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 loadersComponentsMemoryRemembrallOn this pageRemembrallThis page covers how to use the Remembrall ecosystem within LangChain.What is Remembrall?‚ÄãRemembrall gives your language model long-term memory, retrieval augmented generation, and complete observability with just a few lines of code.It works as a light-weight proxy on top of your OpenAI calls and simply augments the context of the chat calls at runtime with relevant facts that have been collected.Setup‚ÄãTo get started, sign in with Github on the Remembrall platform and copy your API key from the settings page.Any request that you send with the modified openai_api_base (see below) and Remembrall API key will automatically be tracked in the Remembrall dashboard. You never have to share your OpenAI key with our platform and this information is never stored by the Remembrall systems.Enable Long Term Memory‚ÄãIn addition to setting the openai_api_base and Remembrall API key via x-gp-api-key, you should specify a UID to maintain memory for. This will usually be a unique user identifier (like email).from langchain.chat_models import ChatOpenAIchat_model = ChatOpenAI(openai_api_base="https://remembrall.dev/api/openai/v1",                        model_kwargs={                            "headers":{                                "x-gp-api-key": "remembrall-api-key-here",                                "x-gp-remember": "user@email.com",                            }                        })chat_model.predict("My favorite color is
This page covers how to use the Remembrall ecosystem within LangChain.
This page covers how to use the Remembrall ecosystem within LangChain. ->: 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 loadersComponentsMemoryRemembrallOn this pageRemembrallThis page covers how to use the Remembrall ecosystem within LangChain.What is Remembrall?‚ÄãRemembrall gives your language model long-term memory, retrieval augmented generation, and complete observability with just a few lines of code.It works as a light-weight proxy on top of your OpenAI calls and simply augments the context of the chat calls at runtime with relevant facts that have been collected.Setup‚ÄãTo get started, sign in with Github on the Remembrall platform and copy your API key from the settings page.Any request that you send with the modified openai_api_base (see below) and Remembrall API key will automatically be tracked in the Remembrall dashboard. You never have to share your OpenAI key with our platform and this information is never stored by the Remembrall systems.Enable Long Term Memory‚ÄãIn addition to setting the openai_api_base and Remembrall API key via x-gp-api-key, you should specify a UID to maintain memory for. This will usually be a unique user identifier (like email).from langchain.chat_models import ChatOpenAIchat_model = ChatOpenAI(openai_api_base="https://remembrall.dev/api/openai/v1",                        model_kwargs={                            "headers":{                                "x-gp-api-key": "remembrall-api-key-here",                                "x-gp-remember": "user@email.com",                            }                        })chat_model.predict("My favorite color is