AI/ML: add integration guide on Jupyter notebooks + chdb

docs/cloud/guides/SQL_console/connection_details.md

@@ -0,0 +1,11 @@
+---
+slug: /cloud/guides/sql-console/gather-connection-details
+sidebar_label: 'Gather your connection details'
+title: 'Gather your connection details'
+description: 'Gather your connection details'
+doc_type: 'guide'
+---
+
+import ConnectionDetails from '@site/docs/_snippets/_gather_your_details_http.mdx';
+
+<ConnectionDetails />

docs/use-cases/AI_ML/index.md

@@ -2,7 +2,7 @@
 description: 'Landing page for Machine Learning and GenAI use case guides'
 pagination_prev: null
 pagination_next: null
-slug: /use-cases/AI
+slug: /use-cases/AI/ask-ai
 title: 'Machine learning and GenAI'
 keywords: ['machine learning', 'genAI', 'AI']
 doc_type: 'landing-page'
@@ -15,6 +15,8 @@ With ClickHouse, it's easier than ever to unleash GenAI on your analytics data.
 In this section, you'll find some guides around how ClickHouse is used for 
 Machine Learning and GenAI.
 
-| Section                  | Description                                                                                |
-|--------------------------|--------------------------------------------------------------------------------------------|
-| [MCP](/use-cases/AI/MCP) | A collection of guides to get you setup using Model Context Protocol (MCP) with ClickHouse |
+| Section                                                              | Description                                                                                                                                      |
+|----------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|
+| [AI chat](/use-cases/AI_ML/AIChat)                                   | This guide explains how to enable and use the AI Chat feature in the ClickHouse Cloud Console.                                                   |
+| [MCP](/use-cases/AI/MCP)                                             | A collection of guides to get you setup using Model Context Protocol (MCP) with ClickHouse                                                       |
+| [AI-powered SQL generation](/use-cases/AI/ai-powered-sql-generation) | This feature allows users to describe their data requirements in plain text, which the system then translates into corresponding SQL statements. |

docs/use-cases/AI_ML/jupyter-notebook.md

@@ -0,0 +1,321 @@
+---
+slug: /use-cases/AI/jupyter-notebook
+sidebar_label: 'Exploring data in Jupyter notebooks with chDB'
+title: 'Exploring data in Jupyter notebooks with chDB'
+description: 'This guide explains how to setup and use chDB to explore data from ClickHouse Cloud or local files in Jupyer notebooks'
+keywords: ['ML', 'Jupyer', 'chDB', 'pandas']
+doc_type: 'guide'
+---
+
+import Image from '@theme/IdealImage';
+import image_1 from '@site/static/images/use-cases/AI_ML/jupyter/1.png';
+import image_2 from '@site/static/images/use-cases/AI_ML/jupyter/2.png';
+import image_3 from '@site/static/images/use-cases/AI_ML/jupyter/3.png';
+import image_4 from '@site/static/images/use-cases/AI_ML/jupyter/4.png';
+import image_5 from '@site/static/images/use-cases/AI_ML/jupyter/5.png';
+import image_6 from '@site/static/images/use-cases/AI_ML/jupyter/6.png';
+import image_7 from '@site/static/images/use-cases/AI_ML/jupyter/7.png';
+import image_8 from '@site/static/images/use-cases/AI_ML/jupyter/8.png';
+import image_9 from '@site/static/images/use-cases/AI_ML/jupyter/9.png';
+
+# Exploring data with Jupyter notebooks and chDB
+
+In this guide, you will learn how you can explore a dataset on ClickHouse Cloud data in Jupyter notebook with the help of [chDB](/chdb) - a fast in-process SQL OLAP Engine powered by ClickHouse.
+
+**Prerequisites**:
+- a virtual environment
+- a working ClickHouse Cloud service and your [connection details](/cloud/guides/sql-console/gather-connection-details)
+
+**What you'll learn:**
+- Connect to ClickHouse Cloud from Jupyter notebooks using chDB
+- Query remote datasets and convert results to Pandas DataFrames
+- Combine cloud data with local CSV files for analysis
+- Visualize data using matplotlib
+
+We'll be using the UK Property Price dataset which is available on ClickHouse Cloud as one of the starter datasets.
+It contains data about the prices that houses were sold for in the United Kingdom from 1995 to 2024.
+
+## Setup {#setup}
+
+To add this dataset to an existing ClickHouse Cloud service, login to [console.clickhouse.cloud](https://console.clickhouse.cloud/) with your account details.
+
+In the left hand menu, click on `Data sources`. Then click `Predefined sample data`:
+
+<Image size="md" img={image_1} alt="Add example data set"/>
+
+Select `Get started` in the UK property price paid data (4GB) card: 
+
+<Image size="md" img={image_2} alt="Select UK price paid dataset"/>
+
+Then click `Import dataset`:
+
+<Image size="md" img={image_3} alt="Import UK price paid dataset"/>
+
+ClickHouse will automatically create the `pp_complete` table in the `default` database and fill the table with 28.92 million rows of price point data.
+
+In order to reduce the likelihood of exposing your credentials, we recommend to add your Cloud username and password as environment variables on your local machine.
+From a terminal run the following command to add your username and password as environment variables:
+
+```bash
+export CLICKHOUSE_USER=default
+export CLICKHOUSE_PASSWORD=your_actual_password
+```
+
+:::note
+The environment variables above persist only as long as your terminal session.
+To set them permanently, add them to your shell configuration file.
+:::
+
+Now activate your virtual environment.
+From within your virtual environment, install Jupyter Notebook with the following command:
+
+```python
+pip install notebook
+```
+
+launch Jupyter Notebook with the following command:
+
+```python
+jupyter notebook
+```
+
+A new browser window should open with the Jupyter interface on `localhost:8888`.
+Click `File` > `New` > `Notebook` to create a new Notebook.
+
+<Image size="md" img={image_4} alt="Create a new notebook"/>
+
+You will be prompted to select a kernel.
+Select any Python kernel available to you, in this example we will select the `ipykernel`:
+
+<Image size="md" img={image_5} alt="Select kernel"/>
+
+In a blank cell, you can type the following command to install chDB which we will be using connect to our remote ClickHouse Cloud instance:
+
+```python
+pip install chdb
+```
+
+You can now import chDB and run a simple query to check that everything is set up correctly:
+
+```python
+import chdb
+
+result = chdb.query("SELECT 'Hello, ClickHouse!' as message")
+print(result)
+```
+
+## Exploring the data {#exploring-the-data}
+
+With the UK price paid data set up and chDB up and running in a Jupyter notebook, we can now get started exploring our data.
+
+Let's imagine we are interested in checking how price has changed with time for a specific area in the UK such as the capital city, London.
+ClickHouse's [`remoteSecure`](/sql-reference/table-functions/remote) function allows you to easily retrieve the data from ClickHouse Cloud.
+You can instruct chDB to return this data in process as a Pandas data frame - which is a convenient and familiar way of working with data.
+
+Write the following query to fetch the UK price paid data from your ClickHouse Cloud service and turn it into a `pandas.DataFrame`:
+
+```python
+import os
+from dotenv import load_dotenv
+import chdb
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+# Load environment variables from .env file
+load_dotenv()
+
+username = os.environ.get('CLICKHOUSE_USER')
+password = os.environ.get('CLICKHOUSE_PASSWORD')
+
+query = f"""
+SELECT 
+    toYear(date) AS year,
+    avg(price) AS avg_price
+FROM remoteSecure(
+'****.europe-west4.gcp.clickhouse.cloud',
+default.pp_complete,
+'{username}',
+'{password}'
+)
+WHERE town = 'LONDON'
+GROUP BY toYear(date)
+ORDER BY year;
+"""
+
+df = chdb.query(query, "DataFrame")
+df.head()
+```
+
+In the snippet above, `chdb.query(query, "DataFrame")` runs the specified query and outputs the result to the terminal as a Pandas DataFrame.
+In the query we are using the `remoteSecure` function to connect to ClickHouse Cloud.
+The `remoteSecure` functions takes as parameters:
+- a connection string
+- the name of the database and table to use
+- your username
+- your password
+
+As a security best practice, you should prefer using environment variables for the username and password parameters rather than specifying them directly in the function, although this is possible if you wish.
+
+The `remoteSecure` function connects to the remote ClickHouse Cloud service, runs the query and returns the result.
+Depending on the size of your data, this could take a few seconds.
+In this case we return an average price point per year, and filter by `town='LONDON'`.
+The result is then stored as a DataFrame in a variable called `df`.
+
+`df.head` displays only the first few rows of the returned data:
+
+<Image size="md" img={image_6} alt="dataframe preview"/>
+
+Run the following command in a new cell to check the types of the columns:
+
+```python
+df.dtypes
+```
+
+```response
+year          uint16
+avg_price    float64
+dtype: object
+```
+
+Notice that while `date` is of type `Date` in ClickHouse, in the resulting data frame it is of type `uint16`.
+chDB automatically infers the most appropriate type when returning the DataFrame.
+
+With the data now available to us in a familiar form, let's explore how prices of property in London have changed with time.
+
+In a new cell, run the following command to build a simple chart of time vs price for London using matplotlib:
+
+```python
+plt.figure(figsize=(12, 6))
+plt.plot(df['year'], df['avg_price'], marker='o')
+plt.xlabel('Year')
+plt.ylabel('Price (£)')
+plt.title('Price of London property over time')
+
+# Show every 2nd year to avoid crowding
+years_to_show = df['year'][::2]  # Every 2nd year
+plt.xticks(years_to_show, rotation=45)
+
+plt.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.show()
+```
+
+<Image size="md" img={image_7} alt="dataframe preview"/>
+
+Perhaps unsurprisingly, property prices in London have increased substantially over time.
+
+A fellow data scientist has sent us a .csv file with additional housing related variables and is curious how 
+the number of houses sold in London has changed over time.
+Let's plot some of these against the housing prices and see if we can discover any correlation.
+
+You can use the `file` table engine to read files directly on your local machine.
+In a new cell, run the following command to make a new DataFrame from the local .csv file.
+
+```python
+query = f"""
+SELECT 
+    toYear(date) AS year,
+    sum(houses_sold)*1000
+    FROM file('/Users/datasci/Desktop/housing_in_london_monthly_variables.csv')
+WHERE area = 'city of london' AND houses_sold IS NOT NULL
+GROUP BY toYear(date)
+ORDER BY year;
+"""
+
+df_2 = chdb.query(query, "DataFrame")
+df_2.head()
+```
+
+<details>
+<summary>Read from multiple sources in a single step</summary>
+It's also possible to read from multiple sources in a single step. You could use the query below using a `JOIN` to do so:
+
+```python
+query = f"""
+SELECT 
+    toYear(date) AS year,
+    avg(price) AS avg_price, housesSold
+FROM remoteSecure(
+'****.europe-west4.gcp.clickhouse.cloud',
+default.pp_complete,
+'{username}',
+'{password}'
+) AS remote
+JOIN (
+  SELECT 
+    toYear(date) AS year,
+    sum(houses_sold)*1000 AS housesSold
+    FROM file('/Users/datasci/Desktop/housing_in_london_monthly_variables.csv')
+  WHERE area = 'city of london' AND houses_sold IS NOT NULL
+  GROUP BY toYear(date)
+  ORDER BY year
+) AS local ON local.year = remote.year
+WHERE town = 'LONDON'
+GROUP BY toYear(date)
+ORDER BY year;
+"""
+```
+</details>
+
+<Image size="md" img={image_8} alt="dataframe preview"/>
+
+Although we are missing data from 2020 onwards, we can plot the two datasets against each other for the years 1995 to 2019.
+In a new cell run the following command:
+
+```python
+# Create a figure with two y-axes
+fig, ax1 = plt.subplots(figsize=(14, 8))
+
+# Plot houses sold on the left y-axis
+color = 'tab:blue'
+ax1.set_xlabel('Year')
+ax1.set_ylabel('Houses Sold', color=color)
+ax1.plot(df_2['year'], df_2['houses_sold'], marker='o', color=color, label='Houses Sold', linewidth=2)
+ax1.tick_params(axis='y', labelcolor=color)
+ax1.grid(True, alpha=0.3)
+
+# Create a second y-axis for price data
+ax2 = ax1.twinx()
+color = 'tab:red'
+ax2.set_ylabel('Average Price (£)', color=color)
+
+# Plot price data up until 2019
+ax2.plot(df[df['year'] <= 2019]['year'], df[df['year'] <= 2019]['avg_price'], marker='s', color=color, label='Average Price', linewidth=2)
+ax2.tick_params(axis='y', labelcolor=color)
+
+# Format price axis with currency formatting
+ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'£{x:,.0f}'))
+
+# Set title and show every 2nd year
+plt.title('London Housing Market: Sales Volume vs Prices Over Time', fontsize=14, pad=20)
+
+# Use years only up to 2019 for both datasets
+all_years = sorted(list(set(df_2[df_2['year'] <= 2019]['year']).union(set(df[df['year'] <= 2019]['year']))))
+years_to_show = all_years[::2]  # Every 2nd year
+ax1.set_xticks(years_to_show)
+ax1.set_xticklabels(years_to_show, rotation=45)
+
+# Add legends
+ax1.legend(loc='upper left')
+ax2.legend(loc='upper right')
+
+plt.tight_layout()
+plt.show()
+```
+
+<Image size="md" img={image_9} alt="Plot of remote data set and local data set"/>
+
+From the plotted data, we see that sales started around 160,000 in the year 1995 and surged quickly, peaking at around 540,000 in 1999.
+After that, volumes declined sharply through the mid-2000s, dropping severely during the 2007-2008 financial crisis and falling to around 140,000.
+Prices on the other hand showed steady, consistent growth from about £150,000 in 1995 to around £300,000 by 2005.
+Growth accelerated significantly after 2012, rising steeply from roughly £400,000 to over £1,000,000 by 2019.
+Unlike sales volume, prices showed minimal impact from the 2008 crisis and maintained an upward trajectory. Yikes!
+
+## Summary {#summary}
+
+This guide demonstrated how chDB enables seamless data exploration in Jupyter notebooks by connecting ClickHouse Cloud with local data sources.
+Using the UK Property Price dataset, we showed how to query remote ClickHouse Cloud data with the `remoteSecure()` function, read local CSV files with the `file()` table engine, and convert results directly to Pandas DataFrames for analysis and visualization.
+Through chDB, data scientists can leverage ClickHouse's powerful SQL capabilities alongside familiar Python tools like Pandas and matplotlib, making it easy to combine multiple data sources for comprehensive analysis.
+
+While many a London-based data scientist may not be able to afford their own home or apartment any time soon, at least they can analyze the market that priced them out!

docs/use-cases/index.md

@@ -9,9 +9,9 @@ doc_type: 'landing-page'
 
 In this section of the docs you can find our use case guides.
 
-| Page                                        | Description                                                                   |
-|---------------------------------------------|-------------------------------------------------------------------------------|
-| [Observability](observability/index.md)     | Use case guide on how to setup and use ClickHouse for Observability           |
-| [Time-Series](time-series/index.md)         | Use case guide on how to setup and use ClickHouse for time-series             |
-| [Data Lake](data_lake/index.md)             | Use case guide on Data Lakes in ClickHouse                                    |
-| [Machine Learning and GenAI](/use-cases/AI) | Use case guides for Machine Learning and GenAI applications with ClickHouse   |
+| Page                           | Description                                                                   |
+|--------------------------------|-------------------------------------------------------------------------------|
+| [Observability](observability/index.md) | Use case guide on how to setup and use ClickHouse for Observability           |
+| [Time-Series](time-series/index.md) | Use case guide on how to setup and use ClickHouse for time-series             |
+| [Data Lake](data_lake/index.md) | Use case guide on Data Lakes in ClickHouse                                    |
+| [Machine Learning and GenAI](/use-cases/AI/ask-ai) | Use case guides for Machine Learning and GenAI applications with ClickHouse   |

scripts/aspell-ignore/en/aspell-dict.txt

@@ -2534,6 +2534,8 @@ marimo's
 matcher
 materializations
 materializedview
+matplot
+matplotlib
 maxIntersections
 maxIntersectionsPosition
 maxMap
@@ -3733,4 +3735,4 @@ setinputsizes
 setoutputsizes
 stmt
 subclasses
-truncations
+truncations