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Analytics, Streamlit, Cortex and the Open Route Service to optimize vehicle routes in order to distribute goods to chosen destinations on time.","paragraphs":["\u003Cblockquote\u003E\n","\u003Cp\u003ENote: This guide is no longer maintained and will not work.\nThe content has been consolidated into a single comprehensive quickstart.\nPlease see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/oss-install-openrouteservice-native-app/\"\u003EBuild Routing Solution in Snowflake with Snowflake CoCo\u003C/a\u003E for the latest version.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/intro-map.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, we will be leveraging the the tools within Snowflake to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualize\u003C/strong\u003E the location of Delivery Points anywhere in the world understand the best routes for vehicles to deliver goods or services from a designated depo. We will use the multi layer mapping capabilities of pydeck to create easy to understand routing plans\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDiscover\u003C/strong\u003E what it would look like to route goods to real world points of interest such as restaurants or supermarkets using the Overture Point of Interest dataset provided freely on the marketplace by Carto.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnderstand\u003C/strong\u003E numerous routing scenarios across a variety of industries anywhere in the world.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you would prefer to skip to quickly see how the route optimization service might work for you, you can quickly use the \u003Cstrong\u003Efree api service\u003C/strong\u003E using the instructions as option 2 for creating the functions.\u003C/p\u003E\n","\u003Cp\u003EYou will be leveraging \u003Ca href=\"https://openrouteservice.org/\"\u003EOpen Route Service\u003C/a\u003E to optimize vehicle routes in order to distribute goods to chosen destinations on time.\u003C/p\u003E\n","\u003Cp\u003EYou will be creating \u003Cstrong\u003EDirections\u003C/strong\u003E, \u003Cstrong\u003ERoute Optimization\u003C/strong\u003E and \u003Ca href=\"https://en.wikipedia.org/wiki/Isochrone_map\"\u003E\u003Cstrong\u003EIsochrone\u003C/strong\u003E\u003C/a\u003E functions.\u003C/p\u003E\n","\u003Cp\u003EThe quickstart contains two options.  Both options require distinct prerequisites.  With either option, Snowflake allows for creation of a fully interactive route simulator which will benefit many vehicle centric industries such as \u003Cstrong\u003Eretail\u003C/strong\u003E, \u003Cstrong\u003Edistribution\u003C/strong\u003E, \u003Cstrong\u003Ehealthcare\u003C/strong\u003E and more.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption 1\u003C/strong\u003E\nUse Snowpark Containers with a native app using the Open Route Service\u003C/p\u003E\n","\u003Ch3\u003ERoute Planning And Optimization Architecture\u003C/h3\u003E\n","\u003Cp\u003EThe architecture below shows the solution which uses a native app and container services to power sophisticated routing and optimisation functions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/ors-architecture.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is a self contained service which is managed by you.  There are no api calls outside of snowflake and no api limitations.  This quickstart uses a medium CPU pool which is capable of running unlimited service calls within \u003Cstrong\u003ENew York City\u003C/strong\u003E.  if you wish to use a larger map such as Europe or the World, you can increase the size of the compute.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThis is what you will need\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration\"\u003EExternal Access Integration Activated\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003ENOTE:\u003C/em\u003E\u003C/strong\u003E - External Access Integration is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.  You will need this to securely download the map and config files from the provider account.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services Activated\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003ENOTE:\u003C/em\u003E\u003C/strong\u003E This is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access to the account.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.docker.com/products/docker-desktop/\"\u003EDocker Desktop\u003C/a\u003E installed\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E installed\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E(Recommended) \u003Ca href=\"https://git-scm.com/downloads\"\u003EGit\u003C/a\u003E installed.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEither download the zip or use git to copy the contents of the the git repo here: https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://code.visualstudio.com/download\"\u003EVSCode\u003C/a\u003E with the Snowflake extension installed.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption 2\u003C/strong\u003E\nUse External Access Integration with Python Functions to call and retrieve data from the Open Route Service.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/api-architecture.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EYou will need access to a Snowflake Account\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration\"\u003EExternal Access Integration\u003C/a\u003E\nNB - External Access Integration is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.  This is for connecting to the open route service api.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAn free account with \u003Ca href=\"https://openrouteservice.org/\"\u003EOpen Route Service\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access to the account.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA more advanced understanding of \u003Cstrong\u003EGeospatial\u003C/strong\u003E data in Snowflake\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUsing \u003Cstrong\u003EAISQL\u003C/strong\u003E functions with Snowpark\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate 3 user defined functions which either call the open route service API or you will learn how to create the service in snowflake using a snowpark container services native app.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003Ecreate simple and multi waypoint directions point to point functions based on the road network and vehicle profile\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERoute Optimization to match the demands with vehicle availability\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate an isochrone for catchment analysis\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreating a location centric application using Streamlit\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAn insight to the Carto Overture Places dataset to build an innovative route planning simulation solution\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELeverage vehicle capabilities and matching with job specifics\u003C/li\u003E\u003Cli\u003Euse a real dataset to simulate route plans for a specific depot\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA streamlit application to simulate route plans for potential customers anywhere in the world.  This could be for a potential new depot or simply to try out route optimisation which you will later replace with a real data pipeline.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOption 1 - Native app & SPCS\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/ors-architecture.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\nUse Snowpark Containers with a native app using the Open Route Service\u003C/p\u003E\n","\u003Cp\u003EThis will create the necessary snowflake database and stages within the public schema.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen up visual studio code with the downloaded github repository as per the prerequisites.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUse the Snowflake \u003Cstrong\u003Eadd-in\u003C/strong\u003E to login to your snowflake account\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vscode-addon.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EWithin the Repo, navigate to:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENative_app\u003C/strong\u003E > \u003Cstrong\u003EProvider_setup\u003C/strong\u003E >  \u003Cstrong\u003Eenv_setup.sql\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPress run all or ctrl + enter / command + enter to run the code within visual studio code.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou will now have a database which contains an empty repository and three stages.  You can view these stages easily with the VSCode addin.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/setup-stages.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe  \u003Cstrong\u003EORS_SPCS_STAGE\u003C/strong\u003E stage will contain a map extract and a config file.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EORS_GRAPHS_SPCS_STAGE\u003C/strong\u003E stage will contain files in a graphs structure to easily calculate route optimisations.  The graphs created will depend on the map uploaded and which vehicle profiles are enabled.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EORS_ELEVATION_CACHE_SPCS_STAGE\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis cache stores elevation data based on the chosen map extract.  This improves performance when enabled.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to the folder \u003Cstrong\u003EProvider_setup > staged files\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn here you will see two files.  One of which is a map file.\u003C/p\u003E\n","\u003Cp\u003EAn example map file you can use is of San Francisco which is provided in the staged_files folder.  To use this file, it needs to be uploaded to the \u003Cstrong\u003EORS_SPCS_STAGE\u003C/strong\u003E.  You can do this either manually within snowsight, or more conveniently, using the snowflake add-in.\u003C/p\u003E\n","\u003Cp\u003EYou can choose from a range of map files from websites such as these\u003C/p\u003E\n","\u003Cp\u003Ehttps://download.geofabrik.de/\nhttps://download.bbbike.org/osm/\u003C/p\u003E\n","\u003Cp\u003EThe file below is the original weekly updated open street map which contains the whole planet.\nhttps://planet.openstreetmap.org/pbf/\u003C/p\u003E\n","\u003Cp\u003EBear in mind the bigger the map, the longer it will take to create the graphs.  You may also require a larger compute for the container to run if you are using a larger map. You might also need to update parameter XMX (Max RAM assigned to Java) in file \u003Ccode\u003Eservices/openrouteservice/openrouteservice.yaml\u003C/code\u003E. As a Rule of Thumb, set it to: \u003Ccode\u003E<PBF-size> * <profiles> * 2\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EAlso please note that the size of the files uploaded using the put command is limited to 5G.  If you wish to use the world file, you will need to initially store in a cloud storage like S3 bucket or Azure Blob Storage and then copy using the copy command.\u003C/p\u003E\n","\u003Cp\u003EThe ors-config file is a configuration file for the app.  This does a variety of things.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpen up the ors-config.yml file to take a look at it\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou will see at the beginning of the yml file is a source file locator.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E\nors:\n  engine:\n    profile_default:\n      build:  \n        source_file: /home/ors/files/sanFrancisco.osm.pbf\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is what the URL will be to point to the right map file.   If you would like to use a different map, as well as uploading the alternative map you will need to change the source file parameter here.\u003C/p\u003E\n","\u003Cp\u003ENext you will see a profiles configuration area\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E    profiles:\n      driving-car:\n        enabled: true\n      cycling-road:\n        enabled: true\n      driving-hgv:\n        enabled: true\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is where you can configure multiple types of vehicles.  If you look at the commented out profiles in here, you can also  configure each profile further as well as adding additional profiles.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eedit the config yml file and add \u003Cstrong\u003Ecycling-electric\u003C/strong\u003E and \u003Cstrong\u003Efoot-walking\u003C/strong\u003E profiles:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E    profiles:\n      driving-car:\n        enabled: true\n      cycling-road:\n        enabled: true\n      driving-hgv:\n        enabled: true\n      cycling-electric:\n        enabled: true\n      foot-walking:\n        enabled: true\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere is where you can change the amount of maximum visited nodes.\u003C/p\u003E\n","\u003Cp\u003EThe nodes are locations where route optimization algorithms are implemented and processed. These nodes are crucial for efficiently planning and executing delivery or service routes, minimizing travel time and cost.  The number of nodes required will depend on how many vehicles, what the vehicle profile is, the length of each journey and how many jobs are involved.  Here, the default number of visited nodes are much lower than the overridden default below. Same for maximum_routes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E    matrix:\n      maximum_visited_nodes: 1000000000\n      maximum_routes: 25000000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThere are also other options available for each profile - and each option will depend on what the profile is.\u003C/p\u003E\n","\u003Ch3\u003EImporting new files into a stage using the Snowflake Add-In.\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload a new map file for \u003Cstrong\u003Enew york city\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ca href=\"https://download.bbbike.org/osm/bbbike/NewYork/NewYork.osm.pbf\"\u003Ehere\u003C/a\u003E to download the New York City OSM.PBF file.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWithin the snowflake add-in navigate to the newly created \u003Cstrong\u003EORS_SPCS_STAGE\u003C/strong\u003E.   You will see this in the \u003Cstrong\u003EObject Explorer\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/view-stages.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the upload icon - \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/upload-icon.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENavigate to the newly \u003Cstrong\u003Edownloaded New York\u003C/strong\u003E file to upload the map file to the snowflake stage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/new-york-map.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EModify the \u003Cstrong\u003Econfig file\u003C/strong\u003E by changing the source file location to the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003E    build:  \n        source_file: /home/ors/files/NewYork.osm.pbf\n        instructions: false\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, use the upload tool again to upload the modified config file to snowflake.\u003C/p\u003E\n","\u003Cp\u003EYou should see the new files appear in th stages area\u003C/p\u003E\n","\u003Cp\u003EOnce the files are uploaded, refresh the cache of the stage\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER STAGE OPENROUTESERVICE_SETUP.PUBLIC.ORS_SPCS_STAGE REFRESH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExecute the following to ensure the files are registered on the stage directory\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E select * from directory(@OPENROUTESERVICE_SETUP.PUBLIC.ORS_SPCS_STAGE);\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/stage-directory.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate the image and services.\u003C/h3\u003E\n","\u003Cp\u003EYou will now load the docker images to the snowflake repository\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to the provider_setup > spcs_setup.sh and openn the file.\u003C/li\u003E\u003Cli\u003EAmend where it says \u003Cstrong\u003EYOUR_CONNECTION\u003C/strong\u003E with your \u003Cstrong\u003Esnowcli\u003C/strong\u003E connection.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003ENOTE:\u003C/em\u003E\u003C/strong\u003E  If you have not created a connection before, please navigate to the following \u003Ca href=\"/en/developers/guides/getting-started-with-snowflake-cli/\"\u003EQuickStart\u003C/a\u003E before proceeding which will explain how these are created.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003EExecute the following to ensure you have the correct privileges to run the bash file.  Open up a terminal from the \u003Cstrong\u003E/native_app\u003C/strong\u003E directory within vscode and run the following:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Echmod +x provider_setup/spcs_setup.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003Espcs_setup.sh\u003C/strong\u003E file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E./provider_setup/spcs_setup.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will need to ensure that you have docker desktop running before you run the file.\u003C/p\u003E\n","\u003Cp\u003EYou now have an 4 docker images inside the previously created repository:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWithin the env_setup.sql, run the following command:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW IMAGES IN IMAGE REPOSITORY IMAGE_REPOSITORY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see four images pushed to the image repository like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/images-uploaded.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Edownloader\u003C/strong\u003E image will copy the config and map file from the setup stage to the consumer app.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Erouting_reverse_proxy\u003C/strong\u003E will securely manage traffic between  the other three services.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Eopenrouteservice\u003C/strong\u003E contains all the apis which the openrouteservice offers\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Evroom service\u003C/strong\u003E manages the route optimization service.\u003C/p\u003E\n","\u003Cp\u003ENow the assets are all setup in the repository and stages, you will now configure the app.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUsing the same terminal in the same directory as before, execute the following snow CLI command\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow app run -c <CONNECTION_NAME>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will do the following:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe Manifest File\u003C/strong\u003E\nuse the manifest file to compile to package all 4 images stored inside the image repository\u003C/p\u003E\n","\u003Cp\u003EAllow permissions for the consumer to create pools for running services.\u003C/p\u003E\n","\u003Cp\u003ESpecify the default streamlit for configuring the app.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe setup script\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen a consumer installs the app, it will add all the services for each image and create all objects needed to run the application.\u003C/p\u003E\n","\u003Cp\u003EThis also includes the functions that we will later use in streamlit.  The following functions are created:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDirections\u003C/li\u003E\u003Cli\u003EIsochrones\u003C/li\u003E\u003Cli\u003EOptimisation\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou will also note that an additional function (download) is created which calls the downloader service to download the map and config file from the provider stage to the consumer stage.\u003C/p\u003E\n","\u003Cp\u003EOnce you application package is installed, you will see a new installed app appear in the \u003Cstrong\u003Eapps\u003C/strong\u003E section of Snowsight.  This is a locally installed app for testing purposes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/native-app.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will also see an application package which you can use to share with other accounts either privately or via the marketplace.\u003C/p\u003E\n","\u003Ch3\u003EActivate the app\u003C/h3\u003E\n","\u003Cp\u003EIf you login to snow sight you will see the following newly created app within \u003Cstrong\u003EData Products > Apps\u003C/strong\u003E.  This is an app local to this snowflake for testing purposes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/activate-app.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpen the app and grant the permissions as requested by the application.  Once granted, you can then press \u003Cstrong\u003EActivate\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou will need to wait a few minutes for the graphs to update.  Within the graphs stage you should see the following folders appear:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/graphs-stage.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENB: you may need to refresh the stages to view the profiles. in the directory.\u003C/p\u003E\n","\u003Cp\u003EIf you open the functions part of the app you will see the following functions appear\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/app-functions.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will learn how to use these functions after option 2 of the quickstart which produces the same functions using rest api calls to the external service.  If you wish, skip option 2 and navigate to the Snowflake Marketplace section.  You will need a dataset provided by \u003Cstrong\u003ECarto\u003C/strong\u003E on the market place for the part of the notebook and the streamlit to run.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOption 2 Calling ORS APIs\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/api-architecture.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUse External Access Integration with Python Functions to call and retrieve data from the Open Route Service\u003C/p\u003E\n","\u003Cp\u003EThe open route service is free to use but there are restrictions in the number of calls to the freely available api api.\u003C/p\u003E\n","\u003Cp\u003Ehttps://openrouteservice.org/plans/\u003C/p\u003E\n","\u003Ch3\u003ERegister to Open Route Service and retrieve a key\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EVisit \u003Ca href=\"https://openrouteservice.org\"\u003EOpenRouteService\u003C/a\u003E.  Register here and then retrieve your key.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOpen up a new SQL worksheet and run the following commands. To open up a new SQL worksheet, select Projects » Worksheets, then click the blue plus button and select SQL worksheet.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR;\nCREATE WAREHOUSE IF NOT EXISTS ROUTING_ANALYTICS;\n\nCREATE SCHEMA IF NOT EXISTS CORE;\nCREATE SCHEMA IF NOT EXISTS DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the create secret command and replace the secret string with your secret token provided by Open Route Service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE SECRET IF NOT EXISTS CORE.ROUTING_TOKEN\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '<replace with your secret token>'\n  COMMENT = 'token for routing demo'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a Network Rule and External Integration\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nCREATE OR REPLACE NETWORK RULE open_route_api\n  MODE = EGRESS\n  TYPE = HOST_PORT\n  VALUE_LIST = ('api.openrouteservice.org');\n\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION open_route_integration\n  ALLOWED_NETWORK_RULES = (open_route_api)\n  ALLOWED_AUTHENTICATION_SECRETS = all\n  ENABLED = true;\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a simple directions function\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDirections Function 1 - for simple point to point directions\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nCREATE OR REPLACE FUNCTION CORE.DIRECTIONS (method varchar, jstart array, jend array)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.10\nhandler = 'get_directions'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_directions(method,jstart,jend):\n    request = f'''https://api.openrouteservice.org/v2/directions/{method}'''\n    key = _snowflake.get_generic_secret_string('cred')\n\n    PARAMS = {'api_key':key,\n            'start':f'{jstart[0]},{jstart[1]}', 'end':f'{jend[0]},{jend[1]}'}\n\n    r = requests.get(url = request, params = PARAMS)\n    response = r.json()\n    \n    return response\n$$;\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a Directions function with Way Points\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION CORE.DIRECTIONS (method varchar, locations variant)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_directions'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\nimport json\n\ndef get_directions(method,locations):\n    request_directions = f'''https://api.openrouteservice.org/v2/directions/{method}/geojson'''\n    key = _snowflake.get_generic_secret_string('cred')\n\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = locations\n\n    r = requests.post(url = request_directions,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n\n    $$;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ECreate an Optimisation Function\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nCREATE OR REPLACE FUNCTION CORE.OPTIMIZATION (jobs array, vehicles array)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_optimization'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_optimization(jobs,vehicles):\n    request_optimization = f'''https://api.openrouteservice.org/optimization'''\n    key = _snowflake.get_generic_secret_string('cred')\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = {"jobs":jobs,"vehicles":vehicles}\n\n    r = requests.post(url = request_optimization,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E.   Create an Isochrone function\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION CORE.ISOCHRONES(method string, lon float, lat float, range int)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_isochrone'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_isochrone(method,lon,lat,range):\n    request_isochrone = f'''https://api.openrouteservice.org/v2/isochrones/{method}'''\n    key = _snowflake.get_generic_secret_string('cred')\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = {'locations':[[lon,lat]],\n                    'range':[range*60],\n                    'location_type':'start',\n                    'range_type':'time',\n                    'smoothing':10}\n\n    r = requests.post(url = request_isochrone,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will now see the functions below ready to use.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/functions-created.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003EBefore you try out your functions, you will get a dataset from the marketplace.  This is the Carto Overture dataset which includes an extensive point of interest map across the whole world.  It is also useful for routing simulations.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENavigate to the Snowflake Marketplace - this is under Data Products > Snowflake Marketplace\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-navigation.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESearch for Overture Maps - Places\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-search-overture.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the following dataset then press \u003Cstrong\u003EGet\u003C/strong\u003E Do not change the database name.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-get-dataset.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERouting functions with AISQL\u003C/h2\u003E\n","\u003Cp\u003EYou will now test out all the functions which you have created. You will be using data simulated by \u003Cstrong\u003EAISQL\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis notebook covers using the functions, how to apply them and how to visualize the results.  At the end you will have a good understand of how the route optimisation service works well with Snowflake Advanced analytical capabilites - which will also lead onto creating the streamlit datasets which will be covered in the next section.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETo ensure the AI LLM model will work in your region and cloud, please run the following command:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ERun the following SQL to setup a new database and schema for collecting Views/Tables and notebooks for the simulator:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR;\nCREATE WAREHOUSE IF NOT EXISTS ROUTING_ANALYTICS;\n\nCREATE SCHEMA IF NOT EXISTS DATA;\nCREATE SCHEMA IF NOT EXISTS NOTEBOOKS;\nCREATE SCHEMA IF NOT EXISTS STREAMLITS;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload following \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Notebook/routing_setup.ipynb\"\u003Enotebook\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the following \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Notebook/environment.yml\"\u003Eenvironment file\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate 1 stage to store the notebook assets\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE STAGE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.notebook DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EImport the downloaded notebook and environment file into the stage using a method of choice such as the Snowsight UI or Visual Studio Code.\u003C/li\u003E\u003Cli\u003ERun the following to create your notebook\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE NOTEBOOK VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.EXPLORE_ROUTING_FUNCTIONS_WITH_AISQL\nFROM '@VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.NOTEBOOK'\nMAIN_FILE = 'routing_setup.ipynb'\nQUERY_WAREHOUSE = 'ROUTING_ANALYTICS'\nCOMMENT = '{"origin":"sf_sit-is", "name":"Route Optimization with Open Route Service", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"notebook"}}';\n\nALTER NOTEBOOK VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.EXPLORE_ROUTING_FUNCTIONS_WITH_AISQL ADD LIVE VERSION FROM LAST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will now be able to try out how the functions work and use them in conjunction with \u003Cstrong\u003EAISQL\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the notebook and follow the provided instructions.  In order to run the streamlit, it is essential that you run from the cell \u003Cstrong\u003Eadd_carto_data\u003C/strong\u003E AND BELOW.  This is to ensure that you have all the correct dependencies needed.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure you run all the code below this section \u003Cstrong\u003EBEFORE\u003C/strong\u003E you move to the streamlit.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/notebook-carto-section.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy the Streamlit\u003C/h2\u003E\n","\u003Cp\u003ENow you can see how all the functions work with AISQL, lets now build a route simulator streamlit application.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Streamlit/streamlit.zip\"\u003Ehere\u003C/a\u003E to download the files needed for the streamlit app.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUnzip all files ready for uploading to a stage.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate 1 stage to store streamlit assets\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE STAGE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR.STREAMLITS.STREAMLIT DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to the Streamlit Stage or the VSCode add-in to import the files.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpload all files with the exception of config.toml to the streamlit stage\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUpload the the config.toml file to a folder called .streamlit within the streamlit stage.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate the streamlit using the following script\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STREAMLIT VEHICLE_ROUTING_SIMULATOR.STREAMLITS.SIMULATOR\nROOT_LOCATION = '@VEHICLE_ROUTING_SIMULATOR.STREAMLITS.streamlit'\nFROM 'routing.py'\nQUERY_WAREHOUSE = 'ROUTING_ANALYTICS'\nCOMMENT = '{"origin":"sf_sit-is", "name":"Route Optimization with Open Route Service", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"Streamlit"}}';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGo to the homepage in Snowsight\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003EStreamlits\u003C/strong\u003E and run the \u003Cstrong\u003ESIMULATOR\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the Streamlit\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/streamlit-main.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe streamlit app which you have open simulates potential routes to 29 delivery locations for selected customer types - all coming from a user definable wholesaler.  Currently there are 3 types of distributor available although with the notebook, you can create limitless industry categories:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFood\u003C/li\u003E\u003Cli\u003EHealth\u003C/li\u003E\u003Cli\u003ECosmetics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you wish to add additional choice of distributor types, you can with the provided notebook.\u003C/p\u003E\n","\u003Cp\u003EBefore you choose your category, you must select where the routing specific functions are.  This app works with both the api call method and the native app method.  If you followed the instructions and went through both options, you can test out either option using the supplied radio selector.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/function-source-selector.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe places you will work with are real as they are based on the Carto Overture points of interest maps which is a dataset freely available on the marketplace.  This allows you to create a location relevant scenario based on the needs of a specific usecase.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPlease note:\u003C/strong\u003E  For this simulation the data has been restricted to new york city.  You will need to revise the initial notebook should you require an additional location.  This is an extract filtered by \u003Cstrong\u003EGEOHASH\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf you have built the native app and require an alternative city, you will need to upload the new map to the configuration stage.\u003C/p\u003E\n","\u003Ch3\u003EEnd to End with Streamlit Dynamic Simulator Overview Diagram\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/overview-diagram.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESetting the Context of the Routing Scenario\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen up the side menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the industry type.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EChoose the LLM model in order to search for a location.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EType in a word or phrase in the world which will help locate the simulation.\u003Cbr\u003E\n\u003Cstrong\u003ENB\u003C/strong\u003E You will only return results in the New York City boundary.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EChoose the distance in KM for how wide you would like the app to search for nearby distributors.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/sidebar-cortex-filter.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EScroll down to get a map which highlights the place plus where all the nearby distributors are.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EScroll further down in the sidebar to select a specific distributor. - This is sorted by distance from the centre point.  You should have relevent wholesalers based on location and industry.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/distributor-list.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EChoose the type of customers you want to deliver goods to.  In this case, we are choosing supermarkets and restaurants.  Customer types can be configured using the provided notebook.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThere is an order acceptance catchment time - this will be used to generate an isochrone which will filter possible delivery locations within that isochrone.  The isochrone produced is a polygon shaped to all the possible places you can drive within the acceptable drive time.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/isochrone-filter.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou may close the side bar.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWholesaler Routing Walkthrough.\u003C/h3\u003E\n","\u003Cp\u003EThis is an example scenario based on the previously selected fields.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHudson Produce\u003C/strong\u003E is in New York City.  This week they have 3 vehicles assigned to make up to 30 deliveries today.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/depot-vehicles-overview.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVehicle 1\u003C/strong\u003E will start between 8HRS and 17HRS - this vehicle is a car.  [hover over information]  the vehicle has a capacity limit of 4 and been assigned a skill level of 1 - this vehicle does not have a freezer so can only carry fresh food.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVehicle 2\u003C/strong\u003E will operate between 12 and 17hrs [change vehicle 2 from 8 till 12].  This will also be a car but has a skill level of 2 which means they can deliver frozen food.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVehicle 3\u003C/strong\u003E will also operate between 8hrs and 17hrs and has a skill level of 3 - they can carry  the premium food items - this vehicle will be an road bicycle [select cycling-road].\u003C/p\u003E\n","\u003Cp\u003EYou can look at the vehicle skill level by hovering over the '?' against each vehicle.\u003C/p\u003E\n","\u003Cp\u003EOnce the selections are made you can choose the scope for the jobs - this is based on a catchment time.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect 25mins based on how far you can cycle in that time.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/catchment-diagram.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-skills-assignment.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will note that orders of the Non Perishable orders will only go to vehicle 3, the fresh food will go to vehicle 2 and the frozen food will go to vehicle 1.\u003C/p\u003E\n","\u003Cp\u003E(if i have more vehicles that have the same skills it will also look at the time slots as well).\u003C/p\u003E\n","\u003Cp\u003ENext we look at the map\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-routes-map.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EVehicle 3 has the least amount of things to deliver but takes the longest to deliver them.  This is probably because the vehicle is a bicycle.  [change bicycle to hgv and re run]\u003C/p\u003E\n","\u003Cp\u003EWhen looking at the map itself, you will see the lines of the route for each vehicle, this is colour coded - you will also see circles which also represent the drops for each vehicle.  The hoverover will tell you what the point represents.\u003C/p\u003E\n","\u003Cp\u003ETabs - this will give instructions for each segment of the drivers journey - the final stop is the return back to the wholesaler.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-itinerary.png?v=f6f4f8e4\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EHow does it work\u003C/h3\u003E\n","\u003Cp\u003EYou can see that in a couple of clicks you can create a vehicle optimisation scenario from anywhere.\u003C/p\u003E\n","\u003Ch4\u003EFinding the place\u003C/h4\u003E\n","\u003Cp\u003Ethe app is using an LLM to retrieve a Latitude and longitude based on the word entered into the search.\u003C/p\u003E\n","\u003Cp\u003ESnowflake will use the ST_DWITHIN geospatial function to filter the overture maps to find all places of interest within an Xkm radius.\u003C/p\u003E\n","\u003Ch4\u003EThe previously run notebook\u003C/h4\u003E\n","\u003Cp\u003EThe previously ran notebook contains the standing data which you can go back to to customize the demo.  If you want to change the types of places to be hotels, then that is quite possible.\u003C/p\u003E\n","\u003Cp\u003EWithin the notebook, you have also created:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EThe overture dataset and included optimisation on geo and the category variant column to help with faster searching.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAn industry lookup table to add relevant context\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA job sample table\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EThe mapping\u003C/h4\u003E\n","\u003Cp\u003EThe solution leverages Pydeck to plot points, linestrings and polygons on a map.  The isochrone is the polygon, the routes are linestrings and the places/points of interest are points.  You would have seen how this works in the original notebook. AISQL is useful to quickly generate python code to test the maps.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Streamlit Code\u003C/h2\u003E\n","\u003Cp\u003EThis final Section, gives you some explanation as to how the streamlit code works.\u003C/p\u003E\n","\u003Cp\u003EThe Streamlit puts all of the above components together. I will now explain how the main aspects of the code works.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESetup Theming\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EAn important feature for better user experience is what the application looks like. I have themed the app to be consistant with Snowflake Branding. This is so much easier and flexible now we can add styles to Streamlit in Snowflake.\u003C/p\u003E\n","\u003Cp\u003EFor the theming, a style sheet was added to the streamlit project.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ewith open('extra.css') as f:\n    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIndustry Lookup\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EAn  industry lookup snowpark dataframe is created. We then create a second dataframe which only selects the industry name. This will be used for the first sidebar filter\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nlookup = session.table('LOOKUP')\nindustry = lookup.select('INDUSTRY')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBased on the selected industry, key variables are generated for added context to the standing data and filtering the points of interest dataset. The user selects the chosen industry from the sidebar, which then assign the variables\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n#sidebar\nwith st.sidebar:\n    st.image(image)\n    choice = st.radio('select industry', industry)\n    lookup = lookup.filter(col('INDUSTRY')==choice)\n    lookup = lookup.with_column('IND',array_to_string('IND',lit(',')))\n    lookup = lookup.with_column('IND2',array_to_string('IND2',lit(',')))\n    \n    lookuppd = lookup.to_pandas()\n\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n#assign variables\n\npa = lookuppd.PA.iloc[0]\npb = lookuppd.PB.iloc[0]\npc = lookuppd.PC.iloc[0]\nind = lookuppd.IND.iloc[0]\nind2 = lookuppd.IND2.iloc[0]\nctype = json.loads(lookuppd.CTYPE.iloc[0])\nstype = json.loads(lookuppd.STYPE.iloc[0])\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVehicle Type Dropdown\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThese vehicle types will be assigned to each of the 3 vehicles. These will be configured by the user.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nmethod =[\n             'driving-car',\n             'driving-hgv',\n             'cycling-regular',\n             'cycling-road',\n             'cycling-mountain',\n             'cycling-electric']\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELocations Dataset\u003C/strong\u003E\nHere, a Snowpark Dataframe is created from the previously configured places dataset.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_f = session.table('places')\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eplaces_f = places_f.select('GEOMETRY',call_function('ST_X',\n                           col('GEOMETRY')).alias('LON'),\n                           call_function('ST_Y',\n                                  col('GEOMETRY')).alias('LAT'),\n                                  col('ADDRESS'),\n                                  col('CATEGORY'),\n                                  col('ALTERNATE'),\n                                  col('PHONES'),col('NAME'),\n                                  col('GEOMETRY').alias('POINT')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex map filter\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is where Cortex is used to filter the places dataset. The prompt is asking the model to 'give me the Latitude an Longitude which centers the following place.' The 'following place' is a free text field which the user enters such as: the Statue of Liberty, or London, or Heathrow Airport. They enter whatever they like and cortex will try and make sense of it.\nThe user also chooses an LLM model (I have found mistral-large2 works very well) and a distance. Different LLMs produce varying results of accuracy. For better accuracy, perhaps use Cortex Fine Tuning to load good examples into the model - such as the Overture Points of Interest itself. I found that mistral large 2 produced the result accuracy I needed without fine tuning. The distance is not really used for the LLM, but is used later to filter out potential distributors by straight line distance.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nwith st.sidebar:\n    st.markdown('##### Cortex Powered Map Filter')\n    st.info(prompt)\n    model = st.selectbox('Choose Model:',['reka-flash','mistral-large2'],1)\n    place = st.text_input('Choose Input','Heathrow Airport')\n    distance = st.number_input('Distance from location in KM',1,300,5)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we need to do some prompt engineering. Below is the initial prompt to work with.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nprompt = ('give me the LAT and LON which centers the following place')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe idea is that the prompt feed the LLM and return the results to the LLM in the correct format. However, further engineering will be needed before we get reliably good results.\u003C/p\u003E\n","\u003Cp\u003EI engineered the prompt by adding text to the prompts such as ' return 3 value which are as follows…. and 'use the following json template'. This is to ensure that what is returned is very likely to be in the format that I would expect.\u003C/p\u003E\n","\u003Cp\u003EThe results are returned as a single string which is simply converted to json by using the 'parse_json' function. You will note that before the json is parsed I removed characters that are sometimes generated in order to return the resulting json as markdown. This is great for display purposes but not so great if I only want the json. The replace function removes these characters if they exist.\u003C/p\u003E\n","\u003Cp\u003EOnce parsed, I used standard geo and semi structured features in Snowflake to calculate points, present results in a structured form and order the returned results by distance.\u003C/p\u003E\n","\u003Cp\u003EThis has all been wrapped with a function called choose_place(place,model,distance).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nprompt = ('give me the LAT and LON which centers the following place')\n\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n@st.cache_data\ndef choose_place(place,model,distance):\n    json_template = str({'LAT':44.3452,'LON':2.345,'DISTANCE':100})\n    min_max = session.createDataFrame([{'PLACE':place}])\\\n    .with_column('CENTER',\n        call_function('snowflake.cortex.complete',\n        model,\n        concat(lit(prompt),\n        col('PLACE'),lit(f'return 3 values which \\\n        are as follows LAT and LON with {distance} as DISTANCE'),\n        lit('use the following json template'),\n        lit(json_template),\n        lit('return only json. DO NOT RETURN COMMENTRY OR VERBIAGE'))\n                                  )\n    min_max = min_max.select(parse_json(replace(replace('CENTER',\n                              "```",\n                              ''),\n                              'json',\n                              '')).alias('CENTER'))\n   return min_max.select(col('CENTER')['LAT'].astype(FloatType()).alias('LAT'),\n     col('CENTER')['LON'].astype(FloatType()).alias('LON'),\n     call_function('ST_ASWKT',\n     call_function('ST_MAKEPOINT',\n     col('LON'),col('LAT'))).alias('POINT'),\n     col('CENTER')['DISTANCE'].astype(FloatType()).alias('"DISTANCE1"'),\n     lit(0).alias('DISTANCE'),\n     lit(place).alias('NAME')).to_pandas()\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will call this function in the next step of the app.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nbbox = choose_place(place,model,distance)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreating a scatter plot for suggested location based on user input\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is a pydeck layer - which generates a map based on the returned result of the previously created function. The returned results will be a single blue spot. We will later create another layer which will scatter all the available depots based on this location and within the distance chosen by the user.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ncontext = pdk.Layer(\n    'ScatterplotLayer',\n    bbox,\n    get_position=['LON', 'LAT'],\n    filled=True,\n    stroked=False,\n    radius_min_pixels=6,\n    radius_max_pixels=20,\n    auto_highlight=True,\n    get_fill_color=[41, 181, 232],\n    pickable=True)\n    st.divider()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPreview the results in a pydeck chart. The view state navigates the map to the position of the blue spot.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nview_state = pdk.ViewState(bbox.LON.iloc[0], bbox.LAT.iloc[0], zoom=4)\n    st.pydeck_chart(pdk.Deck(layers=[context],map_style=None,initial_view_state=view_state))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/pydeck-context-map.png?v=f6f4f8e4\" alt=\"map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will next add a new layer which will show all industry related industry suggestions that are within X distance of the blue spot.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESearching the data for the right type of place (The What).\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will search the 'what' by using the SEARCH function. This will search multiple columns within the same row to see if it matches the keywords stated in the industry lookup table. The industry must match the one which the user selected earlier. You will note that this search is being repeated twice - this is to search for two different concepts.\u003C/p\u003E\n","\u003Cp\u003EFor example:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ESearch one must contain one of these words:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ehospital\u003C/li\u003E\u003Cli\u003Ehealth\u003C/li\u003E\u003Cli\u003Epharmaceutical\u003C/li\u003E\u003Cli\u003Edrug\u003C/li\u003E\u003Cli\u003Ehealthcare\u003C/li\u003E\u003Cli\u003Epharmacy\u003C/li\u003E\u003Cli\u003Esurgical\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFrom the results of search 1, search 2 must contain one of these words:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Esupplies\u003C/li\u003E\u003Cli\u003Ewarehouse\u003C/li\u003E\u003Cli\u003Edepot\u003C/li\u003E\u003Cli\u003Edistribution\u003C/li\u003E\u003Cli\u003Ewholesaler\u003C/li\u003E\u003Cli\u003Edistributors\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eplaces_w = places_f.filter(call_function('ST_DWITHIN', \n      places_f['GEOMETRY'],\n      to_geography(lit(bbox.POINT.iloc[0])),\n      lit(bbox.DISTANCE1.iloc[0])*1000))#.cache_result()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_1 = places_w.filter(expr(f'''search((CATEGORY,\n      ALTERNATE,\n      NAME),'{ind}',\n      analyzer=>'DEFAULT_ANALYZER')''')).cache_result()\nplaces_1 = places_1.filter(expr(f'''search((CATEGORY,\n      ALTERNATE,\n      NAME),\n      '{ind2}',\n      analyzer=>'DEFAULT_ANALYZER')''')).cache_result()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe search is using the default analyzer, meaning it will accept any of the words in any order. This is useful to avoid missing things, but providing a second context by searching twice, you will get a better result accuracy. All search words are found in the industry lookup table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFilter the places by the 'where'\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will filter by distance from the previously allocated point which was returned by the LLM.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_1 = places_1.with_column('DISTANCE',\n                call_function('ST_DISTANCE',\n                call_function('ST_MAKEPOINT',\n                 col('LON'),\n                 col('LAT')),\n                 call_function('ST_MAKEPOINT',\n                      lit(bbox.LON.iloc[0]),\n                      lit(bbox.LAT.iloc[0]))))\n\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eplaces_1 = places_1.with_column('DISTANCE',\n           round(col('DISTANCE')/1000,2)).order_by('DISTANCE')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualise the map of depots\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis creates a map function which returns the what and where as a scatter plot map within the sidebar. You will note that there is an additional layer here. This layer will return multiple plots as apposed to 1 which is what the first layer generated.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n@st.cache_data\n    def places_cached(distance,bbox,ind):\n        return places_1.to_pandas()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA tool tip is constructed to reveal the name of each potential distributor and the straight line distance.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntooltip = {\n   "html": """<b>Name:</b> {NAME} <b><br>Distance From Centre:</b> {DISTANCE}""",\n   "style": {\n       "width":"50%",\n        "backgroundColor": "steelblue",\n        "color": "white",\n       "text-wrap": "balance"\n            }\n        }\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Edefining the second layer for  potential distributors\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ewholesalers = pdk.Layer(\n    'ScatterplotLayer',\n    places_cached(distance,bbox,ind),\n    get_position=['LON', 'LAT'],\n    filled=True,\n    opacity=0.5,\n    stroked=False,\n    radius_min_pixels=6,\n    radius_max_pixels=10,\n    auto_highlight=True,\n    get_fill_color=[0, 0, 0],\n    pickable=True)\n    st.divider()\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nview_state = pdk.ViewState(bbox.LON.iloc[0], bbox.LAT.iloc[0], zoom=4)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMap Layout\u003C/strong\u003E\nBelow will render the map.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nst.pydeck_chart(pdk.Deck(layers=[wholesalers,context],\n  map_style=None,\n  initial_view_state=view_state, \n  tooltip=tooltip))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/pydeck-wholesalers-map.png?v=f6f4f8e4\" alt=\"map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe returned results will also generate a list of places to select from using a drop down list:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n@st.cache_data\ndef warehouses(distance,bbox,ind):\n  return places_1.group_by(col('NAME'))\\\n        .agg(avg('DISTANCE').alias('DISTANCE'))\\\n        .sort(col('DISTANCE').asc()).to_pandas()\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ns_warehouse = st.selectbox('Choose Wholesaler to distribute goods from:',\n                            warehouses(distance,\n                                       bbox,\n                                       ind))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/choose-depot-dropdown.png?v=f6f4f8e4\" alt=\"choose depot\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EJob Template\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe job template is joined to the the industry lookups for provide context to the types of goods being delivered. we will call this 'time slots'\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntime_slots = session.table('JOB_TEMPLATE')\npa = time_slots.filter(col('PRODUCT')=='pa').join(lookup.select('PA'))\npb = time_slots.filter(col('PRODUCT')=='pb').join(lookup.select('PB'))\npc = time_slots.filter(col('PRODUCT')=='pc').join(lookup.select('PC'))\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntime_slots = pa.union(pb).union(pc).with_column('PRODUCT',\n                                        col('PA')).drop('PA')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECustomer Catchment Generation\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we need to generate another dataset - this time for potential customers which are located within catchment of a chosen depot.  The user will define the catchment based on a drive time.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nrange_minutes = st.number_input('Order Acceptance catchment time:',0,120,20)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/catchment-time-input.png?v=f6f4f8e4\" alt=\"catchment_time\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will now focus on filtering a new point of interest dataset by drive time. This dataset will simulate typical customers within the catchment. For this, we will leverage the 'isochrone' function which calls the open route service api to build a catchment polygon.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nisochrone = session.create_dataframe([{'LON':start_1[0], \n                      'LAT':start_1[1], \n                      'METHOD':smethod,\n                      'RANGE_MINS':range_minutes}])\nst.write(isochrone)\n        \nisochrone = isochrone.select(call_function('UTILS.ISOCHRONES',\n                          (col('METHOD'), \n                           col('LON'), \n                           col('LAT'), \n                           col('RANGE_MINS'))).alias('ISOCHRONE'))\n\nisochrone2 = isochrone.select(to_geography(col('ISOCHRONE')['features'][0]['geometry']).alias('GEO')).cache_result()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see that after calling the isochrone function, we then join the resulting polygon dataset to the point of interest dataset using 'ST_WITHIN'. This ensures only jobs will be created within the catchment area of the polygon.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECustomer Type Filter\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENow lets filter \u003Cstrong\u003E'the what'\u003C/strong\u003E on the customer dataset. We have all points of interests around the catchment of a depo, however, we have not specified what type of organisations these customers are. This is what the next drop down list is for. The user will pick the type of customer which is relevant to the industry. This example filter selection below will only retain organisations which are categorised as hospitals, dentists and pharmacies. Because the categories are in two fields, we will use the \u003Cstrong\u003ESEARCH\u003C/strong\u003E function again.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/customer-type-filter.png?v=f6f4f8e4\" alt=\"cust_type_filter\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_2 = places_f.filter(expr(f'''search((CATEGORY,ALTERNATE,NAME),\n                                '{" ".join(customer_type)}',\n                                analyzer=>'DEFAULT_ANALYZER')'''))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will limit our customer results to match the number of 'time slots' we have created from the job template, and generate a sample of the results.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_2 = places_2.join(isochrone2,\n  call_function('ST_WITHIN',\n  places_2['POINT'],\n  isochrone2['GEO'])).sample(0.5).limit(time_slots.count()).cache_result()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we will create a row number after ordering the sample by a random number. This row number will effectively become our unique \u003Cstrong\u003E'consignment number'\u003C/strong\u003E which will be used in the optimisation service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nwindow_spec = Window.order_by(random())\nplaces_2 = places_2.with_column('ID',row_number().over(window_spec))\nplaces_2 = places_2.join(time_slots,'ID')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we will join to the time slots by 'ID' which has been randomly assigned to \u003Cstrong\u003E'our customers'\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_2 = places_2.join(time_slots,'ID')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow we will format the table in to presentable jobs. This will assign skills, time slots and capacity requirements.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_2_table = places_2.select('ID',\n        col('PRODUCT').alias('"Product"'),\n        col('SLOT_START').alias('"Slot Start"'),\n        col('SLOT_END').alias('"Slot End"'),\n        col('NAME').alias('Name'),\n        col('CATEGORY').alias('"Category"'),\n        col('ADDRESS')['freeform'].astype(StringType()).alias('"Address"'),\n        col('ADDRESS')['locality'].astype(StringType()).alias('"Locality"'),\n        col('ADDRESS')['postcode'].astype(StringType()).alias('"Postcode"'),\n        col('ADDRESS')['region'].astype(StringType()).alias('"Region"'),\n        col('PHONES').alias('"Phone Number"'))\n\n    \nplaces_2 = places_2.with_column('JOB',\n              object_construct(lit('id'),col('ID'),\n              lit('capacity'),lit([2]),\n              lit('skills'),array_construct(col('SKILLS')),\n               lit('time_window'),\n               array_construct(col('SLOT_START')*60*60,col('SLOT_END')*60*60),\n               lit('location'),array_construct(col('LON'),col('LAT'))\n                      py))\n\n    \n\njobs = places_2.select(array_agg('JOB').alias('JOB'))\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe Vehicles\u003C/strong\u003E\nThe example I have created, is an example of only 3 vehicles at pre defined skill levels.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-config-panel.png?v=f6f4f8e4\" alt=\"vehicle_config\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe vehicle location is then Aligned to the previously selected depot. In reality, vehicles might have varying start destinations - however, for simplicity all vehicle starting points are the same.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_vehicles = places_1.filter(col('NAME')==s_warehouse).cache_result()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConstruct each configurable vehicle. Below is an example of one of the vehicles. You will see that we are converting the start and end time of each vehicle to seconds - likewise for the customers, the agreed delivery times for the optimisation service to work are also in seconds.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nvehicle_1 = places_vehicles.select(object_construct(lit('profile'),\n                      lit(smethod),\n                      lit('skills'),\n                      lit(veh1_skills),\n                      lit('id'),\n                      lit(1),\n                      lit('start'),\n                      array_construct(col('LON'),col('LAT')),\n                      lit('end'),\n                      array_construct(col('LON'),col('LAT')),\n                      lit('time_windows'),\n                      array_construct(lit(start_time_0*60*60),\n                                      lit(end_time_0*60*60)),\n                      lit('capacity'),\n                      lit(veh1_capacity)).alias('VEHICLE'))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, we present the configurable aspects of each vehicle to the user. You will note that this is an example of utilising the previously configured styling.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nst.markdown('<h4 class="veh2">Vehicle 2</h2>', \n    unsafe_allow_html=True, \n    help=f'''Vehicle 2 {skill_types[veh2_skills[0]-1]} and a capacity of {veh2_capacity[0]}''')\n    \n    col4,col5,col6 = st.columns(3)\n    with col4:\n        start_time_1 = st.number_input('Start Time in Hours:',0,24,8,key=3)\n    with col5:\n        end_time_1 = st.number_input('End Time in Hours:',start_time_1,24,17,key=4)\n    with col6:\n        smethod_1 = st.selectbox('Choose Method:',method, key=6)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor further formatting - when the job results are returned after going through the optimisation service, it will be nice to clearly see what vehicles are aligned to what job. This detail is added to the dataframe which gives data driven colour coding.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n##### ADD VEHICLE_COLOR TO VEHICLES)\n    vehicle_1 = vehicle_1.with_column('R',lit(125))\n    vehicle_1 = vehicle_1.with_column('G',lit(68))  \n    vehicle_1 = vehicle_1.with_column('B',lit(207)) \n\n    vehicle_2 = vehicle_2.with_column('R',lit(212))\n    vehicle_2 = vehicle_2.with_column('G',lit(91))  \n    vehicle_2 = vehicle_2.with_column('B',lit(144)) \n\n    vehicle_3 = vehicle_3.with_column('R',lit(255))\n    vehicle_3 = vehicle_3.with_column('G',lit(159))  \n    vehicle_3 = vehicle_3.with_column('B',lit(54))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we union all vehicles into a new dataset called \u003Cstrong\u003Evehsdet\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nvehsdet = vehicle_1.union(vehicle_2).union(vehicle_3).with_column('ID',\n              col('VEHICLE')['id'])\\\n    .with_column('PROFILE',\n                 col('VEHICLE')['profile'].astype(StringType()))\\\n    .with_column('WINDOW',\n                  col('VEHICLE')['time_windows'].astype(StringType()))\n    vehs = vehsdet.select(array_agg('VEHICLE').alias('VEH'))\n    vehsdet = vehsdet.drop('VEHICLE')\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERoute Optimisation Service\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ESo we have our 'Customers' and we have our 'Vehicles'. We now need to create our route plans for each vehicle using the Route Optimisation Service. This will effectively push our jobs and vehicles into the api which will return our vehicle route plans based on information such as drive time, consignment needs and vehicle availability.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\noptim = jobs.join(vehs).select('JOB',\n                'VEH',call_function('UTILS.OPTIMIZATION',\n                col('JOB'),col('VEH')).alias('OPTIMIZATION'))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe returned results will be an array of various objects. We will extract out the parts we want in order to display the results clearly.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\noptim = optim.with_column('CODES',col('OPTIMIZATION')['codes'])\n        optim = optim.with_column('ROUTES',col('OPTIMIZATION')['routes'])\n        optim = optim.with_column('SUMMARY',col('OPTIMIZATION')['summary'])\n        optim = optim.with_column('UNASSIGNED',col('OPTIMIZATION')['unassigned'])\n    \n    \n    \n        optim = optim.with_column('COST',col('SUMMARY')['cost'])\\\n        .with_column('DURATION',col('SUMMARY')['duration'])\\\n        .with_column('NUMBER_OF_ROUTES',col('SUMMARY')['routes']).drop('SUMMARY')\n\n        optim = optim.join_table_function('flatten',col('ROUTES'))\\\n        .select('VALUE')\n\n        optim = optim.select(col('VALUE')['amount'].alias('AMOUNT'),\n                         col('VALUE')['vehicle'].alias('VEHICLE'),\n                         col('VALUE')['duration'].alias('DURATION'),\n                         col('VALUE')['steps'].alias('STEPS'),\n                        col('VALUE')['location'][0].alias('LON'),\n                         col('VALUE')['location'][0].alias('LAT')p\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs described in the route optimisation function, the output will give steps for the journey along with other measures such as duration and what vehicle will be assigned. \u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Edirections API\u003C/strong\u003E will then be used to get detailed instructions for each step.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\noptim_line = optim_line\\\n        .select('VEHICLE','R','G','B','PROFILE','ID','TOTAL_JOBS',\n            call_function('UTILS.DIRECTIONS_with_way_points',\n                                    col('PROFILE'),\n                            col('LINE')).alias('DIRECTIONS')).cache_result()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will return detailed route plans which include all drop offs, line strings as well as written instructions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/directions-results.png?v=f6f4f8e4\" alt=\"directions\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETwo layers are created for the visualisation - one for the line paths and the other for the drop offs. The layers have been generated by a python function in order to reuse the code for each vehicle. This is so i can show a vehicle plan each containing an independant map within each tab.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ndef veh_journey(dataframe,vehicle):\n            vehicle_1_path = pdk.Layer(\n            type="PathLayer",\n            data=dataframe[dataframe['VEHICLE']==vehicle],\n            pickable=True,\n            get_color=["0+R","0+G","0+B"],\n            width_scale=20,\n            width_min_pixels=4,\n            width_max_pixels=7,\n            get_path="coordinates",\n            get_width=5)\n            return vehicle_1_path\n\n        def vehicle_drops(dataframe,vehicle):\n            layer_end_v1 = pdk.Layer(\n            'ScatterplotLayer',\n            dataframe[dataframe['VEHICLE']==vehicle],\n            get_position=['LON', 'LAT'],\n            filled=True,\n            stroked=False,\n            radius_min_pixels=6,\n            radius_max_pixels=10,\n            line_width_min_pixels=5,\n            auto_highlight=True,\n            get_radius=50,\n            get_line_color=["0+R","0+G","0+B"],\n            get_fill_color=["0+R","0+G","0+B"],\n            pickable=True)\n            return layer_end_v1\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBelow you can see an example of all three vehicles travelling around Paris to drop goods off. This is combining points and line string layers for each vehicle as well as the isochrone layer.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/map-with-vehicle-tabs.png?v=f6f4f8e4\" alt=\"map with tabs\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EConsiderations\u003C/h4\u003E\n","\u003Cp\u003EThe Job details may plot routes outside the agreed time.  The Demo has only vehicles where each vehicle has a unique skill.  We will need more vehicles / less skills to prevent these violations.\u003C/p\u003E\n","\u003Cp\u003EThe app is confined to a B2B model as we do not have public names and addresses of B2C consumers.  If a B2C simulator is needed, then an alternative 'customer' dataset other than the 'places' dataset is needed.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ESo you will now see that by combining AI, freely accessible points of interests, easy to use geospatial functions, the ability to securely call the open route service and the powers of Streamlit in Snowflake - creating innovative geospatial applications  is entirely possible.\u003C/p\u003E\n","\u003Cp\u003ESnowflake provides powerful solutions when you bring Snowflake's advanced analytics, Cortex, Snowpark and Streamlit's visualization capabilities together.  Also, by leveraging the open route service (or even an alternative provider such as the Carto Toolbox) using external integrations provides another level of geospatial capabilites such as route optimisation, directions and isochrones.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EYou will have learned the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to use Snowflake Cortex can be used as a location filter, which can filter a comprehensive point of interest dataset to anywhere in the world.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUse text based Search capabilities for advanced filtering which adds accurate context to a simulation\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to utilise Pydeck to create a multi layered map\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELeverage the open route service to create the following\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Eisochrones (catchements) based on drive time\u003C/li\u003E\u003Cli\u003ESimple Directions and Directions which include waypoints\u003C/li\u003E\u003Cli\u003ERoute Optimisations\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Ch4\u003ESource code\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-Create-a-Route-Optimisation-and-Vehicle-Route-Plan-Simulator\"\u003ESource Code on Github\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EFurther Related Material\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions-geospatial\"\u003EGeospatial Functions\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/developers/guides/building-geospatial-mult-layer-apps-with-snowflake-and-streamlit/\"\u003EBuilding Geospatial Multi-Layer Apps with Snowflake and Streamlit\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://h3geo.org/docs/\"\u003EH3 Indexing\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://deckgl.readthedocs.io/en/latest/index.html#\"\u003EPydeck\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/\"\u003EUsing Cortex and Streamlit With Geospatial Data\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/developers/guides/geo-for-machine-learning/\"\u003EGetting started with Geospatial AI and ML using Snowflake Cortex\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"title":"Create a Route Optimization and Vehicle Route Plan Simulator","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003E Note: This guide is no longer maintained and will not work.\nThe content has been consolidated into a single comprehensive quickstart.\nPlease see [Build Routing Solution in Snowflake with Snowflake CoCo](https://www.snowflake.com/en/developers/guides/oss-install-openrouteservice-native-app/) for the latest version.\n\n\u003C!-- ------------------------ --\u003E\n## Overview \n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/intro-map.png?v=f6f4f8e4)\n\nIn this quickstart, we will be leveraging the the tools within Snowflake to:\n\n- **Visualize** the location of Delivery Points anywhere in the world understand the best routes for vehicles to deliver goods or services from a designated depo. We will use the multi layer mapping capabilities of pydeck to create easy to understand routing plans\n\n- **Discover** what it would look like to route goods to real world points of interest such as restaurants or supermarkets using the Overture Point of Interest dataset provided freely on the marketplace by Carto.\n\n- **Understand** numerous routing scenarios across a variety of industries anywhere in the world.\n\n\n\nIf you would prefer to skip to quickly see how the route optimization service might work for you, you can quickly use the **free api service** using the instructions as option 2 for creating the functions.\n\nYou will be leveraging [Open Route Service](https://openrouteservice.org/) to optimize vehicle routes in order to distribute goods to chosen destinations on time.\n\nYou will be creating **Directions**, **Route Optimization** and [**Isochrone**](https://en.wikipedia.org/wiki/Isochrone_map) functions.\n\n\nThe quickstart contains two options.  Both options require distinct prerequisites.  With either option, Snowflake allows for creation of a fully interactive route simulator which will benefit many vehicle centric industries such as **retail**, **distribution**, **healthcare** and more.\n\n### Prerequisites\n\n**Option 1** \nUse Snowpark Containers with a native app using the Open Route Service\n\n### Route Planning And Optimization Architecture\n\nThe architecture below shows the solution which uses a native app and container services to power sophisticated routing and optimisation functions. \n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/ors-architecture.png?v=f6f4f8e4)\n\nThis is a self contained service which is managed by you.  There are no api calls outside of snowflake and no api limitations.  This quickstart uses a medium CPU pool which is capable of running unlimited service calls within **New York City**.  if you wish to use a larger map such as Europe or the World, you can increase the size of the compute.\n\n**This is what you will need**:\n\n-   [External Access Integration Activated](https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration)\n    \n\u003E **_NOTE:_** - External Access Integration is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.  You will need this to securely download the map and config files from the provider account.\n\n- [Snowpark Container Services Activated](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n\n\u003E **_NOTE:_** This is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.  \n\n-   **ACCOUNTADMIN** access to the account.\n\n-   [Docker Desktop](https://www.docker.com/products/docker-desktop/) installed\n\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index) installed\n\n-  (Recommended) [Git](https://git-scm.com/downloads) installed. \n\n- Either download the zip or use git to copy the contents of the the git repo here: https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator. \n\n\n- [VSCode](https://code.visualstudio.com/download) with the Snowflake extension installed.\n\n**Option 2**\nUse External Access Integration with Python Functions to call and retrieve data from the Open Route Service. \n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/api-architecture.png?v=f6f4f8e4)\n\n-   You will need access to a Snowflake Account\n\n-   [External Access Integration](https://docs.snowflake.com/en/sql-reference/sql/create-external-access-integration)\n    NB - External Access Integration is enabled by default with the exception of Free Trials where you would need to contact your snowflake representative to activate it.  This is for connecting to the open route service api.\n\n-   An free account with [Open Route Service](https://openrouteservice.org/)\n\n-   **ACCOUNTADMIN** access to the account.\n\n\n### What You’ll Learn \n\n- A more advanced understanding of **Geospatial** data in Snowflake\n- Using **AISQL** functions with Snowpark\n- Create 3 user defined functions which either call the open route service API or you will learn how to create the service in snowflake using a snowpark container services native app. \n\n-  create simple and multi waypoint directions point to point functions based on the road network and vehicle profile\n- Route Optimization to match the demands with vehicle availability\n- Create an isochrone for catchment analysis\n- Creating a location centric application using Streamlit \n- An insight to the Carto Overture Places dataset to build an innovative route planning simulation solution\n  - Leverage vehicle capabilities and matching with job specifics\n  - use a real dataset to simulate route plans for a specific depot\n\n### What You’ll Build \n- A streamlit application to simulate route plans for potential customers anywhere in the world.  This could be for a potential new depot or simply to try out route optimisation which you will later replace with a real data pipeline.\n\n\u003C!-- ------------------------ --\u003E\n## Option 1 - Native app & SPCS\n\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/ors-architecture.png?v=f6f4f8e4)\nUse Snowpark Containers with a native app using the Open Route Service\n\nThis will create the necessary snowflake database and stages within the public schema.\n\n- Open up visual studio code with the downloaded github repository as per the prerequisites.\n\n- Use the Snowflake **add-in** to login to your snowflake account\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vscode-addon.png?v=f6f4f8e4)\n\n- Within the Repo, navigate to: \n\n  **Native_app** \u003E **Provider_setup** \u003E  **env_setup.sql**\n\n- Press run all or ctrl + enter / command + enter to run the code within visual studio code.\n\nYou will now have a database which contains an empty repository and three stages.  You can view these stages easily with the VSCode addin.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/setup-stages.png?v=f6f4f8e4)\n\nThe  **ORS_SPCS_STAGE** stage will contain a map extract and a config file.\n\nThe **ORS_GRAPHS_SPCS_STAGE** stage will contain files in a graphs structure to easily calculate route optimisations.  The graphs created will depend on the map uploaded and which vehicle profiles are enabled.\n\nThe **ORS_ELEVATION_CACHE_SPCS_STAGE**. \n\nThis cache stores elevation data based on the chosen map extract.  This improves performance when enabled.\n\n- Navigate to the folder **Provider_setup \u003E staged files**.\n\nIn here you will see two files.  One of which is a map file.  \n\nAn example map file you can use is of San Francisco which is provided in the staged_files folder.  To use this file, it needs to be uploaded to the **ORS_SPCS_STAGE**.  You can do this either manually within snowsight, or more conveniently, using the snowflake add-in.\n\n\n\nYou can choose from a range of map files from websites such as these\n\nhttps://download.geofabrik.de/\nhttps://download.bbbike.org/osm/\n\nThe file below is the original weekly updated open street map which contains the whole planet.\nhttps://planet.openstreetmap.org/pbf/\n\nBear in mind the bigger the map, the longer it will take to create the graphs.  You may also require a larger compute for the container to run if you are using a larger map. You might also need to update parameter XMX (Max RAM assigned to Java) in file `services/openrouteservice/openrouteservice.yaml`. As a Rule of Thumb, set it to: `\u003CPBF-size\u003E * \u003Cprofiles\u003E * 2`\n\nAlso please note that the size of the files uploaded using the put command is limited to 5G.  If you wish to use the world file, you will need to initially store in a cloud storage like S3 bucket or Azure Blob Storage and then copy using the copy command.\n\nThe ors-config file is a configuration file for the app.  This does a variety of things.\n\n-  Open up the ors-config.yml file to take a look at it\n\nYou will see at the beginning of the yml file is a source file locator.  \n\n```yml\n\nors:\n  engine:\n    profile_default:\n      build:  \n        source_file: /home/ors/files/sanFrancisco.osm.pbf\n\n```\nThis is what the URL will be to point to the right map file.   If you would like to use a different map, as well as uploading the alternative map you will need to change the source file parameter here.\n\nNext you will see a profiles configuration area\n\n```yml\n    profiles:\n      driving-car:\n        enabled: true\n      cycling-road:\n        enabled: true\n      driving-hgv:\n        enabled: true\n```\nThis is where you can configure multiple types of vehicles.  If you look at the commented out profiles in here, you can also  configure each profile further as well as adding additional profiles.\n\n- edit the config yml file and add **cycling-electric** and **foot-walking** profiles:\n\n```yml\n    profiles:\n      driving-car:\n        enabled: true\n      cycling-road:\n        enabled: true\n      driving-hgv:\n        enabled: true\n      cycling-electric:\n        enabled: true\n      foot-walking:\n        enabled: true\n```\n\nHere is where you can change the amount of maximum visited nodes.\n\nThe nodes are locations where route optimization algorithms are implemented and processed. These nodes are crucial for efficiently planning and executing delivery or service routes, minimizing travel time and cost.  The number of nodes required will depend on how many vehicles, what the vehicle profile is, the length of each journey and how many jobs are involved.  Here, the default number of visited nodes are much lower than the overridden default below. Same for maximum_routes.\n\n\n```yml\n    matrix:\n      maximum_visited_nodes: 1000000000\n      maximum_routes: 25000000\n```\n\nThere are also other options available for each profile - and each option will depend on what the profile is.\n\n### Importing new files into a stage using the Snowflake Add-In.\n\n- Download a new map file for **new york city**.\n\n- Click [here](https://download.bbbike.org/osm/bbbike/NewYork/NewYork.osm.pbf) to download the New York City OSM.PBF file.\n\n\n-   Within the snowflake add-in navigate to the newly created **ORS_SPCS_STAGE**.   You will see this in the **Object Explorer**\n\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/view-stages.png?v=f6f4f8e4)\n\n-   Click on the upload icon - ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/upload-icon.png?v=f6f4f8e4) \n\nNavigate to the newly **downloaded New York** file to upload the map file to the snowflake stage.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/new-york-map.png?v=f6f4f8e4)\n\nModify the **config file** by changing the source file location to the following:\n\n```yml\n    build:  \n        source_file: /home/ors/files/NewYork.osm.pbf\n        instructions: false\n```\n\nFinally, use the upload tool again to upload the modified config file to snowflake.  \n\nYou should see the new files appear in th stages area\n\n\nOnce the files are uploaded, refresh the cache of the stage\n\n```sql\nALTER STAGE OPENROUTESERVICE_SETUP.PUBLIC.ORS_SPCS_STAGE REFRESH;\n ```\n\nExecute the following to ensure the files are registered on the stage directory\n\n```sql\n select * from directory(@OPENROUTESERVICE_SETUP.PUBLIC.ORS_SPCS_STAGE);\n\n ```\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/stage-directory.png?v=f6f4f8e4)\n\n### Create the image and services.\n\nYou will now load the docker images to the snowflake repository\n\n- Navigate to the provider_setup \u003E spcs_setup.sh and openn the file.\n- Amend where it says **YOUR_CONNECTION** with your **snowcli** connection.  \n\n\u003E **_NOTE:_**  If you have not created a connection before, please navigate to the following [QuickStart](/en/developers/guides/getting-started-with-snowflake-cli/) before proceeding which will explain how these are created.\n\n- Execute the following to ensure you have the correct privileges to run the bash file.  Open up a terminal from the **/native_app** directory within vscode and run the following:\n\n```bash\nchmod +x provider_setup/spcs_setup.sh\n```\n\nRun the **spcs_setup.sh** file. \n\n```bash\n./provider_setup/spcs_setup.sh\n```\n\nYou will need to ensure that you have docker desktop running before you run the file.\n\n\nYou now have an 4 docker images inside the previously created repository:\n\n- Within the env_setup.sql, run the following command:  \n\n```sql\nSHOW IMAGES IN IMAGE REPOSITORY IMAGE_REPOSITORY;\n```\nYou should see four images pushed to the image repository like this:\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/images-uploaded.png?v=f6f4f8e4)\n\nThe **downloader** image will copy the config and map file from the setup stage to the consumer app.\n\nThe **routing_reverse_proxy** will securely manage traffic between  the other three services.\n\nThe **openrouteservice** contains all the apis which the openrouteservice offers\n\nThe **vroom service** manages the route optimization service.\n\nNow the assets are all setup in the repository and stages, you will now configure the app.\n\n- Using the same terminal in the same directory as before, execute the following snow CLI command\n\n```bash\nsnow app run -c \u003CCONNECTION_NAME\u003E\n```\n\nThis will do the following:\n\n**The Manifest File**\nuse the manifest file to compile to package all 4 images stored inside the image repository\n\nAllow permissions for the consumer to create pools for running services.\n\nSpecify the default streamlit for configuring the app.\n\n**The setup script**\n\nWhen a consumer installs the app, it will add all the services for each image and create all objects needed to run the application.\n\nThis also includes the functions that we will later use in streamlit.  The following functions are created:\n-   Directions\n-   Isochrones\n-   Optimisation\n\nYou will also note that an additional function (download) is created which calls the downloader service to download the map and config file from the provider stage to the consumer stage.\n\nOnce you application package is installed, you will see a new installed app appear in the **apps** section of Snowsight.  This is a locally installed app for testing purposes.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/native-app.png?v=f6f4f8e4)\n\nYou will also see an application package which you can use to share with other accounts either privately or via the marketplace.\n\n\n### Activate the app\n\nIf you login to snow sight you will see the following newly created app within **Data Products \u003E Apps**.  This is an app local to this snowflake for testing purposes.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/activate-app.png?v=f6f4f8e4)\n\n- Open the app and grant the permissions as requested by the application.  Once granted, you can then press **Activate**\n\nYou will need to wait a few minutes for the graphs to update.  Within the graphs stage you should see the following folders appear:\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/graphs-stage.png?v=f6f4f8e4)\n\nNB: you may need to refresh the stages to view the profiles. in the directory.\n\nIf you open the functions part of the app you will see the following functions appear\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/app-functions.png?v=f6f4f8e4)\n\nYou will learn how to use these functions after option 2 of the quickstart which produces the same functions using rest api calls to the external service.  If you wish, skip option 2 and navigate to the Snowflake Marketplace section.  You will need a dataset provided by **Carto** on the market place for the part of the notebook and the streamlit to run. \n\n\u003C!-- ------------------------ --\u003E\n## Option 2 Calling ORS APIs\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/api-architecture.png?v=f6f4f8e4)\n\nUse External Access Integration with Python Functions to call and retrieve data from the Open Route Service\n\nThe open route service is free to use but there are restrictions in the number of calls to the freely available api api.\n\nhttps://openrouteservice.org/plans/\n\n### Register to Open Route Service and retrieve a key\n\n-   Visit [OpenRouteService](https://openrouteservice.org).  Register here and then retrieve your key.\n\n\nOpen up a new SQL worksheet and run the following commands. To open up a new SQL worksheet, select Projects » Worksheets, then click the blue plus button and select SQL worksheet.\n\n```sql\nCREATE DATABASE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR;\nCREATE WAREHOUSE IF NOT EXISTS ROUTING_ANALYTICS;\n\nCREATE SCHEMA IF NOT EXISTS CORE;\nCREATE SCHEMA IF NOT EXISTS DATA;\n```\n\nCopy the create secret command and replace the secret string with your secret token provided by Open Route Service.\n\n```sql\nCREATE SECRET IF NOT EXISTS CORE.ROUTING_TOKEN\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '\u003Creplace with your secret token\u003E'\n  COMMENT = 'token for routing demo'\n```\nCreate a Network Rule and External Integration\n\n```sql\n\nCREATE OR REPLACE NETWORK RULE open_route_api\n  MODE = EGRESS\n  TYPE = HOST_PORT\n  VALUE_LIST = ('api.openrouteservice.org');\n\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION open_route_integration\n  ALLOWED_NETWORK_RULES = (open_route_api)\n  ALLOWED_AUTHENTICATION_SECRETS = all\n  ENABLED = true;\n\n```\n\n-   Create a simple directions function\n\nDirections Function 1 - for simple point to point directions\n\n```sql\n\nCREATE OR REPLACE FUNCTION CORE.DIRECTIONS (method varchar, jstart array, jend array)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.10\nhandler = 'get_directions'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_directions(method,jstart,jend):\n    request = f'''https://api.openrouteservice.org/v2/directions/{method}'''\n    key = _snowflake.get_generic_secret_string('cred')\n\n    PARAMS = {'api_key':key,\n            'start':f'{jstart[0]},{jstart[1]}', 'end':f'{jend[0]},{jend[1]}'}\n\n    r = requests.get(url = request, params = PARAMS)\n    response = r.json()\n    \n    return response\n$$;\n\n```\n-   Create a Directions function with Way Points\n\n```sql\nCREATE OR REPLACE FUNCTION CORE.DIRECTIONS (method varchar, locations variant)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_directions'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\nimport json\n\ndef get_directions(method,locations):\n    request_directions = f'''https://api.openrouteservice.org/v2/directions/{method}/geojson'''\n    key = _snowflake.get_generic_secret_string('cred')\n\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = locations\n\n    r = requests.post(url = request_directions,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n\n    $$;\n```\n\n-   Create an Optimisation Function\n\n```sql\n\nCREATE OR REPLACE FUNCTION CORE.OPTIMIZATION (jobs array, vehicles array)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_optimization'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_optimization(jobs,vehicles):\n    request_optimization = f'''https://api.openrouteservice.org/optimization'''\n    key = _snowflake.get_generic_secret_string('cred')\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = {\"jobs\":jobs,\"vehicles\":vehicles}\n\n    r = requests.post(url = request_optimization,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n$$;\n```\n.   Create an Isochrone function\n\n```sql\nCREATE OR REPLACE FUNCTION CORE.ISOCHRONES(method string, lon float, lat float, range int)\nRETURNS VARIANT\nlanguage python\nruntime_version = 3.9\nhandler = 'get_isochrone'\nexternal_access_integrations = (OPEN_ROUTE_INTEGRATION)\nPACKAGES = ('snowflake-snowpark-python','requests')\nSECRETS = ('cred' = CORE.ROUTING_TOKEN )\n\nAS\n$$\nimport requests\nimport _snowflake\ndef get_isochrone(method,lon,lat,range):\n    request_isochrone = f'''https://api.openrouteservice.org/v2/isochrones/{method}'''\n    key = _snowflake.get_generic_secret_string('cred')\n    HEADERS = { 'Accept': 'application/json, application/geo+json, application/gpx+xml, img/png; charset=utf-8',\n               'Authorization':key,\n               'Content-Type': 'application/json; charset=utf-8'}\n\n    body = {'locations':[[lon,lat]],\n                    'range':[range*60],\n                    'location_type':'start',\n                    'range_type':'time',\n                    'smoothing':10}\n\n    r = requests.post(url = request_isochrone,json = body, headers=HEADERS)\n    response = r.json()\n    \n    return response\n$$;\n```\n\nYou will now see the functions below ready to use.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/functions-created.png?v=f6f4f8e4)\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Marketplace\n\nBefore you try out your functions, you will get a dataset from the marketplace.  This is the Carto Overture dataset which includes an extensive point of interest map across the whole world.  It is also useful for routing simulations.\n-   Navigate to the Snowflake Marketplace - this is under Data Products \u003E Snowflake Marketplace\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-navigation.png?v=f6f4f8e4)\n\n\nSearch for Overture Maps - Places\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-search-overture.png?v=f6f4f8e4)\n\nClick on the following dataset then press **Get** Do not change the database name.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/marketplace-get-dataset.png?v=f6f4f8e4)\n\n\u003C!-- ------------------------ --\u003E\n## Routing functions with AISQL\n\nYou will now test out all the functions which you have created. You will be using data simulated by **AISQL**.  \n\nThis notebook covers using the functions, how to apply them and how to visualize the results.  At the end you will have a good understand of how the route optimisation service works well with Snowflake Advanced analytical capabilites - which will also lead onto creating the streamlit datasets which will be covered in the next section.\n\n- To ensure the AI LLM model will work in your region and cloud, please run the following command:\n\n```sql  \nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n```\n\n- Run the following SQL to setup a new database and schema for collecting Views/Tables and notebooks for the simulator:\n\n```sql\nCREATE DATABASE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR;\nCREATE WAREHOUSE IF NOT EXISTS ROUTING_ANALYTICS;\n\nCREATE SCHEMA IF NOT EXISTS DATA;\nCREATE SCHEMA IF NOT EXISTS NOTEBOOKS;\nCREATE SCHEMA IF NOT EXISTS STREAMLITS;\n```\n\n\n-   Download following [notebook](https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Notebook/routing_setup.ipynb) \n\n-   Download the following [environment file](https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Notebook/environment.yml)\n\n\n-    Create 1 stage to store the notebook assets\n\n ```sql\n CREATE STAGE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.notebook DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n```\n- Import the downloaded notebook and environment file into the stage using a method of choice such as the Snowsight UI or Visual Studio Code.\n- Run the following to create your notebook\n\n```sql\nCREATE OR REPLACE NOTEBOOK VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.EXPLORE_ROUTING_FUNCTIONS_WITH_AISQL\nFROM '@VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.NOTEBOOK'\nMAIN_FILE = 'routing_setup.ipynb'\nQUERY_WAREHOUSE = 'ROUTING_ANALYTICS'\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"Route Optimization with Open Route Service\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"notebook\"}}';\n\nALTER NOTEBOOK VEHICLE_ROUTING_SIMULATOR.NOTEBOOKS.EXPLORE_ROUTING_FUNCTIONS_WITH_AISQL ADD LIVE VERSION FROM LAST;\n```\nYou will now be able to try out how the functions work and use them in conjunction with **AISQL**.\n\nNavigate to the notebook and follow the provided instructions.  In order to run the streamlit, it is essential that you run from the cell **add_carto_data** AND BELOW.  This is to ensure that you have all the correct dependencies needed.\n\n- Ensure you run all the code below this section **BEFORE** you move to the streamlit.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/notebook-carto-section.png?v=f6f4f8e4)\n\u003C!-- ------------------------ --\u003E\n## Deploy the Streamlit\n\nNow you can see how all the functions work with AISQL, lets now build a route simulator streamlit application.\n\n\n- Click [here](https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator/blob/1a512439a664c84b6be0cbd329fd591386762370/Streamlit/streamlit.zip) to download the files needed for the streamlit app.\n\n- Unzip all files ready for uploading to a stage.\n\n\n-    Create 1 stage to store streamlit assets\n\n ```sql\n CREATE STAGE IF NOT EXISTS VEHICLE_ROUTING_SIMULATOR.STREAMLITS.STREAMLIT DIRECTORY = (ENABLE = TRUE) ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n```\n- Navigate to the Streamlit Stage or the VSCode add-in to import the files.\n\n- Upload all files with the exception of config.toml to the streamlit stage\n- Upload the the config.toml file to a folder called .streamlit within the streamlit stage.\n- Create the streamlit using the following script\n\n```sql\nCREATE OR REPLACE STREAMLIT VEHICLE_ROUTING_SIMULATOR.STREAMLITS.SIMULATOR\nROOT_LOCATION = '@VEHICLE_ROUTING_SIMULATOR.STREAMLITS.streamlit'\nFROM 'routing.py'\nQUERY_WAREHOUSE = 'ROUTING_ANALYTICS'\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"Route Optimization with Open Route Service\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"Streamlit\"}}';\n```\n- Go to the homepage in Snowsight\n\n- Click on the **Projects** \u003E **Streamlits** and run the **SIMULATOR**.\n\n\u003C!-- ------------------------ --\u003E\n## Run the Streamlit\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/streamlit-main.png?v=f6f4f8e4)\n\nThe streamlit app which you have open simulates potential routes to 29 delivery locations for selected customer types - all coming from a user definable wholesaler.  Currently there are 3 types of distributor available although with the notebook, you can create limitless industry categories:\n\n-   Food\n-   Health\n-   Cosmetics\n\nIf you wish to add additional choice of distributor types, you can with the provided notebook.\n\nBefore you choose your category, you must select where the routing specific functions are.  This app works with both the api call method and the native app method.  If you followed the instructions and went through both options, you can test out either option using the supplied radio selector.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/function-source-selector.png?v=f6f4f8e4)\n\n\nThe places you will work with are real as they are based on the Carto Overture points of interest maps which is a dataset freely available on the marketplace.  This allows you to create a location relevant scenario based on the needs of a specific usecase.\n\n**Please note:**  For this simulation the data has been restricted to new york city.  You will need to revise the initial notebook should you require an additional location.  This is an extract filtered by **GEOHASH**.  \n\nIf you have built the native app and require an alternative city, you will need to upload the new map to the configuration stage.\n\n### End to End with Streamlit Dynamic Simulator Overview Diagram\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/overview-diagram.png?v=f6f4f8e4)\n\n### Setting the Context of the Routing Scenario\n\n- Open up the side menu\n- Select the industry type.\n- Choose the LLM model in order to search for a location.\n- Type in a word or phrase in the world which will help locate the simulation.  \n**NB** You will only return results in the New York City boundary.\n- Choose the distance in KM for how wide you would like the app to search for nearby distributors.\n\n    ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/sidebar-cortex-filter.png?v=f6f4f8e4)\n\n- Scroll down to get a map which highlights the place plus where all the nearby distributors are.  \n\n- Scroll further down in the sidebar to select a specific distributor. - This is sorted by distance from the centre point.  You should have relevent wholesalers based on location and industry.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/distributor-list.png?v=f6f4f8e4)\n\n\n- Choose the type of customers you want to deliver goods to.  In this case, we are choosing supermarkets and restaurants.  Customer types can be configured using the provided notebook.\n\n\n- There is an order acceptance catchment time - this will be used to generate an isochrone which will filter possible delivery locations within that isochrone.  The isochrone produced is a polygon shaped to all the possible places you can drive within the acceptable drive time.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/isochrone-filter.png?v=f6f4f8e4)\n\n- You may close the side bar.\n\n### Wholesaler Routing Walkthrough.\n\nThis is an example scenario based on the previously selected fields.\n\n**Hudson Produce** is in New York City.  This week they have 3 vehicles assigned to make up to 30 deliveries today.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/depot-vehicles-overview.png?v=f6f4f8e4)\n\n**Vehicle 1** will start between 8HRS and 17HRS - this vehicle is a car.  [hover over information]  the vehicle has a capacity limit of 4 and been assigned a skill level of 1 - this vehicle does not have a freezer so can only carry fresh food.\n\n\n\n**Vehicle 2** will operate between 12 and 17hrs [change vehicle 2 from 8 till 12].  This will also be a car but has a skill level of 2 which means they can deliver frozen food.\n\n\n\n**Vehicle 3** will also operate between 8hrs and 17hrs and has a skill level of 3 - they can carry  the premium food items - this vehicle will be an road bicycle [select cycling-road]. \n\nYou can look at the vehicle skill level by hovering over the '?' against each vehicle.\n\nOnce the selections are made you can choose the scope for the jobs - this is based on a catchment time.  \n\n-   Select 25mins based on how far you can cycle in that time.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/catchment-diagram.png?v=f6f4f8e4)\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-skills-assignment.png?v=f6f4f8e4)\n\nYou will note that orders of the Non Perishable orders will only go to vehicle 3, the fresh food will go to vehicle 2 and the frozen food will go to vehicle 1.\n\n(if i have more vehicles that have the same skills it will also look at the time slots as well).\n\n\n\nNext we look at the map\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-routes-map.png?v=f6f4f8e4)\n\nVehicle 3 has the least amount of things to deliver but takes the longest to deliver them.  This is probably because the vehicle is a bicycle.  [change bicycle to hgv and re run]\n\n\n\nWhen looking at the map itself, you will see the lines of the route for each vehicle, this is colour coded - you will also see circles which also represent the drops for each vehicle.  The hoverover will tell you what the point represents.\n\n\n\nTabs - this will give instructions for each segment of the drivers journey - the final stop is the return back to the wholesaler.\n\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-itinerary.png?v=f6f4f8e4)\n\n\n### How does it work\n\nYou can see that in a couple of clicks you can create a vehicle optimisation scenario from anywhere. \n\n\n#### Finding the place\nthe app is using an LLM to retrieve a Latitude and longitude based on the word entered into the search.\n\nSnowflake will use the ST_DWITHIN geospatial function to filter the overture maps to find all places of interest within an Xkm radius. \n\n\n#### The previously run notebook\n\nThe previously ran notebook contains the standing data which you can go back to to customize the demo.  If you want to change the types of places to be hotels, then that is quite possible.\n\nWithin the notebook, you have also created: \n\n- The overture dataset and included optimisation on geo and the category variant column to help with faster searching.\n\n- An industry lookup table to add relevant context\n\n- A job sample table\n\n#### The mapping\nThe solution leverages Pydeck to plot points, linestrings and polygons on a map.  The isochrone is the polygon, the routes are linestrings and the places/points of interest are points.  You would have seen how this works in the original notebook. AISQL is useful to quickly generate python code to test the maps. \n\u003C!-- ------------------------ --\u003E\n## The Streamlit Code\n\nThis final Section, gives you some explanation as to how the streamlit code works.\n\nThe Streamlit puts all of the above components together. I will now explain how the main aspects of the code works.\n\n**Setup Theming**\n\nAn important feature for better user experience is what the application looks like. I have themed the app to be consistant with Snowflake Branding. This is so much easier and flexible now we can add styles to Streamlit in Snowflake.\n\nFor the theming, a style sheet was added to the streamlit project.\n\n```python\nwith open('extra.css') as f:\n    st.markdown(f\"\u003Cstyle\u003E{f.read()}\u003C/style\u003E\", unsafe_allow_html=True)\n```\n\n\n**Industry Lookup**\n\nAn  industry lookup snowpark dataframe is created. We then create a second dataframe which only selects the industry name. This will be used for the first sidebar filter\n\n```python\n\nlookup = session.table('LOOKUP')\nindustry = lookup.select('INDUSTRY')\n```\n\nBased on the selected industry, key variables are generated for added context to the standing data and filtering the points of interest dataset. The user selects the chosen industry from the sidebar, which then assign the variables\n\n```python\n\n#sidebar\nwith st.sidebar:\n    st.image(image)\n    choice = st.radio('select industry', industry)\n    lookup = lookup.filter(col('INDUSTRY')==choice)\n    lookup = lookup.with_column('IND',array_to_string('IND',lit(',')))\n    lookup = lookup.with_column('IND2',array_to_string('IND2',lit(',')))\n    \n    lookuppd = lookup.to_pandas()\n\n\n```\n\n```python\n\n#assign variables\n\npa = lookuppd.PA.iloc[0]\npb = lookuppd.PB.iloc[0]\npc = lookuppd.PC.iloc[0]\nind = lookuppd.IND.iloc[0]\nind2 = lookuppd.IND2.iloc[0]\nctype = json.loads(lookuppd.CTYPE.iloc[0])\nstype = json.loads(lookuppd.STYPE.iloc[0])\n\n```\n\n**Vehicle Type Dropdown**\n\nThese vehicle types will be assigned to each of the 3 vehicles. These will be configured by the user.\n\n```python\n\nmethod =[\n             'driving-car',\n             'driving-hgv',\n             'cycling-regular',\n             'cycling-road',\n             'cycling-mountain',\n             'cycling-electric']\n\n```\n\n**Locations Dataset**\nHere, a Snowpark Dataframe is created from the previously configured places dataset.\n\n```python\n\nplaces_f = session.table('places')\n\n```\n```python\nplaces_f = places_f.select('GEOMETRY',call_function('ST_X',\n                           col('GEOMETRY')).alias('LON'),\n                           call_function('ST_Y',\n                                  col('GEOMETRY')).alias('LAT'),\n                                  col('ADDRESS'),\n                                  col('CATEGORY'),\n                                  col('ALTERNATE'),\n                                  col('PHONES'),col('NAME'),\n                                  col('GEOMETRY').alias('POINT')\n```\n\n**Cortex map filter**\n\nThis is where Cortex is used to filter the places dataset. The prompt is asking the model to 'give me the Latitude an Longitude which centers the following place.' The 'following place' is a free text field which the user enters such as: the Statue of Liberty, or London, or Heathrow Airport. They enter whatever they like and cortex will try and make sense of it.\nThe user also chooses an LLM model (I have found mistral-large2 works very well) and a distance. Different LLMs produce varying results of accuracy. For better accuracy, perhaps use Cortex Fine Tuning to load good examples into the model - such as the Overture Points of Interest itself. I found that mistral large 2 produced the result accuracy I needed without fine tuning. The distance is not really used for the LLM, but is used later to filter out potential distributors by straight line distance.\n\n```python\n\nwith st.sidebar:\n    st.markdown('##### Cortex Powered Map Filter')\n    st.info(prompt)\n    model = st.selectbox('Choose Model:',['reka-flash','mistral-large2'],1)\n    place = st.text_input('Choose Input','Heathrow Airport')\n    distance = st.number_input('Distance from location in KM',1,300,5)\n\n```\n\nNext, we need to do some prompt engineering. Below is the initial prompt to work with.\n\n```python\n\nprompt = ('give me the LAT and LON which centers the following place')\n\n```\n\nThe idea is that the prompt feed the LLM and return the results to the LLM in the correct format. However, further engineering will be needed before we get reliably good results.\n\nI engineered the prompt by adding text to the prompts such as ' return 3 value which are as follows…. and 'use the following json template'. This is to ensure that what is returned is very likely to be in the format that I would expect.\n\nThe results are returned as a single string which is simply converted to json by using the 'parse_json' function. You will note that before the json is parsed I removed characters that are sometimes generated in order to return the resulting json as markdown. This is great for display purposes but not so great if I only want the json. The replace function removes these characters if they exist.\n\nOnce parsed, I used standard geo and semi structured features in Snowflake to calculate points, present results in a structured form and order the returned results by distance.\n\nThis has all been wrapped with a function called choose_place(place,model,distance).\n\n```python\n\nprompt = ('give me the LAT and LON which centers the following place')\n\n\n```\n\n```python\n\n@st.cache_data\ndef choose_place(place,model,distance):\n    json_template = str({'LAT':44.3452,'LON':2.345,'DISTANCE':100})\n    min_max = session.createDataFrame([{'PLACE':place}])\\\n    .with_column('CENTER',\n        call_function('snowflake.cortex.complete',\n        model,\n        concat(lit(prompt),\n        col('PLACE'),lit(f'return 3 values which \\\n        are as follows LAT and LON with {distance} as DISTANCE'),\n        lit('use the following json template'),\n        lit(json_template),\n        lit('return only json. DO NOT RETURN COMMENTRY OR VERBIAGE'))\n                                  )\n    min_max = min_max.select(parse_json(replace(replace('CENTER',\n                              \"```\",\n                              ''),\n                              'json',\n                              '')).alias('CENTER'))\n   return min_max.select(col('CENTER')['LAT'].astype(FloatType()).alias('LAT'),\n     col('CENTER')['LON'].astype(FloatType()).alias('LON'),\n     call_function('ST_ASWKT',\n     call_function('ST_MAKEPOINT',\n     col('LON'),col('LAT'))).alias('POINT'),\n     col('CENTER')['DISTANCE'].astype(FloatType()).alias('\"DISTANCE1\"'),\n     lit(0).alias('DISTANCE'),\n     lit(place).alias('NAME')).to_pandas()\n\n\n```\n\nWe will call this function in the next step of the app.\n\n```python\n\nbbox = choose_place(place,model,distance)\n\n```\n\n**Creating a scatter plot for suggested location based on user input**\n\nThis is a pydeck layer - which generates a map based on the returned result of the previously created function. The returned results will be a single blue spot. We will later create another layer which will scatter all the available depots based on this location and within the distance chosen by the user.\n\n```python\n\ncontext = pdk.Layer(\n    'ScatterplotLayer',\n    bbox,\n    get_position=['LON', 'LAT'],\n    filled=True,\n    stroked=False,\n    radius_min_pixels=6,\n    radius_max_pixels=20,\n    auto_highlight=True,\n    get_fill_color=[41, 181, 232],\n    pickable=True)\n    st.divider()\n\n```\n\nPreview the results in a pydeck chart. The view state navigates the map to the position of the blue spot.\n\n```python\n\nview_state = pdk.ViewState(bbox.LON.iloc[0], bbox.LAT.iloc[0], zoom=4)\n    st.pydeck_chart(pdk.Deck(layers=[context],map_style=None,initial_view_state=view_state))\n\n```\n![map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/pydeck-context-map.png?v=f6f4f8e4)\n\nWe will next add a new layer which will show all industry related industry suggestions that are within X distance of the blue spot.\n\n**Searching the data for the right type of place (The What).**\n\nWe will search the 'what' by using the SEARCH function. This will search multiple columns within the same row to see if it matches the keywords stated in the industry lookup table. The industry must match the one which the user selected earlier. You will note that this search is being repeated twice - this is to search for two different concepts.\n\nFor example:\n\n- Search one must contain one of these words:\n\n    - hospital \n    - health \n    - pharmaceutical\n    - drug \n    - healthcare \n    - pharmacy \n    - surgical\n\n- From the results of search 1, search 2 must contain one of these words:\n    - supplies \n    - warehouse \n    - depot \n    - distribution \n    - wholesaler \n    - distributors\n\n```python\nplaces_w = places_f.filter(call_function('ST_DWITHIN', \n      places_f['GEOMETRY'],\n      to_geography(lit(bbox.POINT.iloc[0])),\n      lit(bbox.DISTANCE1.iloc[0])*1000))#.cache_result()\n```\n\n```python\n\nplaces_1 = places_w.filter(expr(f'''search((CATEGORY,\n      ALTERNATE,\n      NAME),'{ind}',\n      analyzer=\u003E'DEFAULT_ANALYZER')''')).cache_result()\nplaces_1 = places_1.filter(expr(f'''search((CATEGORY,\n      ALTERNATE,\n      NAME),\n      '{ind2}',\n      analyzer=\u003E'DEFAULT_ANALYZER')''')).cache_result()\n\n```\n\nThe search is using the default analyzer, meaning it will accept any of the words in any order. This is useful to avoid missing things, but providing a second context by searching twice, you will get a better result accuracy. All search words are found in the industry lookup table.\n\n**Filter the places by the 'where'**\n\nWe will filter by distance from the previously allocated point which was returned by the LLM.\n\n```python\n\nplaces_1 = places_1.with_column('DISTANCE',\n                call_function('ST_DISTANCE',\n                call_function('ST_MAKEPOINT',\n                 col('LON'),\n                 col('LAT')),\n                 call_function('ST_MAKEPOINT',\n                      lit(bbox.LON.iloc[0]),\n                      lit(bbox.LAT.iloc[0]))))\n\n\n```\n```python\nplaces_1 = places_1.with_column('DISTANCE',\n           round(col('DISTANCE')/1000,2)).order_by('DISTANCE')\n```\n**Visualise the map of depots**\n\nThis creates a map function which returns the what and where as a scatter plot map within the sidebar. You will note that there is an additional layer here. This layer will return multiple plots as apposed to 1 which is what the first layer generated.\n\n```python\n\n@st.cache_data\n    def places_cached(distance,bbox,ind):\n        return places_1.to_pandas()\n\n```\nA tool tip is constructed to reveal the name of each potential distributor and the straight line distance.\n\n```python\n\ntooltip = {\n   \"html\": \"\"\"\u003Cb\u003EName:\u003C/b\u003E {NAME} \u003Cb\u003E\u003Cbr\u003EDistance From Centre:\u003C/b\u003E {DISTANCE}\"\"\",\n   \"style\": {\n       \"width\":\"50%\",\n        \"backgroundColor\": \"steelblue\",\n        \"color\": \"white\",\n       \"text-wrap\": \"balance\"\n            }\n        }\n\n```\n\n\ndefining the second layer for  potential distributors\n\n```python\nwholesalers = pdk.Layer(\n    'ScatterplotLayer',\n    places_cached(distance,bbox,ind),\n    get_position=['LON', 'LAT'],\n    filled=True,\n    opacity=0.5,\n    stroked=False,\n    radius_min_pixels=6,\n    radius_max_pixels=10,\n    auto_highlight=True,\n    get_fill_color=[0, 0, 0],\n    pickable=True)\n    st.divider()\n\n```\n\n```python\n\nview_state = pdk.ViewState(bbox.LON.iloc[0], bbox.LAT.iloc[0], zoom=4)\n\n```\n\n**Map Layout**\nBelow will render the map.\n\n```python\n\nst.pydeck_chart(pdk.Deck(layers=[wholesalers,context],\n  map_style=None,\n  initial_view_state=view_state, \n  tooltip=tooltip))\n\n```\n\n![map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/pydeck-wholesalers-map.png?v=f6f4f8e4)\n\nThe returned results will also generate a list of places to select from using a drop down list:\n\n```python\n\n@st.cache_data\ndef warehouses(distance,bbox,ind):\n  return places_1.group_by(col('NAME'))\\\n        .agg(avg('DISTANCE').alias('DISTANCE'))\\\n        .sort(col('DISTANCE').asc()).to_pandas()\n\n```\n\n```python\n\ns_warehouse = st.selectbox('Choose Wholesaler to distribute goods from:',\n                            warehouses(distance,\n                                       bbox,\n                                       ind))\n\n```\n\n\n![choose depot](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/choose-depot-dropdown.png?v=f6f4f8e4)\n\n\n**Job Template**\n\nThe job template is joined to the the industry lookups for provide context to the types of goods being delivered. we will call this 'time slots'\n\n```python\n\ntime_slots = session.table('JOB_TEMPLATE')\npa = time_slots.filter(col('PRODUCT')=='pa').join(lookup.select('PA'))\npb = time_slots.filter(col('PRODUCT')=='pb').join(lookup.select('PB'))\npc = time_slots.filter(col('PRODUCT')=='pc').join(lookup.select('PC'))\n\n```\n```python\n\ntime_slots = pa.union(pb).union(pc).with_column('PRODUCT',\n                                        col('PA')).drop('PA')\n\n```\n\n**Customer Catchment Generation**\n\nNow we need to generate another dataset - this time for potential customers which are located within catchment of a chosen depot.  The user will define the catchment based on a drive time.\n\n```python\n\nrange_minutes = st.number_input('Order Acceptance catchment time:',0,120,20)\n\n```\n\n![catchment_time](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/catchment-time-input.png?v=f6f4f8e4)\n\nWe will now focus on filtering a new point of interest dataset by drive time. This dataset will simulate typical customers within the catchment. For this, we will leverage the 'isochrone' function which calls the open route service api to build a catchment polygon.\n\n```python\n\nisochrone = session.create_dataframe([{'LON':start_1[0], \n                      'LAT':start_1[1], \n                      'METHOD':smethod,\n                      'RANGE_MINS':range_minutes}])\nst.write(isochrone)\n        \nisochrone = isochrone.select(call_function('UTILS.ISOCHRONES',\n                          (col('METHOD'), \n                           col('LON'), \n                           col('LAT'), \n                           col('RANGE_MINS'))).alias('ISOCHRONE'))\n\nisochrone2 = isochrone.select(to_geography(col('ISOCHRONE')['features'][0]['geometry']).alias('GEO')).cache_result()\n\n```\n\nYou will see that after calling the isochrone function, we then join the resulting polygon dataset to the point of interest dataset using 'ST_WITHIN'. This ensures only jobs will be created within the catchment area of the polygon.\n\n**Customer Type Filter**\n\nNow lets filter **'the what'** on the customer dataset. We have all points of interests around the catchment of a depo, however, we have not specified what type of organisations these customers are. This is what the next drop down list is for. The user will pick the type of customer which is relevant to the industry. This example filter selection below will only retain organisations which are categorised as hospitals, dentists and pharmacies. Because the categories are in two fields, we will use the **SEARCH** function again.\n\n![cust_type_filter](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/customer-type-filter.png?v=f6f4f8e4)\n\n```python\n\nplaces_2 = places_f.filter(expr(f'''search((CATEGORY,ALTERNATE,NAME),\n                                '{\" \".join(customer_type)}',\n                                analyzer=\u003E'DEFAULT_ANALYZER')'''))\n\n```\n\nWe will limit our customer results to match the number of 'time slots' we have created from the job template, and generate a sample of the results.\n\n```python\n\nplaces_2 = places_2.join(isochrone2,\n  call_function('ST_WITHIN',\n  places_2['POINT'],\n  isochrone2['GEO'])).sample(0.5).limit(time_slots.count()).cache_result()\n\n```\nNext, we will create a row number after ordering the sample by a random number. This row number will effectively become our unique **'consignment number'** which will be used in the optimisation service.\n\n```python\n\nwindow_spec = Window.order_by(random())\nplaces_2 = places_2.with_column('ID',row_number().over(window_spec))\nplaces_2 = places_2.join(time_slots,'ID')\n\n```\n\nNext we will join to the time slots by 'ID' which has been randomly assigned to **'our customers'**.\n\n```python\n\nplaces_2 = places_2.join(time_slots,'ID')\n\n```\n\nNow we will format the table in to presentable jobs. This will assign skills, time slots and capacity requirements.\n\n```python\n\nplaces_2_table = places_2.select('ID',\n        col('PRODUCT').alias('\"Product\"'),\n        col('SLOT_START').alias('\"Slot Start\"'),\n        col('SLOT_END').alias('\"Slot End\"'),\n        col('NAME').alias('Name'),\n        col('CATEGORY').alias('\"Category\"'),\n        col('ADDRESS')['freeform'].astype(StringType()).alias('\"Address\"'),\n        col('ADDRESS')['locality'].astype(StringType()).alias('\"Locality\"'),\n        col('ADDRESS')['postcode'].astype(StringType()).alias('\"Postcode\"'),\n        col('ADDRESS')['region'].astype(StringType()).alias('\"Region\"'),\n        col('PHONES').alias('\"Phone Number\"'))\n\n    \nplaces_2 = places_2.with_column('JOB',\n              object_construct(lit('id'),col('ID'),\n              lit('capacity'),lit([2]),\n              lit('skills'),array_construct(col('SKILLS')),\n               lit('time_window'),\n               array_construct(col('SLOT_START')*60*60,col('SLOT_END')*60*60),\n               lit('location'),array_construct(col('LON'),col('LAT'))\n                      py))\n\n    \n\njobs = places_2.select(array_agg('JOB').alias('JOB'))\n\n\n```\n\n**The Vehicles**\nThe example I have created, is an example of only 3 vehicles at pre defined skill levels.\n\n![vehicle_config](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/vehicle-config-panel.png?v=f6f4f8e4)\n\nThe vehicle location is then Aligned to the previously selected depot. In reality, vehicles might have varying start destinations - however, for simplicity all vehicle starting points are the same.\n\n```python\n\nplaces_vehicles = places_1.filter(col('NAME')==s_warehouse).cache_result()\n\n```\n\nConstruct each configurable vehicle. Below is an example of one of the vehicles. You will see that we are converting the start and end time of each vehicle to seconds - likewise for the customers, the agreed delivery times for the optimisation service to work are also in seconds.\n\n```python\n\nvehicle_1 = places_vehicles.select(object_construct(lit('profile'),\n                      lit(smethod),\n                      lit('skills'),\n                      lit(veh1_skills),\n                      lit('id'),\n                      lit(1),\n                      lit('start'),\n                      array_construct(col('LON'),col('LAT')),\n                      lit('end'),\n                      array_construct(col('LON'),col('LAT')),\n                      lit('time_windows'),\n                      array_construct(lit(start_time_0*60*60),\n                                      lit(end_time_0*60*60)),\n                      lit('capacity'),\n                      lit(veh1_capacity)).alias('VEHICLE'))\n\n```\n\nNow, we present the configurable aspects of each vehicle to the user. You will note that this is an example of utilising the previously configured styling.\n\n```python\n\nst.markdown('\u003Ch4 class=\"veh2\"\u003EVehicle 2\u003C/h2\u003E', \n    unsafe_allow_html=True, \n    help=f'''Vehicle 2 {skill_types[veh2_skills[0]-1]} and a capacity of {veh2_capacity[0]}''')\n    \n    col4,col5,col6 = st.columns(3)\n    with col4:\n        start_time_1 = st.number_input('Start Time in Hours:',0,24,8,key=3)\n    with col5:\n        end_time_1 = st.number_input('End Time in Hours:',start_time_1,24,17,key=4)\n    with col6:\n        smethod_1 = st.selectbox('Choose Method:',method, key=6)\n\n```\n\nFor further formatting - when the job results are returned after going through the optimisation service, it will be nice to clearly see what vehicles are aligned to what job. This detail is added to the dataframe which gives data driven colour coding.\n\n```python\n\n##### ADD VEHICLE_COLOR TO VEHICLES)\n    vehicle_1 = vehicle_1.with_column('R',lit(125))\n    vehicle_1 = vehicle_1.with_column('G',lit(68))  \n    vehicle_1 = vehicle_1.with_column('B',lit(207)) \n\n    vehicle_2 = vehicle_2.with_column('R',lit(212))\n    vehicle_2 = vehicle_2.with_column('G',lit(91))  \n    vehicle_2 = vehicle_2.with_column('B',lit(144)) \n\n    vehicle_3 = vehicle_3.with_column('R',lit(255))\n    vehicle_3 = vehicle_3.with_column('G',lit(159))  \n    vehicle_3 = vehicle_3.with_column('B',lit(54))\n\n```\n\nNext, we union all vehicles into a new dataset called **vehsdet**.\n\n```python\n\nvehsdet = vehicle_1.union(vehicle_2).union(vehicle_3).with_column('ID',\n              col('VEHICLE')['id'])\\\n    .with_column('PROFILE',\n                 col('VEHICLE')['profile'].astype(StringType()))\\\n    .with_column('WINDOW',\n                  col('VEHICLE')['time_windows'].astype(StringType()))\n    vehs = vehsdet.select(array_agg('VEHICLE').alias('VEH'))\n    vehsdet = vehsdet.drop('VEHICLE')\n\n\n```\n\n**Route Optimisation Service**\n\nSo we have our 'Customers' and we have our 'Vehicles'. We now need to create our route plans for each vehicle using the Route Optimisation Service. This will effectively push our jobs and vehicles into the api which will return our vehicle route plans based on information such as drive time, consignment needs and vehicle availability.\n\n```python\n\noptim = jobs.join(vehs).select('JOB',\n                'VEH',call_function('UTILS.OPTIMIZATION',\n                col('JOB'),col('VEH')).alias('OPTIMIZATION'))\n\n```\n\nThe returned results will be an array of various objects. We will extract out the parts we want in order to display the results clearly.\n\n```python\n\noptim = optim.with_column('CODES',col('OPTIMIZATION')['codes'])\n        optim = optim.with_column('ROUTES',col('OPTIMIZATION')['routes'])\n        optim = optim.with_column('SUMMARY',col('OPTIMIZATION')['summary'])\n        optim = optim.with_column('UNASSIGNED',col('OPTIMIZATION')['unassigned'])\n    \n    \n    \n        optim = optim.with_column('COST',col('SUMMARY')['cost'])\\\n        .with_column('DURATION',col('SUMMARY')['duration'])\\\n        .with_column('NUMBER_OF_ROUTES',col('SUMMARY')['routes']).drop('SUMMARY')\n\n        optim = optim.join_table_function('flatten',col('ROUTES'))\\\n        .select('VALUE')\n\n        optim = optim.select(col('VALUE')['amount'].alias('AMOUNT'),\n                         col('VALUE')['vehicle'].alias('VEHICLE'),\n                         col('VALUE')['duration'].alias('DURATION'),\n                         col('VALUE')['steps'].alias('STEPS'),\n                        col('VALUE')['location'][0].alias('LON'),\n                         col('VALUE')['location'][0].alias('LAT')p\n\n```\n\nAs described in the route optimisation function, the output will give steps for the journey along with other measures such as duration and what vehicle will be assigned. \n\n\nThe **directions API** will then be used to get detailed instructions for each step.\n\n```python\n\noptim_line = optim_line\\\n        .select('VEHICLE','R','G','B','PROFILE','ID','TOTAL_JOBS',\n            call_function('UTILS.DIRECTIONS_with_way_points',\n                                    col('PROFILE'),\n                            col('LINE')).alias('DIRECTIONS')).cache_result()\n\n```\n\nThis will return detailed route plans which include all drop offs, line strings as well as written instructions.\n\n![directions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/directions-results.png?v=f6f4f8e4)\n\nTwo layers are created for the visualisation - one for the line paths and the other for the drop offs. The layers have been generated by a python function in order to reuse the code for each vehicle. This is so i can show a vehicle plan each containing an independant map within each tab.\n\n```python\n\ndef veh_journey(dataframe,vehicle):\n            vehicle_1_path = pdk.Layer(\n            type=\"PathLayer\",\n            data=dataframe[dataframe['VEHICLE']==vehicle],\n            pickable=True,\n            get_color=[\"0+R\",\"0+G\",\"0+B\"],\n            width_scale=20,\n            width_min_pixels=4,\n            width_max_pixels=7,\n            get_path=\"coordinates\",\n            get_width=5)\n            return vehicle_1_path\n\n        def vehicle_drops(dataframe,vehicle):\n            layer_end_v1 = pdk.Layer(\n            'ScatterplotLayer',\n            dataframe[dataframe['VEHICLE']==vehicle],\n            get_position=['LON', 'LAT'],\n            filled=True,\n            stroked=False,\n            radius_min_pixels=6,\n            radius_max_pixels=10,\n            line_width_min_pixels=5,\n            auto_highlight=True,\n            get_radius=50,\n            get_line_color=[\"0+R\",\"0+G\",\"0+B\"],\n            get_fill_color=[\"0+R\",\"0+G\",\"0+B\"],\n            pickable=True)\n            return layer_end_v1\n\n\n```\n\nBelow you can see an example of all three vehicles travelling around Paris to drop goods off. This is combining points and line string layers for each vehicle as well as the isochrone layer.\n\n![map with tabs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/create-a-route-optimisation-and-vehicle-route-plan-simulator/map-with-vehicle-tabs.png?v=f6f4f8e4)\n\n#### Considerations\nThe Job details may plot routes outside the agreed time.  The Demo has only vehicles where each vehicle has a unique skill.  We will need more vehicles / less skills to prevent these violations.\n\nThe app is confined to a B2B model as we do not have public names and addresses of B2C consumers.  If a B2C simulator is needed, then an alternative 'customer' dataset other than the 'places' dataset is needed.\n\n\n\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n### Conclusion\n\nSo you will now see that by combining AI, freely accessible points of interests, easy to use geospatial functions, the ability to securely call the open route service and the powers of Streamlit in Snowflake - creating innovative geospatial applications  is entirely possible. \n\nSnowflake provides powerful solutions when you bring Snowflake's advanced analytics, Cortex, Snowpark and Streamlit's visualization capabilities together.  Also, by leveraging the open route service (or even an alternative provider such as the Carto Toolbox) using external integrations provides another level of geospatial capabilites such as route optimisation, directions and isochrones.  \n\n\n\n### What You Learned\n\nYou will have learned the following:\n - How to use Snowflake Cortex can be used as a location filter, which can filter a comprehensive point of interest dataset to anywhere in the world.\n\n- Use text based Search capabilities for advanced filtering which adds accurate context to a simulation\n\n- How to utilise Pydeck to create a multi layered map\n\n- Leverage the open route service to create the following\n\n    - isochrones (catchements) based on drive time\n    - Simple Directions and Directions which include waypoints\n    - Route Optimisations\n\n\n### Related Resources\n\n\n#### Source code\n\n- [Source Code on Github](https://github.com/Snowflake-Labs/sfguide-Create-a-Route-Optimisation-and-Vehicle-Route-Plan-Simulator)\n\n\n#### Further Related Material\n\n- [Geospatial Functions](https://docs.snowflake.com/en/sql-reference/functions-geospatial)\n\n- [Building Geospatial Multi-Layer Apps with Snowflake and Streamlit](/en/developers/guides/building-geospatial-mult-layer-apps-with-snowflake-and-streamlit/)\n\n- [H3 Indexing](https://h3geo.org/docs/)\n\n- [Streamlit](https://streamlit.io/)\n\n- [Pydeck](https://deckgl.readthedocs.io/en/latest/index.html#)\n\n- [Using Cortex and Streamlit With Geospatial Data](/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/)\n\n- [Getting started with Geospatial AI and ML using Snowflake Cortex](/en/developers/guides/geo-for-machine-learning/)\n\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-adf83735e3","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-0ff2a26884",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-acea872429","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-23",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c506d58664","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 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