Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsBedrockOn this pageBedrockAmazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case%pip install boto3from langchain.llms import Bedrockllm = Bedrock(    credentials_profile_name="bedrock-admin",    model_id="amazon.titan-text-express-v1")Using in a conversation chain‚Äãfrom langchain.chains import ConversationChainfrom langchain.memory import ConversationBufferMemoryconversation = ConversationChain(    llm=llm, verbose=True, memory=ConversationBufferMemory())conversation.predict(input="Hi there!")Conversation Chain With Streaming‚Äãfrom langchain.llms import Bedrockfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Bedrock(    credentials_profile_name="bedrock-admin",    model_id="amazon.titan-text-express-v1",    streaming=True,
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsBedrockOn this pageBedrockAmazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case%pip install boto3from langchain.llms import Bedrockllm = Bedrock(    credentials_profile_name="bedrock-admin",    model_id="amazon.titan-text-express-v1")Using in a conversation chain‚Äãfrom langchain.chains import ConversationChainfrom langchain.memory import ConversationBufferMemoryconversation = ConversationChain(    llm=llm, verbose=True, memory=ConversationBufferMemory())conversation.predict(input="Hi there!")Conversation Chain With Streaming‚Äãfrom langchain.llms import Bedrockfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Bedrock(    credentials_profile_name="bedrock-admin",    model_id="amazon.titan-text-express-v1",    streaming=True,