mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
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Added redis info
This commit is contained in:
+102
-1
@@ -150,6 +150,24 @@
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" tensorflow-hub"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Install the latest version of redis for low-latency data retrieval"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install the redis package\n",
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"! pip install --upgrade redis"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -1015,6 +1033,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "6LCGvBNvBd8D"
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@@ -1022,7 +1041,9 @@
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"source": [
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"## Create Online Queries\n",
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"\n",
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"After you built your indexes, you may query against the deployed index to find nearest neighbors."
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"After you built your indexes, you may query against the deployed index to find nearest neighbors.\n",
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"\n",
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"Note: For the DOT_PRODUCT_DISTANCE distance type, the \"distance\" property returned with each MatchNeighbor actually refers to the similarity."
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]
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},
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{
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@@ -1087,6 +1108,76 @@
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" )"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Storing and retrieving titles from a Redis data store\n",
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"When you productionize this code into a service, you will need to conver the nearest nearest id's returned from Vertex AI Matching Engine into data usable by downstream services.\n",
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"\n",
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"In this case, you'll need to convert the id's to titles.\n",
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"\n",
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"You can use Google Cloud's Memorystore to deploy a managed Redis instance to save the id-title key-value pairs.\n",
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"\n",
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"See more information on [Memorystore](https://cloud.google.com/memorystore/docs/redis/create-manage-instances?hl=en)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"REDIS_INSTANCE_NAME = \"stackoverflow-questions-unique\"\n",
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"\n",
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"# Create a Redis instance\n",
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"! gcloud redis instances create '{REDIS_INSTANCE_NAME}' --size=5 --region={REGION} --network={VPC_NETWORK_FULL} --connect-mode=private-service-access"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get host and port info\n",
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"REDIS_HOST = ! gcloud redis instances list --region {REGION} --format='value(HOST)'\n",
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"REDIS_PORT = ! gcloud redis instances list --region {REGION} --format='value(PORT)'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Connect to the instance\n",
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"import redis\n",
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"\n",
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"redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert the id -> title relationship into a dict and write to redis\n",
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"redis_client.mset({str(id): str(title) for id, title in zip(df.id, df.title)})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Verify that redis can retrieve the correct information\n",
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"[f\"Actual = {title}, Retrieved = {redis_client.get(str(id))}\" for id, title in list(zip(df.id, df.title))[:10]]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -1123,6 +1214,16 @@
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"# Delete indexes\n",
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"tree_ah_index.delete()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Delete redis instance\n",
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"! gcloud redis instances delete '{REDIS_INSTANCE_NAME}' --region {REGION} --quiet"
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]
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}
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],
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"metadata": {
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