mem0/embedchain/docs/components/embedding-models.mdx

---
title: 🧩 Embedding models
---

## Overview

Embedchain supports several embedding models from the following providers:

<CardGroup cols={4}>
  <Card title="OpenAI" href="#openai"></Card>
  <Card title="GoogleAI" href="#google-ai"></Card>
  <Card title="Azure OpenAI" href="#azure-openai"></Card>
  <Card title="GPT4All" href="#gpt4all"></Card>
  <Card title="Hugging Face" href="#hugging-face"></Card>
  <Card title="Vertex AI" href="#vertex-ai"></Card>
  <Card title="NVIDIA AI" href="#nvidia-ai"></Card>
  <Card title="Cohere" href="#cohere"></Card>
  <Card title="Ollama" href="#ollama"></Card>
  <Card title="Clarifai" href="#clarifai"></Card>
</CardGroup>

## 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:

<CodeGroup>

```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'
```

</CodeGroup>

* 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:

<CodeGroup>

```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'
```

</CodeGroup>

## 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)

<CodeGroup>
```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"
```
</CodeGroup>
<br/>
<Note>
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).
</Note>

## 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:

<CodeGroup>

```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
```
</CodeGroup>

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.

<CodeGroup>

```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
```

</CodeGroup>

## 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:

<CodeGroup>

```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'
    model_kwargs:
        trust_remote_code: True # Only use if you trust your embedder
```

</CodeGroup>

## 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.

<CodeGroup>

```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'
```

</CodeGroup>

## 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:

<CodeGroup>

```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.
```
</CodeGroup>


## 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:

<CodeGroup>

```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'
```

</CodeGroup>

* 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:

<CodeGroup>

```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
```

</CodeGroup>

## Ollama

Ollama enables the use of embedding models, allowing you to generate high-quality embeddings directly on your local machine. Make sure to install [Ollama](https://ollama.com/download) and keep it running before using the embedding model.

You can find the list of models at [Ollama Embedding Models](https://ollama.com/blog/embedding-models).

Below is an example of how to use embedding model Ollama:

<CodeGroup>

```python main.py
import os
from embedchain import App

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

```yaml config.yaml
embedder:
  provider: ollama
  config:
    model: 'all-minilm:latest'
```

</CodeGroup>

## Clarifai

Install related dependencies using the following command:

```bash
pip install --upgrade 'embedchain[clarifai]'
```

set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.

Now you are all set with exploring Embedchain.

<CodeGroup>

```python main.py
import os
from embedchain import App

os.environ["CLARIFAI_PAT"] = "XXX"

# load llm and embedder configuration from config.yaml file
app = App.from_config(config_path="config.yaml")

#Now let's add some data.
app.add("https://www.forbes.com/profile/elon-musk")

#Query the app
response = app.query("what college degrees does elon musk have?")
```
Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) to explore all the State of the Art embedding models available to use.
For passing LLM model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.

```yaml config.yaml
llm:
 provider: clarifai
 config:
   model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
   model_kwargs:
     temperature: 0.5
     max_tokens: 1000  
embedder:
 provider: clarifai
 config:
   model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
```
</CodeGroup>