:
+ +
+

List[str] or None: The follow-up questions, or None if an error occurred.

+
+
+ + +
+
+
+ + def + generate_questions() -> List[str]: + + +
+ + +

Example:

+ +
+
vn.generate_questions()
+# ['What is the average salary of employees?', 'What is the total salary of employees?', ...]
+
+
+ +

Generate questions using the Vanna.AI API.

+ +
Returns:
+ +
+

List[str] or None: The questions, or None if an error occurred.

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

Example:

+ +
+
# RECOMMENDED IN A NOTEBOOK:
+sql, df, fig, followup_questions = vn.ask()
+
+
+sql, df, fig, followup_questions = vn.ask(question="What is the average salary of employees?")
+# SELECT AVG(salary) FROM employees
+
+
+ +

Ask a question using the Vanna.AI API. This generates an SQL query, runs it, and returns the results in a dataframe and a Plotly figure. +If you set print_results to True, the sql, dataframe, and figure will be output to the screen instead of returned.

+ +
Arguments:
+ +
    +
  • question (str): The question to ask. If None, you will be prompted to enter a question.
    • +
  • print_results (bool): Whether to print the SQL query and results.
    • +
  • auto_train (bool): Whether to automatically train the model if the SQL query is incorrect.
    • +
  • generate_followups (bool): Whether to generate follow-up questions.
    • +
+ +
Returns:
+ +
+

str or None: The SQL query, or None if an error occurred. + pd.DataFrame or None: The results of the SQL query, or None if an error occurred. + plotly.graph_objs.Figure or None: The Plotly figure, or None if an error occurred. + List[str] or None: The follow-up questions, or None if an error occurred.

+
+
+ + +
+
+
+ + def + generate_plotly_code( question: Optional[str], sql: Optional[str], df: pandas.core.frame.DataFrame, chart_instructions: Optional[str] = None) -> str: + + +
+ + +

Example:

+ +
+
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")
+
+
+ +

Generate Plotly code using the Vanna.AI API.

+ +
Arguments:
+ +
    +
  • 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.
    • +
  • chart_instructions (str): Optional instructions for how to plot the chart.
    • +
+ +
Returns:
+ +
+

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

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

Example:

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

Get a Plotly figure from a dataframe and Plotly code.

+ +
Arguments:
+ +
    +
  • df (pd.DataFrame): The dataframe to use.
    • +
  • plotly_code (str): The Plotly code to use.
    • +
+ +
Returns:
+ +
+

plotly.graph_objs.Figure: The Plotly figure.

+
+
+ + +
+
+
+ + def + get_results(cs, default_database: str, sql: str) -> pandas.core.frame.DataFrame: + + +
+ + +

DEPRECATED. Use vn.run_sql instead. +Run the SQL query and return the results as a pandas dataframe. This is just a helper function that does not use the Vanna.AI API.

+ +
Arguments:
+ +
    +
  • cs: Snowflake connection cursor.
    • +
  • default_database (str): The default database to use.
    • +
  • sql (str): The SQL query to execute.
    • +
+ +
Returns:
+ +
+

pd.DataFrame: The results of the SQL query.

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

Example:

+ +
+
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.

+ +
Arguments:
+ +
    +
  • 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'")
+# 'What is the name of the student?'
+
+
+ +

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

+ +
Arguments:
+ +
    +
  • sql (str): The SQL query to generate a question for.
    • +
+ +
Returns:
+ +
+

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

+
+
+ + +
+
+
+ + def + get_all_questions() -> pandas.core.frame.DataFrame: + + +
+ + +

Get a list of questions from the Vanna.AI API.

+ +

Example:

+ +
+
questions = vn.get_all_questions()
+
+
+ +
Returns:
+ +
+

pd.DataFrame or None: The list of questions, or None if an error occurred.

+
+
+ + +
+
+
+ + def + get_training_data() -> pandas.core.frame.DataFrame: + + +
+ + +

Get the training data for the current model

+ +

Example:

+ +
+
training_data = vn.get_training_data()
+
+
+ +
Returns:
+ +
+

pd.DataFrame or None: The training data, or None if an error occurred.

+
+
+ + +
+
+
+ + def + connect_to_sqlite(url: str): + + +
+ + +

Connect to a SQLite database. This is just a helper function to set [vn.run_sql][vanna.run_sql]

+ +
Arguments:
+ +
    +
  • url (str): The URL of the database to connect to.
    • +
+ +
Returns:
+ +
+

None

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

Connect to Snowflake using the Snowflake connector. This is just a helper function to set [vn.run_sql][vanna.run_sql]

+ +

Example:

+ +
+
import snowflake.connector
+
+vn.connect_to_snowflake(
+    account="myaccount",
+    username="myusername",
+    password="mypassword",
+    database="mydatabase",
+    role="myrole",
+)
+
+
+ +
Arguments:
+ +
    +
  • account (str): The Snowflake account name.
    • +
  • username (str): The Snowflake username.
    • +
  • password (str): The Snowflake password.
    • +
  • database (str): The default database to use.
    • +
  • role (Union[str, None], optional): The role to use. Defaults to None.
    • +
+
+ + +
+
+
+ + def + connect_to_postgres( host: str = None, dbname: str = None, user: str = None, password: str = None, port: int = None): + + +
+ + +

Connect to postgres using the psycopg2 connector. This is just a helper function to set [vn.run_sql][vanna.run_sql] +Example:

+ +
+
import psycopg2.connect
+vn.connect_to_bigquery(
+    host="myhost",
+    dbname="mydatabase",
+    user="myuser",
+    password="mypassword",
+    port=5432
+)
+
+
+ +
Arguments:
+ +
    +
  • host (str): The postgres host.
    • +
  • dbname (str): The postgres database name.
    • +
  • user (str): The postgres user.
    • +
  • password (str): The postgres password.
    • +
  • port (int): The postgres Port.
    • +
+
+ + +
+
+
+ + def + connect_to_bigquery(cred_file_path: str = None, project_id: str = None): + + +
+ + +

Connect to gcs using the bigquery connector. This is just a helper function to set [vn.run_sql][vanna.run_sql] +Example:

+ +
+
import bigquery.Client
+vn.connect_to_bigquery(
+    project_id="myprojectid",
+    cred_file_path="path/to/credentials.json",
+)
+
+
+ +
Arguments:
+ +
    +
  • project_id (str): The gcs project id.
    • +
  • cred_file_path (str): The gcs credential file path
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/vanna/base.html b/vanna/base.html new file mode 100644 index 000000000..d4aeb49b3 --- /dev/null +++ b/vanna/base.html @@ -0,0 +1,901 @@ + + + + + + + vanna.base API documentation + + + + + + + + + + +
+
+

+vanna.base

+ + + + + +
+
+
+ + class + VannaBase(abc.ABC): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ config + + +
+ + + + +
+
+
+ run_sql_is_set + + +
+ + + + +
+
+
+ + def + generate_sql_from_question(self, question: str, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + generate_embedding(self, data: str, **kwargs) -> list[float]: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + get_similar_question_sql(self, question: str, **kwargs) -> list: + + +
+ + + + +
+ + +
+
+
@abstractmethod
+ + def + add_question_sql(self, question: str, sql: str, **kwargs): + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + add_ddl(self, ddl: str, **kwargs): + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + add_documentation(self, doc: str, **kwargs): + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + get_prompt( self, question: str, question_sql_list: list, ddl_list: list, doc_list: list, **kwargs): + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + submit_prompt(self, prompt, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + generate_question(self, sql: str, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + generate_plotly_code( self, question: str = None, sql: str = None, df_metadata: str = None, **kwargs) -> str: + + +
+ + + + +
+
+
+ + def + connect_to_snowflake( self, account: str, username: str, password: str, database: str, role: Optional[str] = None): + + +
+ + + + +
+
+
+ + def + run_sql(sql: str, **kwargs) -> pandas.core.frame.DataFrame: + + +
+ + + + +
+
+
+ + def + ask( self, question: Optional[str] = None, print_results: bool = True, auto_train: bool = True) -> Optional[Tuple[Optional[str], Optional[pandas.core.frame.DataFrame], Optional[plotly.graph_objs._figure.Figure]]]: + + +
+ + + + +
+
+
+ + def + train( self, question: str = None, sql: str = None, ddl: str = None, documentation: str = None, plan: vanna.types.TrainingPlan = None) -> bool: + + +
+ + +

Example:

+ +
+
vn.train()
+
+
+ +

Train Vanna.AI on a question and its corresponding SQL query. +If you call it with no arguments, it will check if you connected to a database and it will attempt to train on the metadata of that database. +If you call it with the sql argument, it's equivalent to [add_sql()][vanna.add_sql]. +If you call it with the ddl argument, it's equivalent to [add_ddl()][vanna.add_ddl]. +If you call it with the documentation argument, it's equivalent to [add_documentation()][vanna.add_documentation]. +Additionally, you can pass a [TrainingPlan][vanna.TrainingPlan] object. Get a training plan with [vn.get_training_plan_experimental()][vanna.get_training_plan_experimental].

+ +
Arguments:
+ +
    +
  • question (str): The question to train on.
    • +
  • sql (str): The SQL query to train on.
    • +
  • ddl (str): The DDL statement.
    • +
  • documentation (str): The documentation to train on.
    • +
  • plan (TrainingPlan): The training plan to train on.
    • +
+
+ + +
+
+
+ + def + get_training_plan_snowflake( self, filter_databases: Optional[List[str]] = None, filter_schemas: Optional[List[str]] = None, include_information_schema: bool = False, use_historical_queries: bool = True) -> vanna.types.TrainingPlan: + + +
+ + + + +
+
+
+ + def + get_plotly_figure( self, plotly_code: str, df: pandas.core.frame.DataFrame, dark_mode: bool = True) -> plotly.graph_objs._figure.Figure: + + +
+ + +

Example:

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

Get a Plotly figure from a dataframe and Plotly code.

+ +
Arguments:
+ +
    +
  • df (pd.DataFrame): The dataframe to use.
    • +
  • plotly_code (str): The Plotly code to use.
    • +
+ +
Returns:
+ +
+

plotly.graph_objs.Figure: The Plotly figure.

+
+
+ + +
+
+
+
+ + class + SplitStorage(VannaBase): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ + def + get_similar_question_sql(self, embedding: str, **kwargs) -> list: + + +
+ + + + +
+ + +
+
+
@abstractmethod
+ + def + store_question_sql_embedding(self, embedding: str, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + store_ddl_embedding(self, embedding: str, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + store_documentation_embedding(self, embedding: str, **kwargs) -> str: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + get_similar_question_sql_ids(self, embedding: str, **kwargs) -> list: + + +
+ + + + +
+ + +
+
+
@abstractmethod
+ + def + get_question_sql(self, question_sql_ids: list, **kwargs) -> list: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + get_documentation(self, doc_ids: list, **kwargs) -> list: + + +
+ + + + +
+
+
+
@abstractmethod
+ + def + get_ddl(self, ddl_ids: list, **kwargs) -> list: + + +
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/chromadb_vector.html b/vanna/chromadb_vector.html new file mode 100644 index 000000000..9f221660d --- /dev/null +++ b/vanna/chromadb_vector.html @@ -0,0 +1,480 @@ + + + + + + + vanna.chromadb_vector API documentation + + + + + + + + + + +
+
+

+vanna.chromadb_vector

+ + + + + +
+
+
+ default_ef = +<chromadb.utils.embedding_functions.ONNXMiniLM_L6_V2 object> + + +
+ + + + +
+
+
+ + class + ChromaDB_VectorStore(vanna.base.VannaBase): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ chroma_client + + +
+ + + + +
+
+
+ documentation_collection + + +
+ + + + +
+
+
+ ddl_collection + + +
+ + + + +
+
+
+ sql_collection + + +
+ + + + +
+
+
+ + def + generate_embedding(self, data: str, **kwargs) -> list[float]: + + +
+ + + + +
+
+
+ + def + add_question_sql(self, question: str, sql: str, **kwargs): + + +
+ + + + +
+
+
+ + def + add_ddl(self, ddl: str, **kwargs): + + +
+ + + + +
+
+
+ + def + add_documentation(self, doc: str, **kwargs): + + +
+ + + + +
+
+
+ + def + get_similar_question_sql(self, question: str, **kwargs) -> list: + + +
+ + + + +
+ + +
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/exceptions.html b/vanna/exceptions.html new file mode 100644 index 000000000..038bf1d39 --- /dev/null +++ b/vanna/exceptions.html @@ -0,0 +1,518 @@ + + + + + + + vanna.exceptions API documentation + + + + + + + + + + +
+
+

+vanna.exceptions

+ + + + + +
+
+
+ + class + ImproperlyConfigured(builtins.BaseException): + + +
+ + +

Raise for incorrect configuration.

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + DependencyError(builtins.BaseException): + + +
+ + +

Raise for missing dependencies.

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + ConnectionError(builtins.BaseException): + + +
+ + +

Raise for connection

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + OTPCodeError(builtins.BaseException): + + +
+ + +

Raise for invalid otp or not able to send it

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + SQLRemoveError(builtins.BaseException): + + +
+ + +

Raise when not able to remove SQL

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + ExecutionError(builtins.BaseException): + + +
+ + +

Raise when not able to execute Code

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + ValidationError(builtins.BaseException): + + +
+ + +

Raise for validations

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+
+ + class + APIError(builtins.BaseException): + + +
+ + +

Raise for API errors

+
+ + +
+
Inherited Members
+
+
builtins.BaseException
+
BaseException
+
with_traceback
+
add_note
+
args
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/vanna/local.html b/vanna/local.html new file mode 100644 index 000000000..0bbd9c291 --- /dev/null +++ b/vanna/local.html @@ -0,0 +1,332 @@ + + + + + + + vanna.local API documentation + + + + + + + + + + +
+
+

+vanna.local

+ + + + + +
+
+
+ + class + LocalContext_OpenAI(vanna.chromadb_vector.ChromaDB_VectorStore, vanna.openai_chat.OpenAI_Chat): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ + LocalContext_OpenAI(config=None) + + +
+ + + + +
+
+
Inherited Members
+
+ + + +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/mock.html b/vanna/mock.html new file mode 100644 index 000000000..2371313ed --- /dev/null +++ b/vanna/mock.html @@ -0,0 +1,341 @@ + + + + + + + vanna.mock API documentation + + + + + + + + + + +
+
+

+vanna.mock

+ + + + + +
+
+
+ + class + MockModel(vanna.chromadb_vector.ChromaDB_VectorStore): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ + def + get_prompt( self, question: str, question_sql_list: list, ddl_list: list, doc_list: list, **kwargs) -> str: + + +
+ + + + +
+
+
+ + def + submit_prompt(self, prompt: str, **kwargs) -> str: + + +
+ + + + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/openai_chat.html b/vanna/openai_chat.html new file mode 100644 index 000000000..b584a36de --- /dev/null +++ b/vanna/openai_chat.html @@ -0,0 +1,415 @@ + + + + + + + vanna.openai_chat API documentation + + + + + + + + + + +
+
+

+vanna.openai_chat

+ + + + + +
+
+
+ + class + OpenAI_Chat(vanna.base.VannaBase): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+
@staticmethod
+ + def + system_message(message: str) -> dict: + + +
+ + + + +
+
+
+
@staticmethod
+ + def + user_message(message: str) -> dict: + + +
+ + + + +
+
+
+
@staticmethod
+ + def + assistant_message(message: str) -> dict: + + +
+ + + + +
+
+
+ + def + get_prompt( self, question: str, question_sql_list: list, ddl_list: list, doc_list: list, **kwargs) -> str: + + +
+ + + + +
+
+
+ + def + generate_question(self, sql: str, **kwargs) -> str: + + +
+ + + + +
+
+
+ + def + generate_plotly_code( self, question: str = None, sql: str = None, df_metadata: str = None, **kwargs) -> str: + + +
+ + + + +
+
+
+ + def + submit_prompt(self, prompt, **kwargs) -> str: + + +
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/openai_embeddings.html b/vanna/openai_embeddings.html new file mode 100644 index 000000000..79eea8e3e --- /dev/null +++ b/vanna/openai_embeddings.html @@ -0,0 +1,319 @@ + + + + + + + vanna.openai_embeddings API documentation + + + + + + + + + + +
+
+

+vanna.openai_embeddings

+ + + + + +
+
+
+ + class + OpenAI_Embeddings(vanna.base.VannaBase): + + +
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
+ + def + generate_embedding(self, data: str, **kwargs) -> list[float]: + + +
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/vanna/types.html b/vanna/types.html new file mode 100644 index 000000000..c96948428 --- /dev/null +++ b/vanna/types.html @@ -0,0 +1,2601 @@ + + + + + + + vanna.types API documentation + + + + + + + + + + +
+
+

+vanna.types

+ + + + + +
+
+
+
@dataclass
+ + class + Status: + + +
+ + + + +
+
+ + Status(success: bool, message: str) + + +
+ + + + +
+
+
+ success: bool + + +
+ + + + +
+
+
+ message: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionList: + + +
+ + + + +
+
+ + QuestionList(questions: List[vanna.types.FullQuestionDocument]) + + +
+ + + + +
+
+
+ questions: List[vanna.types.FullQuestionDocument] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + FullQuestionDocument: + + +
+ + + + +
+
+ + FullQuestionDocument( id: vanna.types.QuestionId, question: vanna.types.Question, answer: vanna.types.SQLAnswer | None, data: vanna.types.DataResult | None, plotly: vanna.types.PlotlyResult | None) + + +
+ + + + +
+
+
+ id: vanna.types.QuestionId + + +
+ + + + +
+
+
+ question: vanna.types.Question + + +
+ + + + +
+
+
+ answer: vanna.types.SQLAnswer | None + + +
+ + + + +
+
+
+ data: vanna.types.DataResult | None + + +
+ + + + +
+
+
+ plotly: vanna.types.PlotlyResult | None + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionSQLPair: + + +
+ + + + +
+
+ + QuestionSQLPair(question: str, sql: str, tag: Optional[str]) + + +
+ + + + +
+
+
+ question: str + + +
+ + + + +
+
+
+ sql: str + + +
+ + + + +
+
+
+ tag: Optional[str] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + Organization: + + +
+ + + + +
+
+ + Organization( name: str, user: str | None, connection: vanna.types.Connection | None) + + +
+ + + + +
+
+
+ name: str + + +
+ + + + +
+
+
+ user: str | None + + +
+ + + + +
+
+
+ connection: vanna.types.Connection | None + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + OrganizationList: + + +
+ + + + +
+
+ + OrganizationList(organizations: List[str]) + + +
+ + + + +
+
+
+ organizations: List[str] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionStringList: + + +
+ + + + +
+
+ + QuestionStringList(questions: List[str]) + + +
+ + + + +
+
+
+ questions: List[str] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + Visibility: + + +
+ + + + +
+
+ + Visibility(visibility: bool) + + +
+ + + + +
+
+
+ visibility: bool + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + UserEmail: + + +
+ + + + +
+
+ + UserEmail(email: str) + + +
+ + + + +
+
+
+ email: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + NewOrganization: + + +
+ + + + +
+
+ + NewOrganization(org_name: str, db_type: str) + + +
+ + + + +
+
+
+ org_name: str + + +
+ + + + +
+
+
+ db_type: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + NewOrganizationMember: + + +
+ + + + +
+
+ + NewOrganizationMember(org_name: str, email: str, is_admin: bool) + + +
+ + + + +
+
+
+ org_name: str + + +
+ + + + +
+
+
+ email: str + + +
+ + + + +
+
+
+ is_admin: bool + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + UserOTP: + + +
+ + + + +
+
+ + UserOTP(email: str, otp: str) + + +
+ + + + +
+
+
+ email: str + + +
+ + + + +
+
+
+ otp: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + ApiKey: + + +
+ + + + +
+
+ + ApiKey(key: str) + + +
+ + + + +
+
+
+ key: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionId: + + +
+ + + + +
+
+ + QuestionId(id: str) + + +
+ + + + +
+
+
+ id: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + Question: + + +
+ + + + +
+
+ + Question(question: str) + + +
+ + + + +
+
+
+ question: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionCategory: + + +
+ + + + +
+
+ + QuestionCategory(question: str, category: str) + + +
+ + + + +
+
+
+ question: str + + +
+ + + + +
+
+
+ category: str + + +
+ + + + +
+
+
+ NO_SQL_GENERATED = +'No SQL Generated' + + +
+ + + + +
+
+
+ SQL_UNABLE_TO_RUN = +'SQL Unable to Run' + + +
+ + + + +
+
+
+ BOOTSTRAP_TRAINING_QUERY = +'Bootstrap Training Query' + + +
+ + + + +
+
+
+ SQL_RAN = +'SQL Ran Successfully' + + +
+ + + + +
+
+
+ FLAGGED_FOR_REVIEW = +'Flagged for Review' + + +
+ + + + +
+
+
+ REVIEWED_AND_APPROVED = +'Reviewed and Approved' + + +
+ + + + +
+
+
+ REVIEWED_AND_REJECTED = +'Reviewed and Rejected' + + +
+ + + + +
+
+
+ REVIEWED_AND_UPDATED = +'Reviewed and Updated' + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + AccuracyStats: + + +
+ + + + +
+
+ + AccuracyStats(num_questions: int, data: Dict[str, int]) + + +
+ + + + +
+
+
+ num_questions: int + + +
+ + + + +
+
+
+ data: Dict[str, int] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + Followup: + + +
+ + + + +
+
+ + Followup(followup: str) + + +
+ + + + +
+
+
+ followup: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + QuestionEmbedding: + + +
+ + + + +
+
+ + QuestionEmbedding(question: vanna.types.Question, embedding: List[float]) + + +
+ + + + +
+
+
+ question: vanna.types.Question + + +
+ + + + +
+
+
+ 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: + + +
+ + + + +
+
+ + WarehouseDefinition(name: str, tables: List[vanna.types.TableDefinition]) + + +
+ + + + +
+
+
+ name: str + + +
+ + + + +
+
+
+ tables: List[vanna.types.TableDefinition] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + TableDefinition: + + +
+ + + + +
+
+ + TableDefinition( schema_name: str, table_name: str, ddl: str | None, columns: List[vanna.types.ColumnDefinition]) + + +
+ + + + +
+
+
+ schema_name: str + + +
+ + + + +
+
+
+ table_name: str + + +
+ + + + +
+
+
+ ddl: str | None + + +
+ + + + +
+
+
+ columns: List[vanna.types.ColumnDefinition] + + +
+ + + + +
+
+
+
+
@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 + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + StringData: + + +
+ + + + +
+
+ + StringData(data: str) + + +
+ + + + +
+
+
+ data: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + DataFrameJSON: + + +
+ + + + +
+
+ + DataFrameJSON(data: str) + + +
+ + + + +
+
+
+ data: str + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + TrainingData: + + +
+ + + + +
+
+ + TrainingData(questions: List[dict], ddl: List[str], documentation: List[str]) + + +
+ + + + +
+
+
+ questions: List[dict] + + +
+ + + + +
+
+
+ ddl: List[str] + + +
+ + + + +
+
+
+ documentation: List[str] + + +
+ + + + +
+
+
+
+
@dataclass
+ + class + TrainingPlanItem: + + +
+ + + + +
+
+ + TrainingPlanItem(item_type: str, item_group: str, item_name: str, item_value: str) + + +
+ + + + +
+
+
+ item_type: str + + +
+ + + + +
+
+
+ item_group: str + + +
+ + + + +
+
+
+ item_name: str + + +
+ + + + +
+
+
+ item_value: str + + +
+ + + + +
+
+
+ ITEM_TYPE_SQL = +'sql' + + +
+ + + + +
+
+
+ ITEM_TYPE_DDL = +'ddl' + + +
+ + + + +
+
+
+ ITEM_TYPE_IS = +'is' + + +
+ + + + +
+
+
+
+ + class + TrainingPlan: + + +
+ + +

A class representing a training plan. You can see what's in it, and remove items from it that you don't want trained.

+ +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.get_summary()
+
+
+
+ + +
+
+ + TrainingPlan(plan: List[vanna.types.TrainingPlanItem]) + + +
+ + + + +
+
+
+ + def + get_summary(self) -> List[str]: + + +
+ + +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.get_summary()
+
+
+ +

Get a summary of the training plan.

+ +
Returns:
+ +
+

List[str]: A list of strings describing the training plan.

+
+
+ + +
+
+
+ + def + remove_item(self, item: str): + + +
+ + +

Example:

+ +
+
plan = vn.get_training_plan()
+
+plan.remove_item("Train on SQL: What is the average salary of employees?")
+
+
+ +

Remove an item from the training plan.

+ +
Arguments:
+ +
    +
  • item (str): The item to remove.
    • +
+
+ + +
+
+
+ + \ No newline at end of file diff --git a/vanna/utils.html b/vanna/utils.html new file mode 100644 index 000000000..b0645b80d --- /dev/null +++ b/vanna/utils.html @@ -0,0 +1,286 @@ + + + + + + + vanna.utils API documentation + + + + + + + + + + +
+
+

+vanna.utils

+ + + + + +
+
+
+ + def + validate_config_path(path): + + +
+ + + + +
+
+
+ + def + sanitize_model_name(model_name): + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/vn-ask.html b/vn-ask.html new file mode 100644 index 000000000..aaaafe93a --- /dev/null +++ b/vn-ask.html @@ -0,0 +1,8030 @@ + + + + + + + + + + + + + +Vanna Docs: + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ + diff --git a/vn-connect-to-bigquery.html b/vn-connect-to-bigquery.html new file mode 100644 index 000000000..c299496ab --- /dev/null +++ b/vn-connect-to-bigquery.html @@ -0,0 +1,7916 @@ + + + + + + + + + + + + + +Vanna Docs: + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+ + diff --git a/vn-connect-to-postgres.html b/vn-connect-to-postgres.html new file mode 100644 index 000000000..a4c7ba8c2 --- /dev/null +++ b/vn-connect-to-postgres.html @@ -0,0 +1,8585 @@ + + + + + + + + + + + + + +Vanna Docs: + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + diff --git a/vn-train.html b/vn-train.html new file mode 100644 index 000000000..8fb0206ab --- /dev/null +++ b/vn-train.html @@ -0,0 +1,7848 @@ + + + + + + + + + + + + + +Vanna Docs: Snowflake + + + + + + + + + + + + + + + + + + + + +
+ + + + Run Using Colab + + + + Open in GitHub + + + + + + + + +
+ + diff --git a/workflow.md b/workflow.md new file mode 100644 index 000000000..af69ca5bd --- /dev/null +++ b/workflow.md @@ -0,0 +1,19 @@ +# What's the Workflow? +```mermaid +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 +``` \ No newline at end of file