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 modelsEmbaasOn this pageEmbaasembaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models.In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.Prerequisites‚ÄãCreate your free embaas account at https://embaas.io/register and generate an API key.# Set API keyembaas_api_key = "YOUR_API_KEY"# or set environment variableos.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"from langchain.embeddings import EmbaasEmbeddingsembeddings = EmbaasEmbeddings()# Create embeddings for a single documentdoc_text = "This is a test document."doc_text_embedding = embeddings.embed_query(doc_text)# Print created embeddingprint(doc_text_embedding)# Create embeddings for multiple documentsdoc_texts = ["This is a test document.", "This is another test document."]doc_texts_embeddings = embeddings.embed_documents(doc_texts)# Print created embeddingsfor i, doc_text_embedding in enumerate(doc_texts_embeddings):    print(f"Embedding for document {i + 1}: {doc_text_embedding}")# Using a different model and/or custom instructionembeddings = EmbaasEmbeddings(    model="instructor-large",
embaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models.
embaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models. ->: 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 modelsEmbaasOn this pageEmbaasembaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models.In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.Prerequisites‚ÄãCreate your free embaas account at https://embaas.io/register and generate an API key.# Set API keyembaas_api_key = "YOUR_API_KEY"# or set environment variableos.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"from langchain.embeddings import EmbaasEmbeddingsembeddings = EmbaasEmbeddings()# Create embeddings for a single documentdoc_text = "This is a test document."doc_text_embedding = embeddings.embed_query(doc_text)# Print created embeddingprint(doc_text_embedding)# Create embeddings for multiple documentsdoc_texts = ["This is a test document.", "This is another test document."]doc_texts_embeddings = embeddings.embed_documents(doc_texts)# Print created embeddingsfor i, doc_text_embedding in enumerate(doc_texts_embeddings):    print(f"Embedding for document {i + 1}: {doc_text_embedding}")# Using a different model and/or custom instructionembeddings = EmbaasEmbeddings(    model="instructor-large",