Embedding models

📄️ Alibaba Tongyi

The AlibabaTongyiEmbeddings class uses the Alibaba Tongyi API to generate embeddings for a given text.

📄️ Azure OpenAI

Azure OpenAI is a cloud service to help you quickly develop generative AI experiences with a diverse set of prebuilt and curated models from OpenAI, Meta and beyond.

📄️ Baidu Qianfan

The BaiduQianfanEmbeddings class uses the Baidu Qianfan API to generate embeddings for a given text.

📄️ Bedrock

Amazon Bedrock is a fully managed service that makes base models from Amazon and third-party model providers accessible through an API.

📄️ Cloudflare Workers AI

If you're deploying your project in a Cloudflare worker, you can use Cloudflare's built-in Workers AI embeddings with LangChain.js.

📄️ Cohere

The CohereEmbeddings class uses the Cohere API to generate embeddings for a given text.

The DeepInfraEmbeddings class utilizes the DeepInfra API to generate embeddings for given text inputs. This guide will walk you through the setup and usage of the DeepInfraEmbeddings class, helping you integrate it into your project seamlessly.

📄️ Fireworks

The FireworksEmbeddings class allows you to use the Fireworks AI API to generate embeddings.

📄️ Google AI

You can access Google's generative AI embeddings models through

📄️ Google PaLM

The Google PaLM API is deprecated and will be removed in 0.3.0. Please use the Google GenAI integration instead.

📄️ Google Vertex AI

The GoogleVertexAIEmbeddings class uses Google's Vertex AI PaLM models

📄️ Gradient AI

The GradientEmbeddings class uses the Gradient AI API to generate embeddings for a given text.

📄️ HuggingFace Inference

This Embeddings integration uses the HuggingFace Inference API to generate embeddings for a given text using by default the sentence-transformers/distilbert-base-nli-mean-tokens model. You can pass a different model name to the constructor to use a different model.

📄️ Jina

The JinaEmbeddings class utilizes the Jina API to generate embeddings for given text inputs. This guide will walk you through the setup and usage of the JinaEmbeddings class, helping you integrate it into your project seamlessly.

📄️ Llama CPP

Only available on Node.js.

📄️ Minimax

The MinimaxEmbeddings class uses the Minimax API to generate embeddings for a given text.

📄️ Mistral AI

The MistralAIEmbeddings class uses the Mistral AI API to generate embeddings for a given text.

📄️ Nomic

The NomicEmbeddings class uses the Nomic AI API to generate embeddings for a given text.

📄️ Ollama

The OllamaEmbeddings class uses the /api/embeddings route of a locally hosted Ollama server to generate embeddings for given texts.

📄️ OpenAI

The OpenAIEmbeddings class uses the OpenAI API to generate embeddings for a given text. By default it strips new line characters from the text, as recommended by OpenAI, but you can disable this by passing stripNewLines: false to the constructor.

📄️ Prem AI

The PremEmbeddings class uses the Prem AI API to generate embeddings for a given text.

📄️ Tencent Hunyuan

The TencentHunyuanEmbeddings class uses the Tencent Hunyuan API to generate embeddings for a given text.

📄️ TensorFlow

This Embeddings integration runs the embeddings entirely in your browser or Node.js environment, using TensorFlow.js. This means that your data isn't sent to any third party, and you don't need to sign up for any API keys. However, it does require more memory and processing power than the other integrations.