Top 10 Integrations between Snowflake and ChatGPT Hands-On

Learn to configure Snowflake and ChatGPT integrations by installing plugins in Visual Studio Code, enabling single-click execution of SQL queries and Python code in Snowflake from ChatGPT-generated content.

Configure VSCode for our GitHub project by setting up a Python virtual environment, installing dependencies, and connecting Snowflake with OpenAI via Streamlit, llama index, and long chain.

Create a free Snowflake trial account, choose enterprise edition, select AWS, configure region, set auto resume and auto suspend, and explore credits, warehouses, and the web UI.

Discover how to create ChatGPT API and ChatGPT Plus accounts, compare free playground access with the paid advanced data analysis package, and set up an OpenAI API.

Install and configure the Snowflake and Genie II VSCode plugins to connect to Snowflake, explore objects, and run queries, then use ChatGPT prompts to explain and complete code.

Explore setting up Snowflake and ChatGPT integrations in VS Code, using Python and SQL with Snowpark, OpenAI API, and Streamlit apps for hands-on cloud data workflows.

See how ChatGPT generates SQL and Python code to create Snowflake sample databases, define star schemas, populate tables with synthetic data using Faker, and run analytics queries.

Generate a sample DDL script to create a Snowflake order-entry database with a star schema, including fact and dimension tables, and run it in VSCode with the ChatGPT plugin.

Generate 100 fake but realistic customer records in Python using the Faker library, inserting them into the dim_customer table and verifying results with the Snowflake plugin.

Generate 100 synthetic rows for the dim product table using Snowflake built-in functions, producing insert statements and validating results in the Snowflake plugin.

Generate and run a Python web scraper guided by ChatGPT, using CSS selectors to extract video game names and prices, and output to video games.csv, debugging when pages change.

Generate three sql select queries with group by in snowflake, using the dim product ddl, and save them for execution to observe ChatGPT's query suggestions in action.

Explore how ChatGPT and Snowflake generate sample databases, populate tables with csv files and public data sets, and craft sql queries for data analysis within a data pipeline.

Explore how to build a metadata inspector using Snowflake, Streamlit, and the ChatGPT API, delivering natural-language queries and metadata insights.

Build a streamlit q&a interface with ChatGPT connected to snowflake metadata via the openai api. Manage secrets and configure api keys while querying information schema for table and column metadata.

Learn to generate and run metadata queries against Snowflake using ChatGPT, extract SQL from responses, connect via secrets toml file, and display results in a Streamlit data frame.

Explore building a Streamlit web app with a tab control that switches between text and table outputs, using a sidebar text area and responsive output controls.

Create a Streamlit web app that implements chat controls for a chat with ChatGPT, using session state to store message history and role-based messages (system, user, assistant) and handle errors.

Explore how to wire Snowflake with ChatGPT via Streamlit, using chat completions API, secrets management, and two architectures for a Q&A interface that returns generated queries.

pull a public dataset from snowflake marketplace, extract table metadata, generate sql queries with chatgpt, converse in natural language about data, and build a data analysis bot inspector.

Access free public datasets from the snowflake marketplace, such as Financial and Economic Essentials, and build LM chatbots using OpenAI's GPT in Streamlit on snowflake data.

Connect to Snowflake to extract metadata for tables and views, including column definitions. Use prompt engineering to create a rich system prompt for ChatGPT and markdown templates.

Demonstrate ad hoc data analysis with natural language questions, using a Streamlit inspector to generate and run SQL via ChatGPT, while describing metadata and preserving privacy.

Create a generic ChatGPT bot using the OpenAI API, manage a simple session memory of messages, and display responses in markdown without database connections.

Inspect and execute interactive data analysis with a ChatGPT bot that manages prompts, displays message history, streams responses, and runs SQL queries against a Snowflake database.

Demonstrates interactive data analysis with a public Snowflake marketplace dataset, system prompts to ChatGPT, extracting metadata, and generating and executing SQL queries locally without exposing data.

Discover how to send CSV or PDF data to ChatGPT for advanced data analysis, including charts, graphs, and machine learning experiments like cluster analysis, using the Advanced Data Analysis plugin.

Explore Snowflake data integration with ChatGPT Plus, comparing graphing in text mode to generated graphs and Python analysis, and uploading csvs for Iris data and cluster analysis.

Display tables and charts with GPT-4 Turbo in a VS Code plugin, sorting top five cities by population and rendering a bar chart, highlighting text-mode limitations and data analysis plugins.

Experiment with the ChatGPT plus subscription's advanced data analysis plugin to generate and run Python code, build charts in matplotlib, and explore datasets with uploaded files.

Generate five charts from a bank branches csv using the advanced data analysis plugin in ChatGPT, producing bar, line, pie, scatter, and histogram visuals with Python/matplotlib code.

Explore clustering with the Iris dataset by performing a k-means analysis on sepal length and width, using Snowflake tables and ChatGPT for generated data science experiments.

Activate interactive data analysis with ChatGPT Plus, generating charts from uploaded csv files and performing exploratory and cluster analyses, including enterprise options for uploading pdf, word, excel, PowerPoint, and csv.

Build a Snowflake usage monitoring dashboard with Streamlit using ChatGPT to extract queries and charts. Create a single-page and tab-based Streamlit app, and explore multi-page templates.

Extract Snowflake usage queries and charts with ChatGPT in VSCode; generate plots from data frames for metrics like credit usage, storage utilization, and longest-running queries to build a three-chart dashboard.

Build a single-page streamlit dashboard that renders interactive plotly charts from snowflake queries, like storage usage and longest queries, using data frames.

Create a tab-based dashboard in a Streamlit web app by placing charts on three separate tab pages. Note Streamlit renders all top pages on start, limiting conditional display.

Build a dashboard as a multi-page streamlit web app with a sidebar select box to display chart at a time. Show the SQL query and Python code for each chart.

Explore how ChatGPT aids building usage monitoring dashboards with Snowflake integration, including generating queries and charts in Streamlit, tackling limitations, and using multi-page templates for scalable dashboards.

Explore how Snowflake data cloud enables calling ChatGPT from within Snowflake using external access integration and a user defined function, enabling data enrichment via select statements.

Explore how to call the OpenAI REST API with HTTP post requests, using JSON bodies, authorization via API key, and a minimal curl, Python requests, and Streamlit example using gpt-4-turbo.

Explore building a Snowflake function with external access integration to call ChatGPT for data enrichment, including network rules, secrets management, and an HTTP POST request workflow within a Python function.

Connect to ChatGPT from a snowflake function and build a streamlit app inside snowflake, executing a query to pass a prompt and return the first row result.

Explore how snowflake external functions enable api calls via aws api gateway and lambda, using external access integration to access the OpenAI API and ChatGPT.

Explore how to call ChatGPT from within Snowflake using rest api and external access integrations, enabling data enrichment via select statements and a Streamlit app.

Leverage LlamaIndex to index your personal or custom content, build a vector store knowledge base with embeddings, and query ChatGPT in natural language via a proxy.

Index personal content with llama index to enable retrieval augmented generation using Streamlit and OpenAI, then deploy a data and metadata base with vector embeddings in a knowledge base.

Create and persist a knowledge base index from documents using LlamaIndex, a simple directory reader, and an OpenAI lm client, with vector embeddings stored as json files.

Save the created index as a query engine in the Streamlit session state, and pass a system prompt to ChatGPT via lambda index, guiding responses to document-specific questions only.

Discover how llama index and retrieval augmented generation use a vector store of embeddings to answer questions from personal and web content with no training needed via ChatGPT.

Explore LangChain SQL generation automating queries by inspecting table definitions, from a notebook to a Streamlit app deployed on the Streamlit Community Cloud with connection parameters for Snowflake and ChatGPT.

Connect to the Snowflake sample database from a Jupyter notebook in VS Code, use LangChain with OpenAI to generate and execute natural language SQL queries via Snowpark.

Build a local Streamlit app that uses LangChain as an SQL generator to query Snowflake via Snowpark and OpenAI, display results in a data frame, and log session activity.

Deploy the Streamlit SQL generator to the Streamlit community cloud as a public app. Mask the OpenAI keys and Snowflake credentials, and deploy from GitHub main branch.

Leverage LangChain's SQL generation to translate natural language questions into SQL against Snowflake, using ChatGPT API, with a Streamlit app and GitHub deployment for safe data access.