Run a text embedding model

Modular Team

embeddings

max

openai

Text embeddings are rich numerical representations of text that power many modern natural language processing (NLP) applications. This tutorial shows you how to serve and interact with an embedding model using an OpenAI-compatible endpoint. Specifically, we'll use MAX to serve the all-mpnet-base-v2 model, which is a powerful transformer that excels at capturing semantic relationships in text.

System requirements:

Mac

Linux

WSL

Set up your environment

Create a Python project to install our APIs and CLI tools:

pixi

1. If you don't have it, install pixi:

   curl -fsSL https://pixi.sh/install.sh | sh

   curl -fsSL https://pixi.sh/install.sh | sh

   Then restart your terminal for the changes to take effect.

2. Create a project:

   pixi init embeddings-tutorial \
     -c https://conda.modular.com/max-nightly/ -c conda-forge \
     && cd embeddings-tutorial

   pixi init embeddings-tutorial \
     -c https://conda.modular.com/max-nightly/ -c conda-forge \
     && cd embeddings-tutorial
3. Install the modular conda package:
   Nightly

   pixi add modular

   pixi add modular

   Stable

   pixi add "modular=25.4"

   pixi add "modular=25.4"
4. Start the virtual environment:

   pixi shell

   pixi shell

uv

1. If you don't have it, install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh

   curl -LsSf https://astral.sh/uv/install.sh | sh

   Then restart your terminal to make uv accessible.

2. Create a project:

   uv init embeddings-tutorial && cd embeddings-tutorial

   uv init embeddings-tutorial && cd embeddings-tutorial
3. Create and start a virtual environment:

   uv venv && source .venv/bin/activate

   uv venv && source .venv/bin/activate
4. Install the modular Python package:
   Nightly

   uv pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --index-url https://dl.modular.com/public/nightly/python/simple/ \
     --index-strategy unsafe-best-match --prerelease allow

   uv pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --index-url https://dl.modular.com/public/nightly/python/simple/ \
     --index-strategy unsafe-best-match --prerelease allow

   Stable

   uv pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --extra-index-url https://modular.gateway.scarf.sh/simple/ \
     --index-strategy unsafe-best-match

   uv pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --extra-index-url https://modular.gateway.scarf.sh/simple/ \
     --index-strategy unsafe-best-match

pip

1. Create a project folder:

   mkdir embeddings-tutorial && cd embeddings-tutorial

   mkdir embeddings-tutorial && cd embeddings-tutorial
2. Create and activate a virtual environment:

   python3 -m venv .venv/embeddings-tutorial \
     && source .venv/embeddings-tutorial/bin/activate

   python3 -m venv .venv/embeddings-tutorial \
     && source .venv/embeddings-tutorial/bin/activate
3. Install the modular Python package:
   Nightly

   pip install --pre modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --index-url https://dl.modular.com/public/nightly/python/simple/

   pip install --pre modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --index-url https://dl.modular.com/public/nightly/python/simple/

   Stable

   pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --extra-index-url https://modular.gateway.scarf.sh/simple/

   pip install modular \
     --extra-index-url https://download.pytorch.org/whl/cpu \
     --extra-index-url https://modular.gateway.scarf.sh/simple/

conda

1. If you don't have it, install conda. A common choice is with brew:

   brew install miniconda

   brew install miniconda
2. Initialize conda for shell interaction:

   conda init

   conda init

   If you're on a Mac, instead use:

   conda init zsh

   conda init zsh

   Then restart your terminal for the changes to take effect.

3. Create a project:

   conda create -n embeddings-tutorial

   conda create -n embeddings-tutorial
4. Start the virtual environment:

   conda activate embeddings-tutorial

   conda activate embeddings-tutorial
5. Install the modular conda package:
   Nightly

   conda install -c conda-forge -c https://conda.modular.com/max-nightly/ modular

   conda install -c conda-forge -c https://conda.modular.com/max-nightly/ modular

   Stable

   conda install -c conda-forge -c https://conda.modular.com/max/ modular

   conda install -c conda-forge -c https://conda.modular.com/max/ modular

Serve your model

Use the max serve command to start a local model server with the all-mpnet-base-v2 model:

max serve \
  --model-path sentence-transformers/all-mpnet-base-v2

max serve \
  --model-path sentence-transformers/all-mpnet-base-v2

This will create a server running the all-mpnet-base-v2 embedding model on http://localhost:8000/v1/embeddings, an OpenAI compatible endpoint.

The endpoint is ready when you see this message printed in your terminal:

Server ready on http://0.0.0.0:8000 (Press CTRL+C to quit)

Server ready on http://0.0.0.0:8000 (Press CTRL+C to quit)

For a complete list of max CLI commands and options, refer to the MAX CLI reference.

Interact with your model

MAX supports OpenAI's REST APIs and you can interact with the model using either the OpenAI Python SDK or curl:

Python

You can use OpenAI's Python client to interact with the model.

To interact with MAX's OpenAI-compatible endpoints, install the OpenAI Python SDK:

pixi

pixi add openai

pixi add openai

uv

uv add openai

uv add openai

pip

pip install openai

pip install openai

conda

conda install openai

conda install openai

Then, create a client and make a request to the model:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

# Create embeddings
response = client.embeddings.create(
    model="sentence-transformers/all-mpnet-base-v2",
    input="Run an embedding model with MAX Serve!",
)

print(f"{response.data[0].embedding[:5]}")

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

# Create embeddings
response = client.embeddings.create(
    model="sentence-transformers/all-mpnet-base-v2",
    input="Run an embedding model with MAX Serve!",
)

print(f"{response.data[0].embedding[:5]}")

You should receive a response similar to this:

{"data":[{"index":0,"embedding":[-0.06595132499933243,0.005941616836935282,0.021467769518494606,0.23037832975387573,

{"data":[{"index":0,"embedding":[-0.06595132499933243,0.005941616836935282,0.021467769518494606,0.23037832975387573,

The text has been shortened for brevity. This returns a numerical representation of the input text that can be used for semantic comparisons.

curl

The following curl command sends an embeddings request to the model's chat completions

curl http://localhost:8000/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
    "input": "Run an embedding model with MAX Serve!",
    "model": "sentence-transformers/all-mpnet-base-v2"
}'

curl http://localhost:8000/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
    "input": "Run an embedding model with MAX Serve!",
    "model": "sentence-transformers/all-mpnet-base-v2"
}'

You should receive a response similar to this:

{"data":[{"index":0,"embedding":[-0.06595132499933243,0.005941616836935282,0.021467769518494606,0.23037832975387573,

{"data":[{"index":0,"embedding":[-0.06595132499933243,0.005941616836935282,0.021467769518494606,0.23037832975387573,

The text has been shortened for brevity. This returns a numerical representation of the input text that can be used for semantic comparisons.

For complete details on all available API endpoints and options, see the MAX Serve API documentation.

Next steps

Now that you have successfully set up MAX with an OpenAI-compatible embeddings endpoint, checkout out these other tutorials:

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