Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryAWS DynamoDBCassandramemoryMomento CacheMongodDBMot√∂rheadPostgresRedisRemembrallRocksetSingleStoreDBSQL (SQLAlchemy)SQLiteStreamlitUpstash Redis Chat Message HistoryXataZepCallbacksChat loadersComponentsMemoryXataOn this pageXataXata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.This notebook covers:A simple example showing what XataChatMessageHistory does.A more complex example using a REACT agent that answer questions based on a knowledge based or documentation (stored in Xata as a vector store) and also having a long-term searchable history of its past messages (stored in Xata as a memory store)Setup‚ÄãCreate a database‚ÄãIn the Xata UI create a new database. You can name it whatever you want, in this notepad we'll use langchain. The Langchain integration can auto-create the table used for storying the memory, and this is what we'll use in this example. If you want to pre-create the table, ensure it has the right schema and set create_table to False when creating the class. Pre-creating the table saves one round-trip to the database during each session initialization.Let's first install our dependencies:pip install xata openai langchainNext, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something like this:
Xata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.
Xata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryAWS DynamoDBCassandramemoryMomento CacheMongodDBMot√∂rheadPostgresRedisRemembrallRocksetSingleStoreDBSQL (SQLAlchemy)SQLiteStreamlitUpstash Redis Chat Message HistoryXataZepCallbacksChat loadersComponentsMemoryXataOn this pageXataXata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.This notebook covers:A simple example showing what XataChatMessageHistory does.A more complex example using a REACT agent that answer questions based on a knowledge based or documentation (stored in Xata as a vector store) and also having a long-term searchable history of its past messages (stored in Xata as a memory store)Setup‚ÄãCreate a database‚ÄãIn the Xata UI create a new database. You can name it whatever you want, in this notepad we'll use langchain. The Langchain integration can auto-create the table used for storying the memory, and this is what we'll use in this example. If you want to pre-create the table, ensure it has the right schema and set create_table to False when creating the class. Pre-creating the table saves one round-trip to the database during each session initialization.Let's first install our dependencies:pip install xata openai langchainNext, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something like this: