AwaDB | ü¶úÔ∏èüîó Langchain






Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsAleph AlphaAwaDBAzureOpenAIBaidu QianfanBedrockBGE on Hugging FaceClarifaiCohereDashScopeDeepInfraEDEN AIElasticsearchEmbaasERNIE Embedding-V1Fake EmbeddingsGoogle Vertex AI PaLMGPT4AllGradientHugging FaceInstructEmbeddingsJinaLlama-cppLLMRailsLocalAIMiniMaxModelScopeMosaicMLNLP CloudOllamaOpenAISageMakerSelf HostedSentence TransformersSpaCyTensorflowHubXorbits inference (Xinference)Vector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsText embedding modelsAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook explains how to use AwaEmbeddings in LangChain.# pip install awadbimport the library‚Äãfrom langchain.embeddings import AwaEmbeddingsEmbedding = AwaEmbeddings()Set embedding modelUsers can use Embedding.set_model() to specify the embedding model. \
The input of this function is a string which represents the model's name. \
The list of currently supported models can be obtained here \ \ The default model is all-mpnet-base-v2, it can be used without setting.text = "our embedding test"Embedding.set_model("all-mpnet-base-v2")res_query = Embedding.embed_query("The test information")res_document = Embedding.embed_documents(["test1", "another test"])PreviousAleph AlphaNextAzureOpenAIimport the libraryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.
AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications. ->: AwaDB | ü¶úÔ∏èüîó Langchain






Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsAleph AlphaAwaDBAzureOpenAIBaidu QianfanBedrockBGE on Hugging FaceClarifaiCohereDashScopeDeepInfraEDEN AIElasticsearchEmbaasERNIE Embedding-V1Fake EmbeddingsGoogle Vertex AI PaLMGPT4AllGradientHugging FaceInstructEmbeddingsJinaLlama-cppLLMRailsLocalAIMiniMaxModelScopeMosaicMLNLP CloudOllamaOpenAISageMakerSelf HostedSentence TransformersSpaCyTensorflowHubXorbits inference (Xinference)Vector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsText embedding modelsAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook explains how to use AwaEmbeddings in LangChain.# pip install awadbimport the library‚Äãfrom langchain.embeddings import AwaEmbeddingsEmbedding = AwaEmbeddings()Set embedding modelUsers can use Embedding.set_model() to specify the embedding model. \
The input of this function is a string which represents the model's name. \
The list of currently supported models can be obtained here \ \ The default model is all-mpnet-base-v2, it can be used without setting.text = "our embedding test"Embedding.set_model("all-mpnet-base-v2")res_query = Embedding.embed_query("The test information")res_document = Embedding.embed_documents(["test1", "another test"])PreviousAleph AlphaNextAzureOpenAIimport the libraryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.