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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-99d64857f0","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/agent-verbosity-cortex-evaluation","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroTitle":{"lines":["Agent Verbosity Evaluation with Snowflake Cortex 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-d1b9449f9c","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-bd41a03f64",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-38c67de9a8","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide walks you through building a comprehensive \u003Cstrong\u003Ecross-model verbosity evaluation system\u003C/strong\u003E using Snowflake Cortex REST API. You'll compare how different LLMs (Claude, Mistral, and Llama) handle verbosity constraints across 8 response styles, with automated evaluation pipelines using TruLens, persona compliance testing, and extended thinking capabilities.\u003C/p\u003E\n","\u003Cp\u003EThe system includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E8 Verbosity Agents\u003C/strong\u003E: Minimal, Brief, Standard, Detailed, Verbose, Code-Only, Explain, Step-by-Step\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECross-Model Comparison\u003C/strong\u003E: Claude Sonnet 4 vs Mistral Large 2 vs Llama 3.1 70B\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETruLens Evaluation\u003C/strong\u003E: LLM-as-Judge with SAE (Sparse Autoencoder) analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPersona Compliance\u003C/strong\u003E: 5th Grade, Scholar, Compute, Business personas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMCP Integration\u003C/strong\u003E: Wikipedia retrieval and A/B testing framework\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExtended Thinking\u003C/strong\u003E: Claude reasoning traces with RAG\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_comparison.png?v=6490b034\" alt=\"Cross-Model Comparison Dashboard\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/ModelCompare.png?v=6490b034\" alt=\"Model Compare - Cortex REST & SQL\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with ACCOUNTADMIN role\u003C/li\u003E\u003Cli\u003EPython 3.9+ installed\u003C/li\u003E\u003Cli\u003EProgrammatic Access Token (PAT) configured in \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EBasic familiarity with Streamlit and Cortex AI\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to deploy Cortex Agents with verbosity constraints\u003C/li\u003E\u003Cli\u003ECompare model responses across verbosity levels\u003C/li\u003E\u003Cli\u003EImplement LLM-as-Judge evaluation with TruLens\u003C/li\u003E\u003Cli\u003EUse MCP (Model Context Protocol) for retrieval\u003C/li\u003E\u003Cli\u003ECapture extended thinking traces from Claude\u003C/li\u003E\u003Cli\u003EBuild dbt pipelines for ML feature engineering\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake Account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.9+\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E (\u003Ccode\u003Epip install streamlit\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EPAT token configured for Cortex REST API access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECross-model verbosity comparison dashboard\u003C/li\u003E\u003Cli\u003E24 Cortex Agents (8 verbosity levels × 3 models)\u003C/li\u003E\u003Cli\u003ETruLens evaluation pipeline with SAE analysis\u003C/li\u003E\u003Cli\u003EPersona compliance testing system\u003C/li\u003E\u003Cli\u003EMCP-based RAG with extended thinking\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture\u003C/h2\u003E\n","\u003Cp\u003EThe system architecture consists of multiple components working together:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E┌─────────────────────────────────────────────────────────────────────────────┐\n│                    Agent Verbosity Evaluation System                         │\n├─────────────────────────────────────────────────────────────────────────────┤\n│                                                                             │\n│  CORTEX REST API (/api/v2/cortex/inference:complete)                        │\n│  ├── CLAUDE SONNET 4 (8 verbosity agents)                                  │\n│  ├── MISTRAL LARGE 2 (8 verbosity agents)                                  │\n│  └── LLAMA 3.1 70B (8 verbosity agents)                                    │\n│                                                                             │\n│  EVALUATION PIPELINES                                                       │\n│  ├── TruLens LLM-as-Judge (Mixtral, Arctic)                                │\n│  ├── SAE Feature Analysis (Sparse Autoencoder)                             │\n│  └── Persona Compliance Scoring                                            │\n│                                                                             │\n│  MCP SERVERS                                                                │\n│  ├── Wikipedia MCP (Port 8503) - Document retrieval                        │\n│  └── A/B Testing MCP (Port 8517) - Experiment framework                    │\n│                                                                             │\n│  DATA PIPELINES                                                             │\n│  ├── dbt Models for ML Features                                            │\n│  └── Snowflake Tables for Results                                          │\n│                                                                             │\n└─────────────────────────────────────────────────────────────────────────────┘\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerbosity Levels\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELevel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMax Lines\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMinimal\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle word/number when possible\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBrief\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo preamble or postamble\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStandard\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBalanced with context\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDetailed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E15\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull structure: Issue, Location, Risk, Fix\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerbose\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E50\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComprehensive with edge cases\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECode-Only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E20\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo explanatory text\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplain\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E20\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhy and how behind everything\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep-by-Step\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E25\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENumbered walkthroughs\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n","\u003Ch3\u003EStep 1: Install Dependencies\u003C/h3\u003E\n","\u003Cp\u003ECreate a virtual environment and install the required packages:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\npip install -r requirements.txt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis installs all dependencies including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit, Pandas, Altair\u003C/strong\u003E - Dashboard UI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Connector\u003C/strong\u003E - Database connectivity\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELiteLLM\u003C/strong\u003E - Unified API for OpenAI, Anthropic, Mistral models\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2: Configure Snowflake Connection\u003C/h3\u003E\n","\u003Cp\u003EConnect using Snow CLI for interactive authentication:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection add myaccount\nsnow connection set-default myaccount\nsnow connection test myaccount\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAlternatively, configure PAT token in \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[connections.myaccount]\naccount = "your_account"\nuser = "your_username"\npassword = "your_pat_token"\nwarehouse = "COMPUTE_WH"\ndatabase = "CORTEX_DB"\nschema = "AGENTS"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you see "Authentication token has expired" errors, run \u003Ccode\u003Esnow connection test myaccount\u003C/code\u003E to refresh the connection.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETroubleshooting: Authentication Token Expired (Error 390114)\u003C/h3\u003E\n","\u003Cp\u003EIf you encounter this error in the dashboard:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E390114 (08001): Authentication token has expired. The user must authenticate again.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESolution:\u003C/strong\u003E Run the following command in your terminal to refresh the authentication token:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection test myaccount\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will re-authenticate with Snowflake and refresh your session token. Then restart the Streamlit dashboard:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run dashboard.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe dashboard includes auto-reconnect logic that will attempt to refresh the connection automatically, but if the token is fully expired, a manual refresh via Snow CLI is required.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Verify Cortex Access\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport requests\nimport tomllib\nimport os\n\ndef get_pat_from_toml(connection_name: str = "myaccount") -> str:\n    """Read PAT token from ~/.snowflake/config.toml"""\n    toml_path = os.path.expanduser("~/.snowflake/config.toml")\n    with open(toml_path, "rb") as f:\n        config = tomllib.load(f)\n    return config["connections"][connection_name].get("password", "")\n\n# Test Cortex REST API\npat = get_pat_from_toml()\nurl = "https://your_account.snowflakecomputing.com/api/v2/cortex/inference:complete"\nheaders = {"Authorization": f"Bearer {pat}", "Content-Type": "application/json"}\npayload = {"model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "Hello"}]}\n\nresponse = requests.post(url, headers=headers, json=payload)\nprint(f"Status: {response.status_code}")\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy Cortex Agents\u003C/h2\u003E\n","\u003Cp\u003EDeploy the 24 verbosity-constrained agents using Snowflake SQL:\u003C/p\u003E\n","\u003Ch3\u003EClaude Agents\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CLAUDE MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_MINIMAL_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n- No punctuation unless required\n- No formatting\n$$;\n\n-- CLAUDE BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_BRIEF_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n- Essential information only\n- Code snippets without explanation\n$$;\n\n-- CLAUDE STANDARD\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_STANDARD_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide balanced responses with appropriate detail.\n- 3-6 lines typical\n- Include context when helpful\n- Brief explanation with code\n- Skip obvious details\n$$;\n\n-- CLAUDE DETAILED\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_DETAILED_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide detailed responses with full context.\nStructure: Issue → Location → Risk → Fix → Related\n$$;\n\n-- CLAUDE VERBOSE\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_VERBOSE_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide comprehensive, educational responses.\nInclude: Context, Problem, Vulnerable Code, Secure Alternatives, \nWhy Fix Works, Edge Cases, Related Patterns, References.\n$$;\n\n-- CLAUDE CODE ONLY\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_CODE_ONLY_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou respond with code only, no prose.\n- Only output code blocks\n- No explanations before or after\n- Comments only if essential for understanding\n$$;\n\n-- CLAUDE EXPLAIN\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_EXPLAIN_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou explain the why and how behind everything.\n- Start with WHY something matters\n- Explain HOW to implement\n- Connect to broader concepts\n$$;\n\n-- CLAUDE STEP BY STEP\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_STEP_BY_STEP_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide numbered, sequential walkthroughs.\nFormat: 1. First step 2. Second step...\nEach step should be actionable and clear.\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMistral Agents\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- MISTRAL MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.MISTRAL_MINIMAL_AGENT\n  MODEL = 'mistral-large2'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n$$;\n\n-- MISTRAL BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.MISTRAL_BRIEF_AGENT\n  MODEL = 'mistral-large2'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n$$;\n\n-- Continue for remaining 6 Mistral agents...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELlama Agents\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- LLAMA MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_MINIMAL_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n$$;\n\n-- LLAMA BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_BRIEF_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n$$;\n\n-- LLAMA STANDARD\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_STANDARD_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide balanced responses with appropriate detail.\n- 3-6 lines typical\n- Include context when helpful\n$$;\n\n-- Continue for remaining 5 Llama agents...\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Verbosity Comparison\u003C/h2\u003E\n","\u003Ch3\u003EStreamlit Dashboard\u003C/h3\u003E\n","\u003Cp\u003ECreate the comparison dashboard that tests all three models across all verbosity levels:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nimport pandas as pd\nimport requests\nimport tomllib\nimport os\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nst.set_page_config(page_title="Model Comparison Dashboard", layout="wide")\n\n# ============================================================\n# CONFIG-DRIVEN MODEL CONFIGURATION\n# ============================================================\n@dataclass\nclass ModelConfig:\n    """Configuration for a Cortex model."""\n    name: str\n    model_id: str\n    supports_thinking: bool = False\n    max_tokens: int = 4096\n\n# Load models from config - easily extensible\nMODEL_CONFIGS = {\n    "claude": ModelConfig("Claude Sonnet 4", "claude-sonnet-4-5", supports_thinking=True),\n    "mistral": ModelConfig("Mistral Large 2", "mistral-large2"),\n    "llama": ModelConfig("Llama 3.1 70B", "llama3.1-70b"),\n}\n\n# ============================================================\n# CORTEX REST API CLIENT\n# ============================================================\nclass CortexRESTClient:\n    """Unified client for Snowflake Cortex REST API."""\n    \n    def __init__(self, connection_name: str = "myaccount"):\n        self.connection_name = connection_name\n        self._load_credentials()\n    \n    def _load_credentials(self):\n        """Load PAT from ~/.snowflake/config.toml"""\n        toml_path = os.path.expanduser("~/.snowflake/config.toml")\n        with open(toml_path, "rb") as f:\n            config = tomllib.load(f)\n        conn = config["connections"][self.connection_name]\n        self.account = conn.get("account", "")\n        self.pat = conn.get("password", "")  # PAT stored as password\n        self.base_url = f"https://{self.account}.snowflakecomputing.com"\n    \n    def complete(self, model: str, messages: list, **kwargs) -> dict:\n        """Call Cortex REST API /api/v2/cortex/inference:complete"""\n        url = f"{self.base_url}/api/v2/cortex/inference:complete"\n        headers = {\n            "Authorization": f"Bearer {self.pat}",\n            "Content-Type": "application/json"\n        }\n        payload = {"model": model, "messages": messages, **kwargs}\n        \n        response = requests.post(url, headers=headers, json=payload)\n        response.raise_for_status()\n        return response.json()\n    \n    def chat_completions(self, model: str, messages: list, **kwargs) -> dict:\n        """Call Chat Completions API /api/v2/cortex/chat/completions"""\n        url = f"{self.base_url}/api/v2/cortex/chat/completions"\n        headers = {\n            "Authorization": f"Bearer {self.pat}",\n            "Content-Type": "application/json"\n        }\n        payload = {"model": model, "messages": messages, **kwargs}\n        \n        response = requests.post(url, headers=headers, json=payload)\n        response.raise_for_status()\n        return response.json()\n    \n    def chat_completions_with_thinking(self, model: str, messages: list, \n                                        thinking_budget: int = 8000) -> dict:\n        """Call Chat Completions API with extended thinking enabled."""\n        url = f"{self.base_url}/api/v2/cortex/chat/completions"\n        headers = {\n            "Authorization": f"Bearer {self.pat}",\n            "Content-Type": "application/json"\n        }\n        payload = {\n            "model": model,\n            "messages": messages,\n            "temperature": 1.0,  # Required for thinking\n            "thinking": {"type": "enabled", "budget_tokens": thinking_budget},\n            "stream": True\n        }\n        \n        response = requests.post(url, headers=headers, json=payload, stream=True)\n        return self._parse_streaming_response(response)\n    \n    def _parse_streaming_response(self, response) -> dict:\n        """Parse SSE streaming response with thinking blocks."""\n        thinking_content = ""\n        response_content = ""\n        usage = {}\n        \n        for line in response.iter_lines(decode_unicode=True):\n            if not line or not line.startswith("data: "):\n                continue\n            data = json.loads(line[6:])\n            \n            if data.get("type") == "content_block_delta":\n                delta = data.get("delta", {})\n                if delta.get("type") == "thinking_delta":\n                    thinking_content += delta.get("thinking", "")\n                elif delta.get("type") == "text_delta":\n                    response_content += delta.get("text", "")\n            \n            if "usage" in data:\n                usage = data["usage"]\n        \n        return {\n            "response": response_content,\n            "thinking": thinking_content,\n            "usage": usage\n        }\n\n# Initialize client\nclient = CortexRESTClient()\n\n# ============================================================\n# VERBOSITY CONFIGURATION\n# ============================================================\nVERBOSITY_PROMPTS = {\n    "minimal": "1 line maximum. Single word/number when possible.",\n    "brief": "Maximum 3 lines. No preamble or postamble.",\n    "standard": "3-6 lines typical. Include context when helpful.",\n    "detailed": "Full context with structure: Issue, Location, Risk, Fix.",\n    "verbose": "Comprehensive with background, options, edge cases.",\n    "code_only": "Return ONLY code blocks. No explanatory text.",\n    "explain": "Explain the why and how behind everything.",\n    "step_by_step": "Numbered, sequential walkthroughs."\n}\n\nVERBOSITY_MAX_LINES = {\n    "minimal": 1, "brief": 3, "standard": 6, "detailed": 15,\n    "verbose": 50, "code_only": 20, "explain": 20, "step_by_step": 25\n}\n\nTEST_QUERIES = [\n    {"id": "Q1", "category": "factual", "text": "What is SQL injection?"},\n    {"id": "Q2", "category": "code_fix", "text": "Fix: session.sql(f\\"SELECT * FROM users WHERE id={user_id}\\")"},\n    {"id": "Q3", "category": "explanation", "text": "Why is parameterized SQL safer?"},\n    {"id": "Q4", "category": "binary", "text": "Is eval(user_input) safe in Python?"},\n]\n\ndef call_model(model_key: str, verbosity: str, query: str) -> dict:\n    """Call Cortex model via REST API with verbosity constraints."""\n    config = MODEL_CONFIGS[model_key]\n    system_prompt = f"You provide {verbosity} responses. RULES: {VERBOSITY_PROMPTS[verbosity]}"\n    \n    messages = [\n        {"role": "system", "content": system_prompt},\n        {"role": "user", "content": query}\n    ]\n    \n    # Use Cortex REST API\n    result = client.complete(config.model_id, messages)\n    \n    answer = result.get("choices", [{}])[0].get("message", {}).get("content", "")\n    usage = result.get("usage", {})\n    \n    return {\n        "response": answer,\n        "line_count": len(answer.strip().split("\\n")),\n        "word_count": len(answer.split()),\n        "compliant": len(answer.strip().split("\\n")) <= VERBOSITY_MAX_LINES[verbosity],\n        "prompt_tokens": usage.get("prompt_tokens", 0),\n        "completion_tokens": usage.get("completion_tokens", 0)\n    }\n\n# ============================================================\n# DASHBOARD UI\n# ============================================================\nst.title("Cross-Model Verbosity Comparison")\n\n# Dynamic model selection from config\nmodel_names = [f"**{c.name}**" for c in MODEL_CONFIGS.values()]\nst.markdown(f"Compare {' vs '.join(model_names)} across verbosity levels using Cortex REST API")\n\n# Model selection (config-driven)\nselected_models = st.multiselect(\n    "Select models to compare",\n    list(MODEL_CONFIGS.keys()),\n    default=list(MODEL_CONFIGS.keys()),\n    format_func=lambda k: MODEL_CONFIGS[k].name\n)\n\nselected_verbosities = st.multiselect(\n    "Select verbosity levels",\n    list(VERBOSITY_PROMPTS.keys()),\n    default=["minimal", "brief", "standard"]\n)\n\nif st.button("Run Comparison", type="primary"):\n    results = []\n    progress = st.progress(0)\n    total = len(TEST_QUERIES) * len(selected_verbosities) * len(selected_models)\n    i = 0\n    \n    for query in TEST_QUERIES:\n        for verbosity in selected_verbosities:\n            row = {"query_id": query["id"], "verbosity": verbosity}\n            \n            for model_key in selected_models:\n                result = call_model(model_key, verbosity, query["text"])\n                row[f"{model_key}_lines"] = result["line_count"]\n                row[f"{model_key}_compliant"] = result["compliant"]\n                row[f"{model_key}_tokens"] = result["prompt_tokens"] + result["completion_tokens"]\n                i += 1\n                progress.progress(i / total)\n            \n            results.append(row)\n    \n    df = pd.DataFrame(results)\n    st.dataframe(df, use_container_width=True)\n    \n    # Compliance summary\n    st.subheader("Compliance Summary")\n    for model_key in selected_models:\n        compliance_rate = df[f"{model_key}_compliant"].mean() * 100\n        st.metric(MODEL_CONFIGS[model_key].name, f"{compliance_rate:.1f}%")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/langchain_cortex_rest_api.png?v=6490b034\" alt=\"LangChain with Cortex REST API\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWikipedia Q&A with Vine Copula\u003C/h2\u003E\n","\u003Cp\u003EThe system generates synthetic Q&A pairs from Wikipedia articles using \u003Cstrong\u003EVine Copula\u003C/strong\u003E modeling for statistical diversity.\u003C/p\u003E\n","\u003Ch3\u003EMCP Wikipedia Server\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# wiki_mcp_server.py\nfrom fastapi import FastAPI\nimport wikipedia\n\napp = FastAPI()\n\n@app.get("/search")\nasync def search(query: str, limit: int = 3):\n    """Search Wikipedia articles."""\n    results = wikipedia.search(query, results=limit)\n    return {"articles": results}\n\n@app.get("/content")\nasync def get_content(title: str):\n    """Get article content."""\n    try:\n        page = wikipedia.page(title)\n        return {\n            "title": page.title,\n            "content": page.content[:5000],\n            "summary": page.summary\n        }\n    except Exception as e:\n        return {"error": str(e)}\n\n# Run: uvicorn wiki_mcp_server:app --port 8503\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVine Copula Q&A Generator\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclass VineCopula:\n    """Generate statistically diverse Q&A pairs using Vine Copula."""\n    \n    def __init__(self, dimensions: int = 3):\n        self.dimensions = dimensions\n    \n    def generate_samples(self, n_samples: int) -> np.ndarray:\n        """Generate correlated samples via Gaussian copula."""\n        # Correlation matrix for question difficulty, length, complexity\n        correlation = np.array([\n            [1.0, 0.3, 0.5],\n            [0.3, 1.0, 0.4],\n            [0.5, 0.4, 1.0]\n        ])\n        \n        # Generate multivariate normal samples\n        samples = np.random.multivariate_normal(\n            mean=[0, 0, 0],\n            cov=correlation,\n            size=n_samples\n        )\n        \n        # Transform to uniform via CDF\n        from scipy.stats import norm\n        return norm.cdf(samples)\n\ndef generate_wiki_qa(articles: list, n_questions: int = 10) -> list:\n    """Generate Q&A pairs from Wikipedia articles."""\n    copula = VineCopula()\n    samples = copula.generate_samples(n_questions)\n    \n    qa_pairs = []\n    for i, sample in enumerate(samples):\n        difficulty = "easy" if sample[0] < 0.33 else "medium" if sample[0] < 0.66 else "hard"\n        article = articles[i % len(articles)]\n        \n        qa_pairs.append({\n            "article": article,\n            "difficulty": difficulty,\n            "question": f"Based on {article}, explain...",\n            "copula_params": sample.tolist()\n        })\n    \n    return qa_pairs\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPersona Compliance Testing\u003C/h2\u003E\n","\u003Cp\u003ETest how well models adapt responses to different audience personas:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_comparison.png?v=6490b034\" alt=\"Persona Comparison\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPersona Definitions\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPersona\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETarget Metric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPass Criteria\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5th Grade\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFlesch Reading Ease\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E80-90 (Easy)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScholar\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFlesch Reading Ease\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E20-50 (Difficult)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL Syntax Check\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContains SELECT or ```sql\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBusiness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBusiness Term Count\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EROI, KPI, stakeholder, metric\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ECompliance Scoring\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef compute_flesch_score(text: str) -> float:\n    """Calculate Flesch Reading Ease score."""\n    sentences = text.count('.') + text.count('!') + text.count('?')\n    words = len(text.split())\n    syllables = sum(count_syllables(word) for word in text.split())\n    \n    if sentences == 0 or words == 0:\n        return 0\n    \n    return 206.835 - 1.015 * (words / sentences) - 84.6 * (syllables / words)\n\ndef check_sql_presence(text: str) -> bool:\n    """Check if response contains SQL code."""\n    return "SELECT" in text.upper() or "```sql" in text.lower()\n\ndef count_business_terms(text: str) -> int:\n    """Count business terminology."""\n    terms = ["roi", "kpi", "stakeholder", "metric", "revenue", "profit", "margin"]\n    text_lower = text.lower()\n    return sum(1 for term in terms if term in text_lower)\n\ndef evaluate_persona_compliance(response: str, persona: str) -> float:\n    """Score persona compliance 0-1."""\n    if persona == "5th_grade":\n        score = compute_flesch_score(response)\n        return 1.0 if 80 <= score <= 90 else 0.7 if 70 <= score <= 100 else 0.3\n    \n    elif persona == "scholar":\n        score = compute_flesch_score(response)\n        return 1.0 if 20 <= score <= 50 else 0.7 if 10 <= score <= 60 else 0.3\n    \n    elif persona == "compute":\n        return 1.0 if check_sql_presence(response) else 0.3\n    \n    elif persona == "business":\n        count = count_business_terms(response)\n        return min(1.0, count / 5.0)\n    \n    return 0.5\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_compliance.png?v=6490b034\" alt=\"Persona Compliance Results\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETruLens Evaluation Pipeline\u003C/h2\u003E\n","\u003Cp\u003EImplement LLM-as-Judge evaluation with multiple judge models and SAE (Sparse Autoencoder) analysis for model interpretability.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/trulens_evals.png?v=6490b034\" alt=\"TruLens Evaluations\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EJudge Configuration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport json\n\n# Judge models for LLM-as-Judge evaluation via Cortex REST API\nJUDGE_MODELS = {\n    "mixtral": "mixtral-8x7b",\n    "arctic": "snowflake-arctic"\n}\n\nJUDGE_PROMPT = """You are an expert evaluator assessing if an AI agent's response adheres to its verbosity constraints.\n\n**Verbosity Level**: {verbosity}\n**Expected Constraints**: {constraints}\n**Response to Evaluate**: {response}\n\nEvaluate whether the response adheres to the verbosity criteria above.\n\nScore each dimension 1-5:\n1. **Length Compliance**: Does the response length match the expected verbosity level?\n2. **Information Density**: Is the information appropriately dense for this level?\n3. **Content Appropriateness**: Is the content appropriate for this verbosity level?\n\nReturn JSON:\n{{"length_score": X, "density_score": X, "content_score": X, "overall": X, "reasoning": "..."}}\n"""\n\ndef evaluate_with_judge(response: str, verbosity: str, judge_key: str) -> dict:\n    """Run LLM-as-Judge evaluation via Cortex REST API."""\n    prompt = JUDGE_PROMPT.format(\n        verbosity=verbosity,\n        constraints=VERBOSITY_PROMPTS[verbosity],\n        response=response\n    )\n    \n    # Use Cortex REST API for judge evaluation\n    messages = [{"role": "user", "content": prompt}]\n    result = client.complete(JUDGE_MODELS[judge_key], messages)\n    \n    answer = result.get("choices", [{}])[0].get("message", {}).get("content", "")\n    return json.loads(answer)\n\ndef run_multi_judge_evaluation(response: str, verbosity: str) -> dict:\n    """Run evaluation with multiple judge models via Cortex REST API."""\n    results = {}\n    for judge_name in JUDGE_MODELS:\n        try:\n            results[judge_name] = evaluate_with_judge(response, verbosity, judge_name)\n        except Exception as e:\n            results[judge_name] = {"error": str(e)}\n    \n    # Aggregate scores across judges\n    valid_scores = [r["overall"] for r in results.values() if "overall" in r]\n    avg_score = sum(valid_scores) / len(valid_scores) if valid_scores else 0\n    \n    return {"judges": results, "aggregate_score": avg_score}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESAE Feature Analysis\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/sae_analysis_langtrace.png?v=6490b034\" alt=\"SAE Analysis with LangTrace\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESparse Autoencoder (SAE) analysis decomposes LLM activations into interpretable features:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclass SAEAnalyzer:\n    """Sparse Autoencoder for model interpretability."""\n    \n    def __init__(self, hidden_dim: int = 4096, sparsity_target: float = 0.05):\n        self.hidden_dim = hidden_dim\n        self.sparsity_target = sparsity_target\n    \n    def analyze(self, model: str, layer: str, response: str) -> dict:\n        """Analyze response activations through SAE."""\n        # Simulated SAE metrics\n        return {\n            "model": model,\n            "layer": layer,\n            "feature_activation": np.random.uniform(0.3, 0.5),\n            "feature_sparsity": np.random.uniform(0.04, 0.12),\n            "reconstruction_loss": np.random.uniform(0.02, 0.15),\n            "dead_features": np.random.uniform(0.1, 0.3),\n            "composite_score": np.random.uniform(0.7, 0.85),\n            "status": "HEALTHY" if np.random.random() > 0.3 else "NEEDS_TUNING"\n        }\n\n# SAE Results Example\n# MODEL     LAYER      ACTIVATION   SPARSITY   RECON_LOSS   STATUS\n# claude    layer_24   0.3596       0.0507     0.0212       HEALTHY\n# mistral   layer_24   0.4185       0.1142     0.1026       NEEDS_TUNING\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExtended Thinking and RAG\u003C/h2\u003E\n","\u003Cp\u003ECapture Claude's reasoning process with extended thinking using the \u003Cstrong\u003EChat Completions API\u003C/strong\u003E and combine with retrieval-augmented generation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/mcp_rag_extended_thinking.png?v=6490b034\" alt=\"MCP RAG with Extended Thinking\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EExtended Thinking with Chat Completions\u003C/h3\u003E\n","\u003Cp\u003EUse the \u003Ccode\u003E/api/v2/cortex/chat/completions\u003C/code\u003E endpoint for extended thinking with full usage statistics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport requests\nimport json\n\ndef run_extended_thinking_chat_completions(\n    query: str, \n    context: str, \n    thinking_budget: int = 8000\n) -> dict:\n    """Execute extended thinking via Chat Completions API."""\n    \n    # Chat Completions endpoint for extended thinking\n    url = f"https://{ACCOUNT}.snowflakecomputing.com/api/v2/cortex/chat/completions"\n    \n    headers = {\n        "Authorization": f"Bearer {PAT}",\n        "Content-Type": "application/json"\n    }\n    \n    payload = {\n        "model": "claude-sonnet-4-5",  # Extended thinking supported\n        "messages": [\n            {"role": "system", "content": f"Use this context to answer:\\n\\n{context}"},\n            {"role": "user", "content": query}\n        ],\n        "temperature": 1.0,  # Required for extended thinking\n        "thinking": {\n            "type": "enabled",\n            "budget_tokens": thinking_budget\n        },\n        "stream": True\n    }\n    \n    response = requests.post(url, headers=headers, json=payload, stream=True)\n    \n    thinking_content = ""\n    response_content = ""\n    usage = {}\n    \n    for line in response.iter_lines(decode_unicode=True):\n        if not line or not line.startswith("data: "):\n            continue\n        \n        if line.strip() == "data: [DONE]":\n            break\n            \n        data = json.loads(line[6:])\n        \n        # Parse streaming chunks\n        for choice in data.get("choices", []):\n            delta = choice.get("delta", {})\n            \n            # Capture thinking content\n            if "thinking" in delta:\n                thinking_content += delta.get("thinking", "")\n            \n            # Capture response content  \n            if "content" in delta:\n                response_content += delta.get("content", "")\n        \n        # Capture usage stats (comes in final chunk)\n        if "usage" in data:\n            usage = data["usage"]\n    \n    return {\n        "answer": response_content,\n        "thinking": thinking_content,\n        "prompt_tokens": usage.get("prompt_tokens", 0),\n        "completion_tokens": usage.get("completion_tokens", 0),\n        "reasoning_tokens": usage.get("reasoning_tokens", 0),\n        "total_tokens": usage.get("total_tokens", 0)\n    }\n\n# Example usage\nresult = run_extended_thinking_chat_completions(\n    query="What are the security implications of SQL injection?",\n    context="SQL injection is a code injection technique...",\n    thinking_budget=8000\n)\n\nprint(f"Thinking: {result['thinking'][:500]}...")\nprint(f"Answer: {result['answer']}")\nprint(f"Tokens - Prompt: {result['prompt_tokens']}, Completion: {result['completion_tokens']}, Reasoning: {result['reasoning_tokens']}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ENon-Streaming Chat Completions\u003C/h3\u003E\n","\u003Cp\u003EFor simpler use cases without streaming:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef chat_completion_simple(query: str, model: str = "claude-sonnet-4-5") -> dict:\n    """Simple chat completion without streaming."""\n    \n    url = f"https://{ACCOUNT}.snowflakecomputing.com/api/v2/cortex/chat/completions"\n    \n    payload = {\n        "model": model,\n        "messages": [{"role": "user", "content": query}],\n        "max_tokens": 4096\n    }\n    \n    headers = {"Authorization": f"Bearer {PAT}", "Content-Type": "application/json"}\n    response = requests.post(url, headers=headers, json=payload)\n    result = response.json()\n    \n    return {\n        "content": result["choices"][0]["message"]["content"],\n        "usage": result.get("usage", {})\n    }\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EChat Completions Usage Display\u003C/h3\u003E\n","\u003Cp\u003EThe dashboard displays token usage from the Chat Completions API:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrompt Tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInput tokens sent to model\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompletion Tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOutput tokens generated\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReasoning Tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETokens used for extended thinking\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETotal Tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECombined usage\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEst. Cost\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApproximate API cost\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Display usage in Streamlit\ndef display_chat_completions_usage(result: dict):\n    """Display Chat Completions usage metrics."""\n    st.markdown("#### 📊 Chat Completions Usage")\n    \n    col1, col2, col3, col4 = st.columns(4)\n    with col1:\n        st.metric("Prompt Tokens", f"{result['prompt_tokens']:,}")\n    with col2:\n        st.metric("Completion Tokens", f"{result['completion_tokens']:,}")\n    with col3:\n        st.metric("Reasoning Tokens", f"{result['reasoning_tokens']:,}")\n    with col4:\n        total = result['prompt_tokens'] + result['completion_tokens']\n        est_cost = (result['prompt_tokens'] * 0.003 + result['completion_tokens'] * 0.015) / 1000\n        st.metric("Est. Cost", f"${est_cost:.4f}")\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt Pipelines for ML\u003C/h2\u003E\n","\u003Cp\u003EBuild data pipelines for ML feature engineering with dbt models running on Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/dbt_pipelines_embeddings.png?v=6490b034\" alt=\"dbt Pipelines for Embeddings\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPipeline Lineage\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/dbt_lineage.png?v=6490b034\" alt=\"dbt Lineage\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EML Features Model\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- models/ml_features/ml_file_embeddings.sql\n{{ config(\n    materialized='incremental',\n    unique_key='file_hash',\n    on_schema_change='sync_all_columns'\n) }}\n\nWITH source_files AS (\n    SELECT \n        file_path,\n        file_content,\n        MD5(file_content) as file_hash,\n        LENGTH(file_content) as content_length,\n        CURRENT_TIMESTAMP() as processed_at\n    FROM {{ ref('stg_source_files') }}\n    {% if is_incremental() %}\n    WHERE processed_at > (SELECT MAX(processed_at) FROM {{ this }})\n    {% endif %}\n),\n\nembeddings AS (\n    SELECT\n        file_hash,\n        file_path,\n        -- Generate embeddings via Cortex\n        SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', file_content) as embedding_vector,\n        content_length,\n        processed_at\n    FROM source_files\n)\n\nSELECT * FROM embeddings\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEvaluation Results Model\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- models/trulens_evals/persona_evaluations.sql\n{{ config(\n    materialized='incremental',\n    unique_key='eval_id'\n) }}\n\nSELECT\n    {{ dbt_utils.generate_surrogate_key(['model', 'persona', 'query_id', 'timestamp']) }} as eval_id,\n    model,\n    persona,\n    query_id,\n    response,\n    \n    -- Persona-specific compliance scoring\n    CASE persona\n        WHEN '5th_grade' THEN \n            CASE WHEN flesch_score BETWEEN 80 AND 90 THEN 1.0\n                 WHEN flesch_score BETWEEN 70 AND 100 THEN 0.7\n                 ELSE 0.3 END\n        WHEN 'scholar' THEN\n            CASE WHEN flesch_score BETWEEN 20 AND 50 THEN 1.0\n                 WHEN flesch_score BETWEEN 10 AND 60 THEN 0.7\n                 ELSE 0.3 END\n        WHEN 'compute' THEN\n            CASE WHEN CONTAINS(response, 'SELECT') OR CONTAINS(response, '```sql') THEN 1.0 \n                 ELSE 0.3 END\n        WHEN 'business' THEN\n            LEAST(1.0, REGEXP_COUNT(LOWER(response), 'roi|kpi|stakeholder|metric|revenue') / 5.0)\n    END as persona_compliance,\n    \n    timestamp\nFROM {{ ref('stg_persona_responses') }}\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EA/B Testing with LangGraph\u003C/h2\u003E\n","\u003Cp\u003EImplement experiment frameworks using LangGraph and MCP for A/B testing model configurations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/langgraph_ab_experiment.png?v=6490b034\" alt=\"LangGraph A/B Experiment\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EA/B MCP Server\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# ab_mcp_server.py\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nimport random\n\napp = FastAPI()\n\nclass Experiment(BaseModel):\n    name: str\n    variants: list[str]\n    traffic_split: list[float]\n\nexperiments = {}\n\n@app.post("/experiment/create")\nasync def create_experiment(exp: Experiment):\n    """Create new A/B experiment."""\n    experiments[exp.name] = exp\n    return {"status": "created", "experiment": exp.name}\n\n@app.get("/experiment/assign/{name}/{user_id}")\nasync def assign_variant(name: str, user_id: str):\n    """Assign user to experiment variant."""\n    exp = experiments.get(name)\n    if not exp:\n        return {"error": "Experiment not found"}\n    \n    # Deterministic assignment based on user_id hash\n    hash_val = hash(user_id) % 100 / 100\n    cumulative = 0\n    for variant, split in zip(exp.variants, exp.traffic_split):\n        cumulative += split\n        if hash_val < cumulative:\n            return {"variant": variant, "user_id": user_id}\n    \n    return {"variant": exp.variants[-1], "user_id": user_id}\n\n@app.post("/experiment/{name}/record")\nasync def record_result(name: str, user_id: str, metric: str, value: float):\n    """Record experiment metric."""\n    return {"status": "recorded", "experiment": name, "user_id": user_id}\n\n# Run: uvicorn ab_mcp_server:app --port 8517\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELangGraph Workflow\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom langgraph.graph import StateGraph, END\nfrom typing import TypedDict\n\nclass ExperimentState(TypedDict):\n    user_id: str\n    query: str\n    variant: str\n    response: str\n    metrics: dict\n\ndef assign_variant(state: ExperimentState) -> ExperimentState:\n    """Assign user to experiment variant via MCP."""\n    response = requests.get(f"{AB_MCP_URL}/experiment/assign/verbosity_test/{state['user_id']}")\n    state["variant"] = response.json()["variant"]\n    return state\n\ndef run_model(state: ExperimentState) -> ExperimentState:\n    """Run model based on assigned variant."""\n    verbosity = state["variant"]  # e.g., "minimal", "brief", "standard"\n    result = call_model("claude-sonnet-4-5", verbosity, state["query"])\n    state["response"] = result["response"]\n    state["metrics"] = {"line_count": result["line_count"], "compliant": result["compliant"]}\n    return state\n\ndef record_metrics(state: ExperimentState) -> ExperimentState:\n    """Record experiment results."""\n    requests.post(\n        f"{AB_MCP_URL}/experiment/verbosity_test/record",\n        params={"user_id": state["user_id"], "metric": "compliance", "value": state["metrics"]["compliant"]}\n    )\n    return state\n\n# Build graph\nworkflow = StateGraph(ExperimentState)\nworkflow.add_node("assign", assign_variant)\nworkflow.add_node("run", run_model)\nworkflow.add_node("record", record_metrics)\n\nworkflow.set_entry_point("assign")\nworkflow.add_edge("assign", "run")\nworkflow.add_edge("run", "record")\nworkflow.add_edge("record", END)\n\napp = workflow.compile()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMultimodal Vision with Cortex\u003C/h2\u003E\n","\u003Cp\u003EUse the \u003Cstrong\u003ECortex Chat Completions API\u003C/strong\u003E for image understanding with Claude and GPT-4o vision models. This enables analysis of egocentric frames from AR devices like \u003Cstrong\u003EProject Aria\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MultimodalEgocentric1.png?v=6490b034\" alt=\"Multimodal Egocentric Analysis\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EVision API Call\u003C/h3\u003E\n","\u003Cp\u003EThe Cortex Chat Completions API supports OpenAI-compatible format with base64-encoded images:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport requests\nimport base64\nimport tomllib\nimport os\n\ndef call_vision_model(model: str, prompt: str, image_path: str):\n    """Call Cortex vision model with an image."""\n    # Load credentials\n    with open(os.path.expanduser("~/.snowflake/config.toml"), "rb") as f:\n        config = tomllib.load(f)\n    pat = config["connections"]["myaccount"]["password"]\n    account = config["connections"]["myaccount"]["account"].lower().replace("_", "-")\n    \n    # Encode image to base64\n    with open(image_path, "rb") as img_file:\n        image_base64 = base64.b64encode(img_file.read()).decode("utf-8")\n    \n    url = f"https://{account}.snowflakecomputing.com/api/v2/cortex/v1/chat/completions"\n    \n    payload = {\n        "model": model,  # claude-sonnet-4-5, gpt-4o, etc.\n        "messages": [{\n            "role": "user",\n            "content": [\n                {"type": "text", "text": prompt},\n                {"type": "image_url", "image_url": {\n                    "url": f"data:image/jpeg;base64,{image_base64}"\n                }}\n            ]\n        }],\n        "max_completion_tokens": 1024\n    }\n    \n    headers = {"Authorization": f"Bearer {pat}", "Content-Type": "application/json"}\n    response = requests.post(url, headers=headers, json=payload)\n    return response.json()["choices"][0]["message"]["content"]\n\n# Analyze an egocentric image\nresult = call_vision_model(\n    model="claude-sonnet-4-5",\n    prompt="This is an egocentric view from AR glasses. Describe the scene.",\n    image_path="egocentric_frame.jpg"\n)\nprint(result)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EProject Aria Integration\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://facebookresearch.github.io/projectaria_tools/gen2/\"\u003EProject Aria\u003C/a\u003E is Meta's AR research glasses with a 12MP RGB camera. Extract frames from Aria VRS recordings for vision analysis:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# pip install projectaria-tools[all]\nfrom projectaria_tools.core import data_provider\n\n# Load VRS recording\nprovider = data_provider.create_vrs_data_provider("recording.vrs")\nrgb_stream = provider.get_stream_id_from_label("camera-rgb")\n\n# Extract RGB frame\nimage_data = provider.get_image_data_by_index(rgb_stream, 0)\nimage_array = image_data[0].to_numpy_array()\n\n# Save frame for vision analysis\nfrom PIL import Image\nImage.fromarray(image_array).save("aria_frame.jpg")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MultimodalEgocentric2.png?v=6490b034\" alt=\"Multimodal Vision Comparison\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESupported Vision Models\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EProvider\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUse Case\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eclaude-sonnet-4-5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScene understanding, detailed analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eclaude-sonnet-4-6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELatest Claude vision capabilities\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Egpt-4o\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFast, accurate image understanding\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Egpt-4o-mini\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECost-effective vision tasks\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDashboard Walkthrough\u003C/h2\u003E\n","\u003Cp\u003ELaunch the full cross-model verbosity dashboard:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run compare_models_dashboard.py --server.port 8501\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerbosity Comparison\u003C/h3\u003E\n","\u003Cp\u003EThe main tab lets you compare Claude, Mistral, and Llama across all 8 verbosity levels with compliance scoring and token usage metrics.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/VerbosityCompare.png?v=6490b034\" alt=\"Verbosity Compare\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELive Testing\u003C/h3\u003E\n","\u003Cp\u003ERun live model comparisons with custom prompts and see real-time results from the Cortex REST API.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LiveTest.png?v=6490b034\" alt=\"Live Test\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EResults Analysis\u003C/h3\u003E\n","\u003Cp\u003EAnalyze compliance rates, token efficiency, and response quality across models and verbosity levels.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/ResultsAnalysis.png?v=6490b034\" alt=\"Results Analysis\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPersona Comparison\u003C/h3\u003E\n","\u003Cp\u003ETest persona compliance across all tabs — 5th Grade, Scholar, Compute, and Business personas evaluated against each model.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompareAllTabs.png?v=6490b034\" alt=\"Persona Compare All Tabs\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompare1.png?v=6490b034\" alt=\"Persona Compare — Detail Views\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompare3.png?v=6490b034\" alt=\"Persona Compare — Compliance Scores\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERAG with Extended Thinking\u003C/h3\u003E\n","\u003Cp\u003EMini RAG pipeline with Wikipedia retrieval and Claude extended thinking traces.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MiniRAG.png?v=6490b034\" alt=\"Mini RAG\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/RAG2.png?v=6490b034\" alt=\"RAG with Extended Thinking\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESAE & LangChain Integration\u003C/h3\u003E\n","\u003Cp\u003ESparse Autoencoder feature analysis with LangChain orchestration and LangTrace event-driven hooks for observability.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/SAELangChainLangTraceEventDrivenHooks.png?v=6490b034\" alt=\"SAE LangChain LangTrace Event-Driven Hooks\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/saeFeatureAnalysis.png?v=6490b034\" alt=\"SAE Feature Analysis\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELangGraph Experiments\u003C/h3\u003E\n","\u003Cp\u003EA/B testing framework using LangGraph workflows for experiment-driven model evaluation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LangGraph.png?v=6490b034\" alt=\"LangGraph\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LangGraphexperiments.png?v=6490b034\" alt=\"LangGraph Experiments\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEvaluation & Batch Testing\u003C/h3\u003E\n","\u003Cp\u003ETruLens evaluation demo with LLM-as-Judge scoring and batch test execution across all model-verbosity combinations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/EvalDemo1.png?v=6490b034\" alt=\"Eval Demo\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/TrulensEval.png?v=6490b034\" alt=\"TruLens Eval\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/BatchTest1.png?v=6490b034\" alt=\"Batch Test\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built a comprehensive cross-model verbosity evaluation system using \u003Cstrong\u003ESnowflake Cortex REST API\u003C/strong\u003E that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDeploys 24 Cortex Agents with verbosity constraints (8 levels × 3 models)\u003C/li\u003E\u003Cli\u003ECompares \u003Cstrong\u003EClaude Sonnet 4\u003C/strong\u003E, \u003Cstrong\u003EMistral Large 2\u003C/strong\u003E, and \u003Cstrong\u003ELlama 3.1 70B\u003C/strong\u003E across 8 response styles\u003C/li\u003E\u003Cli\u003EUses config-driven model management for easy extensibility\u003C/li\u003E\u003Cli\u003EImplements TruLens LLM-as-Judge evaluation with SAE analysis\u003C/li\u003E\u003Cli\u003ETests persona compliance (5th Grade, Scholar, Compute, Business)\u003C/li\u003E\u003Cli\u003EIntegrates MCP for Wikipedia retrieval and A/B testing\u003C/li\u003E\u003Cli\u003ECaptures extended thinking traces from Claude via Cortex REST API\u003C/li\u003E\u003Cli\u003EBuilds dbt pipelines for ML feature engineering\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECalling Cortex models via REST API (\u003Ccode\u003E/api/v2/cortex/inference:complete\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EConfig-driven multi-model comparison methodology\u003C/li\u003E\u003Cli\u003ELLM-as-Judge evaluation patterns with multiple judge models\u003C/li\u003E\u003Cli\u003EMCP (Model Context Protocol) integration\u003C/li\u003E\u003Cli\u003EExtended thinking with streaming response parsing\u003C/li\u003E\u003Cli\u003Edbt pipelines for ML on Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex\"\u003ESnowflake Cortex AI Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api\"\u003ECortex REST API Reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.trulens.org/\"\u003ETruLens Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://modelcontextprotocol.io/\"\u003EMCP Specification\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.getdbt.com/\"\u003Edbt Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://langchain-ai.github.io/langgraph/\"\u003ELangGraph Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAdd more models to MODEL_CONFIGS (e.g., GPT-4 via external functions)\u003C/li\u003E\u003Cli\u003EAdd custom evaluation metrics\u003C/li\u003E\u003Cli\u003EDeploy as Streamlit in Snowflake app\u003C/li\u003E\u003Cli\u003EIntegrate with Snowflake ML Model Registry\u003C/li\u003E\u003C/ul\u003E"],"description":"Build a cross-model verbosity evaluation system comparing Claude, Mistral, and Llama across 8 response styles using Snowflake Cortex REST API, TruLens, and MCP","title":"Agent Verbosity Evaluation with Snowflake Cortex AI","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nThis guide walks you through building a comprehensive **cross-model verbosity evaluation system** using Snowflake Cortex REST API. You'll compare how different LLMs (Claude, Mistral, and Llama) handle verbosity constraints across 8 response styles, with automated evaluation pipelines using TruLens, persona compliance testing, and extended thinking capabilities.\n\nThe system includes:\n- **8 Verbosity Agents**: Minimal, Brief, Standard, Detailed, Verbose, Code-Only, Explain, Step-by-Step\n- **Cross-Model Comparison**: Claude Sonnet 4 vs Mistral Large 2 vs Llama 3.1 70B\n- **TruLens Evaluation**: LLM-as-Judge with SAE (Sparse Autoencoder) analysis\n- **Persona Compliance**: 5th Grade, Scholar, Compute, Business personas\n- **MCP Integration**: Wikipedia retrieval and A/B testing framework\n- **Extended Thinking**: Claude reasoning traces with RAG\n\n![Cross-Model Comparison Dashboard](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_comparison.png?v=6490b034)\n\n![Model Compare - Cortex REST & SQL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/ModelCompare.png?v=6490b034)\n\n### Prerequisites\n- Snowflake account with ACCOUNTADMIN role\n- Python 3.9+ installed\n- Programmatic Access Token (PAT) configured in `~/.snowflake/config.toml`\n- Basic familiarity with Streamlit and Cortex AI\n\n### What You'll Learn\n- How to deploy Cortex Agents with verbosity constraints\n- Compare model responses across verbosity levels\n- Implement LLM-as-Judge evaluation with TruLens\n- Use MCP (Model Context Protocol) for retrieval\n- Capture extended thinking traces from Claude\n- Build dbt pipelines for ML feature engineering\n\n### What You'll Need\n- A [Snowflake Account](https://signup.snowflake.com/)\n- [Python 3.9+](https://www.python.org/downloads/)\n- [Streamlit](https://streamlit.io/) (`pip install streamlit`)\n- PAT token configured for Cortex REST API access\n\n### What You'll Build\n- Cross-model verbosity comparison dashboard\n- 24 Cortex Agents (8 verbosity levels × 3 models)\n- TruLens evaluation pipeline with SAE analysis\n- Persona compliance testing system\n- MCP-based RAG with extended thinking\n\n\u003C!-- ------------------------ --\u003E\n## Architecture\n\nThe system architecture consists of multiple components working together:\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│                    Agent Verbosity Evaluation System                         │\n├─────────────────────────────────────────────────────────────────────────────┤\n│                                                                             │\n│  CORTEX REST API (/api/v2/cortex/inference:complete)                        │\n│  ├── CLAUDE SONNET 4 (8 verbosity agents)                                  │\n│  ├── MISTRAL LARGE 2 (8 verbosity agents)                                  │\n│  └── LLAMA 3.1 70B (8 verbosity agents)                                    │\n│                                                                             │\n│  EVALUATION PIPELINES                                                       │\n│  ├── TruLens LLM-as-Judge (Mixtral, Arctic)                                │\n│  ├── SAE Feature Analysis (Sparse Autoencoder)                             │\n│  └── Persona Compliance Scoring                                            │\n│                                                                             │\n│  MCP SERVERS                                                                │\n│  ├── Wikipedia MCP (Port 8503) - Document retrieval                        │\n│  └── A/B Testing MCP (Port 8517) - Experiment framework                    │\n│                                                                             │\n│  DATA PIPELINES                                                             │\n│  ├── dbt Models for ML Features                                            │\n│  └── Snowflake Tables for Results                                          │\n│                                                                             │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Verbosity Levels\n\n| Level | Max Lines | Description |\n|-------|-----------|-------------|\n| Minimal | 1 | Single word/number when possible |\n| Brief | 3 | No preamble or postamble |\n| Standard | 6 | Balanced with context |\n| Detailed | 15 | Full structure: Issue, Location, Risk, Fix |\n| Verbose | 50 | Comprehensive with edge cases |\n| Code-Only | 20 | No explanatory text |\n| Explain | 20 | Why and how behind everything |\n| Step-by-Step | 25 | Numbered walkthroughs |\n\n\u003C!-- ------------------------ --\u003E\n## Environment Setup\n\n### Step 1: Install Dependencies\n\nCreate a virtual environment and install the required packages:\n\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\npip install -r requirements.txt\n```\n\nThis installs all dependencies including:\n- **Streamlit, Pandas, Altair** - Dashboard UI\n- **Snowflake Connector** - Database connectivity\n- **LiteLLM** - Unified API for OpenAI, Anthropic, Mistral models\n\n### Step 2: Configure Snowflake Connection\n\nConnect using Snow CLI for interactive authentication:\n\n```bash\nsnow connection add myaccount\nsnow connection set-default myaccount\nsnow connection test myaccount\n```\n\nAlternatively, configure PAT token in `~/.snowflake/config.toml`:\n\n```toml\n[connections.myaccount]\naccount = \"your_account\"\nuser = \"your_username\"\npassword = \"your_pat_token\"\nwarehouse = \"COMPUTE_WH\"\ndatabase = \"CORTEX_DB\"\nschema = \"AGENTS\"\n```\n\n\u003E **Note:** If you see \"Authentication token has expired\" errors, run `snow connection test myaccount` to refresh the connection.\n\n### Troubleshooting: Authentication Token Expired (Error 390114)\n\nIf you encounter this error in the dashboard:\n\n```\n390114 (08001): Authentication token has expired. The user must authenticate again.\n```\n\n**Solution:** Run the following command in your terminal to refresh the authentication token:\n\n```bash\nsnow connection test myaccount\n```\n\nThis will re-authenticate with Snowflake and refresh your session token. Then restart the Streamlit dashboard:\n\n```bash\nstreamlit run dashboard.py\n```\n\nThe dashboard includes auto-reconnect logic that will attempt to refresh the connection automatically, but if the token is fully expired, a manual refresh via Snow CLI is required.\n\n### Step 3: Verify Cortex Access\n\n```python\nimport requests\nimport tomllib\nimport os\n\ndef get_pat_from_toml(connection_name: str = \"myaccount\") -\u003E str:\n    \"\"\"Read PAT token from ~/.snowflake/config.toml\"\"\"\n    toml_path = os.path.expanduser(\"~/.snowflake/config.toml\")\n    with open(toml_path, \"rb\") as f:\n        config = tomllib.load(f)\n    return config[\"connections\"][connection_name].get(\"password\", \"\")\n\n# Test Cortex REST API\npat = get_pat_from_toml()\nurl = \"https://your_account.snowflakecomputing.com/api/v2/cortex/inference:complete\"\nheaders = {\"Authorization\": f\"Bearer {pat}\", \"Content-Type\": \"application/json\"}\npayload = {\"model\": \"claude-sonnet-4-5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]}\n\nresponse = requests.post(url, headers=headers, json=payload)\nprint(f\"Status: {response.status_code}\")\n```\n\n\u003C!-- ------------------------ --\u003E\n## Deploy Cortex Agents\n\nDeploy the 24 verbosity-constrained agents using Snowflake SQL:\n\n### Claude Agents\n\n```sql\n-- CLAUDE MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_MINIMAL_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n- No punctuation unless required\n- No formatting\n$$;\n\n-- CLAUDE BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_BRIEF_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n- Essential information only\n- Code snippets without explanation\n$$;\n\n-- CLAUDE STANDARD\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_STANDARD_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide balanced responses with appropriate detail.\n- 3-6 lines typical\n- Include context when helpful\n- Brief explanation with code\n- Skip obvious details\n$$;\n\n-- CLAUDE DETAILED\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_DETAILED_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide detailed responses with full context.\nStructure: Issue → Location → Risk → Fix → Related\n$$;\n\n-- CLAUDE VERBOSE\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_VERBOSE_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide comprehensive, educational responses.\nInclude: Context, Problem, Vulnerable Code, Secure Alternatives, \nWhy Fix Works, Edge Cases, Related Patterns, References.\n$$;\n\n-- CLAUDE CODE ONLY\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_CODE_ONLY_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou respond with code only, no prose.\n- Only output code blocks\n- No explanations before or after\n- Comments only if essential for understanding\n$$;\n\n-- CLAUDE EXPLAIN\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_EXPLAIN_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou explain the why and how behind everything.\n- Start with WHY something matters\n- Explain HOW to implement\n- Connect to broader concepts\n$$;\n\n-- CLAUDE STEP BY STEP\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.CLAUDE_STEP_BY_STEP_AGENT\n  MODEL = 'claude-sonnet-4-5'\n  PROMPT = $$\nYou provide numbered, sequential walkthroughs.\nFormat: 1. First step 2. Second step...\nEach step should be actionable and clear.\n$$;\n```\n\n### Mistral Agents\n\n```sql\n-- MISTRAL MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.MISTRAL_MINIMAL_AGENT\n  MODEL = 'mistral-large2'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n$$;\n\n-- MISTRAL BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.MISTRAL_BRIEF_AGENT\n  MODEL = 'mistral-large2'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n$$;\n\n-- Continue for remaining 6 Mistral agents...\n```\n\n### Llama Agents\n\n```sql\n-- LLAMA MINIMAL\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_MINIMAL_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide absolute minimum responses.\n- 1 line maximum\n- Single word/number when possible\n$$;\n\n-- LLAMA BRIEF\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_BRIEF_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide brief, direct responses.\n- Maximum 3 lines\n- No preamble or postamble\n$$;\n\n-- LLAMA STANDARD\nCREATE OR REPLACE CORTEX AGENT CORTEX_DB.AGENTS.LLAMA_STANDARD_AGENT\n  MODEL = 'llama3.1-70b'\n  PROMPT = $$\nYou provide balanced responses with appropriate detail.\n- 3-6 lines typical\n- Include context when helpful\n$$;\n\n-- Continue for remaining 5 Llama agents...\n```\n\n\u003C!-- ------------------------ --\u003E\n## Run Verbosity Comparison\n\n### Streamlit Dashboard\n\nCreate the comparison dashboard that tests all three models across all verbosity levels:\n\n```python\nimport streamlit as st\nimport pandas as pd\nimport requests\nimport tomllib\nimport os\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nst.set_page_config(page_title=\"Model Comparison Dashboard\", layout=\"wide\")\n\n# ============================================================\n# CONFIG-DRIVEN MODEL CONFIGURATION\n# ============================================================\n@dataclass\nclass ModelConfig:\n    \"\"\"Configuration for a Cortex model.\"\"\"\n    name: str\n    model_id: str\n    supports_thinking: bool = False\n    max_tokens: int = 4096\n\n# Load models from config - easily extensible\nMODEL_CONFIGS = {\n    \"claude\": ModelConfig(\"Claude Sonnet 4\", \"claude-sonnet-4-5\", supports_thinking=True),\n    \"mistral\": ModelConfig(\"Mistral Large 2\", \"mistral-large2\"),\n    \"llama\": ModelConfig(\"Llama 3.1 70B\", \"llama3.1-70b\"),\n}\n\n# ============================================================\n# CORTEX REST API CLIENT\n# ============================================================\nclass CortexRESTClient:\n    \"\"\"Unified client for Snowflake Cortex REST API.\"\"\"\n    \n    def __init__(self, connection_name: str = \"myaccount\"):\n        self.connection_name = connection_name\n        self._load_credentials()\n    \n    def _load_credentials(self):\n        \"\"\"Load PAT from ~/.snowflake/config.toml\"\"\"\n        toml_path = os.path.expanduser(\"~/.snowflake/config.toml\")\n        with open(toml_path, \"rb\") as f:\n            config = tomllib.load(f)\n        conn = config[\"connections\"][self.connection_name]\n        self.account = conn.get(\"account\", \"\")\n        self.pat = conn.get(\"password\", \"\")  # PAT stored as password\n        self.base_url = f\"https://{self.account}.snowflakecomputing.com\"\n    \n    def complete(self, model: str, messages: list, **kwargs) -\u003E dict:\n        \"\"\"Call Cortex REST API /api/v2/cortex/inference:complete\"\"\"\n        url = f\"{self.base_url}/api/v2/cortex/inference:complete\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.pat}\",\n            \"Content-Type\": \"application/json\"\n        }\n        payload = {\"model\": model, \"messages\": messages, **kwargs}\n        \n        response = requests.post(url, headers=headers, json=payload)\n        response.raise_for_status()\n        return response.json()\n    \n    def chat_completions(self, model: str, messages: list, **kwargs) -\u003E dict:\n        \"\"\"Call Chat Completions API /api/v2/cortex/chat/completions\"\"\"\n        url = f\"{self.base_url}/api/v2/cortex/chat/completions\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.pat}\",\n            \"Content-Type\": \"application/json\"\n        }\n        payload = {\"model\": model, \"messages\": messages, **kwargs}\n        \n        response = requests.post(url, headers=headers, json=payload)\n        response.raise_for_status()\n        return response.json()\n    \n    def chat_completions_with_thinking(self, model: str, messages: list, \n                                        thinking_budget: int = 8000) -\u003E dict:\n        \"\"\"Call Chat Completions API with extended thinking enabled.\"\"\"\n        url = f\"{self.base_url}/api/v2/cortex/chat/completions\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.pat}\",\n            \"Content-Type\": \"application/json\"\n        }\n        payload = {\n            \"model\": model,\n            \"messages\": messages,\n            \"temperature\": 1.0,  # Required for thinking\n            \"thinking\": {\"type\": \"enabled\", \"budget_tokens\": thinking_budget},\n            \"stream\": True\n        }\n        \n        response = requests.post(url, headers=headers, json=payload, stream=True)\n        return self._parse_streaming_response(response)\n    \n    def _parse_streaming_response(self, response) -\u003E dict:\n        \"\"\"Parse SSE streaming response with thinking blocks.\"\"\"\n        thinking_content = \"\"\n        response_content = \"\"\n        usage = {}\n        \n        for line in response.iter_lines(decode_unicode=True):\n            if not line or not line.startswith(\"data: \"):\n                continue\n            data = json.loads(line[6:])\n            \n            if data.get(\"type\") == \"content_block_delta\":\n                delta = data.get(\"delta\", {})\n                if delta.get(\"type\") == \"thinking_delta\":\n                    thinking_content += delta.get(\"thinking\", \"\")\n                elif delta.get(\"type\") == \"text_delta\":\n                    response_content += delta.get(\"text\", \"\")\n            \n            if \"usage\" in data:\n                usage = data[\"usage\"]\n        \n        return {\n            \"response\": response_content,\n            \"thinking\": thinking_content,\n            \"usage\": usage\n        }\n\n# Initialize client\nclient = CortexRESTClient()\n\n# ============================================================\n# VERBOSITY CONFIGURATION\n# ============================================================\nVERBOSITY_PROMPTS = {\n    \"minimal\": \"1 line maximum. Single word/number when possible.\",\n    \"brief\": \"Maximum 3 lines. No preamble or postamble.\",\n    \"standard\": \"3-6 lines typical. Include context when helpful.\",\n    \"detailed\": \"Full context with structure: Issue, Location, Risk, Fix.\",\n    \"verbose\": \"Comprehensive with background, options, edge cases.\",\n    \"code_only\": \"Return ONLY code blocks. No explanatory text.\",\n    \"explain\": \"Explain the why and how behind everything.\",\n    \"step_by_step\": \"Numbered, sequential walkthroughs.\"\n}\n\nVERBOSITY_MAX_LINES = {\n    \"minimal\": 1, \"brief\": 3, \"standard\": 6, \"detailed\": 15,\n    \"verbose\": 50, \"code_only\": 20, \"explain\": 20, \"step_by_step\": 25\n}\n\nTEST_QUERIES = [\n    {\"id\": \"Q1\", \"category\": \"factual\", \"text\": \"What is SQL injection?\"},\n    {\"id\": \"Q2\", \"category\": \"code_fix\", \"text\": \"Fix: session.sql(f\\\"SELECT * FROM users WHERE id={user_id}\\\")\"},\n    {\"id\": \"Q3\", \"category\": \"explanation\", \"text\": \"Why is parameterized SQL safer?\"},\n    {\"id\": \"Q4\", \"category\": \"binary\", \"text\": \"Is eval(user_input) safe in Python?\"},\n]\n\ndef call_model(model_key: str, verbosity: str, query: str) -\u003E dict:\n    \"\"\"Call Cortex model via REST API with verbosity constraints.\"\"\"\n    config = MODEL_CONFIGS[model_key]\n    system_prompt = f\"You provide {verbosity} responses. RULES: {VERBOSITY_PROMPTS[verbosity]}\"\n    \n    messages = [\n        {\"role\": \"system\", \"content\": system_prompt},\n        {\"role\": \"user\", \"content\": query}\n    ]\n    \n    # Use Cortex REST API\n    result = client.complete(config.model_id, messages)\n    \n    answer = result.get(\"choices\", [{}])[0].get(\"message\", {}).get(\"content\", \"\")\n    usage = result.get(\"usage\", {})\n    \n    return {\n        \"response\": answer,\n        \"line_count\": len(answer.strip().split(\"\\n\")),\n        \"word_count\": len(answer.split()),\n        \"compliant\": len(answer.strip().split(\"\\n\")) \u003C= VERBOSITY_MAX_LINES[verbosity],\n        \"prompt_tokens\": usage.get(\"prompt_tokens\", 0),\n        \"completion_tokens\": usage.get(\"completion_tokens\", 0)\n    }\n\n# ============================================================\n# DASHBOARD UI\n# ============================================================\nst.title(\"Cross-Model Verbosity Comparison\")\n\n# Dynamic model selection from config\nmodel_names = [f\"**{c.name}**\" for c in MODEL_CONFIGS.values()]\nst.markdown(f\"Compare {' vs '.join(model_names)} across verbosity levels using Cortex REST API\")\n\n# Model selection (config-driven)\nselected_models = st.multiselect(\n    \"Select models to compare\",\n    list(MODEL_CONFIGS.keys()),\n    default=list(MODEL_CONFIGS.keys()),\n    format_func=lambda k: MODEL_CONFIGS[k].name\n)\n\nselected_verbosities = st.multiselect(\n    \"Select verbosity levels\",\n    list(VERBOSITY_PROMPTS.keys()),\n    default=[\"minimal\", \"brief\", \"standard\"]\n)\n\nif st.button(\"Run Comparison\", type=\"primary\"):\n    results = []\n    progress = st.progress(0)\n    total = len(TEST_QUERIES) * len(selected_verbosities) * len(selected_models)\n    i = 0\n    \n    for query in TEST_QUERIES:\n        for verbosity in selected_verbosities:\n            row = {\"query_id\": query[\"id\"], \"verbosity\": verbosity}\n            \n            for model_key in selected_models:\n                result = call_model(model_key, verbosity, query[\"text\"])\n                row[f\"{model_key}_lines\"] = result[\"line_count\"]\n                row[f\"{model_key}_compliant\"] = result[\"compliant\"]\n                row[f\"{model_key}_tokens\"] = result[\"prompt_tokens\"] + result[\"completion_tokens\"]\n                i += 1\n                progress.progress(i / total)\n            \n            results.append(row)\n    \n    df = pd.DataFrame(results)\n    st.dataframe(df, use_container_width=True)\n    \n    # Compliance summary\n    st.subheader(\"Compliance Summary\")\n    for model_key in selected_models:\n        compliance_rate = df[f\"{model_key}_compliant\"].mean() * 100\n        st.metric(MODEL_CONFIGS[model_key].name, f\"{compliance_rate:.1f}%\")\n```\n\n![LangChain with Cortex REST API](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/langchain_cortex_rest_api.png?v=6490b034)\n\n\u003C!-- ------------------------ --\u003E\n## Wikipedia Q&A with Vine Copula\n\nThe system generates synthetic Q&A pairs from Wikipedia articles using **Vine Copula** modeling for statistical diversity.\n\n### MCP Wikipedia Server\n\n```python\n# wiki_mcp_server.py\nfrom fastapi import FastAPI\nimport wikipedia\n\napp = FastAPI()\n\n@app.get(\"/search\")\nasync def search(query: str, limit: int = 3):\n    \"\"\"Search Wikipedia articles.\"\"\"\n    results = wikipedia.search(query, results=limit)\n    return {\"articles\": results}\n\n@app.get(\"/content\")\nasync def get_content(title: str):\n    \"\"\"Get article content.\"\"\"\n    try:\n        page = wikipedia.page(title)\n        return {\n            \"title\": page.title,\n            \"content\": page.content[:5000],\n            \"summary\": page.summary\n        }\n    except Exception as e:\n        return {\"error\": str(e)}\n\n# Run: uvicorn wiki_mcp_server:app --port 8503\n```\n\n### Vine Copula Q&A Generator\n\n```python\nclass VineCopula:\n    \"\"\"Generate statistically diverse Q&A pairs using Vine Copula.\"\"\"\n    \n    def __init__(self, dimensions: int = 3):\n        self.dimensions = dimensions\n    \n    def generate_samples(self, n_samples: int) -\u003E np.ndarray:\n        \"\"\"Generate correlated samples via Gaussian copula.\"\"\"\n        # Correlation matrix for question difficulty, length, complexity\n        correlation = np.array([\n            [1.0, 0.3, 0.5],\n            [0.3, 1.0, 0.4],\n            [0.5, 0.4, 1.0]\n        ])\n        \n        # Generate multivariate normal samples\n        samples = np.random.multivariate_normal(\n            mean=[0, 0, 0],\n            cov=correlation,\n            size=n_samples\n        )\n        \n        # Transform to uniform via CDF\n        from scipy.stats import norm\n        return norm.cdf(samples)\n\ndef generate_wiki_qa(articles: list, n_questions: int = 10) -\u003E list:\n    \"\"\"Generate Q&A pairs from Wikipedia articles.\"\"\"\n    copula = VineCopula()\n    samples = copula.generate_samples(n_questions)\n    \n    qa_pairs = []\n    for i, sample in enumerate(samples):\n        difficulty = \"easy\" if sample[0] \u003C 0.33 else \"medium\" if sample[0] \u003C 0.66 else \"hard\"\n        article = articles[i % len(articles)]\n        \n        qa_pairs.append({\n            \"article\": article,\n            \"difficulty\": difficulty,\n            \"question\": f\"Based on {article}, explain...\",\n            \"copula_params\": sample.tolist()\n        })\n    \n    return qa_pairs\n```\n\n\u003C!-- ------------------------ --\u003E\n## Persona Compliance Testing\n\nTest how well models adapt responses to different audience personas:\n\n![Persona Comparison](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_comparison.png?v=6490b034)\n\n### Persona Definitions\n\n| Persona | Target Metric | Pass Criteria |\n|---------|---------------|---------------|\n| 5th Grade | Flesch Reading Ease | 80-90 (Easy) |\n| Scholar | Flesch Reading Ease | 20-50 (Difficult) |\n| Compute | SQL Syntax Check | Contains SELECT or \\`\\`\\`sql |\n| Business | Business Term Count | ROI, KPI, stakeholder, metric |\n\n### Compliance Scoring\n\n```python\ndef compute_flesch_score(text: str) -\u003E float:\n    \"\"\"Calculate Flesch Reading Ease score.\"\"\"\n    sentences = text.count('.') + text.count('!') + text.count('?')\n    words = len(text.split())\n    syllables = sum(count_syllables(word) for word in text.split())\n    \n    if sentences == 0 or words == 0:\n        return 0\n    \n    return 206.835 - 1.015 * (words / sentences) - 84.6 * (syllables / words)\n\ndef check_sql_presence(text: str) -\u003E bool:\n    \"\"\"Check if response contains SQL code.\"\"\"\n    return \"SELECT\" in text.upper() or \"```sql\" in text.lower()\n\ndef count_business_terms(text: str) -\u003E int:\n    \"\"\"Count business terminology.\"\"\"\n    terms = [\"roi\", \"kpi\", \"stakeholder\", \"metric\", \"revenue\", \"profit\", \"margin\"]\n    text_lower = text.lower()\n    return sum(1 for term in terms if term in text_lower)\n\ndef evaluate_persona_compliance(response: str, persona: str) -\u003E float:\n    \"\"\"Score persona compliance 0-1.\"\"\"\n    if persona == \"5th_grade\":\n        score = compute_flesch_score(response)\n        return 1.0 if 80 \u003C= score \u003C= 90 else 0.7 if 70 \u003C= score \u003C= 100 else 0.3\n    \n    elif persona == \"scholar\":\n        score = compute_flesch_score(response)\n        return 1.0 if 20 \u003C= score \u003C= 50 else 0.7 if 10 \u003C= score \u003C= 60 else 0.3\n    \n    elif persona == \"compute\":\n        return 1.0 if check_sql_presence(response) else 0.3\n    \n    elif persona == \"business\":\n        count = count_business_terms(response)\n        return min(1.0, count / 5.0)\n    \n    return 0.5\n```\n\n![Persona Compliance Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/persona_compliance.png?v=6490b034)\n\n\u003C!-- ------------------------ --\u003E\n## TruLens Evaluation Pipeline\n\nImplement LLM-as-Judge evaluation with multiple judge models and SAE (Sparse Autoencoder) analysis for model interpretability.\n\n![TruLens Evaluations](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/trulens_evals.png?v=6490b034)\n\n### Judge Configuration\n\n```python\nimport json\n\n# Judge models for LLM-as-Judge evaluation via Cortex REST API\nJUDGE_MODELS = {\n    \"mixtral\": \"mixtral-8x7b\",\n    \"arctic\": \"snowflake-arctic\"\n}\n\nJUDGE_PROMPT = \"\"\"You are an expert evaluator assessing if an AI agent's response adheres to its verbosity constraints.\n\n**Verbosity Level**: {verbosity}\n**Expected Constraints**: {constraints}\n**Response to Evaluate**: {response}\n\nEvaluate whether the response adheres to the verbosity criteria above.\n\nScore each dimension 1-5:\n1. **Length Compliance**: Does the response length match the expected verbosity level?\n2. **Information Density**: Is the information appropriately dense for this level?\n3. **Content Appropriateness**: Is the content appropriate for this verbosity level?\n\nReturn JSON:\n{{\"length_score\": X, \"density_score\": X, \"content_score\": X, \"overall\": X, \"reasoning\": \"...\"}}\n\"\"\"\n\ndef evaluate_with_judge(response: str, verbosity: str, judge_key: str) -\u003E dict:\n    \"\"\"Run LLM-as-Judge evaluation via Cortex REST API.\"\"\"\n    prompt = JUDGE_PROMPT.format(\n        verbosity=verbosity,\n        constraints=VERBOSITY_PROMPTS[verbosity],\n        response=response\n    )\n    \n    # Use Cortex REST API for judge evaluation\n    messages = [{\"role\": \"user\", \"content\": prompt}]\n    result = client.complete(JUDGE_MODELS[judge_key], messages)\n    \n    answer = result.get(\"choices\", [{}])[0].get(\"message\", {}).get(\"content\", \"\")\n    return json.loads(answer)\n\ndef run_multi_judge_evaluation(response: str, verbosity: str) -\u003E dict:\n    \"\"\"Run evaluation with multiple judge models via Cortex REST API.\"\"\"\n    results = {}\n    for judge_name in JUDGE_MODELS:\n        try:\n            results[judge_name] = evaluate_with_judge(response, verbosity, judge_name)\n        except Exception as e:\n            results[judge_name] = {\"error\": str(e)}\n    \n    # Aggregate scores across judges\n    valid_scores = [r[\"overall\"] for r in results.values() if \"overall\" in r]\n    avg_score = sum(valid_scores) / len(valid_scores) if valid_scores else 0\n    \n    return {\"judges\": results, \"aggregate_score\": avg_score}\n```\n\n### SAE Feature Analysis\n\n![SAE Analysis with LangTrace](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/sae_analysis_langtrace.png?v=6490b034)\n\nSparse Autoencoder (SAE) analysis decomposes LLM activations into interpretable features:\n\n```python\nclass SAEAnalyzer:\n    \"\"\"Sparse Autoencoder for model interpretability.\"\"\"\n    \n    def __init__(self, hidden_dim: int = 4096, sparsity_target: float = 0.05):\n        self.hidden_dim = hidden_dim\n        self.sparsity_target = sparsity_target\n    \n    def analyze(self, model: str, layer: str, response: str) -\u003E dict:\n        \"\"\"Analyze response activations through SAE.\"\"\"\n        # Simulated SAE metrics\n        return {\n            \"model\": model,\n            \"layer\": layer,\n            \"feature_activation\": np.random.uniform(0.3, 0.5),\n            \"feature_sparsity\": np.random.uniform(0.04, 0.12),\n            \"reconstruction_loss\": np.random.uniform(0.02, 0.15),\n            \"dead_features\": np.random.uniform(0.1, 0.3),\n            \"composite_score\": np.random.uniform(0.7, 0.85),\n            \"status\": \"HEALTHY\" if np.random.random() \u003E 0.3 else \"NEEDS_TUNING\"\n        }\n\n# SAE Results Example\n# MODEL     LAYER      ACTIVATION   SPARSITY   RECON_LOSS   STATUS\n# claude    layer_24   0.3596       0.0507     0.0212       HEALTHY\n# mistral   layer_24   0.4185       0.1142     0.1026       NEEDS_TUNING\n```\n\n\u003C!-- ------------------------ --\u003E\n## Extended Thinking and RAG\n\nCapture Claude's reasoning process with extended thinking using the **Chat Completions API** and combine with retrieval-augmented generation.\n\n![MCP RAG with Extended Thinking](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/mcp_rag_extended_thinking.png?v=6490b034)\n\n### Extended Thinking with Chat Completions\n\nUse the `/api/v2/cortex/chat/completions` endpoint for extended thinking with full usage statistics:\n\n```python\nimport requests\nimport json\n\ndef run_extended_thinking_chat_completions(\n    query: str, \n    context: str, \n    thinking_budget: int = 8000\n) -\u003E dict:\n    \"\"\"Execute extended thinking via Chat Completions API.\"\"\"\n    \n    # Chat Completions endpoint for extended thinking\n    url = f\"https://{ACCOUNT}.snowflakecomputing.com/api/v2/cortex/chat/completions\"\n    \n    headers = {\n        \"Authorization\": f\"Bearer {PAT}\",\n        \"Content-Type\": \"application/json\"\n    }\n    \n    payload = {\n        \"model\": \"claude-sonnet-4-5\",  # Extended thinking supported\n        \"messages\": [\n            {\"role\": \"system\", \"content\": f\"Use this context to answer:\\n\\n{context}\"},\n            {\"role\": \"user\", \"content\": query}\n        ],\n        \"temperature\": 1.0,  # Required for extended thinking\n        \"thinking\": {\n            \"type\": \"enabled\",\n            \"budget_tokens\": thinking_budget\n        },\n        \"stream\": True\n    }\n    \n    response = requests.post(url, headers=headers, json=payload, stream=True)\n    \n    thinking_content = \"\"\n    response_content = \"\"\n    usage = {}\n    \n    for line in response.iter_lines(decode_unicode=True):\n        if not line or not line.startswith(\"data: \"):\n            continue\n        \n        if line.strip() == \"data: [DONE]\":\n            break\n            \n        data = json.loads(line[6:])\n        \n        # Parse streaming chunks\n        for choice in data.get(\"choices\", []):\n            delta = choice.get(\"delta\", {})\n            \n            # Capture thinking content\n            if \"thinking\" in delta:\n                thinking_content += delta.get(\"thinking\", \"\")\n            \n            # Capture response content  \n            if \"content\" in delta:\n                response_content += delta.get(\"content\", \"\")\n        \n        # Capture usage stats (comes in final chunk)\n        if \"usage\" in data:\n            usage = data[\"usage\"]\n    \n    return {\n        \"answer\": response_content,\n        \"thinking\": thinking_content,\n        \"prompt_tokens\": usage.get(\"prompt_tokens\", 0),\n        \"completion_tokens\": usage.get(\"completion_tokens\", 0),\n        \"reasoning_tokens\": usage.get(\"reasoning_tokens\", 0),\n        \"total_tokens\": usage.get(\"total_tokens\", 0)\n    }\n\n# Example usage\nresult = run_extended_thinking_chat_completions(\n    query=\"What are the security implications of SQL injection?\",\n    context=\"SQL injection is a code injection technique...\",\n    thinking_budget=8000\n)\n\nprint(f\"Thinking: {result['thinking'][:500]}...\")\nprint(f\"Answer: {result['answer']}\")\nprint(f\"Tokens - Prompt: {result['prompt_tokens']}, Completion: {result['completion_tokens']}, Reasoning: {result['reasoning_tokens']}\")\n```\n\n### Non-Streaming Chat Completions\n\nFor simpler use cases without streaming:\n\n```python\ndef chat_completion_simple(query: str, model: str = \"claude-sonnet-4-5\") -\u003E dict:\n    \"\"\"Simple chat completion without streaming.\"\"\"\n    \n    url = f\"https://{ACCOUNT}.snowflakecomputing.com/api/v2/cortex/chat/completions\"\n    \n    payload = {\n        \"model\": model,\n        \"messages\": [{\"role\": \"user\", \"content\": query}],\n        \"max_tokens\": 4096\n    }\n    \n    headers = {\"Authorization\": f\"Bearer {PAT}\", \"Content-Type\": \"application/json\"}\n    response = requests.post(url, headers=headers, json=payload)\n    result = response.json()\n    \n    return {\n        \"content\": result[\"choices\"][0][\"message\"][\"content\"],\n        \"usage\": result.get(\"usage\", {})\n    }\n```\n\n### Chat Completions Usage Display\n\nThe dashboard displays token usage from the Chat Completions API:\n\n| Metric | Description |\n|--------|-------------|\n| Prompt Tokens | Input tokens sent to model |\n| Completion Tokens | Output tokens generated |\n| Reasoning Tokens | Tokens used for extended thinking |\n| Total Tokens | Combined usage |\n| Est. Cost | Approximate API cost |\n\n```python\n# Display usage in Streamlit\ndef display_chat_completions_usage(result: dict):\n    \"\"\"Display Chat Completions usage metrics.\"\"\"\n    st.markdown(\"#### 📊 Chat Completions Usage\")\n    \n    col1, col2, col3, col4 = st.columns(4)\n    with col1:\n        st.metric(\"Prompt Tokens\", f\"{result['prompt_tokens']:,}\")\n    with col2:\n        st.metric(\"Completion Tokens\", f\"{result['completion_tokens']:,}\")\n    with col3:\n        st.metric(\"Reasoning Tokens\", f\"{result['reasoning_tokens']:,}\")\n    with col4:\n        total = result['prompt_tokens'] + result['completion_tokens']\n        est_cost = (result['prompt_tokens'] * 0.003 + result['completion_tokens'] * 0.015) / 1000\n        st.metric(\"Est. Cost\", f\"${est_cost:.4f}\")\n```\n\n\u003C!-- ------------------------ --\u003E\n## dbt Pipelines for ML\n\nBuild data pipelines for ML feature engineering with dbt models running on Snowflake.\n\n![dbt Pipelines for Embeddings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/dbt_pipelines_embeddings.png?v=6490b034)\n\n### Pipeline Lineage\n\n![dbt Lineage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/dbt_lineage.png?v=6490b034)\n\n### ML Features Model\n\n```sql\n-- models/ml_features/ml_file_embeddings.sql\n{{ config(\n    materialized='incremental',\n    unique_key='file_hash',\n    on_schema_change='sync_all_columns'\n) }}\n\nWITH source_files AS (\n    SELECT \n        file_path,\n        file_content,\n        MD5(file_content) as file_hash,\n        LENGTH(file_content) as content_length,\n        CURRENT_TIMESTAMP() as processed_at\n    FROM {{ ref('stg_source_files') }}\n    {% if is_incremental() %}\n    WHERE processed_at \u003E (SELECT MAX(processed_at) FROM {{ this }})\n    {% endif %}\n),\n\nembeddings AS (\n    SELECT\n        file_hash,\n        file_path,\n        -- Generate embeddings via Cortex\n        SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', file_content) as embedding_vector,\n        content_length,\n        processed_at\n    FROM source_files\n)\n\nSELECT * FROM embeddings\n```\n\n### Evaluation Results Model\n\n```sql\n-- models/trulens_evals/persona_evaluations.sql\n{{ config(\n    materialized='incremental',\n    unique_key='eval_id'\n) }}\n\nSELECT\n    {{ dbt_utils.generate_surrogate_key(['model', 'persona', 'query_id', 'timestamp']) }} as eval_id,\n    model,\n    persona,\n    query_id,\n    response,\n    \n    -- Persona-specific compliance scoring\n    CASE persona\n        WHEN '5th_grade' THEN \n            CASE WHEN flesch_score BETWEEN 80 AND 90 THEN 1.0\n                 WHEN flesch_score BETWEEN 70 AND 100 THEN 0.7\n                 ELSE 0.3 END\n        WHEN 'scholar' THEN\n            CASE WHEN flesch_score BETWEEN 20 AND 50 THEN 1.0\n                 WHEN flesch_score BETWEEN 10 AND 60 THEN 0.7\n                 ELSE 0.3 END\n        WHEN 'compute' THEN\n            CASE WHEN CONTAINS(response, 'SELECT') OR CONTAINS(response, '```sql') THEN 1.0 \n                 ELSE 0.3 END\n        WHEN 'business' THEN\n            LEAST(1.0, REGEXP_COUNT(LOWER(response), 'roi|kpi|stakeholder|metric|revenue') / 5.0)\n    END as persona_compliance,\n    \n    timestamp\nFROM {{ ref('stg_persona_responses') }}\n```\n\n\u003C!-- ------------------------ --\u003E\n## A/B Testing with LangGraph\n\nImplement experiment frameworks using LangGraph and MCP for A/B testing model configurations.\n\n![LangGraph A/B Experiment](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/langgraph_ab_experiment.png?v=6490b034)\n\n### A/B MCP Server\n\n```python\n# ab_mcp_server.py\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nimport random\n\napp = FastAPI()\n\nclass Experiment(BaseModel):\n    name: str\n    variants: list[str]\n    traffic_split: list[float]\n\nexperiments = {}\n\n@app.post(\"/experiment/create\")\nasync def create_experiment(exp: Experiment):\n    \"\"\"Create new A/B experiment.\"\"\"\n    experiments[exp.name] = exp\n    return {\"status\": \"created\", \"experiment\": exp.name}\n\n@app.get(\"/experiment/assign/{name}/{user_id}\")\nasync def assign_variant(name: str, user_id: str):\n    \"\"\"Assign user to experiment variant.\"\"\"\n    exp = experiments.get(name)\n    if not exp:\n        return {\"error\": \"Experiment not found\"}\n    \n    # Deterministic assignment based on user_id hash\n    hash_val = hash(user_id) % 100 / 100\n    cumulative = 0\n    for variant, split in zip(exp.variants, exp.traffic_split):\n        cumulative += split\n        if hash_val \u003C cumulative:\n            return {\"variant\": variant, \"user_id\": user_id}\n    \n    return {\"variant\": exp.variants[-1], \"user_id\": user_id}\n\n@app.post(\"/experiment/{name}/record\")\nasync def record_result(name: str, user_id: str, metric: str, value: float):\n    \"\"\"Record experiment metric.\"\"\"\n    return {\"status\": \"recorded\", \"experiment\": name, \"user_id\": user_id}\n\n# Run: uvicorn ab_mcp_server:app --port 8517\n```\n\n### LangGraph Workflow\n\n```python\nfrom langgraph.graph import StateGraph, END\nfrom typing import TypedDict\n\nclass ExperimentState(TypedDict):\n    user_id: str\n    query: str\n    variant: str\n    response: str\n    metrics: dict\n\ndef assign_variant(state: ExperimentState) -\u003E ExperimentState:\n    \"\"\"Assign user to experiment variant via MCP.\"\"\"\n    response = requests.get(f\"{AB_MCP_URL}/experiment/assign/verbosity_test/{state['user_id']}\")\n    state[\"variant\"] = response.json()[\"variant\"]\n    return state\n\ndef run_model(state: ExperimentState) -\u003E ExperimentState:\n    \"\"\"Run model based on assigned variant.\"\"\"\n    verbosity = state[\"variant\"]  # e.g., \"minimal\", \"brief\", \"standard\"\n    result = call_model(\"claude-sonnet-4-5\", verbosity, state[\"query\"])\n    state[\"response\"] = result[\"response\"]\n    state[\"metrics\"] = {\"line_count\": result[\"line_count\"], \"compliant\": result[\"compliant\"]}\n    return state\n\ndef record_metrics(state: ExperimentState) -\u003E ExperimentState:\n    \"\"\"Record experiment results.\"\"\"\n    requests.post(\n        f\"{AB_MCP_URL}/experiment/verbosity_test/record\",\n        params={\"user_id\": state[\"user_id\"], \"metric\": \"compliance\", \"value\": state[\"metrics\"][\"compliant\"]}\n    )\n    return state\n\n# Build graph\nworkflow = StateGraph(ExperimentState)\nworkflow.add_node(\"assign\", assign_variant)\nworkflow.add_node(\"run\", run_model)\nworkflow.add_node(\"record\", record_metrics)\n\nworkflow.set_entry_point(\"assign\")\nworkflow.add_edge(\"assign\", \"run\")\nworkflow.add_edge(\"run\", \"record\")\nworkflow.add_edge(\"record\", END)\n\napp = workflow.compile()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Multimodal Vision with Cortex\n\nUse the **Cortex Chat Completions API** for image understanding with Claude and GPT-4o vision models. This enables analysis of egocentric frames from AR devices like **Project Aria**.\n\n![Multimodal Egocentric Analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MultimodalEgocentric1.png?v=6490b034)\n\n### Vision API Call\n\nThe Cortex Chat Completions API supports OpenAI-compatible format with base64-encoded images:\n\n```python\nimport requests\nimport base64\nimport tomllib\nimport os\n\ndef call_vision_model(model: str, prompt: str, image_path: str):\n    \"\"\"Call Cortex vision model with an image.\"\"\"\n    # Load credentials\n    with open(os.path.expanduser(\"~/.snowflake/config.toml\"), \"rb\") as f:\n        config = tomllib.load(f)\n    pat = config[\"connections\"][\"myaccount\"][\"password\"]\n    account = config[\"connections\"][\"myaccount\"][\"account\"].lower().replace(\"_\", \"-\")\n    \n    # Encode image to base64\n    with open(image_path, \"rb\") as img_file:\n        image_base64 = base64.b64encode(img_file.read()).decode(\"utf-8\")\n    \n    url = f\"https://{account}.snowflakecomputing.com/api/v2/cortex/v1/chat/completions\"\n    \n    payload = {\n        \"model\": model,  # claude-sonnet-4-5, gpt-4o, etc.\n        \"messages\": [{\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"text\", \"text\": prompt},\n                {\"type\": \"image_url\", \"image_url\": {\n                    \"url\": f\"data:image/jpeg;base64,{image_base64}\"\n                }}\n            ]\n        }],\n        \"max_completion_tokens\": 1024\n    }\n    \n    headers = {\"Authorization\": f\"Bearer {pat}\", \"Content-Type\": \"application/json\"}\n    response = requests.post(url, headers=headers, json=payload)\n    return response.json()[\"choices\"][0][\"message\"][\"content\"]\n\n# Analyze an egocentric image\nresult = call_vision_model(\n    model=\"claude-sonnet-4-5\",\n    prompt=\"This is an egocentric view from AR glasses. Describe the scene.\",\n    image_path=\"egocentric_frame.jpg\"\n)\nprint(result)\n```\n\n### Project Aria Integration\n\n[Project Aria](https://facebookresearch.github.io/projectaria_tools/gen2/) is Meta's AR research glasses with a 12MP RGB camera. Extract frames from Aria VRS recordings for vision analysis:\n\n```python\n# pip install projectaria-tools[all]\nfrom projectaria_tools.core import data_provider\n\n# Load VRS recording\nprovider = data_provider.create_vrs_data_provider(\"recording.vrs\")\nrgb_stream = provider.get_stream_id_from_label(\"camera-rgb\")\n\n# Extract RGB frame\nimage_data = provider.get_image_data_by_index(rgb_stream, 0)\nimage_array = image_data[0].to_numpy_array()\n\n# Save frame for vision analysis\nfrom PIL import Image\nImage.fromarray(image_array).save(\"aria_frame.jpg\")\n```\n\n![Multimodal Vision Comparison](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MultimodalEgocentric2.png?v=6490b034)\n\n### Supported Vision Models\n\n| Model | Provider | Use Case |\n|-------|----------|----------|\n| claude-sonnet-4-5 | Cortex | Scene understanding, detailed analysis |\n| claude-sonnet-4-6 | Cortex | Latest Claude vision capabilities |\n| gpt-4o | Cortex | Fast, accurate image understanding |\n| gpt-4o-mini | Cortex | Cost-effective vision tasks |\n\n\u003C!-- ------------------------ --\u003E\n## Dashboard Walkthrough\n\nLaunch the full cross-model verbosity dashboard:\n\n```bash\nstreamlit run compare_models_dashboard.py --server.port 8501\n```\n\n### Verbosity Comparison\n\nThe main tab lets you compare Claude, Mistral, and Llama across all 8 verbosity levels with compliance scoring and token usage metrics.\n\n![Verbosity Compare](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/VerbosityCompare.png?v=6490b034)\n\n### Live Testing\n\nRun live model comparisons with custom prompts and see real-time results from the Cortex REST API.\n\n![Live Test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LiveTest.png?v=6490b034)\n\n### Results Analysis\n\nAnalyze compliance rates, token efficiency, and response quality across models and verbosity levels.\n\n![Results Analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/ResultsAnalysis.png?v=6490b034)\n\n### Persona Comparison\n\nTest persona compliance across all tabs — 5th Grade, Scholar, Compute, and Business personas evaluated against each model.\n\n![Persona Compare All Tabs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompareAllTabs.png?v=6490b034)\n\n![Persona Compare — Detail Views](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompare1.png?v=6490b034)\n\n![Persona Compare — Compliance Scores](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/PersonaCompare3.png?v=6490b034)\n\n### RAG with Extended Thinking\n\nMini RAG pipeline with Wikipedia retrieval and Claude extended thinking traces.\n\n![Mini RAG](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/MiniRAG.png?v=6490b034)\n\n![RAG with Extended Thinking](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/RAG2.png?v=6490b034)\n\n### SAE & LangChain Integration\n\nSparse Autoencoder feature analysis with LangChain orchestration and LangTrace event-driven hooks for observability.\n\n![SAE LangChain LangTrace Event-Driven Hooks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/SAELangChainLangTraceEventDrivenHooks.png?v=6490b034)\n\n![SAE Feature Analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/saeFeatureAnalysis.png?v=6490b034)\n\n### LangGraph Experiments\n\nA/B testing framework using LangGraph workflows for experiment-driven model evaluation.\n\n![LangGraph](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LangGraph.png?v=6490b034)\n\n![LangGraph Experiments](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/LangGraphexperiments.png?v=6490b034)\n\n### Evaluation & Batch Testing\n\nTruLens evaluation demo with LLM-as-Judge scoring and batch test execution across all model-verbosity combinations.\n\n![Eval Demo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/EvalDemo1.png?v=6490b034)\n\n![TruLens Eval](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/TrulensEval.png?v=6490b034)\n\n![Batch Test](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/agent-verbosity-cortex-evaluation/BatchTest1.png?v=6490b034)\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've built a comprehensive cross-model verbosity evaluation system using **Snowflake Cortex REST API** that:\n\n- Deploys 24 Cortex Agents with verbosity constraints (8 levels × 3 models)\n- Compares **Claude Sonnet 4**, **Mistral Large 2**, and **Llama 3.1 70B** across 8 response styles\n- Uses config-driven model management for easy extensibility\n- Implements TruLens LLM-as-Judge evaluation with SAE analysis\n- Tests persona compliance (5th Grade, Scholar, Compute, Business)\n- Integrates MCP for Wikipedia retrieval and A/B testing\n- Captures extended thinking traces from Claude via Cortex REST API\n- Builds dbt pipelines for ML feature engineering\n\n### What You Learned\n- Calling Cortex models via REST API (`/api/v2/cortex/inference:complete`)\n- Config-driven multi-model comparison methodology\n- LLM-as-Judge evaluation patterns with multiple judge models\n- MCP (Model Context Protocol) integration\n- Extended thinking with streaming response parsing\n- dbt pipelines for ML on Snowflake\n\n### Related Resources\n- [Snowflake Cortex AI Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex)\n- [Cortex REST API Reference](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api)\n- [TruLens Documentation](https://www.trulens.org/)\n- [MCP Specification](https://modelcontextprotocol.io/)\n- [dbt Documentation](https://docs.getdbt.com/)\n- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)\n\n### Next Steps\n- Add more models to MODEL_CONFIGS (e.g., GPT-4 via external functions)\n- Add custom evaluation metrics\n- Deploy as Streamlit in Snowflake app\n- Integrate with Snowflake ML Model Registry\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c45373cbc7","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-d6e700c42b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ec10bb8a36","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e897ba9e30","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. 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