and toolkitsMemoryCallbacksChat loadersProvidersMoreRocksetOn this pageRocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index‚Ñ¢ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. Installation and Setup‚ÄãMake sure you have Rockset account and go to the web console to get the API key. Details can be found on the website.pip install rocksetVector Store‚ÄãSee a usage example.from langchain.vectorstores import Rockset Document Loader‚ÄãSee a usage example.from langchain.document_loaders import RocksetLoaderChat Message History‚ÄãSee a usage example.from langchain.memory.chat_message_histories import RocksetChatMessageHistoryPreviousRoamNextRunhouseInstallation and SetupVector StoreDocument LoaderChat Message HistoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Rockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index‚Ñ¢ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters.
Rockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index‚Ñ¢ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreRocksetOn this pageRocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Index‚Ñ¢ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. Installation and Setup‚ÄãMake sure you have Rockset account and go to the web console to get the API key. Details can be found on the website.pip install rocksetVector Store‚ÄãSee a usage example.from langchain.vectorstores import Rockset Document Loader‚ÄãSee a usage example.from langchain.document_loaders import RocksetLoaderChat Message History‚ÄãSee a usage example.from langchain.memory.chat_message_histories import RocksetChatMessageHistoryPreviousRoamNextRunhouseInstallation and SetupVector StoreDocument LoaderChat Message HistoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.