diff --git a/docs/quick-tour/interacting-with-the-index.ipynb b/docs/quick-tour/interacting-with-the-index.ipynb index 14481da6..99c16b33 100644 --- a/docs/quick-tour/interacting-with-the-index.ipynb +++ b/docs/quick-tour/interacting-with-the-index.ipynb @@ -106,7 +106,17 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], "source": [ "!pip install -qU pandas==2.2.3 pinecone==6.0.2" ] @@ -143,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/conda/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/Users/aarshitaacharya/.pyenv/versions/3.10.16/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -245,7 +255,7 @@ "{\n", " \"name\": \"interacting-with-the-index\",\n", " \"metric\": \"euclidean\",\n", - " \"host\": \"interacting-with-the-index-dojoi3u.svc.aped-4627-b74a.pinecone.io\",\n", + " \"host\": \"interacting-with-the-index-dxs2y97.svc.aped-4627-b74a.pinecone.io\",\n", " \"spec\": {\n", " \"serverless\": {\n", " \"cloud\": \"aws\",\n", @@ -297,7 +307,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The index host is interacting-with-the-index-dojoi3u.svc.aped-4627-b74a.pinecone.io\n" + "The index host is interacting-with-the-index-dxs2y97.svc.aped-4627-b74a.pinecone.io\n" ] } ], @@ -319,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "d686f6a8-5536-4890-a0ca-653a3b62e666", "metadata": {}, "outputs": [], @@ -351,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "analyzed-charity", "metadata": { "colab": { @@ -434,7 +444,7 @@ "4 E [5.0, 5.0]" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -470,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "checked-christopher", "metadata": { "colab": { @@ -494,7 +504,7 @@ "{'upserted_count': 5}" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -504,6 +514,23 @@ "index.upsert(vectors=zip(df.id, df.vector))" ] }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2940b354", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "\n", + "def is_fresh(index):\n", + " stats = index.describe_index_stats()\n", + " vector_count = stats.total_vector_count\n", + " print(f\"Vector count: \", vector_count)\n", + " return vector_count > 0" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -525,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "varied-scene", "metadata": { "colab": { @@ -543,19 +570,33 @@ "tags": [] }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vector count: 0\n", + "Vector count: 0\n", + "Vector count: 0\n", + "Vector count: 5\n" + ] + }, { "data": { "text/plain": [ "FetchResponse(namespace='', vectors={'A': Vector(id='A', values=[1.0, 1.0], metadata=None, sparse_values=None), 'B': Vector(id='B', values=[2.0, 2.0], metadata=None, sparse_values=None)}, usage={'read_units': 1})" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Fetch vectors by ID\n", + "while not is_fresh(index):\n", + " # It takes a few moments for vectors we just upserted\n", + " # to become available for querying\n", + " time.sleep(5)\n", + "\n", "fetch_results = index.fetch(ids=[\"A\", \"B\"])\n", "fetch_results" ] @@ -581,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "id": "dried-demographic", "metadata": { "colab": { @@ -602,10 +643,13 @@ { "data": { "text/plain": [ - "{'matches': [], 'namespace': '', 'usage': {'read_units': 1}}" + "{'matches': [{'id': 'A', 'score': 0.0199999809, 'values': []},\n", + " {'id': 'B', 'score': 1.61999989, 'values': []}],\n", + " 'namespace': '',\n", + " 'usage': {'read_units': 1}}" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -637,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "generic-witness", "metadata": { "colab": { @@ -661,7 +705,7 @@ "FetchResponse(namespace='', vectors={'A': Vector(id='A', values=[1.0, 1.0], metadata=None, sparse_values=None)}, usage={'read_units': 1})" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -674,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "id": "comic-rwanda", "metadata": { "colab": { @@ -698,7 +742,7 @@ "{'upserted_count': 1}" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -710,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "id": "gentle-messenger", "metadata": { "colab": { @@ -734,13 +778,15 @@ "FetchResponse(namespace='', vectors={'A': Vector(id='A', values=[0.1, 0.1], metadata=None, sparse_values=None)}, usage={'read_units': 1})" ] }, - "execution_count": 25, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fetch vector by the same ID again\n", + "\n", + "time.sleep(5)\n", "fetch_result = index.fetch(ids=[\"A\"])\n", "fetch_result" ] @@ -766,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "id": "hispanic-talent", "metadata": { "colab": { @@ -790,7 +836,7 @@ "{}" ] }, - "execution_count": 26, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -802,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "id": "romantic-dubai", "metadata": { "colab": { @@ -823,16 +869,18 @@ { "data": { "text/plain": [ - "FetchResponse(namespace='', vectors={'A': Vector(id='A', values=[0.1, 0.1], metadata=None, sparse_values=None), 'B': Vector(id='B', values=[2.0, 2.0], metadata=None, sparse_values=None)}, usage={'read_units': 1})" + "FetchResponse(namespace='', vectors={'B': Vector(id='B', values=[2.0, 2.0], metadata=None, sparse_values=None)}, usage={'read_units': 1})" ] }, - "execution_count": 28, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Deleted vectors are empty\n", + "\n", + "time.sleep(5)\n", "fetch_results = index.fetch(ids=[\"A\", \"B\"])\n", "fetch_results" ] @@ -858,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "id": "nonprofit-popularity", "metadata": { "colab": { @@ -887,7 +935,7 @@ " 'vector_type': 'dense'}" ] }, - "execution_count": 29, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -918,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "id": "supported-casino", "metadata": { "id": "supported-casino", @@ -943,7 +991,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -957,7 +1005,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.10.16" }, "papermill": { "default_parameters": {},