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Text Embedding Models
Text Embedding Models#
Note
Conceptual Guide
This documentation goes over how to use the Embedding class in LangChain.
The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.
Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
The base Embedding class in LangChain exposes two methods: embed_documents and embed_query. The largest difference is that these two methods have different interfaces: one works over multiple documents, while the other works over a single document. Besides this, another reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
The following integrations exist for text embeddings.
Aleph Alpha
Amazon Bedrock
Azure OpenAI
Cohere
DashScope
DeepInfra
Elasticsearch
Embaas
Fake Embeddings
Google Vertex AI PaLM
Hugging Face Hub
HuggingFace Instruct
Jina
Llama-cpp
MiniMax
ModelScope
MosaicML
OpenAI
SageMaker Endpoint
Self Hosted Embeddings
Sentence Transformers
Tensorflow Hub
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By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
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