docs: add a uv install cell to every example notebook

docs/examples/agent-quickstart.ipynb

@@ -8,6 +8,16 @@
     "# TimeCopilot Agent"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621a76d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,

docs/examples/anomaly-detection-forecaster-quickstart.ipynb

@@ -8,6 +8,16 @@
     "# TimeCopilot Forecaster (Anomaly Detection)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f923f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "85e1ddbe",

docs/examples/aws-bedrock.ipynb

@@ -8,6 +8,16 @@
     "# Using TimeCopilot with AWS Bedrock"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f34942da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio python-dotenv"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "40e50249",

docs/examples/chronos-family.ipynb

@@ -8,6 +8,16 @@
     "# Forecast Using the Chronos Family"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93b9c9a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "85e1ddbe",

docs/examples/cryptocurrency-quickstart.ipynb

@@ -9,6 +9,16 @@
     "This notebook will work to exemplify the use of TimeCopilot in the context of missing data, anomaly detection and forecasting usage on a dataframe that involves distinct ids. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "492c4a54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot kagglehub"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/examples/custom-ensembles.ipynb

@@ -12,6 +12,16 @@
       ],
       "id": "a1b2c3d0"
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "02b01730",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

docs/examples/dask.ipynb

@@ -8,6 +8,16 @@
     "# Using Dask with TimeCopilot"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81854556",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q \"timecopilot[distributed]\" nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,

docs/examples/explaining-foundation-models-and-ensembles.ipynb

@@ -10,6 +10,16 @@
         "This example shows how to give the `TimeCopilot` agent your own foundation models and an ensemble, and then ask plain-English questions to understand and compare them."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dcdd0b18",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 1,

docs/examples/finetuning.ipynb

@@ -14,6 +14,16 @@
         "Finetuning in TimeCopilot is available for **Chronos 2** and **TimeGPT**. See [ChronosFinetuningConfig](https://timecopilot.dev/api/models/foundation/models/#timecopilot.models.foundation.chronos.ChronosFinetuningConfig) and [TimeGPTFinetuningConfig](https://timecopilot.dev/api/models/foundation/models/#timecopilot.models.foundation.timegpt.TimeGPTFinetuningConfig) for API references."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "22c57273",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "bCv-v5rIdiXS",

docs/examples/forecaster-quickstart.ipynb

@@ -8,6 +8,16 @@
     "# TimeCopilot Forecaster"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f0c7f1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "85e1ddbe",

docs/examples/gift-eval.ipynb

@@ -53,7 +53,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install \"timecopilot[gift-eval]>=0.0.22\""
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q \"timecopilot[gift-eval]\""
    ]
   },
   {

docs/examples/google-llms.ipynb

@@ -8,6 +8,16 @@
     "# Using TimeCopilot with Google Endpoints"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7e60ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio python-dotenv"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "51eaf62c",

docs/examples/llm-providers.ipynb

@@ -16,6 +16,16 @@
     "- **Leverage existing infrastructure** by using providers you already have configured"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6c52fbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio python-dotenv"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9c8d5124",

docs/examples/pandas_baseline.ipynb

@@ -10,6 +10,16 @@
     "This is a reference point for using a TimeCopilotForecaster with pandas rather than with the alternative distributed dataframes like pyspark, ray, and dask."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9710b236",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/examples/patchtst-fm-family.ipynb

@@ -8,6 +8,16 @@
     "# Forecast Using the PatchTST-FM Family\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cece0e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7471dfd7",

docs/examples/patchtst-fm.ipynb

@@ -10,6 +10,16 @@
     "This is an example for using PatchTST-FM with the `timecopilot` library."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0fc3170",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/examples/pyspark.ipynb

@@ -13,6 +13,16 @@
     "For this example PySpark version `4.0.2` was used with OpenJDK version `21.0.10` used as the Java version."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73068063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q \"timecopilot[distributed]\" nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

docs/examples/ray.ipynb

@@ -8,6 +8,16 @@
     "# Using Ray with TimeCopilot"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a9ff676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q \"timecopilot[distributed]\" nest-asyncio"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,

docs/examples/sktime.ipynb

@@ -10,6 +10,16 @@
     "This is an example for using sktime based models with the `timecopilot` library."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e720b5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot nest-asyncio sktime"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6140af37",

docs/examples/t0-family.ipynb

@@ -8,6 +8,16 @@
     "# Forecast Using the T0 Family\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7f1ecef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7471dfd7",

docs/examples/tabpfn-family.ipynb

@@ -8,6 +8,16 @@
     "# Forecast Using the TabPFN Family\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "063fc248",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot python-dotenv"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b2fa5716",

docs/examples/timesfm-family.ipynb

@@ -7,6 +7,15 @@
     "# Forecast Using the TimesFM Family"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "448dd930",

docs/examples/tirex-family.ipynb

@@ -8,6 +8,15 @@
         "\n"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "9d46570f",

docs/examples/toto-family.ipynb

@@ -8,6 +8,16 @@
     "# Forecast Using the Toto Family"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0959f0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!UV_CONSTRAINT= UV_BUILD_CONSTRAINT= uv pip install -q timecopilot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "67e73106",