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Overview

Embedchain supports several embedding models from the following providers:

OpenAI

GoogleAI

Azure OpenAI

GPT4All

Hugging Face

Vertex AI

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OpenAI

To use OpenAI embedding function, you have to set the OPENAI_API_KEY environment variable. You can obtain the OpenAI API key from the OpenAI Platform. Once you have obtained the key, you can use it like this: 
import os
from embedchain import App

os.environ['OPENAI_API_KEY'] = 'xxx'

# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")

app.add("https://en.wikipedia.org/wiki/OpenAI")
app.query("What is OpenAI?")
  • OpenAI announced two new embedding models: text-embedding-3-small and text-embedding-3-large. Embedchain supports both these models. Below you can find YAML config for both:
embedder:
  provider: openai
  config:
    model: 'text-embedding-3-small'

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Google AI

To use Google AI embedding function, you have to set the GOOGLE_API_KEY environment variable. You can obtain the Google API key from the Google Maker Suite 
import os
from embedchain import App

os.environ["GOOGLE_API_KEY"] = "xxx"

app = App.from_config(config_path="config.yaml")

For more details regarding the Google AI embedding model, please refer to the Google AI documentation.

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Azure OpenAI

To use Azure OpenAI embedding model, you have to set some of the azure openai related environment variables as given in the code block below: 
import os
from embedchain import App

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
os.environ["AZURE_OPENAI_API_KEY"] = "xxx"
os.environ["OPENAI_API_VERSION"] = "xxx"

app = App.from_config(config_path="config.yaml")
You can find the list of models and deployment name on the Azure OpenAI Platform.

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GPT4ALL

GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer. 
from embedchain import App

# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")

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Hugging Face

Hugging Face supports generating embeddings of arbitrary length documents of text using Sentence Transformer library. Example of how to generate embeddings using hugging face is given below: 
from embedchain import App

# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")

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Vertex AI

Embedchain supports Google’s VertexAI embeddings model through a simple interface. You just have to pass the model_name in the config yaml and it would work out of the box. 
from embedchain import App

# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")