obs)                # populate prompt template output using the fetched feature values.        kwargs["query"] = query        kwargs["account_id"] = account_id        kwargs["account_age"] = df["accountAge"][0]        kwargs["account_country"] = df["accountCountry"][0]        kwargs["payment_rejects_1d_per_user"] = df["numPaymentRejects1dPerUser"][0]        return self.prompt.format(**kwargs)Test‚Äã# Replace the place holders below with actual details of feature store that was created in previous stepsprompt_template = AzureMLFeatureStorePromptTemplate(            subscription_id="",            resource_group="",            feature_store_name="")print(prompt_template.format(account_id="A1829581630230790"))                                    ###                account id = A1829581630230790                account age = 563.0                account country = GB                payment rejects 1d per user = 15.0                ###                Use in a chain‚ÄãWe can now use this in a chain, successfully creating a chain that achieves personalization backed by the AzureML Managed Feature Store.os.environ["OPENAI_API_KEY"]="" # Fill the open ai key herefrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)# NOTE: developer's can further fine tune AzureMLFeatureStorePromptTemplate# for getting even more accurate results for the input querychain.predict(account_id="A1829581630230790", query ="write a small thank you note within 20 words if account age > 10 using the account stats")    'Thank you for being a valued member for over 10 years! We appreciate your continued support.'PreviousPrompt templatesNextCustom prompt templateFeastLoad Feast StorePromptsUse in a chainTectonPrerequisitesDefine and load featuresPromptsUse in a chainFeatureformInitialize FeatureformPromptsUse in a chainAzureML Managed Feature StorePrerequisitesPromptsTestUse in a
Feature stores are a concept from traditional machine learning that make sure data fed into models is up-to-date and relevant. For more on this, see here.
Feature stores are a concept from traditional machine learning that make sure data fed into models is up-to-date and relevant. For more on this, see here. ->: obs)                # populate prompt template output using the fetched feature values.        kwargs["query"] = query        kwargs["account_id"] = account_id        kwargs["account_age"] = df["accountAge"][0]        kwargs["account_country"] = df["accountCountry"][0]        kwargs["payment_rejects_1d_per_user"] = df["numPaymentRejects1dPerUser"][0]        return self.prompt.format(**kwargs)Test‚Äã# Replace the place holders below with actual details of feature store that was created in previous stepsprompt_template = AzureMLFeatureStorePromptTemplate(            subscription_id="",            resource_group="",            feature_store_name="")print(prompt_template.format(account_id="A1829581630230790"))                                    ###                account id = A1829581630230790                account age = 563.0                account country = GB                payment rejects 1d per user = 15.0                ###                Use in a chain‚ÄãWe can now use this in a chain, successfully creating a chain that achieves personalization backed by the AzureML Managed Feature Store.os.environ["OPENAI_API_KEY"]="" # Fill the open ai key herefrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)# NOTE: developer's can further fine tune AzureMLFeatureStorePromptTemplate# for getting even more accurate results for the input querychain.predict(account_id="A1829581630230790", query ="write a small thank you note within 20 words if account age > 10 using the account stats")    'Thank you for being a valued member for over 10 years! We appreciate your continued support.'PreviousPrompt templatesNextCustom prompt templateFeastLoad Feast StorePromptsUse in a chainTectonPrerequisitesDefine and load featuresPromptsUse in a chainFeatureformInitialize FeatureformPromptsUse in a chainAzureML Managed Feature StorePrerequisitesPromptsTestUse in a