Jina | 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 modelsJinaJinaLet's load the Jina Embedding class.from langchain.embeddings import JinaEmbeddingsembeddings = JinaEmbeddings(    jina_auth_token=jina_auth_token, model_name="ViT-B-32::openai")text = "This is a test document."query_result = embeddings.embed_query(text)doc_result = embeddings.embed_documents([text])In the above example, ViT-B-32::openai, OpenAI's pretrained ViT-B-32 model is used. For a full list of models, see here.PreviousInstructEmbeddingsNextLlama-cppCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Let's load the Jina Embedding class.
Let's load the Jina Embedding class. ->: Jina | 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 modelsJinaJinaLet's load the Jina Embedding class.from langchain.embeddings import JinaEmbeddingsembeddings = JinaEmbeddings(    jina_auth_token=jina_auth_token, model_name="ViT-B-32::openai")text = "This is a test document."query_result = embeddings.embed_query(text)doc_result = embeddings.embed_documents([text])In the above example, ViT-B-32::openai, OpenAI's pretrained ViT-B-32 model is used. For a full list of models, see here.PreviousInstructEmbeddingsNextLlama-cppCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.