---
title: 🧩 Embedding models
---
## Overview
Embedchain supports several embedding models from the following providers:
## 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](https://platform.openai.com/account/api-keys).
Once you have obtained the key, you can use it like this:
```python main.py
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?")
```
```yaml config.yaml
embedder:
provider: openai
config:
model: 'text-embedding-3-small'
```
* 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:
```yaml text-embedding-3-small.yaml
embedder:
provider: openai
config:
model: 'text-embedding-3-small'
```
```yaml text-embedding-3-large.yaml
embedder:
provider: openai
config:
model: 'text-embedding-3-large'
```
## 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](https://makersuite.google.com/app/apikey)
```python main.py
import os
from embedchain import App
os.environ["GOOGLE_API_KEY"] = "xxx"
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
embedder:
provider: google
config:
model: 'models/embedding-001'
task_type: "retrieval_document"
title: "Embeddings for Embedchain"
```
For more details regarding the Google AI embedding model, please refer to the [Google AI documentation](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).
## 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:
```python main.py
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")
```
```yaml config.yaml
llm:
provider: azure_openai
config:
model: gpt-35-turbo
deployment_name: your_llm_deployment_name
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
embedder:
provider: azure_openai
config:
model: text-embedding-ada-002
deployment_name: you_embedding_model_deployment_name
```
You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
## GPT4ALL
GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer.
```python main.py
from embedchain import App
# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: gpt4all
config:
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
embedder:
provider: gpt4all
```
## 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:
```python main.py
from embedchain import App
# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: huggingface
config:
model: 'google/flan-t5-xxl'
temperature: 0.5
max_tokens: 1000
top_p: 0.5
stream: false
embedder:
provider: huggingface
config:
model: 'sentence-transformers/all-mpnet-base-v2'
```
## 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.
```python main.py
from embedchain import App
# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: vertexai
config:
model: 'chat-bison'
temperature: 0.5
top_p: 0.5
embedder:
provider: vertexai
config:
model: 'textembedding-gecko'
```
## NVIDIA AI
[NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) let you quickly use NVIDIA's AI models, such as Mixtral 8x7B, Llama 2 etc, through our API. These models are available in the [NVIDIA NGC catalog](https://catalog.ngc.nvidia.com/ai-foundation-models), fully optimized and ready to use on NVIDIA's AI platform. They are designed for high speed and easy customization, ensuring smooth performance on any accelerated setup.
### Usage
In order to use embedding models and LLMs from NVIDIA AI, create an account on [NVIDIA NGC Service](https://catalog.ngc.nvidia.com/).
Generate an API key from their dashboard. Set the API key as `NVIDIA_API_KEY` environment variable. Note that the `NVIDIA_API_KEY` will start with `nvapi-`.
Below is an example of how to use LLM model and embedding model from NVIDIA AI:
```python main.py
import os
from embedchain import App
os.environ['NVIDIA_API_KEY'] = 'nvapi-xxxx'
config = {
"app": {
"config": {
"id": "my-app",
},
},
"llm": {
"provider": "nvidia",
"config": {
"model": "nemotron_steerlm_8b",
},
},
"embedder": {
"provider": "nvidia",
"config": {
"model": "nvolveqa_40k",
"vector_dimension": 1024,
},
},
}
app = App.from_config(config=config)
app.add("https://www.forbes.com/profile/elon-musk")
answer = app.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk is subject to fluctuations based on the market value of his holdings in various companies.
# As of March 1, 2024, his net worth is estimated to be approximately $210 billion. However, this figure can change rapidly due to stock market fluctuations and other factors.
# Additionally, his net worth may include other assets such as real estate and art, which are not reflected in his stock portfolio.
```
## Cohere
To use embedding models and LLMs from COHERE, create an account on [COHERE](https://dashboard.cohere.com/welcome/login?redirect_uri=%2Fapi-keys).
Generate an API key from their dashboard. Set the API key as `COHERE_API_KEY` environment variable.
Once you have obtained the key, you can use it like this:
```python main.py
import os
from embedchain import App
os.environ['COHERE_API_KEY'] = 'xxx'
# load embedding model configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
embedder:
provider: cohere
config:
model: 'embed-english-light-v3.0'
```
* Cohere has few embedding models: `embed-english-v3.0`, `embed-multilingual-v3.0`, `embed-multilingual-light-v3.0`, `embed-english-v2.0`, `embed-english-light-v2.0` and `embed-multilingual-v2.0`. Embedchain supports all these models. Below you can find YAML config for all:
```yaml embed-english-v3.0.yaml
embedder:
provider: cohere
config:
model: 'embed-english-v3.0'
vector_dimension: 1024
```
```yaml embed-multilingual-v3.0.yaml
embedder:
provider: cohere
config:
model: 'embed-multilingual-v3.0'
vector_dimension: 1024
```
```yaml embed-multilingual-light-v3.0.yaml
embedder:
provider: cohere
config:
model: 'embed-multilingual-light-v3.0'
vector_dimension: 384
```
```yaml embed-english-v2.0.yaml
embedder:
provider: cohere
config:
model: 'embed-english-v2.0'
vector_dimension: 4096
```
```yaml embed-english-light-v2.0.yaml
embedder:
provider: cohere
config:
model: 'embed-english-light-v2.0'
vector_dimension: 1024
```
```yaml embed-multilingual-v2.0.yaml
embedder:
provider: cohere
config:
model: 'embed-multilingual-v2.0'
vector_dimension: 768
```