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 loadersComponentsLLMsManifestOn this pageManifestThis notebook goes over how to use Manifest and LangChain.For more detailed information on manifest, and how to use it with local huggingface models like in this example, see https://github.com/HazyResearch/manifestAnother example of using Manifest with Langchain.pip install manifest-mlfrom manifest import Manifestfrom langchain.llms.manifest import ManifestWrappermanifest = Manifest(    client_name="huggingface", client_connection="http://127.0.0.1:5000")print(manifest.client_pool.get_current_client().get_model_params())llm = ManifestWrapper(    client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 256})# Map reduce examplefrom langchain.prompts import PromptTemplatefrom langchain.text_splitter import CharacterTextSplitterfrom langchain.chains.mapreduce import MapReduceChain_prompt = """Write a concise summary of the following:{text}CONCISE SUMMARY:"""prompt =
This notebook goes over how to use Manifest and LangChain.
This notebook goes over how to use Manifest and LangChain. ->: 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 loadersComponentsLLMsManifestOn this pageManifestThis notebook goes over how to use Manifest and LangChain.For more detailed information on manifest, and how to use it with local huggingface models like in this example, see https://github.com/HazyResearch/manifestAnother example of using Manifest with Langchain.pip install manifest-mlfrom manifest import Manifestfrom langchain.llms.manifest import ManifestWrappermanifest = Manifest(    client_name="huggingface", client_connection="http://127.0.0.1:5000")print(manifest.client_pool.get_current_client().get_model_params())llm = ManifestWrapper(    client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 256})# Map reduce examplefrom langchain.prompts import PromptTemplatefrom langchain.text_splitter import CharacterTextSplitterfrom langchain.chains.mapreduce import MapReduceChain_prompt = """Write a concise summary of the following:{text}CONCISE SUMMARY:"""prompt =