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You build models in Python with \u003Cstrong\u003EPyRel\u003C/strong\u003E: \u003Cem\u003Econcepts\u003C/em\u003E (suppliers, parts, orders), \u003Cem\u003Eproperties\u003C/em\u003E (a supplier's region) and \u003Cem\u003Erelationships\u003C/em\u003E (a part contains another part), plus \u003Cem\u003Erules\u003C/em\u003E that derive new facts.\u003C/p\u003E\n","\u003Cp\u003EThis guide builds on the official \u003Ca href=\"https://docs.relational.ai/get-started/start-building-with-pyrel\"\u003EStart building with PyRel\u003C/a\u003E guide: same local setup, a richer model, and a question that shows what PyRel adds next to SQL and an LLM.\u003C/p\u003E\n","\u003Cp\u003EThe case: a tier-3 supplier stops delivering. Nobody buys from it directly. Which customer orders are at risk, why, and what do we do about it? Answering it means following two networks of unknown depth, the supplier tiers and the bill of materials, and the supplier network contains a cycle. SQL can do it, with complications. PyRel does it with a few rules. Cortex \u003Ccode\u003EAI_COMPLETE\u003C/code\u003E then writes the mitigation plan from the facts PyRel derived.\u003C/p\u003E\n","\u003Cp\u003EThe notebooks are only the surface for showing these concepts. They run locally in Jupyter for this demonstration, not as a recommended production setup. The reasoning runs in the RelationalAI app inside your Snowflake account, next to the data.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhy PyRel needs a semantic model (ontology) over your tables, and how to map tables to it\u003C/li\u003E\u003Cli\u003EHow recursive rules handle multi-hop questions and cycles, compared with recursive CTEs\u003C/li\u003E\u003Cli\u003EHow to hand derived facts to an LLM, and check the plan it writes against those facts\u003C/li\u003E\u003Cli\u003EHow to connect PyRel to Snowflake with \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E, and reuse its session for SQL and Cortex\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003ETwo notebooks on a synthetic electronics supply chain (20 products, 315 parts, 80 suppliers in four tiers, 1,500 orders):\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/01_relationalai_setup.ipynb\"\u003E01_relationalai_setup.ipynb\u003C/a\u003E: connect, load the data, first query.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/02_supplier_risk_to_plan.ipynb\"\u003E02_supplier_risk_to_plan.ipynb\u003C/a\u003E: impact of a supplier failure, a check of the alternative supplier, a mitigation plan and a grounding check.\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow this guide was made.\u003C/strong\u003E The ontology, the PyRel rules and queries, the notebooks, the sample data and most of this text were generated with generative AI (Cortex Code), not written by hand. Every code cell was then run against the loaded tables and checked against the planted answers in \u003Ccode\u003Eassets/data/expected_results.json\u003C/code\u003E. Treat the code as a working example to review and adapt, not as reference style for PyRel. In a production setting, an agent with an LLM will be used to generate the queries against the ontology. Building the ontology will be assisted by generative AI tooling as well.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTYZOOIX8H/relationalai-relationalai\"\u003ERelationalAI Native App\u003C/a\u003E installed and activated, with user access granted, as described in \u003Ca href=\"https://docs.relational.ai/get-started/set-up-relationalai-in-snowflake\"\u003ESet up RelationalAI in Snowflake\u003C/a\u003E. You need the role \u003Ccode\u003ERAI_DEVELOPER\u003C/code\u003E and a \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E from your invite link.\u003C/li\u003E\u003Cli\u003EAn administrator who can run one short setup script as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EPython 3.10–3.14 on your machine\u003C/li\u003E\u003Cli\u003EAccess to Cortex \u003Ccode\u003EAI_COMPLETE\u003C/code\u003E and a supported model in your region, or cross-region inference enabled\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGet the example files.\u003C/strong\u003E Use this page's \u003Cstrong\u003EFork\u003C/strong\u003E button to open the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis\"\u003Eguide's source folder on GitHub\u003C/a\u003E. The notebooks, CSV data and SQL scripts are included in its \u003Ccode\u003Eassets/\u003C/code\u003E folder; they are downloaded from GitHub, not served by this website. Fork the repository if you want your own editable copy. To download just this guide from the public repository, run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone --depth 1 --filter=blob:none --sparse https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts\ngit sparse-checkout set site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis\ncd site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following setup commands from this guide folder. Keep the \u003Ccode\u003Eassets/\u003C/code\u003E directory structure intact so the notebooks can find the data and scripts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Warehouse and schema (administrator).\u003C/strong\u003E Run \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/setup.sql\"\u003E\u003Ccode\u003Eassets/scripts/setup.sql\u003C/code\u003E\u003C/a\u003E as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. It creates the warehouse \u003Ccode\u003ERAI_QS_WH\u003C/code\u003E, with a 600-second statement timeout, and the schema \u003Ccode\u003ERAI_QUICKSTART.SUPPLY_CHAIN\u003C/code\u003E, and grants both to \u003Ccode\u003ERAI_DEVELOPER\u003C/code\u003E. Nothing else is needed: no integrations, network rules or extra roles. The RelationalAI setup already created the role and the OAuth integration that PyRel signs in with.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Python environment.\u003C/strong\u003E From the guide folder downloaded above, create an environment, install PyRel and Jupyter, and register the environment as a Jupyter kernel:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython -m venv .venv && source .venv/bin/activate\npip install "relationalai>=1.30.1,<2.0" jupyter\npython -m ipykernel install --user --name rai-supply-chain --display-name "RAI supply chain"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe last line makes the environment show up as \u003Cstrong\u003ERAI supply chain\u003C/strong\u003E in the kernel picker of Jupyter, VS Code or Cortex Code, wherever the environment lives. Prefer conda? Use \u003Ccode\u003Econda create -n rai-supply-chain python=3.12 -y && conda activate rai-supply-chain\u003C/code\u003E instead of the first line.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Connection.\u003C/strong\u003E Save the \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E from your invite link in the guide folder, next to \u003Ccode\u003Eassets/\u003C/code\u003E, and set the warehouse to \u003Ccode\u003ERAI_QS_WH\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Edefault_connection: sf\ndirect_access: true\nconnections:\n  sf:\n    type: snowflake\n    authenticator: oauth_authorization_code\n    account: <org>-<account>\n    warehouse: RAI_QS_WH\n    user: <your_user>\n    role: RAI_DEVELOPER\n    oauth_client_id: <oauth_client_id>\n    oauth_redirect_uri: http://localhost:54321/snowflake/oauth-redirect\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck it with \u003Ccode\u003Erai connect\u003C/code\u003E from the guide folder. Then open the notebooks in \u003Ccode\u003Eassets/notebooks/\u003C/code\u003E and select the kernel \u003Cstrong\u003ERAI supply chain\u003C/strong\u003E. PyRel looks for \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E in the notebook's folder and its parent folders, so it finds the file two levels up.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Load the data.\u003C/strong\u003E Notebook 01 uploads the CSV files from \u003Ccode\u003Eassets/data/\u003C/code\u003E to a stage and runs \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/load_data.sql\"\u003E\u003Ccode\u003Eassets/scripts/load_data.sql\u003C/code\u003E\u003C/a\u003E. The script creates eleven tables with \u003Ccode\u003ECHANGE_TRACKING = TRUE\u003C/code\u003E and checks the row counts. RelationalAI keeps its copy of source tables in sync through change data capture (CDC), which is why change tracking is on.\u003C/p\u003E\n","\u003Cp\u003EThe first PyRel query in notebook 01 starts a reasoning engine and indexes the tables. That takes a few minutes; later queries take seconds.\u003C/p\u003E\n","\u003Cp\u003EThe data is synthetic, with a few cases planted deliberately. \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/data/README.md\"\u003E\u003Ccode\u003Eassets/data/README.md\u003C/code\u003E\u003C/a\u003E describes each one, and what the notebooks are looking for. This guide uses these:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIn the data\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHidden single point of failure\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESensor \u003Ccode\u003EP-141\u003C/code\u003E has two tier-1 suppliers, but both depend on tier-3 supplier \u003Ccode\u003ES-T3-07\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShared subassembly\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EP-141\u003C/code\u003E sits in control board \u003Ccode\u003EP-040\u003C/code\u003E, which is used in 8 products, at different depths\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECycle\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ES-T2-11\u003C/code\u003E and \u003Ccode\u003ES-T2-14\u003C/code\u003E depend on each other\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQualified alternative\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ES-T1-01\u003C/code\u003E is approved for \u003Ccode\u003EP-141\u003C/code\u003E: 1,500 per week, 70 days lead time\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Semantic Model\u003C/h2\u003E\n","\u003Cp\u003EPyRel doesn't reason over tables directly. It reasons over an \u003Cstrong\u003Eontology\u003C/strong\u003E: the business concepts, how they relate, and which table populates what. \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/supply_chain_ontology.py\"\u003Esupply_chain_ontology.py\u003C/a\u003E defines it once, and every rule and query in notebook 02 builds on it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EPart = model.Concept("Part", identify_by={"id": String})\nSupplier = model.Concept("Supplier", identify_by={"id": String})\nSupply = model.Concept("Supply", identify_by={"supplier": Supplier, "part": Part})\n\nPart.contains = model.Property(f"{Part:parent} contains {Part:child} {Integer:qty} times")\nSupplier.depends_on = model.Relationship(f"{Supplier:buyer} depends on {Supplier:upstream}")\n\nbom = model.Table("RAI_QUICKSTART.SUPPLY_CHAIN.BOM")\nmodel.define(Part.lookup(id=bom.PARENT_PART_ID).contains(Part.lookup(id=bom.CHILD_PART_ID), bom.QTY_PER))\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Eidentify_by\u003C/code\u003E sets a concept's key. \u003Ccode\u003ESupply\u003C/code\u003E is keyed by a supplier and a part, because the pair carries its own data: capacity, lead time and price.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EProperty\u003C/code\u003E holds one value per key: here, one quantity per (parent, child) pair. \u003Ccode\u003ERelationship\u003C/code\u003E is a set of facts with no key, for many-to-many links that carry no value, like \u003Ccode\u003Edepends_on\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ESame-type slots get role names (\u003Ccode\u003Eparent\u003C/code\u003E, \u003Ccode\u003Echild\u003C/code\u003E), so the direction is explicit.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Emodel.Table()\u003C/code\u003E maps a Snowflake table; column names are upper case, as Snowflake stores them.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe full model used in this guide is shown below. It describes an electronics manufacturer from order to raw supplier: customers place orders for products, each product is built from a bill of materials of parts, and parts are delivered by suppliers who depend on other suppliers, up to tier 4. It contains seven concepts (the boxes), their attributes (the text in each box) and the links between them (the arrows).\u003C/p\u003E\n","\u003Cp\u003EAn arrow means "is linked to", not "is a PyRel \u003Ccode\u003ERelationship\u003C/code\u003E". Each arrow is one of three things in the code:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ea \u003Cstrong\u003E\u003Ccode\u003EProperty\u003C/code\u003E pointing to another concept\u003C/strong\u003E, when each entity has exactly one: an order \u003Cem\u003Eis placed by\u003C/em\u003E one customer, \u003Cem\u003Eis for\u003C/em\u003E one product. \u003Ccode\u003Econtains\u003C/code\u003E is also a Property, with one quantity per (parent, child) pair.\u003C/li\u003E\u003Cli\u003Ea \u003Cstrong\u003E\u003Ccode\u003ERelationship\u003C/code\u003E\u003C/strong\u003E, when the link is many-to-many and carries no value: \u003Ccode\u003Edepends_on\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003Ea \u003Cstrong\u003Ekey field of a link concept\u003C/strong\u003E. \u003Ccode\u003ESupply\u003C/code\u003E and \u003Ccode\u003EQualifiedSupply\u003C/code\u003E are entities keyed by a supplier and a part, so \u003Cem\u003Eof\u003C/em\u003E and \u003Cem\u003Eby\u003C/em\u003E point to the two ends of the key.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESolid arrows come from the tables. Dashed arrows are facts that the rules in the next section derive. Yes/no facts are marked with \u003Ccode\u003Eflag:\u003C/code\u003E inside their concept.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-relational-ai-for-supply-chain-analysis/supply_chain_model.png?v=69fc288a\" alt=\"Supply-chain model: orders link to customers and products; products contain parts; current and qualified supplies link parts to suppliers. Dashed links show derived dependencies.\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe question flows through it like this: \u003Ccode\u003ES-T3-07\u003C/code\u003E is down, so everyone who \u003Ccode\u003Erelies_on\u003C/code\u003E it is down. A \u003Ccode\u003EPart\u003C/code\u003E whose every \u003Ccode\u003ESupply\u003C/code\u003E has a down supplier \u003Ccode\u003Ehas_no_supplier\u003C/code\u003E. An \u003Ccode\u003EOrder\u003C/code\u003E is at risk if its product's top part \u003Ccode\u003Eincludes\u003C/code\u003E that part. The diagram was generated with generative AI from \u003Ccode\u003Esupply_chain_ontology.py\u003C/code\u003E and the notebook rules.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFind the Hidden Risk\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe tier-1 view.\u003C/strong\u003E \u003Ccode\u003EP-141\u003C/code\u003E has two suppliers, in different regions. That looks safe.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe SQL attempt.\u003C/strong\u003E A recursive CTE can walk up the supplier tiers and down the bill of materials. Because of the cycle, you have to carry the visited path in an array, skip suppliers already on it, and cap the depth. Without that guard, a recursive query over the supplier network can run forever. When we built this guide, one ran for over an hour, which is why \u003Ccode\u003ERAI_QS_WH\u003C/code\u003E has a statement timeout. The notebook shows the guarded query. It gives the right answer, but \u003Ccode\u003Edepth < 10\u003C/code\u003E is a guess, and each follow-up question needs the guard again.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe PyRel rules.\u003C/strong\u003E  "Relies on, at some tier" is two lines: a direct dependency, or a dependency of something you already rely on.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ESupplier.relies_on = model.Relationship(f"{Supplier:buyer} relies on {Supplier:upstream} at some tier")\ns1, s2, s3 = Supplier.ref(), Supplier.ref(), Supplier.ref()\nmodel.where(s1.depends_on(s2)).define(s1.relies_on(s2))\nmodel.where(s1.relies_on(s2), s2.depends_on(s3)).define(s1.relies_on(s3))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERelationalAI keeps deriving facts until nothing new appears. A known fact isn't derived again, so the cycle ends on its own. The notebook adds four more rules in the same style: \u003Ccode\u003Eis_down\u003C/code\u003E, \u003Ccode\u003Ehas_no_supplier\u003C/code\u003E, \u003Ccode\u003Eincludes\u003C/code\u003E (any level of the BOM) and \u003Ccode\u003Eneeds\u003C/code\u003E (quantity per unit, multiplied along the path). A last rule, \u003Ccode\u003EQualifiedSupply.is_usable\u003C/code\u003E, checks that the alternative supplier doesn't itself rely on the failed one.\u003C/p\u003E\n","\u003Cp\u003EExpected result:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E \u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E \u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESuppliers down\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ES-T3-07\u003C/code\u003E, \u003Ccode\u003ES-T2-03\u003C/code\u003E, \u003Ccode\u003ES-T2-08\u003C/code\u003E, \u003Ccode\u003ES-T1-02\u003C/code\u003E, \u003Ccode\u003ES-T1-05\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPart without a working supplier\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EP-141\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrders at risk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E568, worth 6,840,136.49, in all three regions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESensors needed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E29,286\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUsable alternative\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ES-T1-01\u003C/code\u003E, first delivery 2027-01-04\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWrite the Plan with Cortex\u003C/h2\u003E\n","\u003Cp\u003EThe notebook builds a small \u003Cstrong\u003Efacts pack\u003C/strong\u003E from what PyRel derived: the dependency chain, the impact by region, the gap until the alternative delivers, and the shortfall. Python computes every number. The LLM gets the JSON and a prompt with three rules: use only the facts, copy numbers exactly, list unknowns as open questions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eplan = sql("""\nSELECT AI_COMPLETE(\n    model => ?,\n    prompt => ?,\n    model_parameters => {'temperature': 0, 'max_tokens': 1500}\n) AS plan\n""", params=[LLM, prompt]).plan[0]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe prompt goes in as a bind parameter, so the facts never become part of the SQL text. \u003Ccode\u003EAI_COMPLETE\u003C/code\u003E runs inside Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECheck the plan.\u003C/strong\u003E A final cell collects every number in the plan and looks it up in the facts pack. An LLM tends to derive numbers of its own, such as a shortfall from demand and stock. Even when the arithmetic is right, that number hasn't been checked. So the facts pack already contains the derived numbers the plan needs, and the check reports any number that isn't in it. It doesn't judge the advice itself; a planner still reads the plan.\u003C/p\u003E\n","\u003Cp\u003EThe notebook writes \u003Ccode\u003EORDERS_AT_RISK\u003C/code\u003E and \u003Ccode\u003EMITIGATION_PLANS\u003C/code\u003E back to the schema, so SQL, dashboards and tasks can use them.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETroubleshooting\u003C/h2\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFix\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENo module named 'relationalai'\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENotebook runs on another kernel\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESelect the kernel \u003Cstrong\u003ERAI supply chain\u003C/strong\u003E; if it's missing, rerun the \u003Ccode\u003Eipykernel install\u003C/code\u003E line with the environment active\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erai connect\u003C/code\u003E or the first cell can't find a config\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E not in the guide folder or \u003Ccode\u003Eassets/notebooks/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMove it there; PyRel searches the current folder and its parents\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESign-in fails, or a role isn't allowed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole in \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E isn't \u003Ccode\u003ERAI_DEVELOPER\u003C/code\u003E, or your user lacks it\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESet \u003Ccode\u003Erole: RAI_DEVELOPER\u003C/code\u003E and ask the administrator for the grant\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EWarehouse RAI_QS_WH\u003C/code\u003E check fails\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esetup.sql\u003C/code\u003E not run, or a different warehouse in \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun \u003Ccode\u003Esetup.sql\u003C/code\u003E and set \u003Ccode\u003Ewarehouse: RAI_QS_WH\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E[Unknown Table] Failed to fetch schema for table\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETables not loaded yet, or wrong name\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun notebook 01; use the fully qualified upper-case name\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EModuleNotFoundError: supply_chain_ontology\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENotebook opened from a copy outside \u003Ccode\u003Eassets/notebooks/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpen the notebook in \u003Ccode\u003Eassets/notebooks/\u003C/code\u003E, next to \u003Ccode\u003Esupply_chain_ontology.py\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFirst query takes minutes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEngine start and table indexing, or a new table being synced\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWait; later queries are fast\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAI_COMPLETE\u003C/code\u003E fails with an unknown or unavailable model\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModel not offered in your region\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPick another model for \u003Ccode\u003ELLM\u003C/code\u003E, or enable cross-region inference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA query runs until the 600-second timeout\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnguarded recursion on the supplier cycle\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse the guarded CTE, or the PyRel rules\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003ERun the following statements, also available in \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/tear_down.sql\"\u003Etear_down.sql\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP DATABASE IF EXISTS RAI_QUICKSTART;\nDROP WAREHOUSE IF EXISTS RAI_QS_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis leaves the RelationalAI app, its roles and the OAuth integration in place. To stop the app's compute, see \u003Ca href=\"https://docs.relational.ai/manage/app/shut-down\"\u003EShut down the app\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou found a supplier risk that a tier-1 view hides, using PyRel rules over Snowflake tables from a local notebook, and turned the result into a checked mitigation plan with Cortex.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAfter the RelationalAI setup, a warehouse, a schema and a \u003Ccode\u003Eraiconfig.yaml\u003C/code\u003E are all you need: no integrations or extra roles\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Emodel.config.get_session()\u003C/code\u003E gives you a Snowpark session on the same connection, for SQL, uploads and Cortex\u003C/li\u003E\u003Cli\u003EAn ontology maps tables to concepts once; rules and queries build on it\u003C/li\u003E\u003Cli\u003ERecursive rules handle multi-hop questions and cycles without a hand-written guard\u003C/li\u003E\u003Cli\u003EAn LLM writes the plan best from a small facts pack, and a simple check keeps its numbers honest\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.relational.ai/get-started/start-building-with-pyrel\"\u003EStart building with PyRel\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.relational.ai/get-started/set-up-relationalai-in-snowflake\"\u003ESet up RelationalAI in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.relational.ai/build/templates\"\u003ERelationalAI project templates\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_complete\"\u003EAI_COMPLETE\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with RelationalAI for Supply Chain Analysis",":type":"snowflake-site/components/contentfragment","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n[RelationalAI](https://docs.relational.ai/) adds a semantic modelling and reasoning layer to Snowflake. You build models in Python with **PyRel**: *concepts* (suppliers, parts, orders), *properties* (a supplier's region) and *relationships* (a part contains another part), plus *rules* that derive new facts.\n\nThis guide builds on the official [Start building with PyRel](https://docs.relational.ai/get-started/start-building-with-pyrel) guide: same local setup, a richer model, and a question that shows what PyRel adds next to SQL and an LLM.\n\nThe case: a tier-3 supplier stops delivering. Nobody buys from it directly. Which customer orders are at risk, why, and what do we do about it? Answering it means following two networks of unknown depth, the supplier tiers and the bill of materials, and the supplier network contains a cycle. SQL can do it, with complications. PyRel does it with a few rules. Cortex `AI_COMPLETE` then writes the mitigation plan from the facts PyRel derived.\n\nThe notebooks are only the surface for showing these concepts. They run locally in Jupyter for this demonstration, not as a recommended production setup. The reasoning runs in the RelationalAI app inside your Snowflake account, next to the data.\n\n### What You'll Learn\n- Why PyRel needs a semantic model (ontology) over your tables, and how to map tables to it\n- How recursive rules handle multi-hop questions and cycles, compared with recursive CTEs\n- How to hand derived facts to an LLM, and check the plan it writes against those facts\n- How to connect PyRel to Snowflake with `raiconfig.yaml`, and reuse its session for SQL and Cortex\n\n### What You'll Build\nTwo notebooks on a synthetic electronics supply chain (20 products, 315 parts, 80 suppliers in four tiers, 1,500 orders):\n\n1. [01_relationalai_setup.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/01_relationalai_setup.ipynb): connect, load the data, first query.\n2. [02_supplier_risk_to_plan.ipynb](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/02_supplier_risk_to_plan.ipynb): impact of a supplier failure, a check of the alternative supplier, a mitigation plan and a grounding check.\n\n\u003E **How this guide was made.** The ontology, the PyRel rules and queries, the notebooks, the sample data and most of this text were generated with generative AI (Cortex Code), not written by hand. Every code cell was then run against the loaded tables and checked against the planted answers in `assets/data/expected_results.json`. Treat the code as a working example to review and adapt, not as reference style for PyRel. In a production setting, an agent with an LLM will be used to generate the queries against the ontology. Building the ontology will be assisted by generative AI tooling as well.\n\n### What You'll Need\n- The [RelationalAI Native App](https://app.snowflake.com/marketplace/listing/GZTYZOOIX8H/relationalai-relationalai) installed and activated, with user access granted, as described in [Set up RelationalAI in Snowflake](https://docs.relational.ai/get-started/set-up-relationalai-in-snowflake). You need the role `RAI_DEVELOPER` and a `raiconfig.yaml` from your invite link.\n- An administrator who can run one short setup script as `ACCOUNTADMIN`\n- Python 3.10–3.14 on your machine\n- Access to Cortex `AI_COMPLETE` and a supported model in your region, or cross-region inference enabled\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n**Get the example files.** Use this page's **Fork** button to open the [guide's source folder on GitHub](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis). The notebooks, CSV data and SQL scripts are included in its `assets/` folder; they are downloaded from GitHub, not served by this website. Fork the repository if you want your own editable copy. To download just this guide from the public repository, run:\n\n```bash\ngit clone --depth 1 --filter=blob:none --sparse https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts\ngit sparse-checkout set site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis\ncd site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis\n```\n\nRun the following setup commands from this guide folder. Keep the `assets/` directory structure intact so the notebooks can find the data and scripts.\n\n**1. Warehouse and schema (administrator).** Run [`assets/scripts/setup.sql`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/setup.sql) as `ACCOUNTADMIN`. It creates the warehouse `RAI_QS_WH`, with a 600-second statement timeout, and the schema `RAI_QUICKSTART.SUPPLY_CHAIN`, and grants both to `RAI_DEVELOPER`. Nothing else is needed: no integrations, network rules or extra roles. The RelationalAI setup already created the role and the OAuth integration that PyRel signs in with.\n\n**2. Python environment.** From the guide folder downloaded above, create an environment, install PyRel and Jupyter, and register the environment as a Jupyter kernel:\n\n```bash\npython -m venv .venv && source .venv/bin/activate\npip install \"relationalai\u003E=1.30.1,\u003C2.0\" jupyter\npython -m ipykernel install --user --name rai-supply-chain --display-name \"RAI supply chain\"\n```\n\nThe last line makes the environment show up as **RAI supply chain** in the kernel picker of Jupyter, VS Code or Cortex Code, wherever the environment lives. Prefer conda? Use `conda create -n rai-supply-chain python=3.12 -y && conda activate rai-supply-chain` instead of the first line.\n\n**3. Connection.** Save the `raiconfig.yaml` from your invite link in the guide folder, next to `assets/`, and set the warehouse to `RAI_QS_WH`:\n\n```yaml\ndefault_connection: sf\ndirect_access: true\nconnections:\n  sf:\n    type: snowflake\n    authenticator: oauth_authorization_code\n    account: \u003Corg\u003E-\u003Caccount\u003E\n    warehouse: RAI_QS_WH\n    user: \u003Cyour_user\u003E\n    role: RAI_DEVELOPER\n    oauth_client_id: \u003Coauth_client_id\u003E\n    oauth_redirect_uri: http://localhost:54321/snowflake/oauth-redirect\n```\n\nCheck it with `rai connect` from the guide folder. Then open the notebooks in `assets/notebooks/` and select the kernel **RAI supply chain**. PyRel looks for `raiconfig.yaml` in the notebook's folder and its parent folders, so it finds the file two levels up.\n\n**4. Load the data.** Notebook 01 uploads the CSV files from `assets/data/` to a stage and runs [`assets/scripts/load_data.sql`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/load_data.sql). The script creates eleven tables with `CHANGE_TRACKING = TRUE` and checks the row counts. RelationalAI keeps its copy of source tables in sync through change data capture (CDC), which is why change tracking is on.\n\nThe first PyRel query in notebook 01 starts a reasoning engine and indexes the tables. That takes a few minutes; later queries take seconds.\n\nThe data is synthetic, with a few cases planted deliberately. [`assets/data/README.md`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/data/README.md) describes each one, and what the notebooks are looking for. This guide uses these:\n\n| Case | In the data |\n|---|---|\n| Hidden single point of failure | Sensor `P-141` has two tier-1 suppliers, but both depend on tier-3 supplier `S-T3-07` |\n| Shared subassembly | `P-141` sits in control board `P-040`, which is used in 8 products, at different depths |\n| Cycle | `S-T2-11` and `S-T2-14` depend on each other |\n| Qualified alternative | `S-T1-01` is approved for `P-141`: 1,500 per week, 70 days lead time |\n\n\u003C!-- ------------------------ --\u003E\n## The Semantic Model\n\nPyRel doesn't reason over tables directly. It reasons over an **ontology**: the business concepts, how they relate, and which table populates what. [supply_chain_ontology.py](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/notebooks/supply_chain_ontology.py) defines it once, and every rule and query in notebook 02 builds on it.\n\n```python\nPart = model.Concept(\"Part\", identify_by={\"id\": String})\nSupplier = model.Concept(\"Supplier\", identify_by={\"id\": String})\nSupply = model.Concept(\"Supply\", identify_by={\"supplier\": Supplier, \"part\": Part})\n\nPart.contains = model.Property(f\"{Part:parent} contains {Part:child} {Integer:qty} times\")\nSupplier.depends_on = model.Relationship(f\"{Supplier:buyer} depends on {Supplier:upstream}\")\n\nbom = model.Table(\"RAI_QUICKSTART.SUPPLY_CHAIN.BOM\")\nmodel.define(Part.lookup(id=bom.PARENT_PART_ID).contains(Part.lookup(id=bom.CHILD_PART_ID), bom.QTY_PER))\n```\n\n- `identify_by` sets a concept's key. `Supply` is keyed by a supplier and a part, because the pair carries its own data: capacity, lead time and price.\n- `Property` holds one value per key: here, one quantity per (parent, child) pair. `Relationship` is a set of facts with no key, for many-to-many links that carry no value, like `depends_on`.\n- Same-type slots get role names (`parent`, `child`), so the direction is explicit.\n- `model.Table()` maps a Snowflake table; column names are upper case, as Snowflake stores them.\n\nThe full model used in this guide is shown below. It describes an electronics manufacturer from order to raw supplier: customers place orders for products, each product is built from a bill of materials of parts, and parts are delivered by suppliers who depend on other suppliers, up to tier 4. It contains seven concepts (the boxes), their attributes (the text in each box) and the links between them (the arrows).\n\nAn arrow means \"is linked to\", not \"is a PyRel `Relationship`\". Each arrow is one of three things in the code:\n\n- a **`Property` pointing to another concept**, when each entity has exactly one: an order *is placed by* one customer, *is for* one product. `contains` is also a Property, with one quantity per (parent, child) pair.\n- a **`Relationship`**, when the link is many-to-many and carries no value: `depends_on`.\n- a **key field of a link concept**. `Supply` and `QualifiedSupply` are entities keyed by a supplier and a part, so *of* and *by* point to the two ends of the key.\n\nSolid arrows come from the tables. Dashed arrows are facts that the rules in the next section derive. Yes/no facts are marked with `flag:` inside their concept.\n\n![Supply-chain model: orders link to customers and products; products contain parts; current and qualified supplies link parts to suppliers. Dashed links show derived dependencies.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-relational-ai-for-supply-chain-analysis/supply_chain_model.png?v=69fc288a)\n\nThe question flows through it like this: `S-T3-07` is down, so everyone who `relies_on` it is down. A `Part` whose every `Supply` has a down supplier `has_no_supplier`. An `Order` is at risk if its product's top part `includes` that part. The diagram was generated with generative AI from `supply_chain_ontology.py` and the notebook rules.\n\n\u003C!-- ------------------------ --\u003E\n## Find the Hidden Risk\n\n**The tier-1 view.** `P-141` has two suppliers, in different regions. That looks safe.\n\n**The SQL attempt.** A recursive CTE can walk up the supplier tiers and down the bill of materials. Because of the cycle, you have to carry the visited path in an array, skip suppliers already on it, and cap the depth. Without that guard, a recursive query over the supplier network can run forever. When we built this guide, one ran for over an hour, which is why `RAI_QS_WH` has a statement timeout. The notebook shows the guarded query. It gives the right answer, but `depth \u003C 10` is a guess, and each follow-up question needs the guard again.\n\n**The PyRel rules.**  \"Relies on, at some tier\" is two lines: a direct dependency, or a dependency of something you already rely on.\n\n```python\nSupplier.relies_on = model.Relationship(f\"{Supplier:buyer} relies on {Supplier:upstream} at some tier\")\ns1, s2, s3 = Supplier.ref(), Supplier.ref(), Supplier.ref()\nmodel.where(s1.depends_on(s2)).define(s1.relies_on(s2))\nmodel.where(s1.relies_on(s2), s2.depends_on(s3)).define(s1.relies_on(s3))\n```\n\nRelationalAI keeps deriving facts until nothing new appears. A known fact isn't derived again, so the cycle ends on its own. The notebook adds four more rules in the same style: `is_down`, `has_no_supplier`, `includes` (any level of the BOM) and `needs` (quantity per unit, multiplied along the path). A last rule, `QualifiedSupply.is_usable`, checks that the alternative supplier doesn't itself rely on the failed one.\n\nExpected result:\n\n| | |\n|---|---|\n| Suppliers down | `S-T3-07`, `S-T2-03`, `S-T2-08`, `S-T1-02`, `S-T1-05` |\n| Part without a working supplier | `P-141` |\n| Orders at risk | 568, worth 6,840,136.49, in all three regions |\n| Sensors needed | 29,286 |\n| Usable alternative | `S-T1-01`, first delivery 2027-01-04 |\n\n\u003C!-- ------------------------ --\u003E\n## Write the Plan with Cortex\n\nThe notebook builds a small **facts pack** from what PyRel derived: the dependency chain, the impact by region, the gap until the alternative delivers, and the shortfall. Python computes every number. The LLM gets the JSON and a prompt with three rules: use only the facts, copy numbers exactly, list unknowns as open questions.\n\n```python\nplan = sql(\"\"\"\nSELECT AI_COMPLETE(\n    model =\u003E ?,\n    prompt =\u003E ?,\n    model_parameters =\u003E {'temperature': 0, 'max_tokens': 1500}\n) AS plan\n\"\"\", params=[LLM, prompt]).plan[0]\n```\n\nThe prompt goes in as a bind parameter, so the facts never become part of the SQL text. `AI_COMPLETE` runs inside Snowflake.\n\n**Check the plan.** A final cell collects every number in the plan and looks it up in the facts pack. An LLM tends to derive numbers of its own, such as a shortfall from demand and stock. Even when the arithmetic is right, that number hasn't been checked. So the facts pack already contains the derived numbers the plan needs, and the check reports any number that isn't in it. It doesn't judge the advice itself; a planner still reads the plan.\n\nThe notebook writes `ORDERS_AT_RISK` and `MITIGATION_PLANS` back to the schema, so SQL, dashboards and tasks can use them.\n\n\u003C!-- ------------------------ --\u003E\n## Troubleshooting\n\n| Symptom | Cause | Fix |\n|---|---|---|\n| `No module named 'relationalai'` | Notebook runs on another kernel | Select the kernel **RAI supply chain**; if it's missing, rerun the `ipykernel install` line with the environment active |\n| `rai connect` or the first cell can't find a config | `raiconfig.yaml` not in the guide folder or `assets/notebooks/` | Move it there; PyRel searches the current folder and its parents |\n| Sign-in fails, or a role isn't allowed | Role in `raiconfig.yaml` isn't `RAI_DEVELOPER`, or your user lacks it | Set `role: RAI_DEVELOPER` and ask the administrator for the grant |\n| `Warehouse RAI_QS_WH` check fails | `setup.sql` not run, or a different warehouse in `raiconfig.yaml` | Run `setup.sql` and set `warehouse: RAI_QS_WH` |\n| `[Unknown Table] Failed to fetch schema for table` | Tables not loaded yet, or wrong name | Run notebook 01; use the fully qualified upper-case name |\n| `ModuleNotFoundError: supply_chain_ontology` | Notebook opened from a copy outside `assets/notebooks/` | Open the notebook in `assets/notebooks/`, next to `supply_chain_ontology.py` |\n| First query takes minutes | Engine start and table indexing, or a new table being synced | Wait; later queries are fast |\n| `AI_COMPLETE` fails with an unknown or unavailable model | Model not offered in your region | Pick another model for `LLM`, or enable cross-region inference |\n| A query runs until the 600-second timeout | Unguarded recursion on the supplier cycle | Use the guarded CTE, or the PyRel rules |\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nRun the following statements, also available in [tear_down.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/getting-started-with-relational-ai-for-supply-chain-analysis/assets/scripts/tear_down.sql):\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nDROP DATABASE IF EXISTS RAI_QUICKSTART;\nDROP WAREHOUSE IF EXISTS RAI_QS_WH;\n```\n\nThis leaves the RelationalAI app, its roles and the OAuth integration in place. To stop the app's compute, see [Shut down the app](https://docs.relational.ai/manage/app/shut-down).\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou found a supplier risk that a tier-1 view hides, using PyRel rules over Snowflake tables from a local notebook, and turned the result into a checked mitigation plan with Cortex.\n\n### What You Learned\n- After the RelationalAI setup, a warehouse, a schema and a `raiconfig.yaml` are all you need: no integrations or extra roles\n- `model.config.get_session()` gives you a Snowpark session on the same connection, for SQL, uploads and Cortex\n- An ontology maps tables to concepts once; rules and queries build on it\n- Recursive rules handle multi-hop questions and cycles without a hand-written guard\n- An LLM writes the plan best from a small facts pack, and a simple check keeps its numbers honest\n\n### Related Resources\n- [Start building with PyRel](https://docs.relational.ai/get-started/start-building-with-pyrel)\n- [Set up RelationalAI in Snowflake](https://docs.relational.ai/get-started/set-up-relationalai-in-snowflake)\n- [RelationalAI project templates](https://docs.relational.ai/build/templates)\n- [AI_COMPLETE](https://docs.snowflake.com/en/sql-reference/functions/ai_complete)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-04c9aa0ec8","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-eb2f403ff3",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-9c110908ce","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-23",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-69f7c31727","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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