docs: upgrade Quarto 1.6.26 → 1.9.37

.github/workflows/docs-freeze.yml

@@ -0,0 +1,65 @@
+on:
+  workflow_dispatch:
+
+name: Docs - Update freeze cache
+
+env:
+  UV_VERSION: "0.4.x"
+  PYTHON_VERSION: 3.12
+
+permissions:
+  contents: write
+
+jobs:
+  docs-freeze:
+    runs-on: ubuntu-latest
+
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+
+    steps:
+    - name: Check out repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: 🔵 Set up Quarto
+      uses: quarto-dev/quarto-actions/setup@v2
+      with:
+        version: 1.9.37
+
+    - name: 🚀 Install uv
+      uses: astral-sh/setup-uv@v3
+      with:
+        version: ${{ env.UV_VERSION }}
+
+    - name: 🐍 Set up Python ${{ env.PYTHON_VERSION }}
+      run: uv python install ${{ env.PYTHON_VERSION }}
+
+    - name: 📦 Install chatlas and dependencies
+      run: uv sync --python ${{ env.PYTHON_VERSION }} --all-extras
+
+    - name: 🔌 Activate venv
+      run: |
+        source .venv/bin/activate
+        echo "$VIRTUAL_ENV/bin" >> $GITHUB_PATH
+        echo "VIRTUAL_ENV=$VIRTUAL_ENV" >> $GITHUB_ENV
+
+    - name: Run quartodoc
+      run: make quartodoc
+
+    - name: 🧊 Render docs to populate freeze cache
+      run: quarto render docs
+
+    - name: 💾 Commit updated freeze cache
+      run: |
+        git config user.name "github-actions[bot]"
+        git config user.email "github-actions[bot]@users.noreply.github.com"
+        git add docs/_freeze/
+        if git diff --staged --quiet; then
+          echo "No freeze cache changes to commit."
+        else
+          git commit -m "docs: update freeze cache [skip ci]"
+          git push origin HEAD
+        fi

.github/workflows/docs-publish.yml

@@ -34,7 +34,7 @@ jobs:
     - name: 🔵 Set up Quarto
       uses: quarto-dev/quarto-actions/setup@v2
       with:
-        version: 1.6.26
+        version: 1.9.37
 
     - name: 🚀 Install uv
       uses: astral-sh/setup-uv@v3

docs/.gitignore

@@ -8,3 +8,7 @@ objects.txt
 /reference
 
 CHANGELOG.md
+CHANGELOG.html
+/CHANGELOG_files/
+
+**/*.quarto_ipynb

docs/_freeze/structured-data/classification/execute-results/html.json

@@ -0,0 +1,16 @@
+{
+  "hash": "2bba27dce66fc08e608e5325d4da80f1",
+  "result": {
+    "engine": "jupyter",
+    "markdown": "---\ntitle: Classification\ncallout-appearance: simple\n---\n\nThe following example, which [closely inspired by the Claude documentation](https://github.com/anthropics/anthropic-cookbook/blob/main/tool_use/extracting_structured_json.ipynb), shows how `.chat_structured()` can be used to perform text classification.\n\n::: {#3a69def2 .cell execution_count=1}\n``` {.python .cell-code}\nfrom typing import Literal\n\nfrom chatlas import ChatOpenAI\nfrom pydantic import BaseModel, Field\nimport pandas as pd\n\ntext = \"The new quantum computing breakthrough could revolutionize the tech industry.\"\n\n\nclass Classification(BaseModel):\n    name: Literal[\n        \"Politics\", \"Sports\", \"Technology\", \"Entertainment\", \"Business\", \"Other\"\n    ] = Field(description=\"The category name\")\n\n    score: float = Field(\n        description=\"The classification score for the category, ranging from 0.0 to 1.0.\"\n    )\n\n\nclass Classifications(BaseModel):\n    \"\"\"Array of classification results. The scores should sum to 1.\"\"\"\n    classifications: list[Classification]\n\n\nchat = ChatOpenAI()\ndata = chat.chat_structured(text, data_model=Classifications)\npd.DataFrame([c.model_dump() for c in data.classifications])\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Technology</td>\n      <td>0.94</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Business</td>\n      <td>0.04</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Other</td>\n      <td>0.02</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n```\n:::\n:::\n\n\n",
+    "supporting": [
+      "classification_files"
+    ],
+    "filters": [],
+    "includes": {
+      "include-in-header": [
+        "<script src=\"https://cdn.jsdelivr.net/npm/requirejs@2.3.6/require.min.js\" integrity=\"sha384-c9c+LnTbwQ3aujuU7ULEPVvgLs+Fn6fJUvIGTsuu1ZcCf11fiEubah0ttpca4ntM sha384-6V1/AdqZRWk1KAlWbKBlGhN7VG4iE/yAZcO6NZPMF8od0vukrvr0tg4qY6NSrItx\" crossorigin=\"anonymous\"></script>\n<script src=\"https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.min.js\" integrity=\"sha384-ZvpUoO/+PpLXR1lu4jmpXWu80pZlYUAfxl5NsBMWOEPSjUn/6Z/hRTt8+pR6L4N2\" crossorigin=\"anonymous\" data-relocate-top=\"true\"></script>\n<script type=\"application/javascript\">define('jquery', [],function() {return window.jQuery;})</script>\n"
+      ]
+    }
+  }
+}

docs/_freeze/structured-data/entity-recognition/execute-results/html.json

@@ -0,0 +1,16 @@
+{
+  "hash": "99534743ffcbbb600a2d7fa317246f3d",
+  "result": {
+    "engine": "jupyter",
+    "markdown": "---\ntitle: Entity recognition\ncallout-appearance: simple\n---\n\nThe following example, which [closely inspired by the Claude documentation](https://github.com/anthropics/anthropic-cookbook/blob/main/tool_use/extracting_structured_json.ipynb), shows how `.chat_structured()` can be used to perform entity recognition.\n\n::: {#8a46015f .cell execution_count=1}\n``` {.python .cell-code}\nfrom chatlas import ChatOpenAI\nfrom pydantic import BaseModel, Field\nimport pandas as pd\n\n# | warning: false\ntext = \"John works at Google in New York. He met with Sarah, the CEO of Acme Inc., last week in San Francisco.\"\n\n\nclass NamedEntity(BaseModel):\n    \"\"\"Named entity in the text.\"\"\"\n\n    name: str = Field(description=\"The extracted entity name\")\n\n    type_: str = Field(description=\"The entity type, e.g. 'person', 'location', 'organization'\")\n\n    context: str = Field(description=\"The context in which the entity appears in the text.\")\n\n\nclass NamedEntities(BaseModel):\n    \"\"\"Named entities in the text.\"\"\"\n\n    entities: list[NamedEntity] = Field(description=\"Array of named entities\")\n\n\nchat = ChatOpenAI()\ndata = chat.chat_structured(text, data_model=NamedEntities)\npd.DataFrame([e.model_dump() for e in data.entities])\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>type_</th>\n      <th>context</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>John</td>\n      <td>person</td>\n      <td>John works at Google in New York.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Google</td>\n      <td>organization</td>\n      <td>John works at Google in New York.</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>New York</td>\n      <td>location</td>\n      <td>John works at Google in New York.</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Sarah</td>\n      <td>person</td>\n      <td>He met with Sarah, the CEO of Acme Inc., last ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Acme Inc.</td>\n      <td>organization</td>\n      <td>He met with Sarah, the CEO of Acme Inc., last ...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>San Francisco</td>\n      <td>location</td>\n      <td>He met with Sarah, the CEO of Acme Inc., last ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n```\n:::\n:::\n\n\n",
+    "supporting": [
+      "entity-recognition_files"
+    ],
+    "filters": [],
+    "includes": {
+      "include-in-header": [
+        "<script src=\"https://cdn.jsdelivr.net/npm/requirejs@2.3.6/require.min.js\" integrity=\"sha384-c9c+LnTbwQ3aujuU7ULEPVvgLs+Fn6fJUvIGTsuu1ZcCf11fiEubah0ttpca4ntM sha384-6V1/AdqZRWk1KAlWbKBlGhN7VG4iE/yAZcO6NZPMF8od0vukrvr0tg4qY6NSrItx\" crossorigin=\"anonymous\"></script>\n<script src=\"https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.min.js\" integrity=\"sha384-ZvpUoO/+PpLXR1lu4jmpXWu80pZlYUAfxl5NsBMWOEPSjUn/6Z/hRTt8+pR6L4N2\" crossorigin=\"anonymous\" data-relocate-top=\"true\"></script>\n<script type=\"application/javascript\">define('jquery', [],function() {return window.jQuery;})</script>\n"
+      ]
+    }
+  }
+}