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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ETroves of enterprise data exists as video and audio, but its utility has been limited due to the difficulty of processing it to extract insights. That barrier to entry has rapidly crumbled in the last few years, with the advances of AI models that enable cheap and fast Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR), as well as powerful Vision Language Models (VLMs), able to extract meaning and grounding data from video.\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, we will employ all three techniques to analyze meeting video to extract insights. We are using the \u003Ca href=\"https://groups.inf.ed.ac.uk/ami/corpus/\"\u003EAMI Corpus\u003C/a\u003E, which is a \u003Cstrong\u003Emulti-modal dataset of 100s of hours of simulated scenario meetings in which particpants play different roles in a design team, taking a design project from kick-off to completion over the course of a day\u003C/strong\u003E. The same technques shown here are broadly applicable to other meeting video, especially when valuable information is displayed on-screen, such as presentation slides or demos.\u003C/p\u003E\n","\u003Ch3\u003EPreqrequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic understanding of Snowflake and containers.\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake Account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EInstallation of \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.docker.com/get-started/\"\u003EDocker Desktop\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://huggingface.co\"\u003EHugging Face\u003C/a\u003E account and \u003Ca href=\"https://huggingface.co/docs/hub/en/security-tokens\"\u003EAccess Token\u003C/a\u003E for downloading models\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EYou will build a multi-step pipeline that uses \u003Ca href=\"/en/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E for OCR and ASR, and VLM deployed on \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/\"\u003ESnowpark Container Services\u003C/a\u003E to extract structured data from meeting video. You’ll store the output from all three models into structured Snowflake tables and build a simple chatbot allowing you to use language to generate rich analytical queries. You will be able to assess meeting effectiveness, identify decision points, and extract action items—directly within the AI Data Cloud.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/1_arch_diagram.png\" alt=\"1\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n","\u003Cp\u003EYou will gain hands-on experience with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex \u003Ccode\u003EAI_TRANSCRIBE\u003C/code\u003E\u003C/strong\u003E for ASR\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex \u003Ccode\u003EPARSE_DOCUMENT\u003C/code\u003E\u003C/strong\u003E for OCR\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowpark Container Services (SPCS)\u003C/strong\u003E for loading the \u003Ca href=\"https://github.com/QwenLM/Qwen2.5-VL\"\u003EQwen2.5-VL\u003C/a\u003E large vision model from Hugging Face and running online inference\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EComponent Overview\u003C/h2\u003E\n","\u003Cp\u003ESnowpark Container Services and Snowflake Cortex AI are two of the major components that are utilized within this Quickstart. Below is an overview of them.\u003C/p\u003E\n","\u003Ch3\u003EOverview of Snowpark Container Services\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E is a fully managed container offering designed to facilitate the deployment, management, and scaling of containerized applications within the Snowflake ecosystem. This service enables users to run containerized workloads directly within Snowflake, ensuring that data doesn’t need to be moved out of the Snowflake environment for processing. Unlike traditional container orchestration platforms like Docker or Kubernetes, Snowpark Container Services offers an OCI runtime execution environment specifically optimized for Snowflake. This integration allows for the seamless execution of OCI images, leveraging Snowflake’s robust data platform.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/spcs_diagram.png\" alt=\"spcs\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EOverview of Cortex AI\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E enables you to quickly analyze unstructured data and build generative AI applications using fully managed LLMs, RAG and text-to-SQL services. Enable multiple users to use AI services with no-code, SQL and REST API interfaces.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/platform-cortex-2025.jpeg\" alt=\"Cortex_AI\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Your Environment\u003C/h2\u003E\n","\u003Ch3\u003ECreate Snowflake Account\u003C/h3\u003E\n","\u003Cp\u003ELog into Snowflake using your unique credentials if you have a Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EFor this guide, you will only need Snowflake's \u003Cstrong\u003EStandard Edition\u003C/strong\u003E on AWS. You may want to select \u003Cstrong\u003EEnterprise\u003C/strong\u003E to try out advanced features such as Time Travel, materialized views, or Failover.\u003C/p\u003E\n","\u003Cp\u003EChoose \u003Cstrong\u003EUS West (Oregon)\u003C/strong\u003E for the AWS Region and log in.\u003C/p\u003E\n","\u003Ch3\u003ESet up SQL Environment\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EGit clone the Quickstart's \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis\"\u003Erepo\u003C/a\u003E to your local machine.\u003C/li\u003E\u003Cli\u003ETo prepare your Snowflake environment, in Snowsight, create a SQL file by clicking on \u003Cstrong\u003E+ Create\u003C/strong\u003E, then \u003Cstrong\u003ESQL File\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EName the file \u003Ccode\u003Erun.sql\u003C/code\u003E and copy the contents \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/run.sql\"\u003Erun.sql\u003C/a\u003E from the cloned repository\u003C/li\u003E\u003C/ol\u003E\n<!-- TODO needs updated due to filename change-->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/4_create_setup_sql_file.png\" alt=\"4\"\u003E\u003C/p\u003E\n<!-- TODO feels like we should hold off this until they install CLI\n4. To run the Common Setup, click at the beginning of a SQL line and click the blue Run button above your `run.sql` file. \n\n![5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/5_run_setup.png)\n\nThe output of the SQL command will appear in the results box.\n\n![6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/6_setup_output.png)\n\n--> \n","\u003Ch3\u003EInstall Snowflake CLI\u003C/h3\u003E\n","\u003Cp\u003EInstall the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E. Snowflake CLI can be used to upload the video and audio files to a Stage, check resources, and push container images to the Image Registrykk.\u003C/p\u003E\n","\u003Cp\u003EUse Snowsight's \u003Cstrong\u003EConnect a Tool\u003C/strong\u003E to \u003Ca href=\"https://docs.snowflake.com/user-guide/gen-conn-config#using-sf-web-interface-to-get-connection-settings\"\u003Econfigure Snowflake CLI\u003C/a\u003E to access your Snowflake account.  Viewing \u003Cstrong\u003EAccount Details\u003C/strong\u003E and then \u003Cstrong\u003EConfig File\u003C/strong\u003E will provide you with the .toml file necessary to configure Snowflake CLI to connect to your account. Add the following modified for your account to your snow CLI config file (e.g. \u003Ccode\u003E~/.snowflake.config.toml\u003C/code\u003E):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-TOML\"\u003Edefault_connection_name = "hol"    -- sets the below connection to be implicitly used\n[connections.hol]\naccount = "SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI" -- from Connection Tool\nuser = "<username>"    -- update username\npassword = "<password>"  -- update password\nrole = "container_user_role"  -- update from Quickstart\nwarehouse = "hol_warehouse"\ndatabase = "hol_db"\nschema = "public"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the above contents into \u003Ccode\u003Econfig.toml\u003C/code\u003E in the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-cli#location-of-the-toml-configuration-file\"\u003ESnowflake CLI configuration directory\u003C/a\u003E (e.g. \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E)\u003C/p\u003E\n","\u003Cp\u003EVerify SnowCLI is correctly configured by running:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Esnow connection list\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esnow connection test --connection hol\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esnow connection test\u003C/code\u003E  to verify the default value\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E+-----------------------------------------------------------------------------------------+\n| key             | value                                                                 |\n|-----------------+-----------------------------------------------------------------------|\n| Connection name | hol                                                                   |\n| Status          | OK                                                                    |\n| Host            | SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI.snowflakecomputing.com |\n| Account         | SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI                        |\n| User            | USER                                                                  |\n| Role            | CONTAINER_USER_ROLE                                                   |\n| Database        | HOL_DB                                                                |\n| Warehouse       | HOL_WAREHOUSE                                                         |\n+-----------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun Common Setup\u003C/h3\u003E\n","\u003Cp\u003EExecute the first section of the \u003Ccode\u003Erun.sql\u003C/code\u003E file labeled with the comment \u003Ccode\u003ECOMMON SETUP\u003C/code\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the \u003Ccode\u003Ehol_user_role\u003C/code\u003E Role, \u003Ccode\u003Ehol_db\u003C/code\u003E Database, and \u003Ccode\u003Ehol_warehouse\u003C/code\u003E Warehouse which we will use to run subsequent steps.\u003C/li\u003E\u003Cli\u003ETo use Snowpark Container Services, we'll create the \u003Ccode\u003Ehol_compute_pool\u003C/code\u003E Compute Pool, which provides computing resources to run our containerized Service.\u003C/li\u003E\u003Cli\u003EWe'll also need to enable our Service to reach external sites such as Hugging Face and PyPI, so we create the \u003Ccode\u003Edependencies_access_integration\u003C/code\u003E External Access Integration. Snowflake accounts are secure by default and do not allow external access.\u003C/li\u003E\u003Cli\u003EWe create the \u003Ccode\u003Ehol_db.public.repo\u003C/code\u003E Image Repository for storing container images\u003C/li\u003E\u003Cli\u003EFinally, we create Stages \u003Ccode\u003E@video\u003C/code\u003E and \u003Ccode\u003E@model\u003C/code\u003E for storing files used in later stages\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUpload Files to Stage\u003C/h3\u003E\n","\u003Cp\u003EOur application will process video and audio files that are stored on a Snowflake Stage. We'll need to first upload the video and audio files from the Github repo to our Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EUsing the Snow CLI, list the Stage you previously created\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow stage list\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n|        |        |        |        |     |         |        |         |        |        |         |       | notifi |         |        |         |        |\n|        |        | databa |        |     | has_cre | has_en |         |        |        |         |       | cation | storage |        | owner_r | direct |\n| create |        | se_nam | schema |     | dential | crypti |         | commen |        |         |       | _chann | _integr | endpoi | ole_typ | ory_en |\n| d_on   | name   | e      | _name  | url | s       | on_key | owner   | t      | region | type    | cloud | el     | ation   | nt     | e       | abled  |\n|--------+--------+--------+--------+-----+---------+--------+---------+--------+--------+---------+-------+--------+---------+--------+---------+--------|\n| 2025-0 | VIDEOS | HOL_DB | PUBLIC |     | N       | N      | ACCOUNT |        | None   | INTERNA | None  | None   | None    | None   | ROLE    | Y      |\n| 5-29   |        |        |        |     |         |        | ADMIN   |        |        | L NO    |       |        |         |        |         |        |\n| 15:21: |        |        |        |     |         |        |         |        |        | CSE     |       |        |         |        |         |        |\n| 23.957 |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n| 000-07 |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n| :00    |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUpload the contents of the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/videos\"\u003E\u003Ccode\u003Evideos\u003C/code\u003E\u003C/a\u003E directory and \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/chatbot\"\u003Echatbot\u003C/a\u003E directories from cloned repo the Stage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow stage copy --recursive ./videos @hol_db.public.videos\n$ snow stage copy --recursive ./chatbot @hol_db.public.model\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EList the contents of the Stage\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow stage list-files @hol_db.public.videos\n+-------------------------------------------------------------------------------------------------------------------------------------------+\n| name                                                        | size     | md5                              | last_modified                 |\n|-------------------------------------------------------------+----------+----------------------------------+-------------------------------|\n| videos/amicorpus/IS1004/audio/IS1004a.Mix-Lapel.mp3         | 12748652 | f9ee1bfce574d6ec1de89717465ebf3b | Fri, 30 May 2025 22:33:00 GMT |\n| videos/amicorpus/IS1004/audio/IS1004c.Mix-Lapel.mp3         | 36222956 | 6c25066bfdecf7db3a302c7a43f6173b | Fri, 30 May 2025 22:32:57 GMT |\n...\n+-------------------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMeeting Source Setup\u003C/h3\u003E\n","\u003Cp\u003EExecute the two lines in \u003Ccode\u003Erun.sql\u003C/code\u003E that set the \u003Ccode\u003Emeeting_id\u003C/code\u003E and \u003Ccode\u003Emeeting_part\u003C/code\u003E variables. This will determine which video, audio, and image files you just uploaded will be used by subsequent analysis steps. This should match files that you uploaded to the Stage.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVideo Analysis\u003C/h2\u003E\n","\u003Ch3\u003EBuild Docker Container\u003C/h3\u003E\n","\u003Cp\u003EIn your downloaded \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/videos\"\u003Erepo\u003C/a\u003E, navigate to the \u003Ccode\u003Evideo_analysis\u003C/code\u003E directory.\u003C/p\u003E\n","\u003Cp\u003EUsing the Snow CLI, list your Image Registry:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow spcs image-repository list\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| created_on                 | name | database_name | schema_name | repository_url             | owner        | owner_role_type | comment | encryption    |\n|----------------------------+------+---------------+-------------+----------------------------+--------------+-----------------+---------+---------------|\n| 2025-05-29                 | REPO | HOL_DB        | PUBLIC      | sfsehol-summit25-unstr-dat | ACCOUNTADMIN | ROLE            |         | SNOWFLAKE_SSE |\n| 15:21:19.834000-07:00      |      |               |             | a-processtest-bchxei.regis |              |                 |         |               |\n|                            |      |               |             | try.snowflakecomputing.com |              |                 |         |               |\n|                            |      |               |             | /hol_db/public/repo        |              |                 |         |               |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGet the Image Registry URL\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow spcs image-repository url repo\nsfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBuild the Docker Container\nThe syntax of this command is \u003Ccode\u003Edocker build --rm --platform linux/amd64 -t <repository_url>/<image_name> .\u003C/code\u003E where \u003Ccode\u003Erepository_url\u003C/code\u003E is from the previous command and \u003Ccode\u003Eimage_name\u003C/code\u003E is \u003Ccode\u003Eprocess_video:latest\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ docker build --rm --platform linux/amd64 -t sfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video:latest .\n\n<...build output...>\n\n$ docker images\nREPOSITORY                                                                                                        TAG       IMAGE ID       CREATED             SIZE\nsfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video   latest    8a051200cd1d   About an hour ago   16.5GB\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELogin to your Repository with Docker using Snow CLI\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow spcs image-registry login\nLogin Succeeded\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPush Container to Registry\u003C/h3\u003E\n","\u003Cp\u003ENow that you've built the container, we need to upload or Push the container image to the registry. The Snowpark Container Services (SPCS) Container Registry is a secure, Snowflake-managed repository for storing and versioning container images. It enables developers to seamlessly build, deploy, and run containerized applications within the Snowflake Data Cloud.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ docker push sfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video:latest\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EList the Image in the repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E$ snow spcs image-repository list-images repo\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| created_on                | image_name    | tags   | digest                                                   | image_path                              |\n|---------------------------+---------------+--------+----------------------------------------------------------+-----------------------------------------|\n| 2025-05-30 15:11:34-07:00 | process_video | latest | sha256:077c6883533f7d384b0e6594038895995de6f5470892cb15e | hol_db/public/repo/process_video:latest |\n|                           |               |        | 51e4afcea611a35                                          |                                         |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExecute Containerized Job\u003C/h3\u003E\n","\u003Cp\u003EThe image you built and pushed to SPCS Image Repository contains the \u003Ca href=\"https://docs.vllm.ai/en/latest/\"\u003EvLLM\u003C/a\u003E runtime which provides scalable hosting for large AI models with support for multiple GPU instances. It is pre-configured to load the \u003Ca href=\"https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct\"\u003EQwen2.5-VL-7B-Instruct\u003C/a\u003E VLM from Hugging Face. This model excels at:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVisual understanding: object recognition, but also analyzing slides, charts, and graphics\u003C/li\u003E\u003Cli\u003EUnderstanding long videos and capturing events: for videos of over 1 hour, complete with the ability to capture events and pinpoint relevant video segments\u003C/li\u003E\u003Cli\u003EGenerating structured outputs: returning results in structured formats to be used for Snowflake analytical processing\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe will use \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-services#execute-a-job-service\"\u003ESPCS Jobs\u003C/a\u003E to run the model against meeting videos with the following prompt:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cem\u003EProvide a detailed description of this meeting video, dividing it in to sections with a one sentence description, and capture the most important text that's displayed on screen. Identify the start and end of each section with a timestamp in the 'hh:mm:ss' format. Return the results as JSON"\u003C/em\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe resulting JSON will be parsed into a structured table called \u003Ccode\u003Evideo_analysis\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFollow these steps in the \u003Ccode\u003EVIDEO ANALYSIS\u003C/code\u003E section of \u003Ccode\u003Erun.sql\u003C/code\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E(Optional) Dun the \u003Ccode\u003EDROP SERVICE IF EXISTS\u003C/code\u003E command if you are executing the Job repeatedly to clean up previous runs\u003C/li\u003E\u003Cli\u003ERun the \u003Ccode\u003EEXECUTE JOB SERVICE\u003C/code\u003E command to run the Job\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EReplace the \u003Ccode\u003E<image>:<version>\u003C/code\u003E section with the image path from the previous step\u003C/li\u003E\u003Cli\u003EReplace \u003Ccode\u003E<your_hf_token>\u003C/code\u003E with you Hugging Face token to download the model\u003C/li\u003E\u003Cli\u003EThe exact meeting the model will analyze have already been preset when you set the \u003Ccode\u003E$meeting_id\u003C/code\u003E and \u003Ccode\u003E$meeting_part\u003C/code\u003E SQL variables earlier\u003C/li\u003E\u003Cli\u003EThe command is configured to run asynchronously, so it should complete within a few seconds, however the underlying Job may take 10+ minutes to complete.\n\u003Cul\u003E\u003Cli\u003ETo monitor the Job status and progress, use the \u003Cstrong\u003EJobs\u003C/strong\u003E tab of the  \u003Cstrong\u003EServices & jobs\u003C/strong\u003E area in Snowsight\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003EPROCESS_VIDEO\u003C/code\u003E Job to see the status of the containerized workload. You can also use the \u003Cstrong\u003ELogs\u003C/strong\u003E tab to see the log lines being emitted by the model\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ERun the \u003Ccode\u003ESELECT\u003C/code\u003E statement in the \u003Ccode\u003Evideo_analysis\u003C/code\u003E table when the Job completes to see the model's anslysis of the meeting. The output should look similar to the below:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| MEETING_ID | MEETING_PART | VIDEO_PATH                                   | START_TIME | END_TIME | DESCRIPTION                                                                                                                                                                                                                                                                                                              |\n+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:00:00   | 00:00:21 | The video opens with a group of four individuals entering a room equipped with a large screen displaying a presentation titled "Conceptual Design Meeting." The room is set up for a collaborative discussion, with a table covered in papers and a laptop. The individuals sit down and begin engaging in conversation. |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:00:21   | 00:01:45 | The group continues their discussion, focusing on the content displayed on the screen. The screen shows a slide titled "Method" with bullet points about user preferences and constraints. The individuals take notes and discuss the information presented.                                                             |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:01:45   | 00:03:09 | The screen transitions to a slide titled "Findings," listing various aspects such as user preferences, device requirements, and design constraints. The group discusses these findings, taking notes and sharing insights related to the design process.                                                                 |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:03:09   | 00:04:58 | The screen changes to a slide titled "Interface Concept," which includes a diagram and text discussing the design model and technology. The group engages in a detailed discussion about the interface concept, with one individual standing and pointing at the screen while others take notes.                         |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:04:58   | 00:06:57 | The screen displays a slide titled "Method" again, listing steps for the next phase of the project. The group continues their discussion, focusing on the outlined steps and making decisions based on the information presented.                                                                                        |\n+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESlides Analysis with OCR\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003ENext, we will utilize \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex\"\u003ESnowflake Cortex AI's \u003Ccode\u003EPARSE_DOCUMENT\u003C/code\u003E\u003C/a\u003E to carry out Optical Character Recognition (OCR) of the slide content presented in the meeting. The function can extract full-text and table content from slides. We will employ \u003Cem\u003Elayout mode\u003C/em\u003E to maintain document structure via semantic chunking.\u003C/p\u003E\n","\u003Cp\u003EIn your Snowsight \u003Ccode\u003Erun.sql\u003C/code\u003E file, go to the section labeled \u003Ccode\u003EOCR\u003C/code\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the \u003Ccode\u003Eslides_analysis\u003C/code\u003E table\u003C/li\u003E\u003Cli\u003ERun the \u003Ccode\u003EINSERT\u003C/code\u003E statement, which invokes \u003Ccode\u003EPARSE_DOCUMENT\u003C/code\u003E in layout mode on each slide image (JPG) presented in the meeting\u003C/li\u003E\u003Cli\u003EList the contents of the table, it should contain a row for each slide. Note that the recognized text will still contain a number of irrelevant word fragments and other artifacts, since the input images frequently include the full desktop of the presenter, not just the slide content. This will be addressed in the subsequent analysis.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n| MEETING_ID | MEETING_PART | IMAGE_PATH                                           | TEXT_CONTENT                                                                                                                       |         |\n+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.1.34__72.51.jpg      | werPoint- [Agenda2]Window Help 107% 2) »i Arial 18 18 Design New Slide 18  Real Reaction al Design meeting Agenda                  |         |\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.116.06__135.99.jpg   | werPoint - [Agenda2]Window Help X107% 2) »i Arial 18 Design New Slide Real Reaction  Real Reaction al Design meeting by: S. Marcel |\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.1219.96__1490.04.jpg | 1)lide Shov Window 107% 2)»i Arial 18  # Method # For the Power Source: Solar Cells and Batteries                                  |         |\n+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESpeech Recognition\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EIn this section we will use Snowflake Cortex AI's \u003Ccode\u003EAI_TRANSCRIBE\u003C/code\u003E to carry out Automatic Speech Recognition (ASR) and transcribe the meeting audio. Cortex AI Transcribe provides high-quality transcription of audio files using the latest AI models, allowing us to easily integrate transcription into our application.  We will use Cortex to transcribe the audio into text and then add it into our database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E \u003Ccode\u003EAI_TRANSCRIBE\u003C/code\u003E is currently a \u003Ca href=\"https://docs.snowflake.com/en/release-notes/preview-features\"\u003ESnowflake Preview feature\u003C/a\u003E. Contact your account representative to obtain access.\u003C/p\u003E\n","\u003Cp\u003EIn your Snowsight \u003Ccode\u003Erun.sql\u003C/code\u003E file, go to the section labeled \u003Ccode\u003ESPEECH RECOGNITION\u003C/code\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the \u003Ccode\u003Espeech_analysis\u003C/code\u003E table\u003C/li\u003E\u003Cli\u003ERun the \u003Ccode\u003EINSERT\u003C/code\u003E statement, which invokes \u003Ccode\u003EAI_TRANSCRIBE\u003C/code\u003E on the meeting audio\u003C/li\u003E\u003Cli\u003EList the contents of the table, it should contain a single entry for each audio file, similar to this:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n| MEETING_ID | MEETING_PART | AUDIO_PATH                                           | TEXT_CONTENT                                                                                                                                     |\n+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n| IS1004     | IS1004c      | @videos/amicorpus/IS1004/audio/IS1004c.Mix-Lapel.mp3 | Okay, good afternoon. Hope you have good lunch. Yeah, we had falafel. Oh, nice. And you? Uh, yes, I had something similar, but non-vegetarian... |\n+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EChatbot using Semantic Model\u003C/h2\u003E\n<!-- ------------------------ -->\n","\u003Cp\u003EBy completing the previous steps, we now have 3 distinct data sources about the meeting (semantic analysis of video, audio transcription, OCR of slides), all extracted from a single video file. Lets now turn to the analysis part, where we leverage all three to gain insights into the meeting. We will construct a conversational bot that lets us use language to query the table data generated by the previous steps.\u003C/p\u003E\n","\u003Ch3\u003ECortex Analyst\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ESnowflake Cortex Analyst\u003C/a\u003E allows users to ask questions in natural language and receive direct answers without writing SQL. The structured data we have extracted so far contains columns identifying the meeting part and time stamps. With tweaks to model prompts, additional structured columns can be added to our analysis.\u003C/p\u003E\n","\u003Cp\u003ETo use Cortex Analyst, we create a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst/semantic-model-spec\"\u003E\u003Cstrong\u003Esemantic model\u003C/strong\u003E\u003C/a\u003E which allows us to map our domain specific terminology (meeting, video, transcript, slides, etc) to database schemas and add contextual meaning. For example, when a user asks a query about a specific time in the meeting, we can establish a link with the \u003Ccode\u003Estart_time\u003C/code\u003E and \u003Ccode\u003Eend_time\u003C/code\u003E columns in our \u003Ccode\u003Evideo_analysis\u003C/code\u003E table. This mapping helps Cortex Analyst understand the user’s intent and provide accurate answers.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to the \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E area in Snowsight under \u003Cstrong\u003ESnowflake AI & ML Studio\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESpecify \u003Ccode\u003Ehol_user_role\u003C/code\u003E and \u003Ccode\u003Ehol_warehouse\u003C/code\u003E when prompted\u003C/li\u003E\u003Cli\u003ESelect the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/chatbot/meeting_analysis.yaml\"\u003E\u003Ccode\u003Emeeting_analysis.yaml\u003C/code\u003E\u003C/a\u003E file from the \u003Ccode\u003E@model\u003C/code\u003E stage\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/create_semantic_view.png\" alt=\"semantic_view\"\u003E\u003C/li\u003E\u003Cli\u003EYou should now see the semantic model that we have pre-defined over our three tables\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/semantic_model.png\" alt=\"semantic_model\"\u003E\u003C/li\u003E\u003Cli\u003EYou can use the chat view to test some queries against the data, the \u003Cstrong\u003EVerified Queries\u003C/strong\u003E section provides a good starting point.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/semantic_query.png\" alt=\"semantic_query\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EExplore the model and verified queries that have been pre-defined. Test out any questions you'd like to ask the model and define verified queries when the built text-to-SQL engine is unable to automatically infer the right SQL.\u003C/p\u003E\n","\u003Ch3\u003EStreamlit Chatbot\u003C/h3\u003E\n","\u003Cp\u003EIn this final step, we'll put all the pieces we built so far together into a single LLM-powered chatbot, allowing us to be able to use natural language to learn about the meeting.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EStreamlit\u003C/strong\u003E under \u003Cstrong\u003EProjects\u003C/strong\u003E in Snowsight\u003C/li\u003E\u003Cli\u003ECreate a new Streamlit app called \u003Ccode\u003Emeeting_chat\u003C/code\u003E using \u003Ccode\u003Ehol_db\u003C/code\u003E and \u003Ccode\u003Ehol_warehouse\u003C/code\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/create_streamlit.png\" alt=\"create_streamlit\"\u003E\u003C/li\u003E\u003Cli\u003EIn the \u003Cstrong\u003EPackages\u003C/strong\u003E menu, include \u003Ccode\u003Esnowflake-ml-python\u003C/code\u003E in the list of necessary packages\u003C/li\u003E\u003Cli\u003EReplace the \u003Ccode\u003Estreamlit_app.py\u003C/code\u003E with \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/chatbot/streamlit_app.py\"\u003Ethis file from the repo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ERun the Streamlit app\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should now be able to as the chatbot questions about the meeting. The relevant rows returned by Cortex Analyst are parsed into the LLM's context, which is able to summarize them into clear and accurate answers.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot.png\" alt=\"chatbot\"\u003E\u003C/p\u003E\n","\u003Cp\u003EExpanding the analyst section, you will be able to see the underlying queries being executed against the parsed data.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot_sql.png\" alt=\"chatbot_sql\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf a question you ask is not understood by Cortex Analyst, the chatbot will return some suggestions. You can edit the semantic model's verified queries (see previous step) to allow the model to uderstand more custom questions.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot_suggestions.png\" alt=\"chatbot_suggestions\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EClean up\u003C/h2\u003E\n","\u003Cp\u003EIn the \u003Ccode\u003Erun.sql\u003C/code\u003E file, run the steps in the \u003Ccode\u003ECLEANUP\u003C/code\u003E section. Ensure the following are removed in order to not incur charges:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Emeeting_chat\u003C/code\u003E Streamlit app\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ehol_db\u003C/code\u003E Database\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ehol_warehouse\u003C/code\u003E Warehouse\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ehol_compute_pool\u003C/code\u003E Compute Pool\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, you used Optical Character Recognition (OCR),  Automatic Speech Recognition (ASR), as well as powerful Vision Language Models (VLMs) to extract meeting insights from video files.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating a Snowpark Container Service based application that hosts an AI model\u003C/li\u003E\u003Cli\u003ECalling Snowflake Cortex AI functions.\u003C/li\u003E\u003Cli\u003EUsing Cortex Analyst with semantic models\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGithub repo \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis\"\u003ESnowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESnowpark Container Services \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESnowflake Cortex AI \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003Edocumentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-cortex-analyst/\"\u003EGetting Started with Cortex Analyst Quickstart\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Extracting Insights from Video with Multimodal AI Analysis","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n\nTroves of enterprise data exists as video and audio, but its utility has been limited due to the difficulty of processing it to extract insights. That barrier to entry has rapidly crumbled in the last few years, with the advances of AI models that enable cheap and fast Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR), as well as powerful Vision Language Models (VLMs), able to extract meaning and grounding data from video. \n\nIn this Quickstart, we will employ all three techniques to analyze meeting video to extract insights. We are using the [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/), which is a **multi-modal dataset of 100s of hours of simulated scenario meetings in which particpants play different roles in a design team, taking a design project from kick-off to completion over the course of a day**. The same technques shown here are broadly applicable to other meeting video, especially when valuable information is displayed on-screen, such as presentation slides or demos. \n\n\n### Preqrequisites\n\n* Basic understanding of Snowflake and containers.\n* A [Snowflake Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n* Installation of [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index)\n* [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)\n* [Docker Desktop](https://www.docker.com/get-started/)\n* [Hugging Face](https://huggingface.co) account and [Access Token](https://huggingface.co/docs/hub/en/security-tokens) for downloading models\n\n### What You Will Build\n\nYou will build a multi-step pipeline that uses [Snowflake Cortex AI](/en/product/features/cortex/) for OCR and ASR, and VLM deployed on [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/) to extract structured data from meeting video. You’ll store the output from all three models into structured Snowflake tables and build a simple chatbot allowing you to use language to generate rich analytical queries. You will be able to assess meeting effectiveness, identify decision points, and extract action items—directly within the AI Data Cloud. \n\n![1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/1_arch_diagram.png)\n\n### What You Will Learn\n\nYou will gain hands-on experience with:\n* **Cortex `AI_TRANSCRIBE`** for ASR\n* **Cortex `PARSE_DOCUMENT`** for OCR\n* **Snowpark Container Services (SPCS)** for loading the [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) large vision model from Hugging Face and running online inference\n\n\n## Component Overview\n\n\nSnowpark Container Services and Snowflake Cortex AI are two of the major components that are utilized within this Quickstart. Below is an overview of them.\n\n### Overview of Snowpark Container Services\n\n[Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview) is a fully managed container offering designed to facilitate the deployment, management, and scaling of containerized applications within the Snowflake ecosystem. This service enables users to run containerized workloads directly within Snowflake, ensuring that data doesn’t need to be moved out of the Snowflake environment for processing. Unlike traditional container orchestration platforms like Docker or Kubernetes, Snowpark Container Services offers an OCI runtime execution environment specifically optimized for Snowflake. This integration allows for the seamless execution of OCI images, leveraging Snowflake’s robust data platform.\n\n![spcs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/spcs_diagram.png)\n\n### Overview of Cortex AI\n\n[Snowflake Cortex AI](/en/product/features/cortex/) enables you to quickly analyze unstructured data and build generative AI applications using fully managed LLMs, RAG and text-to-SQL services. Enable multiple users to use AI services with no-code, SQL and REST API interfaces.\n\n![Cortex_AI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/platform-cortex-2025.jpeg)\n\n\u003C!-- ------------------------ --\u003E\n## Prepare Your Environment\n\n\n### Create Snowflake Account\n\nLog into Snowflake using your unique credentials if you have a Snowflake account.\n\n\nFor this guide, you will only need Snowflake's **Standard Edition** on AWS. You may want to select **Enterprise** to try out advanced features such as Time Travel, materialized views, or Failover.\n\nChoose **US West (Oregon)** for the AWS Region and log in.\n\n### Set up SQL Environment\n\n1. Git clone the Quickstart's [repo](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis) to your local machine. \n2. To prepare your Snowflake environment, in Snowsight, create a SQL file by clicking on **+ Create**, then **SQL File**.\n3. Name the file `run.sql` and copy the contents [run.sql](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/run.sql) from the cloned repository\n\n\u003C!-- TODO needs updated due to filename change--\u003E\n![4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/4_create_setup_sql_file.png)\n\n\u003C!-- TODO feels like we should hold off this until they install CLI\n4. To run the Common Setup, click at the beginning of a SQL line and click the blue Run button above your `run.sql` file. \n\n![5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/5_run_setup.png)\n\nThe output of the SQL command will appear in the results box.\n\n![6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/6_setup_output.png)\n\n--\u003E \n\n### Install Snowflake CLI\nInstall the [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index). Snowflake CLI can be used to upload the video and audio files to a Stage, check resources, and push container images to the Image Registrykk. \n\nUse Snowsight's **Connect a Tool** to [configure Snowflake CLI](https://docs.snowflake.com/user-guide/gen-conn-config#using-sf-web-interface-to-get-connection-settings) to access your Snowflake account.  Viewing **Account Details** and then **Config File** will provide you with the .toml file necessary to configure Snowflake CLI to connect to your account. Add the following modified for your account to your snow CLI config file (e.g. `~/.snowflake.config.toml`):\n\n~~~TOML\ndefault_connection_name = \"hol\"    -- sets the below connection to be implicitly used\n[connections.hol]\naccount = \"SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI\" -- from Connection Tool\nuser = \"\u003Cusername\u003E\"    -- update username\npassword = \"\u003Cpassword\u003E\"  -- update password\nrole = \"container_user_role\"  -- update from Quickstart\nwarehouse = \"hol_warehouse\"\ndatabase = \"hol_db\"\nschema = \"public\"\n~~~\n\nCopy the above contents into `config.toml` in the [Snowflake CLI configuration directory](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-cli#location-of-the-toml-configuration-file) (e.g. `~/.snowflake/config.toml`)\n\nVerify SnowCLI is correctly configured by running:\n1. `snow connection list`\n2. `snow connection test --connection hol`\n3. `snow connection test`  to verify the default value\n\n~~~\n+-----------------------------------------------------------------------------------------+\n| key             | value                                                                 |\n|-----------------+-----------------------------------------------------------------------|\n| Connection name | hol                                                                   |\n| Status          | OK                                                                    |\n| Host            | SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI.snowflakecomputing.com |\n| Account         | SFSEHOL-SUMMIT25_UNSTR_DATA_PROCESSTEST_BCHXEI                        |\n| User            | USER                                                                  |\n| Role            | CONTAINER_USER_ROLE                                                   |\n| Database        | HOL_DB                                                                |\n| Warehouse       | HOL_WAREHOUSE                                                         |\n+-----------------------------------------------------------------------------------------+\n~~~\n\n### Run Common Setup \nExecute the first section of the `run.sql` file labeled with the comment `COMMON SETUP`: \n1. Create the `hol_user_role` Role, `hol_db` Database, and `hol_warehouse` Warehouse which we will use to run subsequent steps.\n2. To use Snowpark Container Services, we'll create the `hol_compute_pool` Compute Pool, which provides computing resources to run our containerized Service. \n3. We'll also need to enable our Service to reach external sites such as Hugging Face and PyPI, so we create the `dependencies_access_integration` External Access Integration. Snowflake accounts are secure by default and do not allow external access.  \n4. We create the `hol_db.public.repo` Image Repository for storing container images\n5. Finally, we create Stages `@video` and `@model` for storing files used in later stages\n\n### Upload Files to Stage\n\nOur application will process video and audio files that are stored on a Snowflake Stage. We'll need to first upload the video and audio files from the Github repo to our Snowflake account.\n\nUsing the Snow CLI, list the Stage you previously created\n~~~bash\n$ snow stage list\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n|        |        |        |        |     |         |        |         |        |        |         |       | notifi |         |        |         |        |\n|        |        | databa |        |     | has_cre | has_en |         |        |        |         |       | cation | storage |        | owner_r | direct |\n| create |        | se_nam | schema |     | dential | crypti |         | commen |        |         |       | _chann | _integr | endpoi | ole_typ | ory_en |\n| d_on   | name   | e      | _name  | url | s       | on_key | owner   | t      | region | type    | cloud | el     | ation   | nt     | e       | abled  |\n|--------+--------+--------+--------+-----+---------+--------+---------+--------+--------+---------+-------+--------+---------+--------+---------+--------|\n| 2025-0 | VIDEOS | HOL_DB | PUBLIC |     | N       | N      | ACCOUNT |        | None   | INTERNA | None  | None   | None    | None   | ROLE    | Y      |\n| 5-29   |        |        |        |     |         |        | ADMIN   |        |        | L NO    |       |        |         |        |         |        |\n| 15:21: |        |        |        |     |         |        |         |        |        | CSE     |       |        |         |        |         |        |\n| 23.957 |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n| 000-07 |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n| :00    |        |        |        |     |         |        |         |        |        |         |       |        |         |        |         |        |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n~~~\n\nUpload the contents of the [`videos`](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/videos) directory and [chatbot](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/chatbot) directories from cloned repo the Stage:\n\n~~~bash\n$ snow stage copy --recursive ./videos @hol_db.public.videos\n$ snow stage copy --recursive ./chatbot @hol_db.public.model\n~~~\n\nList the contents of the Stage\n~~~bash\n$ snow stage list-files @hol_db.public.videos\n+-------------------------------------------------------------------------------------------------------------------------------------------+\n| name                                                        | size     | md5                              | last_modified                 |\n|-------------------------------------------------------------+----------+----------------------------------+-------------------------------|\n| videos/amicorpus/IS1004/audio/IS1004a.Mix-Lapel.mp3         | 12748652 | f9ee1bfce574d6ec1de89717465ebf3b | Fri, 30 May 2025 22:33:00 GMT |\n| videos/amicorpus/IS1004/audio/IS1004c.Mix-Lapel.mp3         | 36222956 | 6c25066bfdecf7db3a302c7a43f6173b | Fri, 30 May 2025 22:32:57 GMT |\n...\n+-------------------------------------------------------------------------------------------------------------------------------------------+\n~~~\n\n### Meeting Source Setup\nExecute the two lines in `run.sql` that set the `meeting_id` and `meeting_part` variables. This will determine which video, audio, and image files you just uploaded will be used by subsequent analysis steps. This should match files that you uploaded to the Stage.\n\n\n\u003C!-- ------------------------ --\u003E\n## Video Analysis\n\n\n### Build Docker Container\n\nIn your downloaded [repo](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/tree/main/videos), navigate to the `video_analysis` directory.\n\nUsing the Snow CLI, list your Image Registry:\n\n~~~bash\n$ snow spcs image-repository list\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| created_on                 | name | database_name | schema_name | repository_url             | owner        | owner_role_type | comment | encryption    |\n|----------------------------+------+---------------+-------------+----------------------------+--------------+-----------------+---------+---------------|\n| 2025-05-29                 | REPO | HOL_DB        | PUBLIC      | sfsehol-summit25-unstr-dat | ACCOUNTADMIN | ROLE            |         | SNOWFLAKE_SSE |\n| 15:21:19.834000-07:00      |      |               |             | a-processtest-bchxei.regis |              |                 |         |               |\n|                            |      |               |             | try.snowflakecomputing.com |              |                 |         |               |\n|                            |      |               |             | /hol_db/public/repo        |              |                 |         |               |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n~~~\n\nGet the Image Registry URL\n~~~bash\n$ snow spcs image-repository url repo\nsfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo \n~~~\n\nBuild the Docker Container\nThe syntax of this command is `docker build --rm --platform linux/amd64 -t \u003Crepository_url\u003E/\u003Cimage_name\u003E .` where `repository_url` is from the previous command and `image_name` is `process_video:latest`\n\n~~~bash\n$ docker build --rm --platform linux/amd64 -t sfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video:latest .\n\n\u003C...build output...\u003E\n\n$ docker images\nREPOSITORY                                                                                                        TAG       IMAGE ID       CREATED             SIZE\nsfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video   latest    8a051200cd1d   About an hour ago   16.5GB\n~~~\n\nLogin to your Repository with Docker using Snow CLI\n~~~bash\n$ snow spcs image-registry login\nLogin Succeeded\n~~~\n\n### Push Container to Registry\n\nNow that you've built the container, we need to upload or Push the container image to the registry. The Snowpark Container Services (SPCS) Container Registry is a secure, Snowflake-managed repository for storing and versioning container images. It enables developers to seamlessly build, deploy, and run containerized applications within the Snowflake Data Cloud.\n\n~~~bash\n$ docker push sfsehol-summit25-unstr-data-processtest-bchxei.registry.snowflakecomputing.com/hol_db/public/repo/process_video:latest\n~~~\n\nList the Image in the repository:\n~~~bash\n$ snow spcs image-repository list-images repo\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| created_on                | image_name    | tags   | digest                                                   | image_path                              |\n|---------------------------+---------------+--------+----------------------------------------------------------+-----------------------------------------|\n| 2025-05-30 15:11:34-07:00 | process_video | latest | sha256:077c6883533f7d384b0e6594038895995de6f5470892cb15e | hol_db/public/repo/process_video:latest |\n|                           |               |        | 51e4afcea611a35                                          |                                         |\n+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n~~~\n\n\n\n### Execute Containerized Job\nThe image you built and pushed to SPCS Image Repository contains the [vLLM](https://docs.vllm.ai/en/latest/) runtime which provides scalable hosting for large AI models with support for multiple GPU instances. It is pre-configured to load the [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) VLM from Hugging Face. This model excels at:\n- Visual understanding: object recognition, but also analyzing slides, charts, and graphics\n- Understanding long videos and capturing events: for videos of over 1 hour, complete with the ability to capture events and pinpoint relevant video segments\n- Generating structured outputs: returning results in structured formats to be used for Snowflake analytical processing\n\nWe will use [SPCS Jobs](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/working-with-services#execute-a-job-service) to run the model against meeting videos with the following prompt:\n\n\u003E*Provide a detailed description of this meeting video, dividing it in to sections with a one sentence description, and capture the most important text that's displayed on screen. Identify the start and end of each section with a timestamp in the 'hh:mm:ss' format. Return the results as JSON\"*\n\nThe resulting JSON will be parsed into a structured table called `video_analysis`.\n\nFollow these steps in the `VIDEO ANALYSIS` section of `run.sql`:\n1. (Optional) Dun the `DROP SERVICE IF EXISTS` command if you are executing the Job repeatedly to clean up previous runs\n2. Run the `EXECUTE JOB SERVICE` command to run the Job\n  - Replace the `\u003Cimage\u003E:\u003Cversion\u003E` section with the image path from the previous step\n  - Replace `\u003Cyour_hf_token\u003E` with you Hugging Face token to download the model\n  - The exact meeting the model will analyze have already been preset when you set the `$meeting_id` and `$meeting_part` SQL variables earlier\n  - The command is configured to run asynchronously, so it should complete within a few seconds, however the underlying Job may take 10+ minutes to complete.\n    - To monitor the Job status and progress, use the **Jobs** tab of the  **Services & jobs** area in Snowsight \n    - Select the `PROCESS_VIDEO` Job to see the status of the containerized workload. You can also use the **Logs** tab to see the log lines being emitted by the model\n3. Run the `SELECT` statement in the `video_analysis` table when the Job completes to see the model's anslysis of the meeting. The output should look similar to the below:\n\n~~~bash\n+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| MEETING_ID | MEETING_PART | VIDEO_PATH                                   | START_TIME | END_TIME | DESCRIPTION                                                                                                                                                                                                                                                                                                              |\n+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:00:00   | 00:00:21 | The video opens with a group of four individuals entering a room equipped with a large screen displaying a presentation titled \"Conceptual Design Meeting.\" The room is set up for a collaborative discussion, with a table covered in papers and a laptop. The individuals sit down and begin engaging in conversation. |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:00:21   | 00:01:45 | The group continues their discussion, focusing on the content displayed on the screen. The screen shows a slide titled \"Method\" with bullet points about user preferences and constraints. The individuals take notes and discuss the information presented.                                                             |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:01:45   | 00:03:09 | The screen transitions to a slide titled \"Findings,\" listing various aspects such as user preferences, device requirements, and design constraints. The group discusses these findings, taking notes and sharing insights related to the design process.                                                                 |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:03:09   | 00:04:58 | The screen changes to a slide titled \"Interface Concept,\" which includes a diagram and text discussing the design model and technology. The group engages in a detailed discussion about the interface concept, with one individual standing and pointing at the screen while others take notes.                         |\n| IS1004     | IS1004c      | /videos/amicorpus/IS1004/video/IS1004c.C.mp4 | 00:04:58   | 00:06:57 | The screen displays a slide titled \"Method\" again, listing steps for the next phase of the project. The group continues their discussion, focusing on the outlined steps and making decisions based on the information presented.                                                                                        |\n+------------+--------------+----------------------------------------------+------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n\n~~~\n\n\n## Slides Analysis with OCR\n\u003C!-- ------------------------ --\u003E\n\n\nNext, we will utilize [Snowflake Cortex AI's `PARSE_DOCUMENT`](https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex) to carry out Optical Character Recognition (OCR) of the slide content presented in the meeting. The function can extract full-text and table content from slides. We will employ *layout mode* to maintain document structure via semantic chunking.\n\nIn your Snowsight `run.sql` file, go to the section labeled `OCR`:\n\n1. Create the `slides_analysis` table\n2. Run the `INSERT` statement, which invokes `PARSE_DOCUMENT` in layout mode on each slide image (JPG) presented in the meeting\n3. List the contents of the table, it should contain a row for each slide. Note that the recognized text will still contain a number of irrelevant word fragments and other artifacts, since the input images frequently include the full desktop of the presenter, not just the slide content. This will be addressed in the subsequent analysis.\n\n~~~bash\n+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n| MEETING_ID | MEETING_PART | IMAGE_PATH                                           | TEXT_CONTENT                                                                                                                       |         |\n+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.1.34__72.51.jpg      | werPoint- [Agenda2]Window Help 107% 2) »i Arial 18 18 Design New Slide 18  Real Reaction al Design meeting Agenda                  |         |\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.116.06__135.99.jpg   | werPoint - [Agenda2]Window Help X107% 2) »i Arial 18 Design New Slide Real Reaction  Real Reaction al Design meeting by: S. Marcel |\n| IS1004     | IS1004c      | amicorpus/IS1004/slides/IS1004c.1219.96__1490.04.jpg | 1)lide Shov Window 107% 2)»i Arial 18  # Method # For the Power Source: Solar Cells and Batteries                                  |         |\n+------------+--------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------+\n\n~~~\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Speech Recognition\n\u003C!-- ------------------------ --\u003E\n\n\nIn this section we will use Snowflake Cortex AI's `AI_TRANSCRIBE` to carry out Automatic Speech Recognition (ASR) and transcribe the meeting audio. Cortex AI Transcribe provides high-quality transcription of audio files using the latest AI models, allowing us to easily integrate transcription into our application.  We will use Cortex to transcribe the audio into text and then add it into our database.\n\n**Note:** `AI_TRANSCRIBE` is currently a [Snowflake Preview feature](https://docs.snowflake.com/en/release-notes/preview-features). Contact your account representative to obtain access. \n\nIn your Snowsight `run.sql` file, go to the section labeled `SPEECH RECOGNITION`:\n1. Create the `speech_analysis` table\n2. Run the `INSERT` statement, which invokes `AI_TRANSCRIBE` on the meeting audio\n3. List the contents of the table, it should contain a single entry for each audio file, similar to this:\n\n~~~bash\n+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n| MEETING_ID | MEETING_PART | AUDIO_PATH                                           | TEXT_CONTENT                                                                                                                                     |\n+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n| IS1004     | IS1004c      | @videos/amicorpus/IS1004/audio/IS1004c.Mix-Lapel.mp3 | Okay, good afternoon. Hope you have good lunch. Yeah, we had falafel. Oh, nice. And you? Uh, yes, I had something similar, but non-vegetarian... |\n+------------+--------------+------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+\n~~~\n\n\u003C!-- ------------------------ --\u003E\n\n## Chatbot using Semantic Model\n\u003C!-- ------------------------ --\u003E\n\n\nBy completing the previous steps, we now have 3 distinct data sources about the meeting (semantic analysis of video, audio transcription, OCR of slides), all extracted from a single video file. Lets now turn to the analysis part, where we leverage all three to gain insights into the meeting. We will construct a conversational bot that lets us use language to query the table data generated by the previous steps.\n\n### Cortex Analyst\n[Snowflake Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) allows users to ask questions in natural language and receive direct answers without writing SQL. The structured data we have extracted so far contains columns identifying the meeting part and time stamps. With tweaks to model prompts, additional structured columns can be added to our analysis. \n\nTo use Cortex Analyst, we create a [**semantic model**](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst/semantic-model-spec) which allows us to map our domain specific terminology (meeting, video, transcript, slides, etc) to database schemas and add contextual meaning. For example, when a user asks a query about a specific time in the meeting, we can establish a link with the `start_time` and `end_time` columns in our `video_analysis` table. This mapping helps Cortex Analyst understand the user’s intent and provide accurate answers.\n\n1. Navigate to the **Cortex Analyst** area in Snowsight under **Snowflake AI & ML Studio**\n2. Specify `hol_user_role` and `hol_warehouse` when prompted\n3. Select the [`meeting_analysis.yaml`](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/chatbot/meeting_analysis.yaml) file from the `@model` stage\n![semantic_view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/create_semantic_view.png)\n4. You should now see the semantic model that we have pre-defined over our three tables\n![semantic_model](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/semantic_model.png)\n5. You can use the chat view to test some queries against the data, the **Verified Queries** section provides a good starting point.\n![semantic_query](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/semantic_query.png)\n\nExplore the model and verified queries that have been pre-defined. Test out any questions you'd like to ask the model and define verified queries when the built text-to-SQL engine is unable to automatically infer the right SQL.\n\n### Streamlit Chatbot\nIn this final step, we'll put all the pieces we built so far together into a single LLM-powered chatbot, allowing us to be able to use natural language to learn about the meeting. \n\n1. Navigate to **Streamlit** under **Projects** in Snowsight\n2. Create a new Streamlit app called `meeting_chat` using `hol_db` and `hol_warehouse`\n![create_streamlit](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/create_streamlit.png)\n3. In the **Packages** menu, include `snowflake-ml-python` in the list of necessary packages\n3. Replace the `streamlit_app.py` with [this file from the repo](https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-video-with-multimodal-ai-analysis/blob/main/chatbot/streamlit_app.py)\n4. Run the Streamlit app\n\nYou should now be able to as the chatbot questions about the meeting. The relevant rows returned by Cortex Analyst are parsed into the LLM's context, which is able to summarize them into clear and accurate answers.\n![chatbot](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot.png)\n\nExpanding the analyst section, you will be able to see the underlying queries being executed against the parsed data.\n![chatbot_sql](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot_sql.png)\n\nIf a question you ask is not understood by Cortex Analyst, the chatbot will return some suggestions. You can edit the semantic model's verified queries (see previous step) to allow the model to uderstand more custom questions.\n![chatbot_suggestions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/extracting-insights-from-video-with-multimodal-ai-analysis/chatbot_suggestions.png)\n\n\n## Clean up\n\n\nIn the `run.sql` file, run the steps in the `CLEANUP` section. 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