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vector embeddings to TurboPuffer hybrid search, Shannon entropy distribution analysis, and installing reusable \u003Cstrong\u003ECortex Code (CoCo) skills\u003C/strong\u003E. Also includes direct Cortex Inference access with PAT/JWT auth, streaming SSE, and tool calling.\u003C/p\u003E\n","\u003Cp\u003EAll examples use the \u003Ccode\u003Ecortex_rest.py\u003C/code\u003E client included in this workspace.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EArchitecture\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode\u003EPython client (httpx)\n  ├── Auth: PAT  → Authorization: Bearer <token>\n  │               X-Snowflake-Authorization-Token-Type: PROGRAMMATIC_ACCESS_TOKEN\n  └── Auth: JWT  → Authorization: Bearer <signed_jwt>\n                   X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\n       ↓\nPOST https://<account>.snowflakecomputing.com/api/v2/cortex/inference:complete\n       ↓\n  Snowflake Cortex (claude-4-sonnet, llama3.3-70b, mistral-large2, ...)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EPrerequisites\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install httpx PyJWT cryptography rich\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with Cortex enabled.\u003C/li\u003E\u003Cli\u003EPAT (Programmatic Access Token) in \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E, or an RSA key pair registered on your user.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAll runnable code lives under \u003Ccode\u003Eassets/\u003C/code\u003E. Run scripts from that directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd assets\npython distribution_demo.py\npython test_cortex_rest.py\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 0 — Auth Check\u003C/h2\u003E\n","\u003Cp\u003EVerifies that the PAT loads correctly from \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E and shows the endpoint that will be called.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_rest import _load_pat\n\nhost, token = _load_pat()           # reads connections.myaccount.password\nprint(host)                          # e.g. myorg-myaccount.snowflakecomputing.com\nprint(token[:8] + "...")             # ...\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 1 — Simple Single-Turn Complete\u003C/h2\u003E\n","\u003Cp\u003ENon-streaming completion with token usage stats.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()     # auto PAT from config.toml\nresp = client.complete(\n    "claude-4-sonnet",\n    [{"role": "user", "content": "In one sentence: what is Snowflake Cortex?"}],\n    max_tokens=150,\n)\nprint(resp["choices"][0]["message"]["content"])\nprint(resp["usage"])   # {'prompt_tokens': 21, 'completion_tokens': 51, 'total_tokens': 72}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s1_simple_complete.svg?v=540fc932\" alt=\"Simple complete\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 2 — Multi-Turn Conversation\u003C/h2\u003E\n","\u003Cp\u003EPass the full conversation history; Cortex maintains context.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Emessages = [\n    {"role": "user",      "content": "My name is Ada. What's 12 × 12?"},\n    {"role": "assistant", "content": "12 × 12 = 144."},\n    {"role": "user",      "content": "What's my name and what was the answer?"},\n]\nresp = client.complete("claude-4-sonnet", messages, max_tokens=120)\n# → "Your name is Ada, and the answer to 12 × 12 was 144."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s2_multi_turn.svg?v=540fc932\" alt=\"Multi-turn\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 3 — Streaming SSE Response\u003C/h2\u003E\n","\u003Cp\u003EThe endpoint returns \u003Ca href=\"https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events\"\u003EServer-Sent Events\u003C/a\u003E. \u003Ccode\u003Ecomplete_stream()\u003C/code\u003E parses the \u003Ccode\u003Edata:\u003C/code\u003E lines and yields token chunks incrementally.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor chunk in client.complete_stream(\n    "claude-4-sonnet",\n    [{"role": "user", "content": "Count slowly from 1 to 5, one number per line."}],\n    max_tokens=80,\n):\n    delta = chunk["choices"][0]["delta"]\n    text  = delta.get("content") or delta.get("text", "")\n    print(text, end="", flush=True)\n\n# Raw SSE shape:\n# data: {"id":"...", "model":"claude-4-sonnet",\n#        "choices":[{"delta":{"type":"text","content":"1\\n","text":"1\\n"}}], "usage":{}}\n# data: {"id":"...", ..., "usage":{"prompt_tokens":15,"completion_tokens":20,"total_tokens":35}}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s3_streaming.svg?v=540fc932\" alt=\"Streaming\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 4 — Tool Calling (Function Calling)\u003C/h2\u003E\n","\u003Cp\u003ESnowflake uses \u003Ccode\u003Etool_spec\u003C/code\u003E format. Here the model is asked to draft a customer-service reply to a defective-product review — but first it must call \u003Ccode\u003Eget_product_details(product_id)\u003C/code\u003E to look up the product's metadata. This mirrors the \u003Ccode\u003Eproduct_id\u003C/code\u003E field in the \u003Ccode\u003Ecortex_ibis\u003C/code\u003E reviews dataset (P001–P005).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etools = [{\n    "tool_spec": {\n        "type": "generic",\n        "name": "get_product_details",\n        "description": "Look up product metadata (name, category, price) by product_id from the product catalog.",\n        "input_schema": {\n            "type": "object",\n            "properties": {\n                "product_id": {"type": "string", "description": "Product identifier, e.g. P001, P002."},\n            },\n            "required": ["product_id"],\n        },\n    }\n}]\n\n# Review from the turbopuffer_demo dataset — product P003, defective unit\nmessages = [{\n    "role": "user",\n    "content": (\n        "A customer left this review for product P003: "\n        "'Defective unit out of the box. USB port doesn't work at all.' "\n        "Before drafting a response, look up the product details for P003."\n    ),\n}]\n\nresp = client.complete("claude-4-sonnet", messages, tools=tools, max_tokens=250)\n\n# Model responds with a tool_use call:\n# [{"type": "tool_use", "tool_use": {"name": "get_product_details", "input": {"product_id": "P003"}}}]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s4_tool_calling.svg?v=540fc932\" alt=\"Tool calling\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 5 — Temperature & Sampling Parameters\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E accepts \u003Ccode\u003Etemperature\u003C/code\u003E, \u003Ccode\u003Etop_p\u003C/code\u003E, \u003Ccode\u003Etop_k\u003C/code\u003E. \u003Cstrong\u003ENote:\u003C/strong\u003E these parameters were removed for \u003Ccode\u003Eclaude-opus-4-7\u003C/code\u003E and newer (any non-default value returns 400).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Works for claude-4-sonnet:\nresp = client.complete(\n    "claude-4-sonnet",\n    [{"role": "user", "content": "ping"}],\n    max_tokens=20,\n    temperature=0.7,\n)\n\n# For Opus 4.7 — strip the param before sending:\n#   payload.pop("temperature", None)   # in pre_call_hook.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s5_temperature.svg?v=540fc932\" alt=\"Temperature\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESection 6 — Error Handling\u003C/h2\u003E\n","\u003Cp\u003EA bad model name returns \u003Ccode\u003EHTTP 400\u003C/code\u003E with a JSON error body. \u003Ccode\u003Ehttpx\u003C/code\u003E raises \u003Ccode\u003EHTTPStatusError\u003C/code\u003E; catch it to extract the message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport httpx\nfrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()\ntry:\n    client.complete("this-model-does-not-exist", [{"role":"user","content":"ping"}])\nexcept httpx.HTTPStatusError as exc:\n    print(exc.response.status_code)    # 400\n    print(exc.response.json())         # {"message": "unknown model \\"this-model-does-not-exist\\"", ...}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s6_error_handling.svg?v=540fc932\" alt=\"Error handling\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EJWT Key-Pair Auth\u003C/h2\u003E\n","\u003Cp\u003ETo use key-pair JWT instead of a PAT (e.g. in CI or service accounts):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# 1. Generate RSA key pair (once)\n#    openssl genrsa -out snowflake_rsa_key.p8 2048\n#    openssl rsa -in snowflake_rsa_key.p8 -pubout -out snowflake_rsa_key.pub\n#\n# 2. Register the public key on your Snowflake user:\n#    ALTER USER <user> SET RSA_PUBLIC_KEY='<contents of .pub>';\n#\n# 3. Use JWT auth:\nclient = CortexInferenceClient(\n    auth="jwt",\n    account="myorg-myaccount",\n    user="you@example.com",\n    private_key_path="~/.ssh/snowflake_rsa_key.p8",\n)\nresp = client.complete("claude-4-sonnet", [{"role": "user", "content": "ping"}])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E_build_jwt()\u003C/code\u003E function in \u003Ccode\u003Ecortex_rest.py\u003C/code\u003E signs a short-lived JWT (default 60 min) using PyJWT + RSA, computing the public key fingerprint as \u003Ccode\u003ESHA256:<base64>\u003C/code\u003E.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EModule-Level Convenience Functions\u003C/h2\u003E\n","\u003Cp\u003EFor quick scripts that don't need the full client:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_rest import complete, stream\n\n# One-shot\nprint(complete("What is 2+2?"))\n\n# Streaming (yields text chunks)\nfor chunk in stream("Explain vector search in two sentences."):\n    print(chunk, end="", flush=True)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EDistribution Analysis — Shannon Entropy\u003C/h2\u003E\n","\u003Cp\u003EShannon entropy measures how diverse a product's review categories are. A product with all defect reviews has low entropy (focused root cause). A product with mixed billing/delivery/defect/positive reviews has high entropy (needs broad support coverage).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESynthetic dataset entropy profiles:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EProduct\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EProfile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EH (bits)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENorm H\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP004\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll positive\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.00\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E90% product defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.45\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.23\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP007\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E90% billing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.80\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.40\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP005\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBimodal delivery+defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.97\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.49\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP008\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrimodal billing/delivery/defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.16\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.58\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP003\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBimodal billing+delivery\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.82\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.91\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP001\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUniform (all 4 categories)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.84\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.92\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP006\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESlight positive skew\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.88\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.94\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScreenshot:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s_entropy.svg?v=540fc932\" alt=\"Shannon entropy distribution analysis\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EFile Layout\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecortex-inference-ibis-integration-skills/\n├── cortex-inference-ibis-integration-skills.md   ← this guide\n└── assets/\n    ├── SKILL.md                  ← Cortex Code skill entry point (routing table)\n    ├── cortex_ibis.py            ← Ibis UDFs for all Cortex AI functions (SQL path)\n    ├── cortex_rest.py            ← Direct REST client — PAT/JWT, streaming, tool calling\n    ├── demo.py                   ← End-to-end Ibis enrichment walkthrough\n    ├── distribution_demo.py      ← Shannon entropy & category distribution analysis\n    ├── synthetic_data.py         ← 300-row synthetic review dataset (8 products, Dirichlet)\n    ├── turbopuffer_demo.py       ← Cortex + Ibis + TurboPuffer pipeline\n    ├── test_cortex_rest.py       ← 7-section validation suite + SVG screenshot capture\n    ├── __init__.py\n    ├── requirements.txt\n    ├── s1_simple_complete.svg\n    ├── s2_multi_turn.svg\n    ├── s3_streaming.svg\n    ├── s4_tool_calling.svg\n    ├── s5_temperature.svg\n    ├── s6_error_handling.svg\n    └── s_entropy.svg             ← Shannon entropy bar chart output\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ECortex Code Skill\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Eassets/SKILL.md\u003C/code\u003E + the reference sections in this file also ship as a \u003Cstrong\u003ECortex Code (CoCo) skill\u003C/strong\u003E — install it once and any CoCo session will auto-invoke it for Cortex + Ibis questions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Install the skill into Cortex Code\ncortex skill add /path/to/assets\n\n# Verify\ncortex skill list | grep cortex-ibis\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe skill routes to the right reference section based on intent:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAsk about\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERoutes to\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAI_*\u003C/code\u003E, \u003Ccode\u003ESNOWFLAKE.CORTEX.*\u003C/code\u003E, \u003Ccode\u003E.mutate()\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex + Ibis API Reference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EEnrichmentPipeline\u003C/code\u003E, fluent chain\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnrichmentPipeline Reference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEmbeddings, \u003Ccode\u003EEMBED_TEXT_768/1024\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEmbeddings Reference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic search, vector similarity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic Search Reference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EREST API, \u003Ccode\u003ECortexInferenceClient\u003C/code\u003E, PAT/JWT, streaming\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex REST API Reference\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETurboPuffer, ANN, BM25, hybrid search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETurboPuffer Integration Reference\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Chr\u003E\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\u003E401 Unauthorized\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPAT expired or wrong token\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegenerate PAT in Snowsight\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E400 — temperature is deprecated\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModel is Opus 4.7+\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERemove \u003Ccode\u003Etemperature\u003C/code\u003E/\u003Ccode\u003Etop_p\u003C/code\u003E/\u003Ccode\u003Etop_k\u003C/code\u003E from payload\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E400 — unknown model\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModel name typo or unavailable in region\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck \u003Ccode\u003ECURRENT_REGION()\u003C/code\u003E and use \u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETunnel connection failed: 403\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERunning inside sandboxed env\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse \u003Ccode\u003Edangerously_disable_sandbox=True\u003C/code\u003E or run outside\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EKEYPAIR_JWT\u003C/code\u003E 401\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWrong account/user in JWT issuer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMatch \u003Ccode\u003ECURRENT_ACCOUNT()\u003C/code\u003E / \u003Ccode\u003ECURRENT_USER()\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Chr\u003E\n","\u003Ch1\u003ECortex + Ibis API Reference\u003C/h1\u003E\n","\u003Cp\u003EAll functions in \u003Ccode\u003Ecortex_ibis.py\u003C/code\u003E. Use these before writing custom SQL.\u003C/p\u003E\n","\u003Ch2\u003EAI_* Functions (new unprefixed — preferred)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import (\n    ai_complete, ai_sentiment, ai_translate,\n    ai_classify, ai_extract, ai_filter, ai_redact,\n    ai_summarize_agg, ai_agg,          # aggregates\n)\n\n# Scalar\ntable.mutate(sentiment=ai_sentiment(table.body))\ntable.mutate(translated=ai_translate(table.body, "en", "es"))\ntable.mutate(reply=ai_complete("claude-4-sonnet", "Reply to: " + table.body))\ntable.filter(ai_filter("Is this a complaint? " + table.body))\n\n# Aggregate (use inside .agg())\ntable.group_by("product_id").agg(summary=ai_summarize_agg(table.body))\ntable.group_by("product_id").agg(\n    top_issue=ai_agg("What is the main complaint?", table.body)\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESNOWFLAKE.CORTEX.* Functions (classic namespaced)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import (\n    cortex_complete, cortex_summarize, cortex_sentiment,\n    cortex_translate, cortex_extract_answer,\n)\n\n# cortex_sentiment returns float in [-1, 1]\ntable.mutate(score=cortex_sentiment(table.body))\n\n# cortex_extract_answer returns VARIANT {answer, score}\nraw = cortex_extract_answer(table.body, "What product is reviewed?")\ntable.mutate(answer=variant_str(raw, "answer"), conf=variant_float(raw, "score"))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EVARIANT Helpers\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import variant_str, variant_float, variant_int, unpack_classify\n\n# Unpack AI_CLASSIFY → {label, score}\ncls = ai_classify(table.body, ["billing", "delivery", "product quality"])\ntable.mutate(\n    category=variant_str(cls, "label"),\n    score=variant_float(cls, "score"),\n)\n\n# Shorthand\nunpacked = unpack_classify(cls)   # {'label': StringColumn, 'score': FloatingColumn}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EHigh-Level Helpers\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import add_sentiment, add_summary, add_classification, add_extraction, add_embeddings\n\ntable = add_sentiment(table, "body")                                      # → float 'sentiment'\ntable = add_summary(table, "body")                                        # → str 'summary'\ntable = add_classification(table, "body", ["billing", "delivery"])        # → 'category', 'category_score'\ntable = add_extraction(table, "body", {"order_id": {"type": "string"}})   # → VARIANT 'extracted'\ntable = add_embeddings(table, "body", model="snowflake-arctic-embed-m-v1.5", dims=768)  # → VECTOR 'embedding'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESQL Preview (always do this before .execute())\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport ibis\nprint(ibis.to_sql(table_expr, dialect="snowflake"))\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch1\u003EEnrichmentPipeline Reference\u003C/h1\u003E\n","\u003Cp\u003EFluent builder for composing Cortex enrichment steps. Nothing runs until \u003Ccode\u003E.execute()\u003C/code\u003E or \u003Ccode\u003E.cache()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import EnrichmentPipeline\n\nresult = (\n    EnrichmentPipeline(con.table("CUSTOMER_REVIEWS"))\n    .filter_ai("Is this written in English? ", "body")       # drops non-English rows\n    .classify("body", ["billing", "delivery", "product quality", "support"])\n    .sentiment("body")                                         # float score column\n    .summarize("body")                                         # abstractive summary\n    .embed("body", model="snowflake-arctic-embed-m-v1.5", dims=768)\n    .translate("body", "en", "es", out="body_es")\n    .complete("body", "Write a brief customer-service reply to: ", model="claude-4-sonnet")\n    .execute()                                                 # → pandas DataFrame\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EAvailable Chain Methods\u003C/h2\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMethod\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOutput column(s)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.classify(col, categories)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecategory\u003C/code\u003E, \u003Ccode\u003Ecategory_score\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_CLASSIFY + VARIANT unpack\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.sentiment(col)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esentiment\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE.CORTEX.SENTIMENT float\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.summarize(col)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esummary\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE.CORTEX.SUMMARIZE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.embed(col, model, dims)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eembedding\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEMBED_TEXT_768 or EMBED_TEXT_1024\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.translate(col, src, tgt)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etranslated\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESNOWFLAKE.CORTEX.TRANSLATE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.complete(col, prefix, model)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecompletion\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_COMPLETE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.filter_ai(condition, col)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E— (filters rows)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_FILTER\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EMaterialise to Snowflake Table\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Returns an Ibis table expression pointing at the new table\nenriched = (\n    EnrichmentPipeline(reviews)\n    .classify("body", ["billing", "support"])\n    .sentiment("body")\n).cache(con, "REVIEWS_ENRICHED", overwrite=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EInspect SQL Without Running\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epipeline = EnrichmentPipeline(reviews).sentiment("body").summarize("body")\nprint(pipeline.sql())    # compiled Snowflake SQL\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPattern: Filter First, Enrich Only Relevant Rows\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Cheap vector pre-filter → expensive LLM only on matched subset\nfrom cortex_ibis import embed_768, vector_cosine_similarity\nimport ibis\n\nquery_vec = embed_768("snowflake-arctic-embed-m-v1.5", ibis.literal("refund request"))\nrelevant = (\n    embed_tbl\n    .mutate(sim=vector_cosine_similarity(embed_tbl.embedding, query_vec))\n    .filter(ibis._.sim > 0.75)\n)\n# Now enrich only ~relevant rows (much cheaper than enriching everything)\nresult = EnrichmentPipeline(relevant).classify("body", ["billing"]).execute()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch1\u003EEmbeddings Reference\u003C/h1\u003E\n","\u003Cp\u003ETwo embedding functions available as Ibis built-in UDFs.\u003C/p\u003E\n","\u003Ch2\u003EFunctions\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import embed_768, embed_1024\n\n# Returns VECTOR(FLOAT, 768) — annotated as Array(float32) for Ibis compatibility\nvec_col = embed_768("snowflake-arctic-embed-m-v1.5", table.body)\n\n# Returns VECTOR(FLOAT, 1024)\nvec_col = embed_1024("snowflake-arctic-embed-l-v2", table.body)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003Eadd_embeddings Helper\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import add_embeddings\n\ntable = add_embeddings(\n    table, "body",\n    model="snowflake-arctic-embed-m-v1.5",\n    dims=768,\n    out="embedding",\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003Ecache_embeddings — Pre-compute Once, Query Many Times\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import cache_embeddings\n\nembed_tbl = cache_embeddings(\n    con,\n    source_table="CUSTOMER_REVIEWS",\n    text_col="body",\n    dest_table="CUSTOMER_REVIEWS_EMBEDDINGS",\n    id_cols=["id", "product_id"],\n    model="snowflake-arctic-embed-m-v1.5",\n    dims=768,\n    overwrite=True,\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EOn-the-fly Query Embedding (runs inside Snowflake)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport ibis\nfrom cortex_ibis import embed_768\n\nquery_vec = embed_768("snowflake-arctic-embed-m-v1.5", ibis.literal("your query text"))\n# Compiled to: SNOWFLAKE.CORTEX.EMBED_TEXT_768('model', 'your query text')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EVector Similarity Functions\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import vector_cosine_similarity, vector_l2_distance, vector_inner_product\n\n# Cosine: higher = more similar (range [-1, 1])\nsim = vector_cosine_similarity(table.embedding, query_vec)\n\n# L2: lower = more similar\ndist = vector_l2_distance(table.embedding, query_vec)\n\n# Inner product: for normalised vectors ≡ cosine\ndot = vector_inner_product(table.embedding, query_vec)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EIbis Type Note\u003C/h2\u003E\n","\u003Cp\u003ESnowflake returns \u003Ccode\u003EVECTOR(FLOAT, N)\u003C/code\u003E which has no direct Ibis type. The functions annotate the return as \u003Ccode\u003EArray(float32)\u003C/code\u003E so Ibis accepts the expression — the emitted SQL is valid Snowflake.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch1\u003ESemantic Search Reference\u003C/h1\u003E\n","\u003Cp\u003EUses \u003Ccode\u003ESNOWFLAKE.CORTEX.EMBED_TEXT_768/1024\u003C/code\u003E + \u003Ccode\u003EVECTOR_COSINE_SIMILARITY / L2 / INNER_PRODUCT\u003C/code\u003E.\nQuery embedding is computed \u003Cstrong\u003Einside Snowflake\u003C/strong\u003E — no Python-side API call needed.\u003C/p\u003E\n","\u003Ch2\u003Esemantic_search() Helper\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import semantic_search\n\nresults = semantic_search(\n    embed_tbl,                                 # table with pre-computed 'embedding' column\n    text_col="body",\n    query="delayed shipment and missing item",\n    top_k=10,\n    metric="cosine",                           # "cosine" | "l2" | "inner_product"\n    model="snowflake-arctic-embed-m-v1.5",\n    dims=768,\n    id_cols=["id", "product_id", "body"],      # columns to include in result\n)\n# Returns: id | product_id | body | similarity, ordered by similarity DESC\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPre-compute and Cache Embeddings (recommended)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import cache_embeddings\n\nembed_tbl = cache_embeddings(\n    con,\n    source_table="CUSTOMER_REVIEWS",\n    text_col="body",\n    dest_table="CUSTOMER_REVIEWS_EMBEDDINGS",\n    id_cols=["id", "product_id"],\n    model="snowflake-arctic-embed-m-v1.5",\n    dims=768,\n    overwrite=True,\n)\n# Created: CUSTOMER_REVIEWS_EMBEDDINGS (id, product_id, body, embedding VECTOR(FLOAT,768))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EManual Similarity with Threshold\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import embed_768, vector_cosine_similarity\nimport ibis\n\nquery_vec = embed_768("snowflake-arctic-embed-m-v1.5", ibis.literal("broken product"))\n\nresults = (\n    embed_tbl\n    .mutate(sim=vector_cosine_similarity(embed_tbl.embedding, query_vec))\n    .filter(ibis._.sim > 0.7)                   # threshold\n    .select("id", "product_id", "body", "sim")\n    .order_by(ibis.desc("sim"))\n    .limit(20)\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESupported Models\u003C/h2\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDims\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUse for\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake-arctic-embed-m-v1.5\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E768\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeneral semantic search (default)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake-arctic-embed-l-v2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigher accuracy, slower\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ee5-base-v2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E768\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAlternative general-purpose\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Env-embed-qa-4\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQ&A / retrieval tasks\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Chr\u003E\n","\u003Ch1\u003ECortex REST API Reference\u003C/h1\u003E\n","\u003Cp\u003EDirect HTTP client in \u003Ccode\u003Ecortex_rest.py\u003C/code\u003E. Use when you need streaming, tool calling, or want to bypass the SQL connector.\u003C/p\u003E\n","\u003Ch2\u003EPAT Auth (default — auto-loaded from config.toml)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()   # reads ~/.snowflake/config.toml → connections.myaccount.password\n\n# Headers sent:\n# Authorization: Bearer <token>\n# X-Snowflake-Authorization-Token-Type: PROGRAMMATIC_ACCESS_TOKEN\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EJWT Key-Pair Auth\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclient = CortexInferenceClient(\n    auth="jwt",\n    account="myorg-myaccount",\n    user="you@example.com",\n    private_key_path="~/.ssh/snowflake_rsa_key.p8",\n)\n# Headers sent:\n# Authorization: Bearer <signed_jwt>\n# X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESimple Complete\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eresp = client.complete(\n    "claude-4-sonnet",\n    [{"role": "user", "content": "Summarise this review in one line."}],\n    max_tokens=100,\n)\ntext = resp["choices"][0]["message"]["content"]\nusage = resp["usage"]   # {prompt_tokens, completion_tokens, total_tokens}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EStreaming SSE\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor event in client.complete_stream("claude-4-sonnet", messages, max_tokens=500):\n    delta = event["choices"][0]["delta"]\n    chunk = delta.get("content") or delta.get("text", "")\n    print(chunk, end="", flush=True)\n# Last event has: event["usage"]["total_tokens"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETool Calling (Snowflake tool_spec format)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etools = [{\n    "tool_spec": {\n        "type": "generic",\n        "name": "get_product_details",\n        "description": "Look up product metadata by product_id.",\n        "input_schema": {\n            "type": "object",\n            "properties": {"product_id": {"type": "string"}},\n            "required": ["product_id"],\n        },\n    }\n}]\nresp = client.complete("claude-4-sonnet", messages, tools=tools, max_tokens=250)\n# Tool call in response:\ncontent_list = resp["choices"][0]["message"]["content_list"]\ntool_calls = [c for c in content_list if c.get("type") == "tool_use"]\n# → [{"type": "tool_use", "tool_use": {"name": "get_product_details", "input": {"product_id": "P003"}}}]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESampling Parameters — Important\u003C/h2\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003Etemperature / top_p / top_k\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E, \u003Ccode\u003Ellama3.3-70b\u003C/code\u003E, etc.\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccepted\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eclaude-opus-4-7\u003C/code\u003E and newer Opus\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERemoved\u003C/strong\u003E — returns 400 on any non-default value\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EStrip before sending for Opus 4.7+:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor k in ("temperature", "top_p", "top_k"):\n    payload.pop(k, None)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EError Handling\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport httpx\ntry:\n    resp = client.complete("bad-model", messages)\nexcept httpx.HTTPStatusError as exc:\n    print(exc.response.status_code)   # 400\n    print(exc.response.json())        # {"message": "unknown model \\"bad-model\\""}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EModule-Level Shortcuts\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_rest import complete, stream\n\n# One-shot (returns string)\nprint(complete("What is 2+2?"))\n\n# Streaming (yields chunks)\nfor chunk in stream("Explain vector search in two sentences."):\n    print(chunk, end="", flush=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ELiteLLM Integration Note\u003C/h2\u003E\n","\u003Cp\u003EWhen routing through LiteLLM proxy, prefix the token with \u003Ccode\u003Epat/\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eapi_key: os.environ/SNOWFLAKE_PAT   # .env: SNOWFLAKE_PAT=pat/<raw_token>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor direct \u003Ccode\u003ECortexInferenceClient\u003C/code\u003E, use the raw token (no prefix).\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch1\u003EShannon Entropy & Distribution Analysis Reference\u003C/h1\u003E\n","\u003Cp\u003EFunctions in \u003Ccode\u003Ecortex_ibis.py\u003C/code\u003E section 10. Use to measure category diversity per group.\u003C/p\u003E\n","\u003Ch2\u003EIntuition\u003C/h2\u003E\n","\u003Cp\u003EShannon entropy quantifies how unpredictable a distribution is:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EH (bits)\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMeaning for 4-category reviews\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPerfectly uniform — equal spread across billing/delivery/defect/positive\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.0–1.9\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMixed — 2–3 categories dominant\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.3–1.0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConcentrated — one category dominates (~70–90%)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle category — 100% of reviews in one bucket\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EProduct insight\u003C/strong\u003E: high-entropy products need broad support coverage; low-entropy products have a focused root cause.\u003C/p\u003E\n","\u003Ch2\u003Ecategory_entropy() — pure SQL via Ibis\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import category_entropy\n\n# Input: any Ibis table with a group col and a category col\n# (e.g. output of add_classification())\nclassified = add_classification(reviews_tbl, "body",\n                                ["billing issue", "delivery problem",\n                                 "product defect", "positive feedback"])\n\nentropy_tbl = category_entropy(\n    classified,\n    group_cols=["product_id"],\n    category_col="category",\n)\n# → product_id | entropy | dominant_category | dominant_share\n# Ordered by entropy ASC (lowest = most concentrated)\n\n# Preview SQL before running\nprint(ibis.to_sql(entropy_tbl, dialect="snowflake"))\n\n# Execute\ndf = entropy_tbl.execute()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003Enormalized_entropy() — [0, 1] scale\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import normalized_entropy\n\nnorm_tbl = normalized_entropy(\n    classified,\n    group_cols=["product_id"],\n    category_col="category",\n    num_categories=4,       # must match the actual number of distinct labels\n)\n# Adds 'normalized_entropy' column: 0.0 = single class, 1.0 = perfectly uniform\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003Eentropy_from_pandas() — scipy path\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom cortex_ibis import entropy_from_pandas\n\n# From raw rows\nresult = entropy_from_pandas(df, group_col="product_id", category_col="true_category")\n\n# From pre-aggregated counts\ncounts_df = df.groupby(["product_id", "category"]).size().reset_index(name="n")\nresult = entropy_from_pandas(counts_df, "product_id", "category", count_col="n")\n\n# Returns: product_id | entropy | normalized_entropy | dominant_category | dominant_share\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESynthetic Dataset\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Esynthetic_data.py\u003C/code\u003E generates 300 reviews across 8 products with controlled Dirichlet distributions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom synthetic_data import make_reviews, distribution_summary\n\ndf = make_reviews(seed=42)                # 300 rows: id, product_id, body, true_category\nsummary = distribution_summary(df)       # pivot with per-product counts + true_entropy_bits\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EProduct\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EProfile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETrue H\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP001\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUniform\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~2.00 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP002\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E90% product defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.47 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP003\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBimodal billing+delivery\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~1.0 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP004\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll positive\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.0 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP005\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBimodal delivery+defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~1.0 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP006\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESlight positive skew\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~1.9 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP007\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E90% billing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.47 bits\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EP008\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrimodal billing/delivery/defect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~1.58 bits\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EMiller–Madow Bias Note\u003C/h2\u003E\n","\u003Cp\u003EThe plug-in (MLE) entropy estimator is \u003Cstrong\u003Enegatively biased in expectation\u003C/strong\u003E — it systematically underestimates true entropy for finite samples. The leading-order bias is given by the Miller–Madow correction:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBias ≈ (K − 1) / (2N · ln 2)  bits\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Ewhere K is the number of categories and N is the sample size.\u003C/p\u003E\n","\u003Cp\u003EFor \u003Cstrong\u003EK = 4 categories\u003C/strong\u003E:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003En\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExpected bias (bits)\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.22\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E30\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.07\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E40\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.05\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E300\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~0.007\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe bias shrinks as 1/N and is negligible (< 0.01 bits) at n ≥ 300.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant caveat:\u003C/strong\u003E the formula gives the \u003Cem\u003Eexpected\u003C/em\u003E bias. Individual realizations — especially with skewed or Dirichlet-sampled distributions at small n — can deviate substantially in either direction due to sampling variance. A product with n=40 reviews may show sample H above or below the true H depending on which reviews happened to be drawn.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReferences:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMiller, G. A. (1955). Note on the bias of information estimates. \u003Cem\u003EInformation Theory in Psychology: Problems and Methods\u003C/em\u003E, 95–100. \u003Cem\u003E(Original derivation of the correction.)\u003C/em\u003E\u003C/li\u003E\u003Cli\u003EPaninski, L. (2003). Estimation of entropy and mutual information. \u003Cem\u003ENeural Computation\u003C/em\u003E, 15(6), 1191–1253. \u003Cem\u003E(Proves no unbiased estimator exists; characterizes bias analytically.)\u003C/em\u003E\u003C/li\u003E\u003Cli\u003EDe Gregorio et al. (2024). Entropy estimators for Markovian sequences: A comparative analysis. \u003Cem\u003EarXiv:2310.07547.\u003C/em\u003E \u003Cem\u003E(Quantitative bias/variance comparison across estimators and sample sizes.)\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EUse \u003Ccode\u003Edistribution_summary(df)\u003C/code\u003E to compare true vs sample entropy on the synthetic dataset.\u003C/p\u003E\n","\u003Ch2\u003EFull Demo\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epython distribution_demo.py\n# Prints rich entropy table + ASCII bar chart + exports assets/s_entropy.svg\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Ch1\u003ETurboPuffer Integration Reference\u003C/h1\u003E\n","\u003Cp\u003EPipeline: pandas → Ibis memtable → Cortex AI enrichment → Cortex embeddings → TurboPuffer index → ANN/hybrid/filtered search.\u003C/p\u003E\n","\u003Cp\u003ESee full example in \u003Ccode\u003Eturbopuffer_demo.py\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install turbopuffer\nexport TURBOPUFFER_API_KEY=tpuf_A1...\nexport TURBOPUFFER_REGION=aws-us-east-1    # match your Snowflake region\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom turbopuffer import Turbopuffer\ntpuf = Turbopuffer(api_key=os.environ["TURBOPUFFER_API_KEY"])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EStep 1 — Enrich with Cortex (via Ibis memtable)\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport ibis\nfrom cortex_ibis import ai_sentiment, ai_classify, variant_str, variant_float\n\ntbl = ibis.memtable(df)           # no CREATE TABLE privilege needed\nenriched = tbl.mutate(\n    sentiment_label=ai_sentiment(tbl.body),\n    sentiment_score=cortex_sentiment(tbl.body),\n    category=variant_str(ai_classify(tbl.body, CATEGORIES), "label"),\n).execute()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EStep 2 — Generate Embeddings\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Uses raw SQL via con.raw_sql() + CAST to VARCHAR for Python connector compatibility\nsql = f"""\n    SELECT id, CAST(SNOWFLAKE.CORTEX.EMBED_TEXT_768('{MODEL}', body) AS VARCHAR) AS vec_str\n    FROM (VALUES {rows_sql}) AS t(id, body)\n"""\nresult = con.raw_sql(sql)\nvec_map = {row[0]: json.loads(row[1]) for row in result.fetchall()}\ndf["vector"] = df["id"].map(vec_map)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EStep 3 — Index into TurboPuffer\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ens = tpuf.namespace("cortex-ibis-reviews")\nns.write(\n    upsert_rows=[{"id": ..., "vector": [...], "body": ..., "category": ..., "sentiment_label": ...}],\n    distance_metric="cosine_distance",\n    schema={"body": {"type": "string", "full_text_search": True}, "category": {"type": "string"}},\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESearch Patterns\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# ANN (vector-only)\nns.query(rank_by=("vector", "ANN", query_vec), limit=5,\n         include_attributes=["body", "category", "sentiment_label"])\n\n# Filtered ANN\nns.query(rank_by=("vector", "ANN", query_vec),\n         filters=("sentiment_label", "Eq", "negative"), limit=5)\n\n# Hybrid (70% vector + 30% BM25)\nns.query(rank_by=("Sum", [\n    ("Product", 0.7, ("vector", "ANN", query_vec)),\n    ("Product", 0.3, ("body", "BM25", query_text)),\n]), limit=5)\n\n# Pure BM25 full-text\nns.query(rank_by=("body", "BM25", "refund missing package"), limit=5)\n\n# Aggregations\nns.query(aggregate_by={"count": ("Count",)}, group_by=["category"])\n\n# Namespace branching (copy-on-write, O(1))\nbranch = tpuf.namespace("cortex-ibis-reviews-branch")\nbranch.write(branch_from_namespace="cortex-ibis-reviews")\n\u003C/code\u003E\u003C/pre\u003E"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"A hands-on guide to **Snowflake Cortex AI functions via Ibis**, covering the full pipeline from LLM enrichment and vector embeddings to TurboPuffer hybrid search, Shannon entropy distribution analysis, and installing reusable **Cortex Code (CoCo) skills**. Also includes direct Cortex Inference access with PAT/JWT auth, streaming SSE, and tool calling.\n\nAll examples use the `cortex_rest.py` client included in this workspace.\n\n---\n\n## Architecture\n\n```\nPython client (httpx)\n  ├── Auth: PAT  → Authorization: Bearer \u003Ctoken\u003E\n  │               X-Snowflake-Authorization-Token-Type: PROGRAMMATIC_ACCESS_TOKEN\n  └── Auth: JWT  → Authorization: Bearer \u003Csigned_jwt\u003E\n                   X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\n       ↓\nPOST https://\u003Caccount\u003E.snowflakecomputing.com/api/v2/cortex/inference:complete\n       ↓\n  Snowflake Cortex (claude-4-sonnet, llama3.3-70b, mistral-large2, ...)\n```\n\n---\n\n## Prerequisites\n\n```bash\npip install httpx PyJWT cryptography rich\n```\n\n- Snowflake account with Cortex enabled.\n- PAT (Programmatic Access Token) in `~/.snowflake/config.toml`, or an RSA key pair registered on your user.\n\nAll runnable code lives under `assets/`. Run scripts from that directory:\n\n```bash\ncd assets\npython distribution_demo.py\npython test_cortex_rest.py\n```\n\n---\n\n## Section 0 — Auth Check\n\nVerifies that the PAT loads correctly from `~/.snowflake/config.toml` and shows the endpoint that will be called.\n\n```python\nfrom cortex_rest import _load_pat\n\nhost, token = _load_pat()           # reads connections.myaccount.password\nprint(host)                          # e.g. myorg-myaccount.snowflakecomputing.com\nprint(token[:8] + \"...\")             # ...\n```\n\n---\n\n## Section 1 — Simple Single-Turn Complete\n\nNon-streaming completion with token usage stats.\n\n```python\nfrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()     # auto PAT from config.toml\nresp = client.complete(\n    \"claude-4-sonnet\",\n    [{\"role\": \"user\", \"content\": \"In one sentence: what is Snowflake Cortex?\"}],\n    max_tokens=150,\n)\nprint(resp[\"choices\"][0][\"message\"][\"content\"])\nprint(resp[\"usage\"])   # {'prompt_tokens': 21, 'completion_tokens': 51, 'total_tokens': 72}\n```\n\n**Screenshot:**\n\n![Simple complete](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s1_simple_complete.svg?v=540fc932)\n\n---\n\n## Section 2 — Multi-Turn Conversation\n\nPass the full conversation history; Cortex maintains context.\n\n```python\nmessages = [\n    {\"role\": \"user\",      \"content\": \"My name is Ada. What's 12 × 12?\"},\n    {\"role\": \"assistant\", \"content\": \"12 × 12 = 144.\"},\n    {\"role\": \"user\",      \"content\": \"What's my name and what was the answer?\"},\n]\nresp = client.complete(\"claude-4-sonnet\", messages, max_tokens=120)\n# → \"Your name is Ada, and the answer to 12 × 12 was 144.\"\n```\n\n**Screenshot:**\n\n![Multi-turn](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s2_multi_turn.svg?v=540fc932)\n\n---\n\n## Section 3 — Streaming SSE Response\n\nThe endpoint returns [Server-Sent Events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events). `complete_stream()` parses the `data:` lines and yields token chunks incrementally.\n\n```python\nfor chunk in client.complete_stream(\n    \"claude-4-sonnet\",\n    [{\"role\": \"user\", \"content\": \"Count slowly from 1 to 5, one number per line.\"}],\n    max_tokens=80,\n):\n    delta = chunk[\"choices\"][0][\"delta\"]\n    text  = delta.get(\"content\") or delta.get(\"text\", \"\")\n    print(text, end=\"\", flush=True)\n\n# Raw SSE shape:\n# data: {\"id\":\"...\", \"model\":\"claude-4-sonnet\",\n#        \"choices\":[{\"delta\":{\"type\":\"text\",\"content\":\"1\\n\",\"text\":\"1\\n\"}}], \"usage\":{}}\n# data: {\"id\":\"...\", ..., \"usage\":{\"prompt_tokens\":15,\"completion_tokens\":20,\"total_tokens\":35}}\n```\n\n**Screenshot:**\n\n![Streaming](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s3_streaming.svg?v=540fc932)\n\n---\n\n## Section 4 — Tool Calling (Function Calling)\n\nSnowflake uses `tool_spec` format. Here the model is asked to draft a customer-service reply to a defective-product review — but first it must call `get_product_details(product_id)` to look up the product's metadata. This mirrors the `product_id` field in the `cortex_ibis` reviews dataset (P001–P005).\n\n```python\ntools = [{\n    \"tool_spec\": {\n        \"type\": \"generic\",\n        \"name\": \"get_product_details\",\n        \"description\": \"Look up product metadata (name, category, price) by product_id from the product catalog.\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"product_id\": {\"type\": \"string\", \"description\": \"Product identifier, e.g. P001, P002.\"},\n            },\n            \"required\": [\"product_id\"],\n        },\n    }\n}]\n\n# Review from the turbopuffer_demo dataset — product P003, defective unit\nmessages = [{\n    \"role\": \"user\",\n    \"content\": (\n        \"A customer left this review for product P003: \"\n        \"'Defective unit out of the box. USB port doesn't work at all.' \"\n        \"Before drafting a response, look up the product details for P003.\"\n    ),\n}]\n\nresp = client.complete(\"claude-4-sonnet\", messages, tools=tools, max_tokens=250)\n\n# Model responds with a tool_use call:\n# [{\"type\": \"tool_use\", \"tool_use\": {\"name\": \"get_product_details\", \"input\": {\"product_id\": \"P003\"}}}]\n```\n\n**Screenshot:**\n\n![Tool calling](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s4_tool_calling.svg?v=540fc932)\n\n---\n\n## Section 5 — Temperature & Sampling Parameters\n\n`claude-4-sonnet` accepts `temperature`, `top_p`, `top_k`. **Note:** these parameters were removed for `claude-opus-4-7` and newer (any non-default value returns 400).\n\n```python\n# Works for claude-4-sonnet:\nresp = client.complete(\n    \"claude-4-sonnet\",\n    [{\"role\": \"user\", \"content\": \"ping\"}],\n    max_tokens=20,\n    temperature=0.7,\n)\n\n# For Opus 4.7 — strip the param before sending:\n#   payload.pop(\"temperature\", None)   # in pre_call_hook.py\n```\n\n**Screenshot:**\n\n![Temperature](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s5_temperature.svg?v=540fc932)\n\n---\n\n## Section 6 — Error Handling\n\nA bad model name returns `HTTP 400` with a JSON error body. `httpx` raises `HTTPStatusError`; catch it to extract the message.\n\n```python\nimport httpx\nfrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()\ntry:\n    client.complete(\"this-model-does-not-exist\", [{\"role\":\"user\",\"content\":\"ping\"}])\nexcept httpx.HTTPStatusError as exc:\n    print(exc.response.status_code)    # 400\n    print(exc.response.json())         # {\"message\": \"unknown model \\\"this-model-does-not-exist\\\"\", ...}\n```\n\n**Screenshot:**\n\n![Error handling](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s6_error_handling.svg?v=540fc932)\n\n---\n\n## JWT Key-Pair Auth\n\nTo use key-pair JWT instead of a PAT (e.g. in CI or service accounts):\n\n```python\n# 1. Generate RSA key pair (once)\n#    openssl genrsa -out snowflake_rsa_key.p8 2048\n#    openssl rsa -in snowflake_rsa_key.p8 -pubout -out snowflake_rsa_key.pub\n#\n# 2. Register the public key on your Snowflake user:\n#    ALTER USER \u003Cuser\u003E SET RSA_PUBLIC_KEY='\u003Ccontents of .pub\u003E';\n#\n# 3. Use JWT auth:\nclient = CortexInferenceClient(\n    auth=\"jwt\",\n    account=\"myorg-myaccount\",\n    user=\"you@example.com\",\n    private_key_path=\"~/.ssh/snowflake_rsa_key.p8\",\n)\nresp = client.complete(\"claude-4-sonnet\", [{\"role\": \"user\", \"content\": \"ping\"}])\n```\n\nThe `_build_jwt()` function in `cortex_rest.py` signs a short-lived JWT (default 60 min) using PyJWT + RSA, computing the public key fingerprint as `SHA256:\u003Cbase64\u003E`.\n\n---\n\n## Module-Level Convenience Functions\n\nFor quick scripts that don't need the full client:\n\n```python\nfrom cortex_rest import complete, stream\n\n# One-shot\nprint(complete(\"What is 2+2?\"))\n\n# Streaming (yields text chunks)\nfor chunk in stream(\"Explain vector search in two sentences.\"):\n    print(chunk, end=\"\", flush=True)\n```\n\n---\n\n## Distribution Analysis — Shannon Entropy\n\nShannon entropy measures how diverse a product's review categories are. A product with all defect reviews has low entropy (focused root cause). A product with mixed billing/delivery/defect/positive reviews has high entropy (needs broad support coverage).\n\n\n\n**Synthetic dataset entropy profiles:**\n\n| Product | Profile | H (bits) | Norm H |\n|---|---|---|---|\n| P004 | All positive | 0.00 | 0.00 |\n| P002 | 90% product defect | 0.45 | 0.23 |\n| P007 | 90% billing | 0.80 | 0.40 |\n| P005 | Bimodal delivery+defect | 0.97 | 0.49 |\n| P008 | Trimodal billing/delivery/defect | 1.16 | 0.58 |\n| P003 | Bimodal billing+delivery | 1.82 | 0.91 |\n| P001 | Uniform (all 4 categories) | 1.84 | 0.92 |\n| P006 | Slight positive skew | 1.88 | 0.94 |\n\n**Screenshot:**\n\n![Shannon entropy distribution analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-inference-ibis-integration-skills/s_entropy.svg?v=540fc932)\n\n---\n\n## File Layout\n\n```\ncortex-inference-ibis-integration-skills/\n├── cortex-inference-ibis-integration-skills.md   ← this guide\n└── assets/\n    ├── SKILL.md                  ← Cortex Code skill entry point (routing table)\n    ├── cortex_ibis.py            ← Ibis UDFs for all Cortex AI functions (SQL path)\n    ├── cortex_rest.py            ← Direct REST client — PAT/JWT, streaming, tool calling\n    ├── demo.py                   ← End-to-end Ibis enrichment walkthrough\n    ├── distribution_demo.py      ← Shannon entropy & category distribution analysis\n    ├── synthetic_data.py         ← 300-row synthetic review dataset (8 products, Dirichlet)\n    ├── turbopuffer_demo.py       ← Cortex + Ibis + TurboPuffer pipeline\n    ├── test_cortex_rest.py       ← 7-section validation suite + SVG screenshot capture\n    ├── __init__.py\n    ├── requirements.txt\n    ├── s1_simple_complete.svg\n    ├── s2_multi_turn.svg\n    ├── s3_streaming.svg\n    ├── s4_tool_calling.svg\n    ├── s5_temperature.svg\n    ├── s6_error_handling.svg\n    └── s_entropy.svg             ← Shannon entropy bar chart output\n```\n\n---\n\n## Cortex Code Skill\n\nThe `assets/SKILL.md` + the reference sections in this file also ship as a **Cortex Code (CoCo) skill** — install it once and any CoCo session will auto-invoke it for Cortex + Ibis questions.\n\n```bash\n# Install the skill into Cortex Code\ncortex skill add /path/to/assets\n\n# Verify\ncortex skill list | grep cortex-ibis\n```\n\nThe skill routes to the right reference section based on intent:\n\n| Ask about | Routes to |\n|---|---|\n| `AI_*`, `SNOWFLAKE.CORTEX.*`, `.mutate()` | Cortex + Ibis API Reference |\n| `EnrichmentPipeline`, fluent chain | EnrichmentPipeline Reference |\n| Embeddings, `EMBED_TEXT_768/1024` | Embeddings Reference |\n| Semantic search, vector similarity | Semantic Search Reference |\n| REST API, `CortexInferenceClient`, PAT/JWT, streaming | Cortex REST API Reference |\n| TurboPuffer, ANN, BM25, hybrid search | TurboPuffer Integration Reference |\n\n---\n\n## Troubleshooting\n\n| Symptom | Cause | Fix |\n|---|---|---|\n| `401 Unauthorized` | PAT expired or wrong token | Regenerate PAT in Snowsight |\n| `400 — temperature is deprecated` | Model is Opus 4.7+ | Remove `temperature`/`top_p`/`top_k` from payload |\n| `400 — unknown model` | Model name typo or unavailable in region | Check `CURRENT_REGION()` and use `claude-4-sonnet` |\n| `Tunnel connection failed: 403` | Running inside sandboxed env | Use `dangerously_disable_sandbox=True` or run outside |\n| `KEYPAIR_JWT` 401 | Wrong account/user in JWT issuer | Match `CURRENT_ACCOUNT()` / `CURRENT_USER()` |\n\n---\n\n# Cortex + Ibis API Reference\n\nAll functions in `cortex_ibis.py`. Use these before writing custom SQL.\n\n## AI_* Functions (new unprefixed — preferred)\n\n```python\nfrom cortex_ibis import (\n    ai_complete, ai_sentiment, ai_translate,\n    ai_classify, ai_extract, ai_filter, ai_redact,\n    ai_summarize_agg, ai_agg,          # aggregates\n)\n\n# Scalar\ntable.mutate(sentiment=ai_sentiment(table.body))\ntable.mutate(translated=ai_translate(table.body, \"en\", \"es\"))\ntable.mutate(reply=ai_complete(\"claude-4-sonnet\", \"Reply to: \" + table.body))\ntable.filter(ai_filter(\"Is this a complaint? \" + table.body))\n\n# Aggregate (use inside .agg())\ntable.group_by(\"product_id\").agg(summary=ai_summarize_agg(table.body))\ntable.group_by(\"product_id\").agg(\n    top_issue=ai_agg(\"What is the main complaint?\", table.body)\n)\n```\n\n## SNOWFLAKE.CORTEX.* Functions (classic namespaced)\n\n```python\nfrom cortex_ibis import (\n    cortex_complete, cortex_summarize, cortex_sentiment,\n    cortex_translate, cortex_extract_answer,\n)\n\n# cortex_sentiment returns float in [-1, 1]\ntable.mutate(score=cortex_sentiment(table.body))\n\n# cortex_extract_answer returns VARIANT {answer, score}\nraw = cortex_extract_answer(table.body, \"What product is reviewed?\")\ntable.mutate(answer=variant_str(raw, \"answer\"), conf=variant_float(raw, \"score\"))\n```\n\n## VARIANT Helpers\n\n```python\nfrom cortex_ibis import variant_str, variant_float, variant_int, unpack_classify\n\n# Unpack AI_CLASSIFY → {label, score}\ncls = ai_classify(table.body, [\"billing\", \"delivery\", \"product quality\"])\ntable.mutate(\n    category=variant_str(cls, \"label\"),\n    score=variant_float(cls, \"score\"),\n)\n\n# Shorthand\nunpacked = unpack_classify(cls)   # {'label': StringColumn, 'score': FloatingColumn}\n```\n\n## High-Level Helpers\n\n```python\nfrom cortex_ibis import add_sentiment, add_summary, add_classification, add_extraction, add_embeddings\n\ntable = add_sentiment(table, \"body\")                                      # → float 'sentiment'\ntable = add_summary(table, \"body\")                                        # → str 'summary'\ntable = add_classification(table, \"body\", [\"billing\", \"delivery\"])        # → 'category', 'category_score'\ntable = add_extraction(table, \"body\", {\"order_id\": {\"type\": \"string\"}})   # → VARIANT 'extracted'\ntable = add_embeddings(table, \"body\", model=\"snowflake-arctic-embed-m-v1.5\", dims=768)  # → VECTOR 'embedding'\n```\n\n## SQL Preview (always do this before .execute())\n\n```python\nimport ibis\nprint(ibis.to_sql(table_expr, dialect=\"snowflake\"))\n```\n\n---\n\n# EnrichmentPipeline Reference\n\nFluent builder for composing Cortex enrichment steps. Nothing runs until `.execute()` or `.cache()`.\n\n```python\nfrom cortex_ibis import EnrichmentPipeline\n\nresult = (\n    EnrichmentPipeline(con.table(\"CUSTOMER_REVIEWS\"))\n    .filter_ai(\"Is this written in English? \", \"body\")       # drops non-English rows\n    .classify(\"body\", [\"billing\", \"delivery\", \"product quality\", \"support\"])\n    .sentiment(\"body\")                                         # float score column\n    .summarize(\"body\")                                         # abstractive summary\n    .embed(\"body\", model=\"snowflake-arctic-embed-m-v1.5\", dims=768)\n    .translate(\"body\", \"en\", \"es\", out=\"body_es\")\n    .complete(\"body\", \"Write a brief customer-service reply to: \", model=\"claude-4-sonnet\")\n    .execute()                                                 # → pandas DataFrame\n)\n```\n\n## Available Chain Methods\n\n| Method | Output column(s) | Notes |\n|---|---|---|\n| `.classify(col, categories)` | `category`, `category_score` | AI_CLASSIFY + VARIANT unpack |\n| `.sentiment(col)` | `sentiment` | SNOWFLAKE.CORTEX.SENTIMENT float |\n| `.summarize(col)` | `summary` | SNOWFLAKE.CORTEX.SUMMARIZE |\n| `.embed(col, model, dims)` | `embedding` | EMBED_TEXT_768 or EMBED_TEXT_1024 |\n| `.translate(col, src, tgt)` | `translated` | SNOWFLAKE.CORTEX.TRANSLATE |\n| `.complete(col, prefix, model)` | `completion` | AI_COMPLETE |\n| `.filter_ai(condition, col)` | — (filters rows) | AI_FILTER |\n\n## Materialise to Snowflake Table\n\n```python\n# Returns an Ibis table expression pointing at the new table\nenriched = (\n    EnrichmentPipeline(reviews)\n    .classify(\"body\", [\"billing\", \"support\"])\n    .sentiment(\"body\")\n).cache(con, \"REVIEWS_ENRICHED\", overwrite=True)\n```\n\n## Inspect SQL Without Running\n\n```python\npipeline = EnrichmentPipeline(reviews).sentiment(\"body\").summarize(\"body\")\nprint(pipeline.sql())    # compiled Snowflake SQL\n```\n\n## Pattern: Filter First, Enrich Only Relevant Rows\n\n```python\n# Cheap vector pre-filter → expensive LLM only on matched subset\nfrom cortex_ibis import embed_768, vector_cosine_similarity\nimport ibis\n\nquery_vec = embed_768(\"snowflake-arctic-embed-m-v1.5\", ibis.literal(\"refund request\"))\nrelevant = (\n    embed_tbl\n    .mutate(sim=vector_cosine_similarity(embed_tbl.embedding, query_vec))\n    .filter(ibis._.sim \u003E 0.75)\n)\n# Now enrich only ~relevant rows (much cheaper than enriching everything)\nresult = EnrichmentPipeline(relevant).classify(\"body\", [\"billing\"]).execute()\n```\n\n---\n\n# Embeddings Reference\n\nTwo embedding functions available as Ibis built-in UDFs.\n\n## Functions\n\n```python\nfrom cortex_ibis import embed_768, embed_1024\n\n# Returns VECTOR(FLOAT, 768) — annotated as Array(float32) for Ibis compatibility\nvec_col = embed_768(\"snowflake-arctic-embed-m-v1.5\", table.body)\n\n# Returns VECTOR(FLOAT, 1024)\nvec_col = embed_1024(\"snowflake-arctic-embed-l-v2\", table.body)\n```\n\n## add_embeddings Helper\n\n```python\nfrom cortex_ibis import add_embeddings\n\ntable = add_embeddings(\n    table, \"body\",\n    model=\"snowflake-arctic-embed-m-v1.5\",\n    dims=768,\n    out=\"embedding\",\n)\n```\n\n## cache_embeddings — Pre-compute Once, Query Many Times\n\n```python\nfrom cortex_ibis import cache_embeddings\n\nembed_tbl = cache_embeddings(\n    con,\n    source_table=\"CUSTOMER_REVIEWS\",\n    text_col=\"body\",\n    dest_table=\"CUSTOMER_REVIEWS_EMBEDDINGS\",\n    id_cols=[\"id\", \"product_id\"],\n    model=\"snowflake-arctic-embed-m-v1.5\",\n    dims=768,\n    overwrite=True,\n)\n```\n\n## On-the-fly Query Embedding (runs inside Snowflake)\n\n```python\nimport ibis\nfrom cortex_ibis import embed_768\n\nquery_vec = embed_768(\"snowflake-arctic-embed-m-v1.5\", ibis.literal(\"your query text\"))\n# Compiled to: SNOWFLAKE.CORTEX.EMBED_TEXT_768('model', 'your query text')\n```\n\n## Vector Similarity Functions\n\n```python\nfrom cortex_ibis import vector_cosine_similarity, vector_l2_distance, vector_inner_product\n\n# Cosine: higher = more similar (range [-1, 1])\nsim = vector_cosine_similarity(table.embedding, query_vec)\n\n# L2: lower = more similar\ndist = vector_l2_distance(table.embedding, query_vec)\n\n# Inner product: for normalised vectors ≡ cosine\ndot = vector_inner_product(table.embedding, query_vec)\n```\n\n## Ibis Type Note\n\nSnowflake returns `VECTOR(FLOAT, N)` which has no direct Ibis type. The functions annotate the return as `Array(float32)` so Ibis accepts the expression — the emitted SQL is valid Snowflake.\n\n---\n\n# Semantic Search Reference\n\nUses `SNOWFLAKE.CORTEX.EMBED_TEXT_768/1024` + `VECTOR_COSINE_SIMILARITY / L2 / INNER_PRODUCT`.\nQuery embedding is computed **inside Snowflake** — no Python-side API call needed.\n\n## semantic_search() Helper\n\n```python\nfrom cortex_ibis import semantic_search\n\nresults = semantic_search(\n    embed_tbl,                                 # table with pre-computed 'embedding' column\n    text_col=\"body\",\n    query=\"delayed shipment and missing item\",\n    top_k=10,\n    metric=\"cosine\",                           # \"cosine\" | \"l2\" | \"inner_product\"\n    model=\"snowflake-arctic-embed-m-v1.5\",\n    dims=768,\n    id_cols=[\"id\", \"product_id\", \"body\"],      # columns to include in result\n)\n# Returns: id | product_id | body | similarity, ordered by similarity DESC\n```\n\n## Pre-compute and Cache Embeddings (recommended)\n\n```python\nfrom cortex_ibis import cache_embeddings\n\nembed_tbl = cache_embeddings(\n    con,\n    source_table=\"CUSTOMER_REVIEWS\",\n    text_col=\"body\",\n    dest_table=\"CUSTOMER_REVIEWS_EMBEDDINGS\",\n    id_cols=[\"id\", \"product_id\"],\n    model=\"snowflake-arctic-embed-m-v1.5\",\n    dims=768,\n    overwrite=True,\n)\n# Created: CUSTOMER_REVIEWS_EMBEDDINGS (id, product_id, body, embedding VECTOR(FLOAT,768))\n```\n\n## Manual Similarity with Threshold\n\n```python\nfrom cortex_ibis import embed_768, vector_cosine_similarity\nimport ibis\n\nquery_vec = embed_768(\"snowflake-arctic-embed-m-v1.5\", ibis.literal(\"broken product\"))\n\nresults = (\n    embed_tbl\n    .mutate(sim=vector_cosine_similarity(embed_tbl.embedding, query_vec))\n    .filter(ibis._.sim \u003E 0.7)                   # threshold\n    .select(\"id\", \"product_id\", \"body\", \"sim\")\n    .order_by(ibis.desc(\"sim\"))\n    .limit(20)\n)\n```\n\n## Supported Models\n\n| Model | Dims | Use for |\n|---|---|---|\n| `snowflake-arctic-embed-m-v1.5` | 768 | General semantic search (default) |\n| `snowflake-arctic-embed-l-v2` | 1024 | Higher accuracy, slower |\n| `e5-base-v2` | 768 | Alternative general-purpose |\n| `nv-embed-qa-4` | 1024 | Q&A / retrieval tasks |\n\n---\n\n# Cortex REST API Reference\n\nDirect HTTP client in `cortex_rest.py`. Use when you need streaming, tool calling, or want to bypass the SQL connector.\n\n## PAT Auth (default — auto-loaded from config.toml)\n\n```python\nfrom cortex_rest import CortexInferenceClient\n\nclient = CortexInferenceClient()   # reads ~/.snowflake/config.toml → connections.myaccount.password\n\n# Headers sent:\n# Authorization: Bearer \u003Ctoken\u003E\n# X-Snowflake-Authorization-Token-Type: PROGRAMMATIC_ACCESS_TOKEN\n```\n\n## JWT Key-Pair Auth\n\n```python\nclient = CortexInferenceClient(\n    auth=\"jwt\",\n    account=\"myorg-myaccount\",\n    user=\"you@example.com\",\n    private_key_path=\"~/.ssh/snowflake_rsa_key.p8\",\n)\n# Headers sent:\n# Authorization: Bearer \u003Csigned_jwt\u003E\n# X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\n```\n\n## Simple Complete\n\n```python\nresp = client.complete(\n    \"claude-4-sonnet\",\n    [{\"role\": \"user\", \"content\": \"Summarise this review in one line.\"}],\n    max_tokens=100,\n)\ntext = resp[\"choices\"][0][\"message\"][\"content\"]\nusage = resp[\"usage\"]   # {prompt_tokens, completion_tokens, total_tokens}\n```\n\n## Streaming SSE\n\n```python\nfor event in client.complete_stream(\"claude-4-sonnet\", messages, max_tokens=500):\n    delta = event[\"choices\"][0][\"delta\"]\n    chunk = delta.get(\"content\") or delta.get(\"text\", \"\")\n    print(chunk, end=\"\", flush=True)\n# Last event has: event[\"usage\"][\"total_tokens\"]\n```\n\n## Tool Calling (Snowflake tool_spec format)\n\n```python\ntools = [{\n    \"tool_spec\": {\n        \"type\": \"generic\",\n        \"name\": \"get_product_details\",\n        \"description\": \"Look up product metadata by product_id.\",\n        \"input_schema\": {\n            \"type\": \"object\",\n            \"properties\": {\"product_id\": {\"type\": \"string\"}},\n            \"required\": [\"product_id\"],\n        },\n    }\n}]\nresp = client.complete(\"claude-4-sonnet\", messages, tools=tools, max_tokens=250)\n# Tool call in response:\ncontent_list = resp[\"choices\"][0][\"message\"][\"content_list\"]\ntool_calls = [c for c in content_list if c.get(\"type\") == \"tool_use\"]\n# → [{\"type\": \"tool_use\", \"tool_use\": {\"name\": \"get_product_details\", \"input\": {\"product_id\": \"P003\"}}}]\n```\n\n## Sampling Parameters — Important\n\n| Model | temperature / top_p / top_k |\n|---|---|\n| `claude-4-sonnet`, `llama3.3-70b`, etc. | Accepted |\n| `claude-opus-4-7` and newer Opus | **Removed** — returns 400 on any non-default value |\n\nStrip before sending for Opus 4.7+:\n```python\nfor k in (\"temperature\", \"top_p\", \"top_k\"):\n    payload.pop(k, None)\n```\n\n## Error Handling\n\n```python\nimport httpx\ntry:\n    resp = client.complete(\"bad-model\", messages)\nexcept httpx.HTTPStatusError as exc:\n    print(exc.response.status_code)   # 400\n    print(exc.response.json())        # {\"message\": \"unknown model \\\"bad-model\\\"\"}\n```\n\n## Module-Level Shortcuts\n\n```python\nfrom cortex_rest import complete, stream\n\n# One-shot (returns string)\nprint(complete(\"What is 2+2?\"))\n\n# Streaming (yields chunks)\nfor chunk in stream(\"Explain vector search in two sentences.\"):\n    print(chunk, end=\"\", flush=True)\n```\n\n## LiteLLM Integration Note\n\nWhen routing through LiteLLM proxy, prefix the token with `pat/`:\n```yaml\napi_key: os.environ/SNOWFLAKE_PAT   # .env: SNOWFLAKE_PAT=pat/\u003Craw_token\u003E\n```\nFor direct `CortexInferenceClient`, use the raw token (no prefix).\n\n---\n\n# Shannon Entropy & Distribution Analysis Reference\n\nFunctions in `cortex_ibis.py` section 10. Use to measure category diversity per group.\n\n## Intuition\n\nShannon entropy quantifies how unpredictable a distribution is:\n\n| H (bits) | Meaning for 4-category reviews |\n|---|---|\n| 2.0 | Perfectly uniform — equal spread across billing/delivery/defect/positive |\n| 1.0–1.9 | Mixed — 2–3 categories dominant |\n| 0.3–1.0 | Concentrated — one category dominates (~70–90%) |\n| 0.0 | Single category — 100% of reviews in one bucket |\n\n**Product insight**: high-entropy products need broad support coverage; low-entropy products have a focused root cause.\n\n## category_entropy() — pure SQL via Ibis\n\n```python\nfrom cortex_ibis import category_entropy\n\n# Input: any Ibis table with a group col and a category col\n# (e.g. output of add_classification())\nclassified = add_classification(reviews_tbl, \"body\",\n                                [\"billing issue\", \"delivery problem\",\n                                 \"product defect\", \"positive feedback\"])\n\nentropy_tbl = category_entropy(\n    classified,\n    group_cols=[\"product_id\"],\n    category_col=\"category\",\n)\n# → product_id | entropy | dominant_category | dominant_share\n# Ordered by entropy ASC (lowest = most concentrated)\n\n# Preview SQL before running\nprint(ibis.to_sql(entropy_tbl, dialect=\"snowflake\"))\n\n# Execute\ndf = entropy_tbl.execute()\n```\n\n## normalized_entropy() — [0, 1] scale\n\n```python\nfrom cortex_ibis import normalized_entropy\n\nnorm_tbl = normalized_entropy(\n    classified,\n    group_cols=[\"product_id\"],\n    category_col=\"category\",\n    num_categories=4,       # must match the actual number of distinct labels\n)\n# Adds 'normalized_entropy' column: 0.0 = single class, 1.0 = perfectly uniform\n```\n\n## entropy_from_pandas() — scipy path\n\n```python\nfrom cortex_ibis import entropy_from_pandas\n\n# From raw rows\nresult = entropy_from_pandas(df, group_col=\"product_id\", category_col=\"true_category\")\n\n# From pre-aggregated counts\ncounts_df = df.groupby([\"product_id\", \"category\"]).size().reset_index(name=\"n\")\nresult = entropy_from_pandas(counts_df, \"product_id\", \"category\", count_col=\"n\")\n\n# Returns: product_id | entropy | normalized_entropy | dominant_category | dominant_share\n```\n\n## Synthetic Dataset\n\n`synthetic_data.py` generates 300 reviews across 8 products with controlled Dirichlet distributions:\n\n```python\nfrom synthetic_data import make_reviews, distribution_summary\n\ndf = make_reviews(seed=42)                # 300 rows: id, product_id, body, true_category\nsummary = distribution_summary(df)       # pivot with per-product counts + true_entropy_bits\n```\n\n| Product | Profile | True H |\n|---|---|---|\n| P001 | Uniform | ~2.00 bits |\n| P002 | 90% product defect | ~0.47 bits |\n| P003 | Bimodal billing+delivery | ~1.0 bits |\n| P004 | All positive | ~0.0 bits |\n| P005 | Bimodal delivery+defect | ~1.0 bits |\n| P006 | Slight positive skew | ~1.9 bits |\n| P007 | 90% billing | ~0.47 bits |\n| P008 | Trimodal billing/delivery/defect | ~1.58 bits |\n\n## Miller–Madow Bias Note\n\nThe plug-in (MLE) entropy estimator is **negatively biased in expectation** — it systematically underestimates true entropy for finite samples. The leading-order bias is given by the Miller–Madow correction:\n\n```\nBias ≈ (K − 1) / (2N · ln 2)  bits\n```\n\nwhere K is the number of categories and N is the sample size.\n\nFor **K = 4 categories**:\n\n| n | Expected bias (bits) |\n|---|---|\n| 10 | ~0.22 |\n| 30 | ~0.07 |\n| 40 | ~0.05 |\n| 300 | ~0.007 |\n\nThe bias shrinks as 1/N and is negligible (\u003C 0.01 bits) at n ≥ 300.\n\n**Important caveat:** the formula gives the *expected* bias. Individual realizations — especially with skewed or Dirichlet-sampled distributions at small n — can deviate substantially in either direction due to sampling variance. A product with n=40 reviews may show sample H above or below the true H depending on which reviews happened to be drawn.\n\n**References:**\n- Miller, G. A. (1955). Note on the bias of information estimates. *Information Theory in Psychology: Problems and Methods*, 95–100. *(Original derivation of the correction.)*\n- Paninski, L. (2003). Estimation of entropy and mutual information. *Neural Computation*, 15(6), 1191–1253. *(Proves no unbiased estimator exists; characterizes bias analytically.)*\n- De Gregorio et al. (2024). Entropy estimators for Markovian sequences: A comparative analysis. *arXiv:2310.07547.* *(Quantitative bias/variance comparison across estimators and sample sizes.)*\n\nUse `distribution_summary(df)` to compare true vs sample entropy on the synthetic dataset.\n\n## Full Demo\n\n```python\npython distribution_demo.py\n# Prints rich entropy table + ASCII bar chart + exports assets/s_entropy.svg\n```\n\n---\n\n# TurboPuffer Integration Reference\n\nPipeline: pandas → Ibis memtable → Cortex AI enrichment → Cortex embeddings → TurboPuffer index → ANN/hybrid/filtered search.\n\nSee full example in `turbopuffer_demo.py`.\n\n## Setup\n\n```bash\npip install turbopuffer\nexport TURBOPUFFER_API_KEY=tpuf_A1...\nexport TURBOPUFFER_REGION=aws-us-east-1    # match your Snowflake region\n```\n\n```python\nfrom turbopuffer import Turbopuffer\ntpuf = Turbopuffer(api_key=os.environ[\"TURBOPUFFER_API_KEY\"])\n```\n\n## Step 1 — Enrich with Cortex (via Ibis memtable)\n\n```python\nimport ibis\nfrom cortex_ibis import ai_sentiment, ai_classify, variant_str, variant_float\n\ntbl = ibis.memtable(df)           # no CREATE TABLE privilege needed\nenriched = tbl.mutate(\n    sentiment_label=ai_sentiment(tbl.body),\n    sentiment_score=cortex_sentiment(tbl.body),\n    category=variant_str(ai_classify(tbl.body, CATEGORIES), \"label\"),\n).execute()\n```\n\n## Step 2 — Generate Embeddings\n\n```python\n# Uses raw SQL via con.raw_sql() + CAST to VARCHAR for Python connector compatibility\nsql = f\"\"\"\n    SELECT id, CAST(SNOWFLAKE.CORTEX.EMBED_TEXT_768('{MODEL}', body) AS VARCHAR) AS vec_str\n    FROM (VALUES {rows_sql}) AS t(id, body)\n\"\"\"\nresult = con.raw_sql(sql)\nvec_map = {row[0]: json.loads(row[1]) for row in result.fetchall()}\ndf[\"vector\"] = df[\"id\"].map(vec_map)\n```\n\n## Step 3 — Index into TurboPuffer\n\n```python\nns = tpuf.namespace(\"cortex-ibis-reviews\")\nns.write(\n    upsert_rows=[{\"id\": ..., \"vector\": [...], \"body\": ..., \"category\": ..., \"sentiment_label\": ...}],\n    distance_metric=\"cosine_distance\",\n    schema={\"body\": {\"type\": \"string\", \"full_text_search\": True}, \"category\": {\"type\": \"string\"}},\n)\n```\n\n## Search Patterns\n\n```python\n# ANN (vector-only)\nns.query(rank_by=(\"vector\", \"ANN\", query_vec), limit=5,\n         include_attributes=[\"body\", \"category\", \"sentiment_label\"])\n\n# Filtered ANN\nns.query(rank_by=(\"vector\", \"ANN\", query_vec),\n         filters=(\"sentiment_label\", \"Eq\", \"negative\"), limit=5)\n\n# Hybrid (70% vector + 30% BM25)\nns.query(rank_by=(\"Sum\", [\n    (\"Product\", 0.7, (\"vector\", \"ANN\", query_vec)),\n    (\"Product\", 0.3, (\"body\", \"BM25\", query_text)),\n]), limit=5)\n\n# Pure BM25 full-text\nns.query(rank_by=(\"body\", \"BM25\", \"refund missing package\"), limit=5)\n\n# Aggregations\nns.query(aggregate_by={\"count\": (\"Count\",)}, group_by=[\"category\"])\n\n# Namespace branching (copy-on-write, O(1))\nbranch = tpuf.namespace(\"cortex-ibis-reviews-branch\")\nbranch.write(branch_from_namespace=\"cortex-ibis-reviews\")\n```\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-ef55c0705d","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-2b111eb346",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-13beec7cb5","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-22",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-9d9b0a82e0","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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