Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew-shot examples for chat modelsFormat template outputTemplate formatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt pipeliningValidate templateExample selectorsLanguage modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreOn this pageConnecting to a Feature StoreFeature 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.This concept is extremely relevant when considering putting LLM applications in production. In order to personalize LLM applications, you may want to combine LLMs with up-to-date information about particular users. Feature stores can be a great way to keep that data fresh, and LangChain provides an easy way to combine that data with LLMs.In this notebook we will show how to connect prompt templates to feature stores. The basic idea is to call a feature store from inside a prompt template to retrieve values that are then formatted into the prompt.Feast‚ÄãTo start, we will use the popular open-source feature store framework Feast.This assumes you have already run the steps in the README around getting started. We will build off of that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics.Load Feast Store‚ÄãAgain, this should be set up according to the instructions in the Feast README.from feast import FeatureStore# You may need to update the path depending on where you
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. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew-shot examples for chat modelsFormat template outputTemplate formatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt pipeliningValidate templateExample selectorsLanguage modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreOn this pageConnecting to a Feature StoreFeature 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.This concept is extremely relevant when considering putting LLM applications in production. In order to personalize LLM applications, you may want to combine LLMs with up-to-date information about particular users. Feature stores can be a great way to keep that data fresh, and LangChain provides an easy way to combine that data with LLMs.In this notebook we will show how to connect prompt templates to feature stores. The basic idea is to call a feature store from inside a prompt template to retrieve values that are then formatted into the prompt.Feast‚ÄãTo start, we will use the popular open-source feature store framework Feast.This assumes you have already run the steps in the README around getting started. We will build off of that example in getting started, and create and LLMChain to write a note to a specific driver regarding their up-to-date statistics.Load Feast Store‚ÄãAgain, this should be set up according to the instructions in the Feast README.from feast import FeatureStore# You may need to update the path depending on where you