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Added delay after upsert and delete for eventual consistency #480

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110 changes: 79 additions & 31 deletions docs/quick-tour/interacting-with-the-index.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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"
]
Expand Down Expand Up @@ -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"
]
}
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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"
]
}
],
Expand All @@ -319,7 +329,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 7,
"id": "d686f6a8-5536-4890-a0ca-653a3b62e666",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -351,7 +361,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 8,
"id": "analyzed-charity",
"metadata": {
"colab": {
Expand Down Expand Up @@ -434,7 +444,7 @@
"4 E [5.0, 5.0]"
]
},
"execution_count": 13,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -470,7 +480,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 9,
"id": "checked-christopher",
"metadata": {
"colab": {
Expand All @@ -494,7 +504,7 @@
"{'upserted_count': 5}"
]
},
"execution_count": 14,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -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",
Expand All @@ -525,7 +552,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 11,
"id": "varied-scene",
"metadata": {
"colab": {
Expand All @@ -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"
]
Expand All @@ -581,7 +622,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 12,
"id": "dried-demographic",
"metadata": {
"colab": {
Expand All @@ -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"
}
Expand Down Expand Up @@ -637,7 +681,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 13,
"id": "generic-witness",
"metadata": {
"colab": {
Expand All @@ -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"
}
Expand All @@ -674,7 +718,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 14,
"id": "comic-rwanda",
"metadata": {
"colab": {
Expand All @@ -698,7 +742,7 @@
"{'upserted_count': 1}"
]
},
"execution_count": 18,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -710,7 +754,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 15,
"id": "gentle-messenger",
"metadata": {
"colab": {
Expand All @@ -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"
]
Expand All @@ -766,7 +812,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 16,
"id": "hispanic-talent",
"metadata": {
"colab": {
Expand All @@ -790,7 +836,7 @@
"{}"
]
},
"execution_count": 26,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -802,7 +848,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 17,
"id": "romantic-dubai",
"metadata": {
"colab": {
Expand All @@ -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"
]
Expand All @@ -858,7 +906,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 18,
"id": "nonprofit-popularity",
"metadata": {
"colab": {
Expand Down Expand Up @@ -887,7 +935,7 @@
" 'vector_type': 'dense'}"
]
},
"execution_count": 29,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -918,7 +966,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 19,
"id": "supported-casino",
"metadata": {
"id": "supported-casino",
Expand All @@ -943,7 +991,7 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -957,7 +1005,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
"version": "3.10.16"
},
"papermill": {
"default_parameters": {},
Expand Down