joke about chickens at the end to make them feel better        Here are the drivers stats:    Conversation rate: 0.4745151400566101    Acceptance rate: 0.055561766028404236    Average Daily Trips: 936        Your response:Use in a chain‚ÄãWe can now use this in a chain, successfully creating a chain that achieves personalization backed by a feature store.from langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)chain.run(1001)    "Hi there! I wanted to update you on your current stats. Your acceptance rate is 0.055561766028404236 and your average daily trips are 936. While your conversation rate is currently 0.4745151400566101, I have no doubt that with a little extra effort, you'll be able to exceed that .5 mark! Keep up the great work! And remember, even chickens can't always cross the road, but they still give it their best shot."Tecton‚ÄãAbove, we showed how you could use Feast, a popular open-source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs.Prerequisites‚ÄãTecton Deployment (sign up at https://tecton.ai)TECTON_API_KEY environment variable set to a valid Service Account keyDefine and load features‚ÄãWe will use the user_transaction_counts Feature View from the Tecton tutorial as part of a Feature Service. For simplicity, we are only using a single Feature View; however, more sophisticated applications may require more feature views to retrieve the features needed for its prompt.user_transaction_metrics = FeatureService(    name = "user_transaction_metrics",    features = [user_transaction_counts])The above Feature Service is expected to be applied to a live workspace. For this example, we will be using the "prod" workspace.import tectonworkspace =
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. ->: joke about chickens at the end to make them feel better        Here are the drivers stats:    Conversation rate: 0.4745151400566101    Acceptance rate: 0.055561766028404236    Average Daily Trips: 936        Your response:Use in a chain‚ÄãWe can now use this in a chain, successfully creating a chain that achieves personalization backed by a feature store.from langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainchain = LLMChain(llm=ChatOpenAI(), prompt=prompt_template)chain.run(1001)    "Hi there! I wanted to update you on your current stats. Your acceptance rate is 0.055561766028404236 and your average daily trips are 936. While your conversation rate is currently 0.4745151400566101, I have no doubt that with a little extra effort, you'll be able to exceed that .5 mark! Keep up the great work! And remember, even chickens can't always cross the road, but they still give it their best shot."Tecton‚ÄãAbove, we showed how you could use Feast, a popular open-source and self-managed feature store, with LangChain. Our examples below will show a similar integration using Tecton. Tecton is a fully managed feature platform built to orchestrate the complete ML feature lifecycle, from transformation to online serving, with enterprise-grade SLAs.Prerequisites‚ÄãTecton Deployment (sign up at https://tecton.ai)TECTON_API_KEY environment variable set to a valid Service Account keyDefine and load features‚ÄãWe will use the user_transaction_counts Feature View from the Tecton tutorial as part of a Feature Service. For simplicity, we are only using a single Feature View; however, more sophisticated applications may require more feature views to retrieve the features needed for its prompt.user_transaction_metrics = FeatureService(    name = "user_transaction_metrics",    features = [user_transaction_counts])The above Feature Service is expected to be applied to a live workspace. For this example, we will be using the "prod" workspace.import tectonworkspace =