text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAleph AlphaAzureOpenAIBedrock EmbeddingsClarifaiCohereDashScopeDeepInfraElasticsearchEmbaasFake EmbeddingsGoogle Cloud Platform Vertex AI PaLMGPT4AllHugging Face HubInstructEmbeddingsJinaLlama-cppLocalAIMiniMaxModelScopeMosaicML embeddingsNLP CloudOpenAISageMaker Endpoint EmbeddingsSelf Hosted EmbeddingsSentence Transformers EmbeddingsSpacy EmbeddingTensorflowHubAgent toolkitsToolsVector storesGrouped by providerIntegrationsText embedding modelsLocalAILocalAILet's load the LocalAI Embedding class. In order to use the LocalAI Embedding class, you need to have the LocalAI service hosted somewhere and configure the embedding models. See the documentation at https://localai.io/basics/getting_started/index.html and https://localai.io/features/embeddings/index.html.from langchain.embeddings import LocalAIEmbeddingsembeddings = LocalAIEmbeddings(openai_api_base="http://localhost:8080", model="embedding-model-name")text = "This is a test document."query_result = embeddings.embed_query(text)doc_result = embeddings.embed_documents([text])Let's load the LocalAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see herefrom langchain.embeddings.openai import LocalAIEmbeddingsembeddings = LocalAIEmbeddings(openai_api_base="http://localhost:8080", model="embedding-model-name")text = "This is a test document."query_result = embeddings.embed_query(text)doc_result =