# Cortex Classify Notebook

> Deploy the Cortex CLASSIFY_TEXT tutorial notebook to the user's Snowflake account and provide a link to open it in Snowsight. Use when user wants to learn text classification through a Jupyter notebook experience.

- Skill: `snowflake-labs/cortex-classify-notebook` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add snowflake-labs/cortex-classify-notebook`
- Raw SKILL.md: https://api.skillmd.com/api/skills/snowflake-labs/cortex-classify-notebook/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: snowflake-labs (https://skillmd.com/u/snowflake-labs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/snowflake-labs/cortex-classify-notebook

---


# Cortex Classify Text - Notebook Deployment Skill

You are deploying the Cortex CLASSIFY_TEXT tutorial notebook to the user's Snowflake account. This skill uploads the notebook and provides a direct link to open it in Snowsight.

## What This Skill Does

1. Fetches latest documentation to ensure current syntax
2. Detects or sets up the target environment (prefers SNOWFLAKE_LEARNING)
3. Creates a user-specific schema for the notebook
4. Uploads the tutorial notebook to a Snowflake stage
5. Creates the notebook in the user's account
6. Provides a direct link to open the notebook in Snowsight

## Deployment Flow

Welcome the user briefly, then proceed through the steps efficiently while keeping them informed. Do NOT ask for confirmation at each step - deploy smoothly and report results.

## Step 0: Fetch Latest Documentation (ALWAYS do this first)

Before starting deployment, use `web_fetch` to retrieve the current official documentation:

```
https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex
https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks
```

This ensures your explanations and any troubleshooting advice are current. If the docs show new parameters or syntax, inform the user.

## Step 1: Environment Detection and Setup

**Run these two queries in parallel:**

```sql
-- Query 1: Check if SNOWFLAKE_LEARNING environment exists
SHOW ROLES LIKE 'SNOWFLAKE_LEARNING_ROLE';
```

```
-- Query 2: Read the notebook file from assets/classify_unstructured_customer_reviews.ipynb
```

**If SNOWFLAKE_LEARNING_ROLE exists** (preferred):
```sql
USE ROLE SNOWFLAKE_LEARNING_ROLE;
USE DATABASE SNOWFLAKE_LEARNING_DB;
USE WAREHOUSE SNOWFLAKE_LEARNING_WH;
```

**If NOT available** (fallback):
```sql
USE ROLE ACCOUNTADMIN;  -- or appropriate role with CREATE DATABASE privilege
CREATE DATABASE IF NOT EXISTS CORTEX_TUTORIALS_DB;
USE DATABASE CORTEX_TUTORIALS_DB;
CREATE WAREHOUSE IF NOT EXISTS CORTEX_TUTORIALS_WH WITH WAREHOUSE_SIZE = 'XSMALL';
USE WAREHOUSE CORTEX_TUTORIALS_WH;
```

## Step 2: Create User Schema and Stage

```sql
-- Create a schema for the notebook (named after current user)
SET schema_name = CONCAT(CURRENT_USER(), '_CORTEX_TUTORIALS');
CREATE SCHEMA IF NOT EXISTS IDENTIFIER($schema_name);
USE SCHEMA IDENTIFIER($schema_name);

-- Create internal stage for notebook files
CREATE STAGE IF NOT EXISTS notebook_stage
    DIRECTORY = (ENABLE = TRUE)
    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');
```

## Step 3: Capture Environment Info (CRITICAL)

**IMPORTANT:** After setting up the environment, you MUST query the actual values to use in subsequent steps. Do NOT guess or assume schema names based on the local username.

```sql
SELECT 
    CURRENT_USER() as current_user,
    CURRENT_SCHEMA() as current_schema, 
    CURRENT_DATABASE() as current_database,
    CURRENT_ROLE() as current_role
```

Store these values for use in the upload and notebook creation steps.

## Step 4: Upload Notebook File to Stage

**Use the `snow stage copy` CLI command** with fully qualified stage path:

```bash
snow stage copy <local_notebook_path> @<DATABASE>.<SCHEMA>.notebook_stage --overwrite --connection <connection_name>
```

**Example (substitute actual values from Step 3):**
```bash
snow stage copy /path/to/assets/classify_unstructured_customer_reviews.ipynb @{DATABASE}.{SCHEMA}.notebook_stage --overwrite --connection {CONNECTION}
```

**IMPORTANT:** 
- Use the ACTUAL schema name from Step 3, NOT a guessed name based on local OS username
- The local path is: `assets/classify_unstructured_customer_reviews.ipynb` relative to this skill's base directory
- Always use `--overwrite` flag to handle re-deployments

## Step 5: Create the Notebook

Use fully qualified names for the notebook creation:

```sql
CREATE OR REPLACE NOTEBOOK <DATABASE>.<SCHEMA>.CLASSIFY_CUSTOMER_REVIEWS
    FROM '@<DATABASE>.<SCHEMA>.notebook_stage'
    MAIN_FILE = 'classify_unstructured_customer_reviews.ipynb'
    COMMENT = 'Tutorial: Classify customer reviews with Cortex AI';
```

**Example (substitute actual values from Step 3):**
```sql
CREATE OR REPLACE NOTEBOOK {DATABASE}.{SCHEMA}.CLASSIFY_CUSTOMER_REVIEWS
    FROM '@{DATABASE}.{SCHEMA}.notebook_stage'
    MAIN_FILE = 'classify_unstructured_customer_reviews.ipynb'
    COMMENT = 'Tutorial: Classify customer reviews with Cortex AI';
```

## Step 6: Generate Snowsight URL

**Get the account identifier for the URL:**

```sql
SELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME() as account_identifier
```

**Build the Snowsight URL:**
```
https://app.snowflake.com/{ORG}/{ACCOUNT}/#/notebooks/{DATABASE}.{SCHEMA}.CLASSIFY_CUSTOMER_REVIEWS
```

Where `{ORG}` and `{ACCOUNT}` come from splitting the account_identifier on `-`.

## Success Message

After successful deployment, provide this summary:

---

## Notebook Deployed Successfully!

**Open your notebook:** [Click here to open in Snowsight]({URL})

**Location:** `{DATABASE}.{SCHEMA}.CLASSIFY_CUSTOMER_REVIEWS`

### What the notebook covers:

1. **Load Sample Data** - Customer reviews from Tasty Bytes food trucks
2. **Classify Text with Python** - Use `cortex.classify_text()` on single strings and DataFrames
3. **Classify Text with SQL** - Use `SNOWFLAKE.CORTEX.CLASSIFY_TEXT()` directly in queries
4. **Analyze Results** - Determine if customers would recommend food trucks based on reviews

### To run the tutorial:

1. Click the link above to open the notebook in Snowsight
2. Click **Run All** to execute all cells
3. Review the classification results showing "Likely", "Unlikely", or "Unsure" recommendations

---

## Handling Issues

### Schema Name Mismatch Errors

If you get "Schema does not exist" errors:
- **Always query `CURRENT_SCHEMA()`** after setup to get the actual schema name
- Do NOT assume the schema name matches the local OS username
- Use the queried values in all subsequent commands

### Stage Copy Failures

If `snow stage copy` fails:
- Verify the stage path uses fully qualified names: `@DATABASE.SCHEMA.stage_name`
- Ensure the connection parameter matches the user's active connection
- Check that the role has WRITE privileges on the stage

### Notebook Already Exists

The skill uses `CREATE OR REPLACE NOTEBOOK` by default, which handles existing notebooks automatically.

### Insufficient Privileges

If user can't create notebooks:
- Check if they have CREATE NOTEBOOK privilege on the schema
- Suggest using SNOWFLAKE_LEARNING_ROLE if available
- Fall back to ACCOUNTADMIN if necessary

### Cortex Not Available

If Cortex functions aren't available in the region:
- Check [Cortex region availability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#availability)
- Verify account has Cortex enabled

## Reference Materials

- `assets/classify_unstructured_customer_reviews.ipynb` - The notebook file to deploy
- Notebook teaches classification of customer reviews into "Likely", "Unlikely", "Unsure" categories