> ## Documentation Index
> Fetch the complete documentation index at: https://docs.embedchain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 🧩 Embedding models

## Overview

Embedchain supports several embedding models from the following providers:

<CardGroup cols={4}>
  <Card title="OpenAI" href="#openai" />

  <Card title="GoogleAI" href="#google-ai" />

  <Card title="Azure OpenAI" href="#azure-openai" />

  <Card title="AWS Bedrock" href="#aws-bedrock" />

  <Card title="GPT4All" href="#gpt4all" />

  <Card title="Hugging Face" href="#hugging-face" />

  <Card title="Vertex AI" href="#vertex-ai" />

  <Card title="NVIDIA AI" href="#nvidia-ai" />

  <Card title="Cohere" href="#cohere" />

  <Card title="Ollama" href="#ollama" />

  <Card title="Clarifai" href="#clarifai" />
</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 theme={null}
  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 theme={null}
  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 theme={null}
  embedder:
    provider: openai
    config:
      model: 'text-embedding-3-small'
  ```

  ```yaml text-embedding-3-large.yaml theme={null}
  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 theme={null}
  import os
  from embedchain import App

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

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

  ```yaml config.yaml theme={null}
  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>

## AWS Bedrock

To use AWS Bedrock embedding function, you have to set the AWS environment variable.

<CodeGroup>
  ```python main.py theme={null}
  import os
  from embedchain import App

  os.environ["AWS_ACCESS_KEY_ID"] = "xxx"
  os.environ["AWS_SECRET_ACCESS_KEY"] = "xxx"
  os.environ["AWS_REGION"] = "us-west-2"

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

  ```yaml config.yaml theme={null}
  embedder:
    provider: aws_bedrock
    config:
      model: 'amazon.titan-embed-text-v2:0'
      vector_dimension: 1024
      task_type: "retrieval_document"
      title: "Embeddings for Embedchain"
  ```
</CodeGroup>

<br />

<Note>
  For more details regarding the AWS Bedrock embedding model, please refer to the [AWS Bedrock documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
</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 theme={null}
  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 theme={null}
  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 theme={null}
  from embedchain import App

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

  ```yaml config.yaml theme={null}
  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 theme={null}
  from embedchain import App

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

  ```yaml config.yaml theme={null}
  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 theme={null}
  from embedchain import App

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

  ```yaml config.yaml theme={null}
  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 theme={null}
  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 theme={null}
  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 theme={null}
  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 theme={null}
  embedder:
    provider: cohere
    config:
      model: 'embed-english-v3.0'
      vector_dimension: 1024
  ```

  ```yaml embed-multilingual-v3.0.yaml theme={null}
  embedder:
    provider: cohere
    config:
      model: 'embed-multilingual-v3.0'
      vector_dimension: 1024
  ```

  ```yaml embed-multilingual-light-v3.0.yaml theme={null}
  embedder:
    provider: cohere
    config:
      model: 'embed-multilingual-light-v3.0'
      vector_dimension: 384
  ```

  ```yaml embed-english-v2.0.yaml theme={null}
  embedder:
    provider: cohere
    config:
      model: 'embed-english-v2.0'
      vector_dimension: 4096
  ```

  ```yaml embed-english-light-v2.0.yaml theme={null}
  embedder:
    provider: cohere
    config:
      model: 'embed-english-light-v2.0'
      vector_dimension: 1024
  ```

  ```yaml embed-multilingual-v2.0.yaml theme={null}
  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 theme={null}
  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 theme={null}
  embedder:
    provider: ollama
    config:
      model: 'all-minilm:latest'
  ```
</CodeGroup>

## Clarifai

Install related dependencies using the following command:

```bash theme={null}
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 theme={null}
  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 theme={null}
  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>