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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# How to use Vanna with various databases\n", | ||
| "\n", | ||
| "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.\n", | ||
| "\n", | ||
| "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." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import vanna as vn" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## **PostgreSQL**" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "import psycopg2\n", | ||
| "\n", | ||
| "conn_details = {...} # fill this with your connection details\n", | ||
| "conn_postgres = psycopg2.connect(**conn_details)\n", | ||
| "\n", | ||
| "def run_sql_postgres(sql: str) -> pd.DataFrame:\n", | ||
| " df = pd.read_sql_query(sql, conn_postgres)\n", | ||
| " return df\n", | ||
| "\n", | ||
| "vn.run_sql = run_sql_postgres" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## **Snowflake**\n", | ||
| "\n", | ||
| "We have a built-in function for Snowflake, so you don't need to write your own.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "vn.connect_to_snowflake(account='my-account', username='my-username', password='my-password', database='my-database')\n" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## **Google BigQuery**" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from google.cloud import bigquery\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "project_id = 'your-project-id' # replace with your Project ID\n", | ||
| "client_bigquery = bigquery.Client(project=project_id)\n", | ||
| "\n", | ||
| "def run_sql_bigquery(sql: str) -> pd.DataFrame:\n", | ||
| " df = client_bigquery.query(sql).to_dataframe()\n", | ||
| " return df\n", | ||
| "\n", | ||
| "vn.run_sql = run_sql_bigquery" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## **Amazon Athena**" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "from pyathena import connect\n", | ||
| "\n", | ||
| "conn_details = {...} # fill this with your connection details\n", | ||
| "conn_athena = connect(**conn_details)\n", | ||
| "\n", | ||
| "def run_sql_athena(sql: str) -> pd.DataFrame:\n", | ||
| " df = pd.read_sql(sql, conn_athena)\n", | ||
| " return df\n", | ||
| "\n", | ||
| "vn.run_sql = run_sql_athena" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## **Amazon Redshift**" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "import psycopg2\n", | ||
| "\n", | ||
| "conn_details = {...} # fill this with your connection details\n", | ||
| "conn_redshift = psycopg2.connect(**conn_details)\n", | ||
| "\n", | ||
| "def run_sql_redshift(sql: str) -> pd.DataFrame:\n", | ||
| " df = pd.read_sql_query(sql, conn_redshift)\n", | ||
| " return df\n", | ||
| "\n", | ||
| "vn.run_sql = run_sql_redshift" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# **Others**\n", | ||
| "\n", | ||
| "You can follow a similar pattern to the others for your database. You just have to provide a `vn.run_sql` function that takes in a SQL query and returns a Pandas DataFrame." | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Manually Training Vanna" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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