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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-9f7eb602ef","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroTitle":{"lines":["Frosty: Build an LLM Chatbot in Streamlit on your Snowflake Data"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Joshua Carroll, Richard Meng, Caroline Frasca","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Snowflake ML functions","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/snowflake-ml-functions","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-426b5283b7","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/frosty-llm-chatbot-on-streamlit-snowflake"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Frosty: Build an LLM Chatbot in Streamlit on your Snowflake Data","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/frosty-llm-chatbot-on-streamlit-snowflake"},"flexible_column_cont":{"id":"flexible-column-container-1f23d15419","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-be279048e2",":items":{"contentfragment":{"id":"contentfragment-b84d6f8416","description":"","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/App_Demo.gif\" alt=\"Preview of final app\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this guide, we will build an LLM-powered chatbot named "Frosty" that performs data exploration and answers questions by writing and executing SQL queries on Snowflake data.\u003C/p\u003E\n","\u003Cp\u003EThe application uses Streamlit and Snowflake and can be plugged into your LLM of choice, alongside data from Snowflake Marketplace. By the end of the session, you will have an interactive web application chatbot that can converse and answer questions based on a financial dataset.\u003C/p\u003E\n","\u003Ch3\u003EKey features & technology\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELarge language models (LLMs)\u003C/li\u003E\u003Cli\u003EStreamlit\u003C/li\u003E\u003Cli\u003ESnowflake Marketplace\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat is Streamlit?\u003C/h3\u003E\n","\u003Cp\u003EStreamlit is an open-source Python library that enables developers to quickly create, deploy, and share web apps from Python scripts. Learn more about \u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat is a large language model (LLM)?\u003C/h3\u003E\n","\u003Cp\u003EA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Some examples of popular LLMs are \u003Ca href=\"https://openai.com/research/gpt-4\"\u003EGPT-4\u003C/a\u003E, \u003Ca href=\"https://openai.com/blog/gpt-3-apps\"\u003EGPT-3\u003C/a\u003E, \u003Ca href=\"https://cloud.google.com/ai-platform/training/docs/algorithms/bert-start\"\u003EBERT\u003C/a\u003E, \u003Ca href=\"https://ai.facebook.com/blog/large-language-model-llama-meta-ai/\"\u003ELLaMA\u003C/a\u003E, and \u003Ca href=\"https://blog.google/technology/ai/lamda/\"\u003ELaMDA\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat is OpenAI?\u003C/h3\u003E\n","\u003Cp\u003EOpenAI is the AI research and deployment company behind ChatGPT, GPT-4 (and its predecessors), DALL-E, and other notable offerings. Learn more about \u003Ca href=\"https://openai.com/\"\u003EOpenAI\u003C/a\u003E. We use OpenAI in this guide, but you are welcome to use the large language model of your choice in its place.\u003C/p\u003E\n","\u003Ch3\u003EWhat is the Snowflake Marketplace?\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"/en/data-cloud/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E provides users with access to a wide range of datasets from third-party data stewards, expanding the data available for transforming business processes and making decisions. Data providers can publish datasets and offer data analytics services to Snowflake customers. Customers can securely access shared datasets directly from their Snowflake accounts and receive automatic real-time updates.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccountadmin role access in Snowflake or a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003ESnowflake trial account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EAn API key for OpenAI or another Large Language Model\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, database concepts, and objects\u003C/li\u003E\u003Cli\u003EFamiliarity with Python (all code for the lab is provided)\u003C/li\u003E\u003Cli\u003EAbility to install and run software on your computer\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://code.visualstudio.com/download\"\u003EVSCode\u003C/a\u003E or the IDE of your choice installed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat you’ll learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a web application from a Python script with Streamlit\u003C/li\u003E\u003Cli\u003EHow to build a chatbot in just a few lines of code using \u003Ca href=\"https://docs.streamlit.io/library/api-reference/chat\"\u003EStreamlit's new chat UI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to use \u003Ca href=\"https://docs.streamlit.io/library/api-reference/connections/st.experimental_connection\"\u003E\u003Ccode\u003Est.experimental_connection\u003C/code\u003E\u003C/a\u003E to connect your Streamlit app to Snowflake\u003C/li\u003E\u003Cli\u003EHow to use \u003Ca href=\"https://docs.streamlit.io/library/api-reference/session-state\"\u003E\u003Ccode\u003Esession state\u003C/code\u003E\u003C/a\u003E to store your chatbot's message history\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare your environment\u003C/h2\u003E\n","\u003Cp\u003EComplete the following steps in your local machine (or an equivalent dev environment):\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EInstall [Anaconda Distribution](https://docs.conda.io/en/latest/miniconda.html](https://www.anaconda.com/download) to manage a separate environment by selecting the appropriate installer link for your operating system and Python version.\u003C/li\u003E\u003Cli\u003EOpen the terminal or command prompt and create a folder for your project. Let's call it \u003Ccode\u003Ellm-chatbot\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EMake sure you are running the latest version of conda by running the following command:\n\u003Cpre\u003E\u003Ccode\u003Econda update -n base conda\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003ERun the following command to create a Python 3.11 conda virtual environment:\n\u003Cpre\u003E\u003Ccode\u003Econda create --name snowpark-llm-chatbot python=3.11\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003EActivate the conda environment by running the following command:\n\u003Cpre\u003E\u003Ccode\u003Econda activate snowpark-llm-chatbot\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003EInstall Snowpark for Python, Streamlit, and OpenAI by running the following command:\n\u003Cpre\u003E\u003Ccode\u003Econda install snowflake-snowpark-python "openai>=1.0.0"\nconda install conda-forge::"streamlit>=1.28.2"\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETroubleshooting \u003Ccode\u003Epyarrow\u003C/code\u003E related issues\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EIf you do not have \u003Ccode\u003Epyarrow\u003C/code\u003E installed, you do not need to install it yourself; installing Snowpark automatically installs the appropriate version.\u003C/li\u003E\u003Cli\u003EDo not reinstall a different version of \u003Ccode\u003Epyarrow\u003C/code\u003E after installing Snowpark.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERunning in GitHub Codespaces\u003C/h3\u003E\n","\u003Cp\u003EIf you prefer to run through the tutorial in a remote environment instead of setting up a Python environment locally, you can use GitHub Codespaces.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou can launch a pre-configured Codespace \u003Ca href=\"https://codespaces.new/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake?quickstart=1\"\u003Ehere\u003C/a\u003E with the environment setup and app code already available.\u003C/li\u003E\u003Cli\u003EYou'll just need to add a \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E file with configuration for connecting to Snowflake and an OpenAI API Key as described in "Setting up Streamlit environment".\u003C/li\u003E\u003Cli\u003EMore information and references on running this quickstart in Codespaces \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake#run-in-codespaces\"\u003Ehere\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAccessing data on Snowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003ESnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\u003C/p\u003E\n","\u003Ch3\u003ELog into Snowsight\u003C/h3\u003E\n","\u003Cp\u003EIf you don't have a Snowflake account, sign up for a 30-day free trial \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn a supported web browser, navigate to \u003Ca href=\"https://app.snowflake.com\"\u003Ehttps://app.snowflake.com\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EProvide your account name or account URL. If you’ve previously signed in to Snowsight, you might see an account name that you can select.\u003C/li\u003E\u003Cli\u003ESign in using your Snowflake account credentials.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou can also access Snowsight from the Classic Console:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESign in to the Classic Console.\u003C/li\u003E\u003Cli\u003EIn the navigation menu, select Snowsight.\u003C/li\u003E\u003Cli\u003ESnowsight opens in a new tab.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EObtain dataset from Snowflake Marketplace\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EAt the top left corner, make sure you are logged in as ACCOUNTADMIN (switch role to ACCOUNTADMIN if not).\u003C/li\u003E\u003Cli\u003ENavigate to the Cybersyn Financial & Economic Essentials listing in the Snowflake Marketplace by clicking \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZAS2KF7/cybersyn-inc-cybersyn-financial-economic-essentials\"\u003Ehere\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003E"Get."\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the appropriate roles to access the database being created and accept the Snowflake consumer terms and Cybersyn's terms of use.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003E"Query Data,"\u003C/strong\u003E which will open a worksheet with example queries.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Cybersyn_Example_Queries.png\" alt=\"Example queries for the Cybersyn Financial & Economic Essentials dataset from the Snowflake Marketplace\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrep database\u003C/h3\u003E\n","\u003Cp\u003EBefore building our app, we need to run a set of SQL statements in Snowflake to create two views. The first view is \u003Ccode\u003EFROSTY_SAMPLE.CYBERSYN_FINANCIAL.FINANCIAL_ENTITY_ATTRIBUTES_LIMITED\u003C/code\u003E, which includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA subset of cybersyn_financial__economic_essentials.cybersyn.financial_institution_attributes:\n\u003Cul\u003E\u003Cli\u003ETotals for assets, real estate loans, securities, deposits; % of deposits insured; total employees\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe second view is \u003Ccode\u003EFROSTY_SAMPLE.CYBERSYN_FINANCIAL.FINANCIAL_ENTITY_ANNUAL_TIME_SERIES\u003C/code\u003E, which includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA modified version of cybersyn_financial__economic_essentials.cybersyn.financial_institution_timeseries as follows:\n\u003Cul\u003E\u003Cli\u003EEntity and attribute metadata is joined directly\n\u003Cul\u003E\u003Cli\u003EOnly the set of attributes from FINANCIAL_ENTITY_ATTRIBUTES_LIMITED are exposed\u003C/li\u003E\u003Cli\u003EOnly the end-of-year metrics (YYYY-12-31) are included, and a YEAR column is provided instead of the date column\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou can copy the SQL statements from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/create-views.sql\"\u003Ethis file\u003C/a\u003E and run them in the worksheet created for your sample queries.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Run_Queries.gif\" alt=\"GIF showing the SQL statements being run in Snowflake\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we've configured the dataset we'll be using for our application, we can get started with Streamlit.\u003C/p\u003E\n","\u003Ch2\u003ESetting up Streamlit environment\u003C/h2\u003E\n","\u003Ch3\u003ERun an example Streamlit app\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EHead back over to the command line and navigate to your \u003Ccode\u003Ellm-chatbot\u003C/code\u003E folder.\u003C/li\u003E\u003Cli\u003ERun an example Streamlit app by entering \u003Ccode\u003Estreamlit hello\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Streamlit_Hello.png\" alt=\"alt_text\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EConfigure secrets file\u003C/h3\u003E\n","\u003Cp\u003ESince our application will connect to Snowflake and OpenAI, we need a way to securely store our credentials. Luckily, \u003Ca href=\"https://docs.streamlit.io/streamlit-community-cloud/get-started/deploy-an-app/connect-to-data-sources/secrets-management\"\u003EStreamlit's secrets management feature\u003C/a\u003E allows us to store secrets securely and access them in our Streamlit app as environment variables.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAdd a folder within your \u003Ccode\u003Ellm-chatbot\u003C/code\u003E folder called \u003Ccode\u003E.streamlit\u003C/code\u003E. Using the command line, you can do this by entering \u003Ccode\u003Emkdir .streamlit\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EWithin the \u003Ccode\u003E.streamlit\u003C/code\u003E folder, add a file called \u003Ccode\u003Esecrets.toml\u003C/code\u003E. Using the command line, you can do this by first navigating to the \u003Ccode\u003E.streamlit\u003C/code\u003E folder via \u003Ccode\u003Ecd .streamlit\u003C/code\u003E and then entering \u003Ccode\u003Etouch secrets.toml.\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EAdd OpenAI credentials to \u003Ccode\u003Esecrets.toml\u003C/code\u003E\u003C/h4\u003E\n","\u003Cp\u003EWe need to add our OpenAI API key to our secrets file. Add your OpenAI key to the secrets file with the following format (replace the placeholder API key with your actual API key).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E# .streamlit/secrets.toml\n\nOPENAI_API_KEY = "sk-2v...X"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EAdd Snowflake credentials to \u003Ccode\u003Esecrets.toml\u003C/code\u003E\u003C/h4\u003E\n","\u003Cp\u003EWe also need to add the Snowflake \u003Ccode\u003Euser\u003C/code\u003E, \u003Ccode\u003Epassword\u003C/code\u003E, \u003Ccode\u003Ewarehouse\u003C/code\u003E, \u003Ccode\u003Erole\u003C/code\u003E, and \u003Ccode\u003Eaccount\u003C/code\u003E to our secrets file. Copy the following format, replacing the placeholder credentials with your actual credentials.\n\u003Ccode\u003Eaccount\u003C/code\u003E should be your Snowflake account identifier, which you can locate by following the instructions outlined \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf you prefer to use browser-based SSO to authenticate, replace \u003Ccode\u003Epassword = "<my_trial_pass>"\u003C/code\u003E with \u003Ccode\u003Eauthenticator=EXTERNALBROWSER\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E# .streamlit/secrets.toml\n\n[connections.snowflake]\nuser = "<jdoe>"\npassword = "<my_trial_pass>"\nwarehouse = "COMPUTE_WH"\nrole = "ACCOUNTADMIN"\naccount = "<account-id>"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EFull contents of secrets.toml\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E# .streamlit/secrets.toml\n\nOPENAI_API_KEY = "sk-2v...X"\n\n[connections.snowflake]\nuser = "<username>"\npassword = "<password>"\nwarehouse = "COMPUTE_WH"\nrole = "ACCOUNTADMIN"\naccount = "<account-id>"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EValidate credentials\u003C/h3\u003E\n","\u003Cp\u003ELet's validate that our Snowflake and OpenAI credentials are working as expected.\u003C/p\u003E\n","\u003Ch4\u003EOpenAI credentials\u003C/h4\u003E\n","\u003Cp\u003EFirst, we'll validate our OpenAI credentials by asking GPT-3.5 a simple question: what is Streamlit?\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAdd a file called \u003Ccode\u003Evalidate_credentials.py\u003C/code\u003E at the root of your \u003Ccode\u003Ellm-chatbot\u003C/code\u003E folder.\u003C/li\u003E\u003Cli\u003EAdd the below code to \u003Ccode\u003Evalidate_credentials.py\u003C/code\u003E. This snippet does the following:\n\u003Cul\u003E\u003Cli\u003EImports the Streamlit and OpenAI Python packages\u003C/li\u003E\u003Cli\u003ERetrieves our OpenAI API key from the secrets file\u003C/li\u003E\u003Cli\u003ESends GPT-3.5 the question "What is Streamlit?"\u003C/li\u003E\u003Cli\u003EPrints GPT-3.5's response to the UI using \u003Ccode\u003Est.write\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nfrom openai import OpenAI\n\nclient = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])\n\ncompletion = client.chat.completions.create(\n  model="gpt-3.5-turbo",\n  messages=[\n    {"role": "user", "content": "What is Streamlit?"}\n  ]\n)\n\nst.write(completion.choices[0].message.content)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ERun your Streamlit app by entering \u003Ccode\u003Estreamlit run validate_credentials.py\u003C/code\u003E in the command line.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Validate_OpenAI_Creds.png\" alt=\"alt_text\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003ESnowflake credentials\u003C/h4\u003E\n","\u003Cp\u003ENext, let's validate that our Snowflake credentials are working as expected.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAppend the following to \u003Ccode\u003Evalidate_credentials.py\u003C/code\u003E. This snippet does the following:\n\u003Cul\u003E\u003Cli\u003ECreates a Snowpark connection\u003C/li\u003E\u003Cli\u003EExecutes a query to pull the current warehouse and writes the result to the UI\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econn = st.connection("snowflake")\ndf = conn.query("select current_warehouse()")\nst.write(df)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ERun your Streamlit app by entering \u003Ccode\u003Estreamlit run validate_credentials.py\u003C/code\u003E in the command line.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Validate_Snowflake_Creds.png\" alt=\"alt_text\"\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild a simple chatbot application\u003C/h2\u003E\n","\u003Cp\u003EWe're ready to start building our app! We're going to first build a simple version of the chatbot app that simply passes user-inputted messages to GPT-3.5 and returns GPT-3.5's response. We'll build on the app's complexity in subsequent sections.\u003C/p\u003E\n","\u003Cp\u003EWe'll break down the Python file snippet-by-snippet so that you understand the functionality of each section, but if you'd like to skip ahead and download the full file, you can do so \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/simple_chatbot.py\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a file called \u003Ccode\u003Esimple_chatbot.py\u003C/code\u003E. Add import statements and give your app a title.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom openai import OpenAI\nimport streamlit as st\n\nst.title("☃️ Frosty")\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EInitialize the chatbot's message history by adding the first message that we want the chatbot to display, "How can I help?", to \u003Ca href=\"https://docs.streamlit.io/library/api-reference/session-state\"\u003Esession state\u003C/a\u003E.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Initialize the chat messages history\nif "messages" not in st.session_state.keys():\n    st.session_state.messages = [{"role": "assistant", "content": "How can I help?"}]\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EPrompt the user to enter chat input by using Streamlit's \u003Ccode\u003Est.chat_input()\u003C/code\u003E feature. If the user has entered a message, add that message to the chat history by storing it in session state.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Prompt for user input and save\nif prompt := st.chat_input():\n    st.session_state.messages.append({"role": "user", "content": prompt})\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EDisplay the chatbot's message history by iterating through the values stored in session state associated with the key "messages" and printing each value.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# display the existing chat messages\nfor message in st.session_state.messages:\n    with st.chat_message(message["role"]):\n        st.write(message["content"])\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EIf the last message is not from the assistant, send the message to GPT-3.5 via the \u003Ccode\u003Eopenai\u003C/code\u003E Python package. Display a spinner while the app is retrieving GPT-3.5's response via Streamlit's \u003Ca href=\"https://docs.streamlit.io/library/api-reference/status/st.spinner\"\u003E\u003Ccode\u003Est.spinner\u003C/code\u003E\u003C/a\u003E feature and use \u003Ccode\u003Est.write\u003C/code\u003E to display the chatbot's response in the UI. Append the chatbot's response to the chat history stored in session state.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# If last message is not from assistant, we need to generate a new response\nif st.session_state.messages[-1]["role"] != "assistant":\n    # Call LLM\n    with st.chat_message("assistant"):\n        with st.spinner("Thinking..."):\n            r = OpenAI().chat.completions.create(\n                messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages],\n                model="gpt-3.5-turbo",\n            )\n            response = r.choices[0].message.content\n            st.write(response)\n\n    message = {"role": "assistant", "content": response}\n    st.session_state.messages.append(message)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ERun the Streamlit app via \u003Ccode\u003Estreamlit run simple_chatbot.py\u003C/code\u003E. Give it a whirl – ask Frosty a question!\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Simple_Chatbot.gif\" alt=\"GIF demonstrating the simple chatbot app\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe full contents of the Python file for this simple chatbot app are below, or you can download the file from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/simple_chatbot.py\"\u003EGitHub\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom openai import OpenAI\nimport streamlit as st\n\nst.title("☃️ Frosty")\n\n# Initialize the chat messages history\nif "messages" not in st.session_state.keys():\n    st.session_state.messages = [{"role": "assistant", "content": "How can I help?"}]\n\n# Prompt for user input and save\nif prompt := st.chat_input():\n    st.session_state.messages.append({"role": "user", "content": prompt})\n\n# display the existing chat messages\nfor message in st.session_state.messages:\n    with st.chat_message(message["role"]):\n        st.write(message["content"])\n\n# If last message is not from assistant, we need to generate a new response\nif st.session_state.messages[-1]["role"] != "assistant":\n    # Call LLM\n    with st.chat_message("assistant"):\n        with st.spinner("Thinking..."):\n            r = OpenAI().chat.completions.create(\n                messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages],\n                model="gpt-3.5-turbo",\n            )\n            response = r.choices[0].message.content\n            st.write(response)\n\n    message = {"role": "assistant", "content": response}\n    st.session_state.messages.append(message)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd prompt engineering and SQL extraction\u003C/h2\u003E\n","\u003Cp\u003ENow that we've built a simple version of the chatbot app, let's expand the functionality to enable Frosty to translate our requests into SQL statements and execute those statements using the Cybersyn dataset stored in our Snowflake database.\u003C/p\u003E\n","\u003Ch3\u003ECreate a prompt file\u003C/h3\u003E\n","\u003Cp\u003EWe're also going to create a prompt Python file before building out the main file of our chatbot app. The primary purpose of this file is to create the function \u003Ccode\u003Eget_system_prompt()\u003C/code\u003E, which will be called in our main Python file and will do a few things:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERetrieves basic information about the database we're going to be using, including the table name, table description, and variable names\u003C/li\u003E\u003Cli\u003EComposes a system message for GPT-3.5, which shares basic information about the dataset with the model and instructs the model to:\n\u003Cul\u003E\u003Cli\u003ERespond in the character of an AI Snowflake SQL expert named Frosty\u003C/li\u003E\u003Cli\u003EInclude a SQL query in each answer based on the question and the table.\u003C/li\u003E\u003Cli\u003EFormat SQL queries properly via markdown.\u003C/li\u003E\u003Cli\u003ELimit the number of responses to a SQL query to 10 (unless otherwise specified).\u003C/li\u003E\u003Cli\u003EGenerate a single SQL code snippet.\u003C/li\u003E\u003Cli\u003EOnly use the specified table columns and table.\u003C/li\u003E\u003Cli\u003EAvoid starting variable names with numbers.\u003C/li\u003E\u003Cli\u003EUse "ilike %keyword%" for fuzzy match queries.\u003C/li\u003E\u003Cli\u003EStart the conversation by briefly introducing yourself, describing the table, sharing available metrics in a few sentences, and providing three example questions.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis file should be placed in the root of your \u003Ccode\u003Ellm-chatbot\u003C/code\u003E folder. You can download the file from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/prompts.py\"\u003Ehere\u003C/a\u003E or create an empty Python file and paste the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\n\nSCHEMA_PATH = st.secrets.get("SCHEMA_PATH", "FROSTY_SAMPLE.CYBERSYN_FINANCIAL")\nQUALIFIED_TABLE_NAME = f"{SCHEMA_PATH}.FINANCIAL_ENTITY_ANNUAL_TIME_SERIES"\nTABLE_DESCRIPTION = """\nThis table has various metrics for financial entities (also referred to as banks) since 1983.\nThe user may describe the entities interchangeably as banks, financial institutions, or financial entities.\n"""\n# This query is optional if running Frosty on your own table, especially a wide table.\n# Since this is a deep table, it's useful to tell Frosty what variables are available.\n# Similarly, if you have a table with semi-structured data (like JSON), it could be used to provide hints on available keys.\n# If altering, you may also need to modify the formatting logic in get_table_context() below.\nMETADATA_QUERY = f"SELECT VARIABLE_NAME, DEFINITION FROM {SCHEMA_PATH}.FINANCIAL_ENTITY_ATTRIBUTES_LIMITED;"\n\nGEN_SQL = """\nYou will be acting as an AI Snowflake SQL Expert named Frosty.\nYour goal is to give correct, executable sql query to users.\nYou will be replying to users who will be confused if you don't respond in the character of Frosty.\nYou are given one table, the table name is in <tableName> tag, the columns are in <columns> tag.\nThe user will ask questions, for each question you should respond and include a sql query based on the question and the table. \n\n{context}\n\nHere are 6 critical rules for the interaction you must abide:\n<rules>\n1. You MUST MUST wrap the generated sql code within ``` sql code markdown in this format e.g\n```sql\n(select 1) union (select 2)\n```\n2. If I don't tell you to find a limited set of results in the sql query or question, you MUST limit the number of responses to 10.\n3. Text / string where clauses must be fuzzy match e.g ilike %keyword%\n4. Make sure to generate a single snowflake sql code, not multiple. \n5. You should only use the table columns given in <columns>, and the table given in <tableName>, you MUST NOT hallucinate about the table names\n6. DO NOT put numerical at the very front of sql variable.\n</rules>\n\nDon't forget to use "ilike %keyword%" for fuzzy match queries (especially for variable_name column)\nand wrap the generated sql code with ``` sql code markdown in this format e.g:\n```sql\n(select 1) union (select 2)\n```\n\nFor each question from the user, make sure to include a query in your response.\n\nNow to get started, please briefly introduce yourself, describe the table at a high level, and share the available metrics in 2-3 sentences.\nThen provide 3 example questions using bullet points.\n"""\n\n@st.cache_data(show_spinner="Loading Frosty's context...")\ndef get_table_context(table_name: str, table_description: str, metadata_query: str = None):\n    table = table_name.split(".")\n    conn = st.connection("snowflake")\n    columns = conn.query(f"""\n        SELECT COLUMN_NAME, DATA_TYPE FROM {table[0].upper()}.INFORMATION_SCHEMA.COLUMNS\n        WHERE TABLE_SCHEMA = '{table[1].upper()}' AND TABLE_NAME = '{table[2].upper()}'\n        """, show_spinner=False,\n    )\n    columns = "n".join(\n        [\n            f"- **{columns['COLUMN_NAME'][i]}**: {columns['DATA_TYPE'][i]}"\n            for i in range(len(columns["COLUMN_NAME"]))\n        ]\n    )\n    context = f"""\nHere is the table name <tableName> {'.'.join(table)} </tableName>\n\n<tableDescription>{table_description}</tableDescription>\n\nHere are the columns of the {'.'.join(table)}\n\n<columns>nn{columns}nn</columns>\n    """\n    if metadata_query:\n        metadata = conn.query(metadata_query, show_spinner=False)\n        metadata = "n".join(\n            [\n                f"- **{metadata['VARIABLE_NAME'][i]}**: {metadata['DEFINITION'][i]}"\n                for i in range(len(metadata["VARIABLE_NAME"]))\n            ]\n        )\n        context = context + f"nnAvailable variables by VARIABLE_NAME:nn{metadata}"\n    return context\n\ndef get_system_prompt():\n    table_context = get_table_context(\n        table_name=QUALIFIED_TABLE_NAME,\n        table_description=TABLE_DESCRIPTION,\n        metadata_query=METADATA_QUERY\n    )\n    return GEN_SQL.format(context=table_context)\n\n# do `streamlit run prompts.py` to view the initial system prompt in a Streamlit app\nif __name__ == "__main__":\n    st.header("System prompt for Frosty")\n    st.markdown(get_system_prompt())\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, you can run this file as a Streamlit app to verify the output is working correctly. Run the prompts generation via \u003Ccode\u003Estreamlit run prompts.py\u003C/code\u003E. Make sure the table information is showing up as expected in the rendered prompt - this will get passed to the chatbot in the next section.\u003C/p\u003E\n","\u003Ch3\u003EBuild the chatbot\u003C/h3\u003E\n","\u003Cp\u003EWe'll break down the Python file snippet-by-snippet so that you understand the functionality of each section, but if you'd like to skip ahead and download the full file, you can do so \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/frosty_app.py\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a file called \u003Ccode\u003Efrosty_app.py\u003C/code\u003E and add the below code snippet, which does the following:\n\u003Cul\u003E\u003Cli\u003EAdds import statements and a title\u003C/li\u003E\u003Cli\u003ERetrieves our OpenAI API key from the secrets file\u003C/li\u003E\u003Cli\u003EInitializes the message history using session state\n\u003Cul\u003E\u003Cli\u003EThis time, the first assistant message from the chatbot will display information about the current table in the database this app is using. \u003Ccode\u003Eget_system_prompt()\u003C/code\u003E retrieves this information.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EPrompts the user to enter a message and upon receiving a message, adds that message to the chat history\u003C/li\u003E\u003Cli\u003EIterates through the message history and displays each message in the app\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom openai import OpenAI\nimport re\nimport streamlit as st\nfrom prompts import get_system_prompt\n\nst.title("☃️ Frosty")\n\n# Initialize the chat messages history\nclient = OpenAI(api_key=st.secrets.OPENAI_API_KEY)\nif "messages" not in st.session_state:\n    # system prompt includes table information, rules, and prompts the LLM to produce\n    # a welcome message to the user.\n    st.session_state.messages = [{"role": "system", "content": get_system_prompt()}]\n\n# Prompt for user input and save\nif prompt := st.chat_input():\n    st.session_state.messages.append({"role": "user", "content": prompt})\n\n# display the existing chat messages\nfor message in st.session_state.messages:\n    if message["role"] == "system":\n        continue\n    with st.chat_message(message["role"]):\n        st.write(message["content"])\n        if "results" in message:\n            st.dataframe(message["results"])\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ECheck the last entry in the chat history to see if it was sent by the user or the chatbot. If it was sent by the user, use GPT-3.5 to generate a response. Instead of displaying the entire response at once, use OpenAI's \u003Ccode\u003Estream\u003C/code\u003E parameter to signify that GPT-3.5's response should be sent incrementally in chunks via an event stream, and display the chunks as they're received.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# If last message is not from assistant, we need to generate a new response\nif st.session_state.messages[-1]["role"] != "assistant":\n    with st.chat_message("assistant"):\n        response = ""\n        resp_container = st.empty()\n        for delta in client.chat.completions.create(\n            model="gpt-3.5-turbo",\n            messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages],\n            stream=True,\n        ):\n            response += (delta.choices[0].delta.content or "")\n            resp_container.markdown(response)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EUse a regular expression to search the newly generated response for the SQL markdown syntax that we instructed GPT-3.5 to wrap any SQL queries in. If a match is found, use \u003Ccode\u003Est.experimental_connection\u003C/code\u003E to execute the SQL query against the database we created in Snowflake. Write the result to the app using \u003Ccode\u003Est.dataframe\u003C/code\u003E, and append the result to the associated message in the message history.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E        message = {"role": "assistant", "content": response}\n        # Parse the response for a SQL query and execute if available\n        sql_match = re.search(r"```sqln(.*)n```", response, re.DOTALL)\n        if sql_match:\n            sql = sql_match.group(1)\n            conn = st.connection("snowflake")\n            message["results"] = conn.query(sql)\n            st.dataframe(message["results"])\n        st.session_state.messages.append(message)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ERun the Streamlit app via \u003Ccode\u003Estreamlit run frosty_app.py\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/App_Demo.gif\" alt=\"Preview of final app\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe full contents of the Python file for this app are below, or you can download the file from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/frosty_app.py\"\u003EGitHub\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom openai import OpenAI\nimport re\nimport streamlit as st\nfrom prompts import get_system_prompt\n\nst.title("☃️ Frosty")\n\n# Initialize the chat messages history\nclient = OpenAI(api_key=st.secrets.OPENAI_API_KEY)\nif "messages" not in st.session_state:\n    # system prompt includes table information, rules, and prompts the LLM to produce\n    # a welcome message to the user.\n    st.session_state.messages = [{"role": "system", "content": get_system_prompt()}]\n\n# Prompt for user input and save\nif prompt := st.chat_input():\n    st.session_state.messages.append({"role": "user", "content": prompt})\n\n# display the existing chat messages\nfor message in st.session_state.messages:\n    if message["role"] == "system":\n        continue\n    with st.chat_message(message["role"]):\n        st.write(message["content"])\n        if "results" in message:\n            st.dataframe(message["results"])\n\n# If last message is not from assistant, we need to generate a new response\nif st.session_state.messages[-1]["role"] != "assistant":\n    with st.chat_message("assistant"):\n        response = ""\n        resp_container = st.empty()\n        for delta in client.chat.completions.create(\n            model="gpt-3.5-turbo",\n            messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages],\n            stream=True,\n        ):\n            response += (delta.choices[0].delta.content or "")\n            resp_container.markdown(response)\n\n        message = {"role": "assistant", "content": response}\n        # Parse the response for a SQL query and execute if available\n        sql_match = re.search(r"```sqln(.*)n```", response, re.DOTALL)\n        if sql_match:\n            sql = sql_match.group(1)\n            conn = st.connection("snowflake")\n            message["results"] = conn.query(sql)\n            st.dataframe(message["results"])\n        st.session_state.messages.append(message)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore the data via natural language\u003C/h2\u003E\n","\u003Cp\u003EFinally, it's time to explore the Cybersyn Financial & Economic Essentials using natural language. Try asking Frosty any of the following questions:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWhich financial institution had the highest total assets in the year 2020?\u003C/li\u003E\u003Cli\u003EWhich financial institutions in California had the highest total assets value between 2010 to 2015?\u003C/li\u003E\u003Cli\u003EWhat was the highest % insured (estimated) value for all financial institutions in the state of New Jersey?\u003C/li\u003E\u003Cli\u003EWhat is the lowest value of total securities for all financial institutions in Texas?\u003C/li\u003E\u003Cli\u003EWhat was the % change in all real estate loans for banks headquartered in California between 2015 and 2020?\u003C/li\u003E\u003Cli\u003EWhat was the average total securities value for banks in the state of Wisconsin between 2015 and 2020?\u003C/li\u003E\u003Cli\u003EHow have the total securities value changed over time for financial institutions in New York City?\u003C/li\u003E\u003Cli\u003EWhat was the maximum % insured (estimated) value for a single financial entity in Illinois between 2010 and 2020?\u003C/li\u003E\u003Cli\u003EWhat was the value of all real estate loans for banks located in Massachusetts in 2020?\u003C/li\u003E\u003Cli\u003EHow many banks headquartered in New Hampshire experienced more than 50% growth in their total assets between 2015 and 2020?\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and next steps\u003C/h2\u003E\n","\u003Cp\u003ECongratulations – you've just built an LLM-powered chatbot capable of translating natural language to SQL queries and running those queries on data stored in Snowflake!\u003C/p\u003E\n","\u003Ch3\u003EWhere to go from here\u003C/h3\u003E\n","\u003Cp\u003EThis tutorial is just a starting point for exploring the possibilities of LLM-powered chat interfaces for data exploration and question-answering using Snowflake and Streamlit. A few next things to try:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EUpdate to run against your private data in Snowflake\u003C/strong\u003E, or other relevant Snowflake Marketplace datasets. The table-specific logic in the app is all specified at the top of \u003Ccode\u003Eprompts.py\u003C/code\u003E, so it should be easy to swap and start playing around!\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd more capabilities\u003C/strong\u003E, such as using the LLM to choose from a set of available tables, summarize the returned data, or even write Streamlit code to visualize the results. You could even use a library like LangChain to convert Frosty into an "Agent" with improved chain of thought reasoning and the ability to respond to errors.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPrepare to run in Streamlit in Snowflake\u003C/strong\u003E (currently in Private Preview): The functionality shown here will soon be available in Streamlit in Snowflake, especially when paired with External Access (also in Private Preview) to simplify access to an external LLM.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECheck out the Frosty session (ML103) from Snowflake Summit 2023 for more ideas and what's coming soon from Snowflake!\u003C/p\u003E\n","\u003Ch3\u003EAdditional resources\u003C/h3\u003E\n","\u003Cp\u003EWant to learn more about the tools and technologies used by your app? Check out the following resources:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/library/api-reference/chat\"\u003EStreamlit's new chat UI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/library/api-reference/connections/st.experimental_connection\"\u003Est.experimental_connection\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/library/api-reference/session-state\"\u003ESession state\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/streamlit-community-cloud/get-started/deploy-an-app/connect-to-data-sources/secrets-management\"\u003ESecrets management\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://platform.openai.com/docs/api-reference/chat\"\u003EOpenAI's ChatCompetion feature\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://blog.streamlit.io/generative-ai-and-streamlit-a-perfect-match/\"\u003EGenerative AI and Streamlit: A perfect match\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://streamlit.io/generative-ai\"\u003EBuild powerful generative AI apps with Streamlit\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://developers.snowflake.com/demos/data-exploration-llm-chatbot/\"\u003EDemo on Snowflake Demo Hub\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Frosty: Build an LLM Chatbot in Streamlit on your Snowflake Data",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\n![Preview of final app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/App_Demo.gif)\r\n\r\nIn this guide, we will build an LLM-powered chatbot named \"Frosty\" that performs data exploration and answers questions by writing and executing SQL queries on Snowflake data.\r\n\r\nThe application uses Streamlit and Snowflake and can be plugged into your LLM of choice, alongside data from Snowflake Marketplace. By the end of the session, you will have an interactive web application chatbot that can converse and answer questions based on a financial dataset.\r\n\r\n### Key features & technology\r\n* Large language models (LLMs)\r\n* Streamlit\r\n* Snowflake Marketplace\r\n\r\n### What is Streamlit?\r\nStreamlit is an open-source Python library that enables developers to quickly create, deploy, and share web apps from Python scripts. Learn more about [Streamlit](https://streamlit.io/).\r\n\r\n### What is a large language model (LLM)?\r\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Some examples of popular LLMs are [GPT-4](https://openai.com/research/gpt-4), [GPT-3](https://openai.com/blog/gpt-3-apps), [BERT](https://cloud.google.com/ai-platform/training/docs/algorithms/bert-start), [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/), and [LaMDA](https://blog.google/technology/ai/lamda/).\r\n\r\n### What is OpenAI?\r\nOpenAI is the AI research and deployment company behind ChatGPT, GPT-4 (and its predecessors), DALL-E, and other notable offerings. Learn more about [OpenAI](https://openai.com/). We use OpenAI in this guide, but you are welcome to use the large language model of your choice in its place.\r\n\r\n### What is the Snowflake Marketplace?\r\nThe [Snowflake Marketplace](/en/data-cloud/marketplace/) provides users with access to a wide range of datasets from third-party data stewards, expanding the data available for transforming business processes and making decisions. Data providers can publish datasets and offer data analytics services to Snowflake customers. Customers can securely access shared datasets directly from their Snowflake accounts and receive automatic real-time updates.\r\n\r\n### Prerequisites\r\n* Accountadmin role access in Snowflake or a [Snowflake trial account](https://signup.snowflake.com/?utm_cta=quickstarts_)\r\n* An API key for OpenAI or another Large Language Model\r\n* Basic knowledge of SQL, database concepts, and objects\r\n* Familiarity with Python (all code for the lab is provided)\r\n* Ability to install and run software on your computer\r\n* [VSCode](https://code.visualstudio.com/download) or the IDE of your choice installed\r\n\r\n### What you’ll learn\r\n* How to create a web application from a Python script with Streamlit\r\n* How to build a chatbot in just a few lines of code using [Streamlit's new chat UI](https://docs.streamlit.io/library/api-reference/chat)\r\n* How to use [`st.experimental_connection`](https://docs.streamlit.io/library/api-reference/connections/st.experimental_connection) to connect your Streamlit app to Snowflake\r\n* How to use [`session state`](https://docs.streamlit.io/library/api-reference/session-state) to store your chatbot's message history\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Prepare your environment\r\n\r\nComplete the following steps in your local machine (or an equivalent dev environment):\r\n\r\n1. Install [Anaconda Distribution](https://docs.conda.io/en/latest/miniconda.html](https://www.anaconda.com/download) to manage a separate environment by selecting the appropriate installer link for your operating system and Python version.\r\n2. Open the terminal or command prompt and create a folder for your project. Let's call it `llm-chatbot`.\r\n3. Make sure you are running the latest version of conda by running the following command:\r\n    ```\r\n    conda update -n base conda\r\n    ```\r\n4. Run the following command to create a Python 3.11 conda virtual environment:\r\n    ```\r\n    conda create --name snowpark-llm-chatbot python=3.11\r\n    ```\r\n5. Activate the conda environment by running the following command:\r\n    ```\r\n    conda activate snowpark-llm-chatbot\r\n    ```\r\n8. Install Snowpark for Python, Streamlit, and OpenAI by running the following command:\r\n    ```\r\n    conda install snowflake-snowpark-python \"openai\u003E=1.0.0\"\r\n    conda install conda-forge::\"streamlit\u003E=1.28.2\"\r\n    ```\r\n\r\n### Troubleshooting `pyarrow` related issues\r\n- If you do not have `pyarrow` installed, you do not need to install it yourself; installing Snowpark automatically installs the appropriate version.\r\n- Do not reinstall a different version of `pyarrow` after installing Snowpark.\r\n\r\n### Running in GitHub Codespaces\r\n\r\nIf you prefer to run through the tutorial in a remote environment instead of setting up a Python environment locally, you can use GitHub Codespaces.\r\n- You can launch a pre-configured Codespace [here](https://codespaces.new/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake?quickstart=1) with the environment setup and app code already available.\r\n- You'll just need to add a `.streamlit/secrets.toml` file with configuration for connecting to Snowflake and an OpenAI API Key as described in \"Setting up Streamlit environment\".\r\n- More information and references on running this quickstart in Codespaces [here](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake#run-in-codespaces).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Accessing data on Snowflake Marketplace\r\n\r\n\r\nSnowflake Marketplace provides visibility to a wide variety of datasets from third-party data stewards which broaden access to data points used to transform business processes. Snowflake Marketplace also removes the need to integrate and model data by providing secure access to data sets fully maintained by the data provider.\r\n\r\n### Log into Snowsight\r\n\r\nIf you don't have a Snowflake account, sign up for a 30-day free trial [here](https://signup.snowflake.com/?utm_cta=quickstarts_).\r\n\r\n1. In a supported web browser, navigate to [https://app.snowflake.com](https://app.snowflake.com).\r\n2. Provide your account name or account URL. If you’ve previously signed in to Snowsight, you might see an account name that you can select.\r\n3. Sign in using your Snowflake account credentials.\r\n\r\nYou can also access Snowsight from the Classic Console:\r\n1. Sign in to the Classic Console.\r\n2. In the navigation menu, select Snowsight.\r\n3. Snowsight opens in a new tab.\r\n\r\n### Obtain dataset from Snowflake Marketplace\r\n\r\n1. At the top left corner, make sure you are logged in as ACCOUNTADMIN (switch role to ACCOUNTADMIN if not).\r\n2. Navigate to the Cybersyn Financial & Economic Essentials listing in the Snowflake Marketplace by clicking [here](https://app.snowflake.com/marketplace/listing/GZTSZAS2KF7/cybersyn-inc-cybersyn-financial-economic-essentials).\r\n3. Select **\"Get.\"**\r\n4. Select the appropriate roles to access the database being created and accept the Snowflake consumer terms and Cybersyn's terms of use.\r\n5. Select **\"Query Data,\"** which will open a worksheet with example queries.\r\n\r\n![Example queries for the Cybersyn Financial & Economic Essentials dataset from the Snowflake Marketplace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Cybersyn_Example_Queries.png)\r\n\r\n### Prep database\r\nBefore building our app, we need to run a set of SQL statements in Snowflake to create two views. The first view is `FROSTY_SAMPLE.CYBERSYN_FINANCIAL.FINANCIAL_ENTITY_ATTRIBUTES_LIMITED`, which includes:\r\n  * A subset of cybersyn_financial__economic_essentials.cybersyn.financial_institution_attributes:\r\n    * Totals for assets, real estate loans, securities, deposits; % of deposits insured; total employees\r\n  \r\n  The second view is `FROSTY_SAMPLE.CYBERSYN_FINANCIAL.FINANCIAL_ENTITY_ANNUAL_TIME_SERIES`, which includes:\r\n  * A modified version of cybersyn_financial__economic_essentials.cybersyn.financial_institution_timeseries as follows:\r\n    * Entity and attribute metadata is joined directly\r\n      * Only the set of attributes from FINANCIAL_ENTITY_ATTRIBUTES_LIMITED are exposed\r\n      * Only the end-of-year metrics (YYYY-12-31) are included, and a YEAR column is provided instead of the date column\r\n\r\nYou can copy the SQL statements from [this file](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/create-views.sql) and run them in the worksheet created for your sample queries.\r\n\r\n![GIF showing the SQL statements being run in Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Run_Queries.gif)\r\n\r\nNow that we've configured the dataset we'll be using for our application, we can get started with Streamlit.\r\n\r\n## Setting up Streamlit environment\r\n\r\n### Run an example Streamlit app\r\n1. Head back over to the command line and navigate to your `llm-chatbot` folder.\r\n2. Run an example Streamlit app by entering `streamlit hello`.\r\n![alt_text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Streamlit_Hello.png)\r\n\r\n### Configure secrets file\r\nSince our application will connect to Snowflake and OpenAI, we need a way to securely store our credentials. Luckily, [Streamlit's secrets management feature](https://docs.streamlit.io/streamlit-community-cloud/get-started/deploy-an-app/connect-to-data-sources/secrets-management) allows us to store secrets securely and access them in our Streamlit app as environment variables.\r\n\r\n1. Add a folder within your `llm-chatbot` folder called `.streamlit`. Using the command line, you can do this by entering `mkdir .streamlit`.\r\n2. Within the `.streamlit` folder, add a file called `secrets.toml`. Using the command line, you can do this by first navigating to the `.streamlit` folder via `cd .streamlit` and then entering `touch secrets.toml.`\r\n\r\n#### Add OpenAI credentials to `secrets.toml`\r\nWe need to add our OpenAI API key to our secrets file. Add your OpenAI key to the secrets file with the following format (replace the placeholder API key with your actual API key).\r\n\r\n```toml\r\n# .streamlit/secrets.toml\r\n\r\nOPENAI_API_KEY = \"sk-2v...X\"\r\n```\r\n\r\n#### Add Snowflake credentials to `secrets.toml`\r\nWe also need to add the Snowflake `user`, `password`, `warehouse`, `role`, and `account` to our secrets file. Copy the following format, replacing the placeholder credentials with your actual credentials.\r\n`account` should be your Snowflake account identifier, which you can locate by following the instructions outlined [here](https://docs.snowflake.com/en/user-guide/admin-account-identifier).\r\n\r\nIf you prefer to use browser-based SSO to authenticate, replace `password = \"\u003Cmy_trial_pass\u003E\"` with `authenticator=EXTERNALBROWSER`.\r\n\r\n```toml\r\n# .streamlit/secrets.toml\r\n\r\n[connections.snowflake]\r\nuser = \"\u003Cjdoe\u003E\"\r\npassword = \"\u003Cmy_trial_pass\u003E\"\r\nwarehouse = \"COMPUTE_WH\"\r\nrole = \"ACCOUNTADMIN\"\r\naccount = \"\u003Caccount-id\u003E\"\r\n```\r\n\r\n#### Full contents of secrets.toml\r\n```toml\r\n# .streamlit/secrets.toml\r\n\r\nOPENAI_API_KEY = \"sk-2v...X\"\r\n\r\n[connections.snowflake]\r\nuser = \"\u003Cusername\u003E\"\r\npassword = \"\u003Cpassword\u003E\"\r\nwarehouse = \"COMPUTE_WH\"\r\nrole = \"ACCOUNTADMIN\"\r\naccount = \"\u003Caccount-id\u003E\"\r\n```\r\n\r\n### Validate credentials\r\nLet's validate that our Snowflake and OpenAI credentials are working as expected.\r\n\r\n#### OpenAI credentials\r\n\r\nFirst, we'll validate our OpenAI credentials by asking GPT-3.5 a simple question: what is Streamlit?\r\n\r\n1. Add a file called `validate_credentials.py` at the root of your `llm-chatbot` folder.\r\n2. Add the below code to `validate_credentials.py`. This snippet does the following:\r\n   * Imports the Streamlit and OpenAI Python packages\r\n   * Retrieves our OpenAI API key from the secrets file\r\n   * Sends GPT-3.5 the question \"What is Streamlit?\"\r\n   * Prints GPT-3.5's response to the UI using `st.write`\r\n\r\n```python\r\nimport streamlit as st\r\nfrom openai import OpenAI\r\n\r\nclient = OpenAI(api_key=st.secrets[\"OPENAI_API_KEY\"])\r\n\r\ncompletion = client.chat.completions.create(\r\n  model=\"gpt-3.5-turbo\",\r\n  messages=[\r\n    {\"role\": \"user\", \"content\": \"What is Streamlit?\"}\r\n  ]\r\n)\r\n\r\nst.write(completion.choices[0].message.content)\r\n```\r\n3. Run your Streamlit app by entering `streamlit run validate_credentials.py` in the command line.\r\n![alt_text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Validate_OpenAI_Creds.png)\r\n\r\n#### Snowflake credentials\r\n\r\nNext, let's validate that our Snowflake credentials are working as expected.\r\n\r\n1. Append the following to `validate_credentials.py`. This snippet does the following:\r\n   * Creates a Snowpark connection\r\n   * Executes a query to pull the current warehouse and writes the result to the UI\r\n\r\n```python\r\nconn = st.connection(\"snowflake\")\r\ndf = conn.query(\"select current_warehouse()\")\r\nst.write(df)\r\n```\r\n\r\n2. Run your Streamlit app by entering `streamlit run validate_credentials.py` in the command line.\r\n![alt_text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Validate_Snowflake_Creds.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Build a simple chatbot application\r\n\r\nWe're ready to start building our app! We're going to first build a simple version of the chatbot app that simply passes user-inputted messages to GPT-3.5 and returns GPT-3.5's response. We'll build on the app's complexity in subsequent sections.\r\n\r\nWe'll break down the Python file snippet-by-snippet so that you understand the functionality of each section, but if you'd like to skip ahead and download the full file, you can do so [here](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/simple_chatbot.py).\r\n\r\n1. Create a file called `simple_chatbot.py`. Add import statements and give your app a title.\r\n```python\r\nfrom openai import OpenAI\r\nimport streamlit as st\r\n\r\nst.title(\"☃️ Frosty\")\r\n```\r\n\r\n2. Initialize the chatbot's message history by adding the first message that we want the chatbot to display, \"How can I help?\", to [session state](https://docs.streamlit.io/library/api-reference/session-state).\r\n\r\n```python\r\n# Initialize the chat messages history\r\nif \"messages\" not in st.session_state.keys():\r\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How can I help?\"}]\r\n```\r\n\r\n3. Prompt the user to enter chat input by using Streamlit's `st.chat_input()` feature. If the user has entered a message, add that message to the chat history by storing it in session state.\r\n\r\n```python\r\n# Prompt for user input and save\r\nif prompt := st.chat_input():\r\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\r\n```\r\n\r\n4. Display the chatbot's message history by iterating through the values stored in session state associated with the key \"messages\" and printing each value.\r\n\r\n```python\r\n# display the existing chat messages\r\nfor message in st.session_state.messages:\r\n    with st.chat_message(message[\"role\"]):\r\n        st.write(message[\"content\"])\r\n```\r\n\r\n4. If the last message is not from the assistant, send the message to GPT-3.5 via the `openai` Python package. Display a spinner while the app is retrieving GPT-3.5's response via Streamlit's [`st.spinner`](https://docs.streamlit.io/library/api-reference/status/st.spinner) feature and use `st.write` to display the chatbot's response in the UI. Append the chatbot's response to the chat history stored in session state.\r\n\r\n```python\r\n# If last message is not from assistant, we need to generate a new response\r\nif st.session_state.messages[-1][\"role\"] != \"assistant\":\r\n    # Call LLM\r\n    with st.chat_message(\"assistant\"):\r\n        with st.spinner(\"Thinking...\"):\r\n            r = OpenAI().chat.completions.create(\r\n                messages=[{\"role\": m[\"role\"], \"content\": m[\"content\"]} for m in st.session_state.messages],\r\n                model=\"gpt-3.5-turbo\",\r\n            )\r\n            response = r.choices[0].message.content\r\n            st.write(response)\r\n\r\n    message = {\"role\": \"assistant\", \"content\": response}\r\n    st.session_state.messages.append(message)\r\n```\r\n6. Run the Streamlit app via `streamlit run simple_chatbot.py`. Give it a whirl – ask Frosty a question!\r\n\r\n![GIF demonstrating the simple chatbot app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/Simple_Chatbot.gif)\r\n\r\nThe full contents of the Python file for this simple chatbot app are below, or you can download the file from [GitHub](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/simple_chatbot.py).\r\n\r\n```python\r\nfrom openai import OpenAI\r\nimport streamlit as st\r\n\r\nst.title(\"☃️ Frosty\")\r\n\r\n# Initialize the chat messages history\r\nif \"messages\" not in st.session_state.keys():\r\n    st.session_state.messages = [{\"role\": \"assistant\", \"content\": \"How can I help?\"}]\r\n\r\n# Prompt for user input and save\r\nif prompt := st.chat_input():\r\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\r\n\r\n# display the existing chat messages\r\nfor message in st.session_state.messages:\r\n    with st.chat_message(message[\"role\"]):\r\n        st.write(message[\"content\"])\r\n\r\n# If last message is not from assistant, we need to generate a new response\r\nif st.session_state.messages[-1][\"role\"] != \"assistant\":\r\n    # Call LLM\r\n    with st.chat_message(\"assistant\"):\r\n        with st.spinner(\"Thinking...\"):\r\n            r = OpenAI().chat.completions.create(\r\n                messages=[{\"role\": m[\"role\"], \"content\": m[\"content\"]} for m in st.session_state.messages],\r\n                model=\"gpt-3.5-turbo\",\r\n            )\r\n            response = r.choices[0].message.content\r\n            st.write(response)\r\n\r\n    message = {\"role\": \"assistant\", \"content\": response}\r\n    st.session_state.messages.append(message)\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Add prompt engineering and SQL extraction\r\n\r\nNow that we've built a simple version of the chatbot app, let's expand the functionality to enable Frosty to translate our requests into SQL statements and execute those statements using the Cybersyn dataset stored in our Snowflake database.\r\n\r\n### Create a prompt file\r\nWe're also going to create a prompt Python file before building out the main file of our chatbot app. The primary purpose of this file is to create the function `get_system_prompt()`, which will be called in our main Python file and will do a few things:\r\n  * Retrieves basic information about the database we're going to be using, including the table name, table description, and variable names\r\n  * Composes a system message for GPT-3.5, which shares basic information about the dataset with the model and instructs the model to:\r\n    * Respond in the character of an AI Snowflake SQL expert named Frosty\r\n    * Include a SQL query in each answer based on the question and the table.\r\n    * Format SQL queries properly via markdown.\r\n    * Limit the number of responses to a SQL query to 10 (unless otherwise specified). \r\n    * Generate a single SQL code snippet.\r\n    * Only use the specified table columns and table.\r\n    * Avoid starting variable names with numbers.\r\n    * Use \"ilike %keyword%\" for fuzzy match queries.\r\n    * Start the conversation by briefly introducing yourself, describing the table, sharing available metrics in a few sentences, and providing three example questions.\r\n\r\nThis file should be placed in the root of your `llm-chatbot` folder. You can download the file from [here](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/prompts.py) or create an empty Python file and paste the following code:\r\n\r\n````python\r\nimport streamlit as st\r\n\r\nSCHEMA_PATH = st.secrets.get(\"SCHEMA_PATH\", \"FROSTY_SAMPLE.CYBERSYN_FINANCIAL\")\r\nQUALIFIED_TABLE_NAME = f\"{SCHEMA_PATH}.FINANCIAL_ENTITY_ANNUAL_TIME_SERIES\"\r\nTABLE_DESCRIPTION = \"\"\"\r\nThis table has various metrics for financial entities (also referred to as banks) since 1983.\r\nThe user may describe the entities interchangeably as banks, financial institutions, or financial entities.\r\n\"\"\"\r\n# This query is optional if running Frosty on your own table, especially a wide table.\r\n# Since this is a deep table, it's useful to tell Frosty what variables are available.\r\n# Similarly, if you have a table with semi-structured data (like JSON), it could be used to provide hints on available keys.\r\n# If altering, you may also need to modify the formatting logic in get_table_context() below.\r\nMETADATA_QUERY = f\"SELECT VARIABLE_NAME, DEFINITION FROM {SCHEMA_PATH}.FINANCIAL_ENTITY_ATTRIBUTES_LIMITED;\"\r\n\r\nGEN_SQL = \"\"\"\r\nYou will be acting as an AI Snowflake SQL Expert named Frosty.\r\nYour goal is to give correct, executable sql query to users.\r\nYou will be replying to users who will be confused if you don't respond in the character of Frosty.\r\nYou are given one table, the table name is in \u003CtableName\u003E tag, the columns are in \u003Ccolumns\u003E tag.\r\nThe user will ask questions, for each question you should respond and include a sql query based on the question and the table. \r\n\r\n{context}\r\n\r\nHere are 6 critical rules for the interaction you must abide:\r\n\u003Crules\u003E\r\n1. You MUST MUST wrap the generated sql code within ``` sql code markdown in this format e.g\r\n```sql\r\n(select 1) union (select 2)\r\n```\r\n2. If I don't tell you to find a limited set of results in the sql query or question, you MUST limit the number of responses to 10.\r\n3. Text / string where clauses must be fuzzy match e.g ilike %keyword%\r\n4. Make sure to generate a single snowflake sql code, not multiple. \r\n5. You should only use the table columns given in \u003Ccolumns\u003E, and the table given in \u003CtableName\u003E, you MUST NOT hallucinate about the table names\r\n6. DO NOT put numerical at the very front of sql variable.\r\n\u003C/rules\u003E\r\n\r\nDon't forget to use \"ilike %keyword%\" for fuzzy match queries (especially for variable_name column)\r\nand wrap the generated sql code with ``` sql code markdown in this format e.g:\r\n```sql\r\n(select 1) union (select 2)\r\n```\r\n\r\nFor each question from the user, make sure to include a query in your response.\r\n\r\nNow to get started, please briefly introduce yourself, describe the table at a high level, and share the available metrics in 2-3 sentences.\r\nThen provide 3 example questions using bullet points.\r\n\"\"\"\r\n\r\n@st.cache_data(show_spinner=\"Loading Frosty's context...\")\r\ndef get_table_context(table_name: str, table_description: str, metadata_query: str = None):\r\n    table = table_name.split(\".\")\r\n    conn = st.connection(\"snowflake\")\r\n    columns = conn.query(f\"\"\"\r\n        SELECT COLUMN_NAME, DATA_TYPE FROM {table[0].upper()}.INFORMATION_SCHEMA.COLUMNS\r\n        WHERE TABLE_SCHEMA = '{table[1].upper()}' AND TABLE_NAME = '{table[2].upper()}'\r\n        \"\"\", show_spinner=False,\r\n    )\r\n    columns = \"n\".join(\r\n        [\r\n            f\"- **{columns['COLUMN_NAME'][i]}**: {columns['DATA_TYPE'][i]}\"\r\n            for i in range(len(columns[\"COLUMN_NAME\"]))\r\n        ]\r\n    )\r\n    context = f\"\"\"\r\nHere is the table name \u003CtableName\u003E {'.'.join(table)} \u003C/tableName\u003E\r\n\r\n\u003CtableDescription\u003E{table_description}\u003C/tableDescription\u003E\r\n\r\nHere are the columns of the {'.'.join(table)}\r\n\r\n\u003Ccolumns\u003Enn{columns}nn\u003C/columns\u003E\r\n    \"\"\"\r\n    if metadata_query:\r\n        metadata = conn.query(metadata_query, show_spinner=False)\r\n        metadata = \"n\".join(\r\n            [\r\n                f\"- **{metadata['VARIABLE_NAME'][i]}**: {metadata['DEFINITION'][i]}\"\r\n                for i in range(len(metadata[\"VARIABLE_NAME\"]))\r\n            ]\r\n        )\r\n        context = context + f\"nnAvailable variables by VARIABLE_NAME:nn{metadata}\"\r\n    return context\r\n\r\ndef get_system_prompt():\r\n    table_context = get_table_context(\r\n        table_name=QUALIFIED_TABLE_NAME,\r\n        table_description=TABLE_DESCRIPTION,\r\n        metadata_query=METADATA_QUERY\r\n    )\r\n    return GEN_SQL.format(context=table_context)\r\n\r\n# do `streamlit run prompts.py` to view the initial system prompt in a Streamlit app\r\nif __name__ == \"__main__\":\r\n    st.header(\"System prompt for Frosty\")\r\n    st.markdown(get_system_prompt())\r\n````\r\n\r\nFinally, you can run this file as a Streamlit app to verify the output is working correctly. Run the prompts generation via `streamlit run prompts.py`. Make sure the table information is showing up as expected in the rendered prompt - this will get passed to the chatbot in the next section.\r\n\r\n### Build the chatbot    \r\n\r\nWe'll break down the Python file snippet-by-snippet so that you understand the functionality of each section, but if you'd like to skip ahead and download the full file, you can do so [here](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/frosty_app.py).\r\n\r\n1. Create a file called `frosty_app.py` and add the below code snippet, which does the following:\r\n   * Adds import statements and a title\r\n   * Retrieves our OpenAI API key from the secrets file\r\n   * Initializes the message history using session state\r\n      * This time, the first assistant message from the chatbot will display information about the current table in the database this app is using. `get_system_prompt()` retrieves this information.\r\n   * Prompts the user to enter a message and upon receiving a message, adds that message to the chat history\r\n   * Iterates through the message history and displays each message in the app\r\n\r\n```python\r\nfrom openai import OpenAI\r\nimport re\r\nimport streamlit as st\r\nfrom prompts import get_system_prompt\r\n\r\nst.title(\"☃️ Frosty\")\r\n\r\n# Initialize the chat messages history\r\nclient = OpenAI(api_key=st.secrets.OPENAI_API_KEY)\r\nif \"messages\" not in st.session_state:\r\n    # system prompt includes table information, rules, and prompts the LLM to produce\r\n    # a welcome message to the user.\r\n    st.session_state.messages = [{\"role\": \"system\", \"content\": get_system_prompt()}]\r\n\r\n# Prompt for user input and save\r\nif prompt := st.chat_input():\r\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\r\n\r\n# display the existing chat messages\r\nfor message in st.session_state.messages:\r\n    if message[\"role\"] == \"system\":\r\n        continue\r\n    with st.chat_message(message[\"role\"]):\r\n        st.write(message[\"content\"])\r\n        if \"results\" in message:\r\n            st.dataframe(message[\"results\"])\r\n```\r\n\r\n2. Check the last entry in the chat history to see if it was sent by the user or the chatbot. If it was sent by the user, use GPT-3.5 to generate a response. Instead of displaying the entire response at once, use OpenAI's `stream` parameter to signify that GPT-3.5's response should be sent incrementally in chunks via an event stream, and display the chunks as they're received.\r\n\r\n```python\r\n# If last message is not from assistant, we need to generate a new response\r\nif st.session_state.messages[-1][\"role\"] != \"assistant\":\r\n    with st.chat_message(\"assistant\"):\r\n        response = \"\"\r\n        resp_container = st.empty()\r\n        for delta in client.chat.completions.create(\r\n            model=\"gpt-3.5-turbo\",\r\n            messages=[{\"role\": m[\"role\"], \"content\": m[\"content\"]} for m in st.session_state.messages],\r\n            stream=True,\r\n        ):\r\n            response += (delta.choices[0].delta.content or \"\")\r\n            resp_container.markdown(response)\r\n```\r\n\r\n3. Use a regular expression to search the newly generated response for the SQL markdown syntax that we instructed GPT-3.5 to wrap any SQL queries in. If a match is found, use `st.experimental_connection` to execute the SQL query against the database we created in Snowflake. Write the result to the app using `st.dataframe`, and append the result to the associated message in the message history.\r\n\r\n```python\r\n        message = {\"role\": \"assistant\", \"content\": response}\r\n        # Parse the response for a SQL query and execute if available\r\n        sql_match = re.search(r\"```sqln(.*)n```\", response, re.DOTALL)\r\n        if sql_match:\r\n            sql = sql_match.group(1)\r\n            conn = st.connection(\"snowflake\")\r\n            message[\"results\"] = conn.query(sql)\r\n            st.dataframe(message[\"results\"])\r\n        st.session_state.messages.append(message)\r\n```\r\n\r\n4. Run the Streamlit app via `streamlit run frosty_app.py`.\r\n\r\n![Preview of final app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/frosty-llm-chatbot-on-streamlit-snowflake/App_Demo.gif)\r\n\r\nThe full contents of the Python file for this app are below, or you can download the file from [GitHub](https://github.com/Snowflake-Labs/sfguide-frosty-llm-chatbot-on-streamlit-snowflake/blob/main/src/frosty_app.py).\r\n\r\n```python\r\nfrom openai import OpenAI\r\nimport re\r\nimport streamlit as st\r\nfrom prompts import get_system_prompt\r\n\r\nst.title(\"☃️ Frosty\")\r\n\r\n# Initialize the chat messages history\r\nclient = OpenAI(api_key=st.secrets.OPENAI_API_KEY)\r\nif \"messages\" not in st.session_state:\r\n    # system prompt includes table information, rules, and prompts the LLM to produce\r\n    # a welcome message to the user.\r\n    st.session_state.messages = [{\"role\": \"system\", \"content\": get_system_prompt()}]\r\n\r\n# Prompt for user input and save\r\nif prompt := st.chat_input():\r\n    st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\r\n\r\n# display the existing chat messages\r\nfor message in st.session_state.messages:\r\n    if message[\"role\"] == \"system\":\r\n        continue\r\n    with st.chat_message(message[\"role\"]):\r\n        st.write(message[\"content\"])\r\n        if \"results\" in message:\r\n            st.dataframe(message[\"results\"])\r\n\r\n# If last message is not from assistant, we need to generate a new response\r\nif st.session_state.messages[-1][\"role\"] != \"assistant\":\r\n    with st.chat_message(\"assistant\"):\r\n        response = \"\"\r\n        resp_container = st.empty()\r\n        for delta in client.chat.completions.create(\r\n            model=\"gpt-3.5-turbo\",\r\n            messages=[{\"role\": m[\"role\"], \"content\": m[\"content\"]} for m in st.session_state.messages],\r\n            stream=True,\r\n        ):\r\n            response += (delta.choices[0].delta.content or \"\")\r\n            resp_container.markdown(response)\r\n\r\n        message = {\"role\": \"assistant\", \"content\": response}\r\n        # Parse the response for a SQL query and execute if available\r\n        sql_match = re.search(r\"```sqln(.*)n```\", response, re.DOTALL)\r\n        if sql_match:\r\n            sql = sql_match.group(1)\r\n            conn = st.connection(\"snowflake\")\r\n            message[\"results\"] = conn.query(sql)\r\n            st.dataframe(message[\"results\"])\r\n        st.session_state.messages.append(message)\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Explore the data via natural language\r\n\r\nFinally, it's time to explore the Cybersyn Financial & Economic Essentials using natural language. Try asking Frosty any of the following questions:\r\n\r\n1. Which financial institution had the highest total assets in the year 2020?\r\n2. Which financial institutions in California had the highest total assets value between 2010 to 2015?\r\n3. What was the highest % insured (estimated) value for all financial institutions in the state of New Jersey?\r\n4. What is the lowest value of total securities for all financial institutions in Texas?\r\n5. What was the % change in all real estate loans for banks headquartered in California between 2015 and 2020?\r\n6. What was the average total securities value for banks in the state of Wisconsin between 2015 and 2020?\r\n7. How have the total securities value changed over time for financial institutions in New York City?\r\n8. What was the maximum % insured (estimated) value for a single financial entity in Illinois between 2010 and 2020?\r\n9. What was the value of all real estate loans for banks located in Massachusetts in 2020?\r\n10. How many banks headquartered in New Hampshire experienced more than 50% growth in their total assets between 2015 and 2020?\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion and next steps\r\n\r\nCongratulations – you've just built an LLM-powered chatbot capable of translating natural language to SQL queries and running those queries on data stored in Snowflake!\r\n\r\n### Where to go from here\r\n\r\nThis tutorial is just a starting point for exploring the possibilities of LLM-powered chat interfaces for data exploration and question-answering using Snowflake and Streamlit. A few next things to try:\r\n\r\n- **Update to run against your private data in Snowflake**, or other relevant Snowflake Marketplace datasets. The table-specific logic in the app is all specified at the top of `prompts.py`, so it should be easy to swap and start playing around!\r\n- **Add more capabilities**, such as using the LLM to choose from a set of available tables, summarize the returned data, or even write Streamlit code to visualize the results. You could even use a library like LangChain to convert Frosty into an \"Agent\" with improved chain of thought reasoning and the ability to respond to errors.\r\n- **Prepare to run in Streamlit in Snowflake** (currently in Private Preview): The functionality shown here will soon be available in Streamlit in Snowflake, especially when paired with External Access (also in Private Preview) to simplify access to an external LLM.\r\n\r\nCheck out the Frosty session (ML103) from Snowflake Summit 2023 for more ideas and what's coming soon from Snowflake!\r\n\r\n### Additional resources\r\nWant to learn more about the tools and technologies used by your app? Check out the following resources:\r\n\r\n* [Streamlit's new chat UI](https://docs.streamlit.io/library/api-reference/chat)\r\n* [st.experimental_connection](https://docs.streamlit.io/library/api-reference/connections/st.experimental_connection)\r\n* [Session state](https://docs.streamlit.io/library/api-reference/session-state)\r\n* [Secrets management](https://docs.streamlit.io/streamlit-community-cloud/get-started/deploy-an-app/connect-to-data-sources/secrets-management)\r\n* [OpenAI's ChatCompetion feature](https://platform.openai.com/docs/api-reference/chat)\r\n* [Generative AI and Streamlit: A perfect match](https://blog.streamlit.io/generative-ai-and-streamlit-a-perfect-match/)\r\n* [Build powerful generative AI apps with Streamlit](https://streamlit.io/generative-ai)\r\n* [Demo on Snowflake Demo Hub](https://developers.snowflake.com/demos/data-exploration-llm-chatbot/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b946c6879d","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-8ab6db6bfc",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ae1d551cb3","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-4bf62975c8","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-5cc37516f7",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-8f6e4fe734",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-39c4edcbd4","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-b3b8feb766",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/frosty-llm-chatbot-on-streamlit-snowflake","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EPrepare your environment\u003C/h2\u003E","\u003Ch2\u003EAccessing data on Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003ESetting up Streamlit environment\u003C/h2\u003E","\u003Ch2\u003EBuild a simple chatbot application\u003C/h2\u003E","\u003Ch2\u003EAdd prompt engineering and SQL extraction\u003C/h2\u003E","\u003Ch2\u003EExplore the data via natural language\u003C/h2\u003E","\u003Ch2\u003EConclusion and next 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