capability, and blazing-fast performance.Momento Vector Index stands out as the most productive, easiest-to-use, fully serverless vector index.For both services, simply grab the SDK, obtain an API key, input a few lines into your code, and you're set to go. Together, they provide a comprehensive solution for your LLM data needs.This page covers how to use the Momento ecosystem within LangChain.Installation and Setup‚ÄãSign up for a free account here to get an API keyInstall the Momento Python SDK with pip install momentoCache‚ÄãUse Momento as a serverless, distributed, low-latency cache for LLM prompts and responses. The standard cache is the primary use case for Momento users in any environment.To integrate Momento Cache into your application:from langchain.cache import MomentoCacheThen, set it up with the following code:from datetime import timedeltafrom momento import CacheClient, Configurations, CredentialProviderfrom langchain.globals import set_llm_cache# Instantiate the Momento clientcache_client = CacheClient(    Configurations.Laptop.v1(),    CredentialProvider.from_environment_variable("MOMENTO_API_KEY"),    default_ttl=timedelta(days=1))# Choose a Momento cache name of your choicecache_name = "langchain"# Instantiate the LLM cacheset_llm_cache(MomentoCache(cache_client, cache_name))Memory‚ÄãMomento can be used as a distributed memory store for LLMs.Chat Message History Memory‚ÄãSee this notebook for a walkthrough of how to use Momento as a memory store for chat message history.Vector Store‚ÄãMomento Vector Index (MVI) can be used as a vector store.See this notebook for a walkthrough of how to use MVI as a vector store.PreviousModern TreasuryNextMongoDB AtlasInstallation and SetupCacheMemoryChat Message History MemoryVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Momento Cache is the world's first truly serverless caching service, offering instant elasticity, scale-to-zero
Momento Cache is the world's first truly serverless caching service, offering instant elasticity, scale-to-zero ->: capability, and blazing-fast performance.Momento Vector Index stands out as the most productive, easiest-to-use, fully serverless vector index.For both services, simply grab the SDK, obtain an API key, input a few lines into your code, and you're set to go. Together, they provide a comprehensive solution for your LLM data needs.This page covers how to use the Momento ecosystem within LangChain.Installation and Setup‚ÄãSign up for a free account here to get an API keyInstall the Momento Python SDK with pip install momentoCache‚ÄãUse Momento as a serverless, distributed, low-latency cache for LLM prompts and responses. The standard cache is the primary use case for Momento users in any environment.To integrate Momento Cache into your application:from langchain.cache import MomentoCacheThen, set it up with the following code:from datetime import timedeltafrom momento import CacheClient, Configurations, CredentialProviderfrom langchain.globals import set_llm_cache# Instantiate the Momento clientcache_client = CacheClient(    Configurations.Laptop.v1(),    CredentialProvider.from_environment_variable("MOMENTO_API_KEY"),    default_ttl=timedelta(days=1))# Choose a Momento cache name of your choicecache_name = "langchain"# Instantiate the LLM cacheset_llm_cache(MomentoCache(cache_client, cache_name))Memory‚ÄãMomento can be used as a distributed memory store for LLMs.Chat Message History Memory‚ÄãSee this notebook for a walkthrough of how to use Momento as a memory store for chat message history.Vector Store‚ÄãMomento Vector Index (MVI) can be used as a vector store.See this notebook for a walkthrough of how to use MVI as a vector store.PreviousModern TreasuryNextMongoDB AtlasInstallation and SetupCacheMemoryChat Message History MemoryVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.