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Add an example for azure ml managed feature store (#8324)
We are adding an example of how one can connect to azure ml managed feature store and use such a prompt template in a llm chain. @baskaryan
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@ -597,6 +597,217 @@
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"source": [
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"chain.run(\"C1410926\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b99ac57",
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"metadata": {},
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"source": [
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"## AzureML Managed Feature Store\n",
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"\n",
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"We will use [AzureML Managed Feature Store](https://learn.microsoft.com/en-us/azure/machine-learning/concept-what-is-managed-feature-store) to run the below example. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "1ebf16d2",
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"metadata": {},
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"source": [
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"### Prerequisites\n",
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"\n",
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"* Create feature store with online materialization using instructions here [Enable online materialization and run online inference](https://github.com/Azure/azureml-examples/blob/featurestore/online/sdk/python/featurestore_sample/notebooks/sdk_only/5.%20Enable%20online%20store%20and%20run%20online%20inference.ipynb).\n",
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"\n",
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"* A successfully created feature store by following the instructions should have an `account` featureset with version as `1`. It will have `accountID` as index column with features `accountAge`, `accountCountry`, `numPaymentRejects1dPerUser`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b1ad8ee",
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"metadata": {},
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"source": [
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"### Prompts\n",
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"\n",
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"* Here we will set up a custom AzureMLFeatureStorePromptTemplate. This prompt template will take in an `account_id` and optional `query`. It then fetches feature values from feature store and format those features into the output prompt. Note that the required input to this prompt template is just `account_id`, since that is the only user defined piece (all other variables are looked up inside the prompt template).\n",
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"\n",
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"* Also note that this is a bootstrap example to showcase how LLM applications can leverage AzureML managed feature store. Developers are welcome to improve the prompt template further to suit their needs."
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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": 1,
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"id": "bd54e256",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ['AZURE_ML_CLI_PRIVATE_FEATURES_ENABLED'] = 'True'"
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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": 2,
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"id": "5f935e7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas\n",
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"\n",
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"from pydantic import Extra\n",
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"from langchain.prompts import PromptTemplate, StringPromptTemplate\n",
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"from azure.identity import AzureCliCredential\n",
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"from azureml.featurestore import FeatureStoreClient, init_online_lookup, get_online_features\n",
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"\n",
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"class AzureMLFeatureStorePromptTemplate(StringPromptTemplate, extra=Extra.allow):\n",
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"\n",
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" def __init__(self, subscription_id: str, resource_group: str, feature_store_name: str, **kwargs):\n",
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" # this is an example template for proof of concept and can be changed to suit the developer needs\n",
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" template = \"\"\"\n",
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" {query}\n",
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" ###\n",
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" account id = {account_id}\n",
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" account age = {account_age}\n",
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" account country = {account_country}\n",
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" payment rejects 1d per user = {payment_rejects_1d_per_user}\n",
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" ###\n",
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" \"\"\"\n",
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" prompt_template=PromptTemplate.from_template(template)\n",
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" super().__init__(prompt=prompt_template, input_variables=[\"account_id\", \"query\"])\n",
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"\n",
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" # use AzureMLOnBehalfOfCredential() in spark context\n",
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" credential = AzureCliCredential()\n",
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"\n",
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" self._fs_client = FeatureStoreClient(\n",
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" credential=credential,\n",
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" subscription_id=subscription_id,\n",
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" resource_group_name=resource_group,\n",
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" name=feature_store_name)\n",
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" \n",
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" self._feature_set = self._fs_client.feature_sets.get(name=\"accounts\", version=1)\n",
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"\n",
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" init_online_lookup(self._feature_set.features, credential, force=True)\n",
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" \n",
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"\n",
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" def format(self, **kwargs) -> str: \n",
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" if \"account_id\" not in kwargs:\n",
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" raise \"account_id needed to fetch details from feature store\"\n",
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" account_id = kwargs.pop(\"account_id\") \n",
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"\n",
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" query=\"\"\n",
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" if \"query\" in kwargs:\n",
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" query = kwargs.pop(\"query\")\n",
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"\n",
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" # feature set is registered with accountID as entity index column.\n",
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" obs = pandas.DataFrame({'accountID': [account_id]})\n",
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"\n",
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" # get the feature details for the input entity from feature store.\n",
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" df = get_online_features(self._feature_set.features, obs) \n",
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"\n",
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" # populate prompt template output using the fetched feature values.\n",
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" kwargs[\"query\"] = query\n",
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" kwargs[\"account_id\"] = account_id\n",
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" kwargs[\"account_age\"] = df[\"accountAge\"][0]\n",
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" kwargs[\"account_country\"] = df[\"accountCountry\"][0]\n",
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" kwargs[\"payment_rejects_1d_per_user\"] = df[\"numPaymentRejects1dPerUser\"][0]\n",
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"\n",
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" return self.prompt.format(**kwargs)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "28f148b0",
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"metadata": {},
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"source": [
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"### Test"
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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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"id": "84571856",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Replace the place holders below with actual details of feature store that was created in previous steps\n",
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"\n",
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"prompt_template = AzureMLFeatureStorePromptTemplate(\n",
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" subscription_id=\"\",\n",
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" resource_group=\"\",\n",
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" feature_store_name=\"\")"
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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": 4,
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"id": "99703f42",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" \n",
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" ###\n",
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" account id = A1829581630230790\n",
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" account age = 563.0\n",
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" account country = GB\n",
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" payment rejects 1d per user = 15.0\n",
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" ###\n",
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" \n"
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]
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}
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],
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"source": [
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"print(prompt_template.format(account_id=\"A1829581630230790\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8830d12",
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"metadata": {},
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"source": [
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"### Use in a chain\n",
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"\n",
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"We can now use this in a chain, successfully creating a chain that achieves personalization backed by the AzureML Managed Feature Store"
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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": 5,
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"id": "33266cb5",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"OPENAI_API_KEY\"]=\"\" # Fill the open ai key here\n",
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"\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain import LLMChain\n",
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"\n",
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"chain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)"
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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": 8,
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"id": "67ae8934",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Thank you for being a valued member for over 10 years! We appreciate your continued support.'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# NOTE: developer's can further fine tune AzureMLFeatureStorePromptTemplate\n",
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"# for getting even more accurate results for the input query\n",
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"chain.predict(account_id=\"A1829581630230790\", query =\"write a small thank you note within 20 words if account age > 10 using the account stats\")"
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]
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}
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],
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"metadata": {
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