This page shows how to:
  • Generate embeddings with a unified API. TensorZero unifies many LLM APIs (e.g. OpenAI) and inference servers (e.g. Ollama).
  • Use any programming language. You can use any OpenAI SDK (Python, Node, Go, etc.) or the OpenAI-compatible HTTP API.
We provide complete code examples on GitHub.

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Generate embeddings from OpenAI

Our example uses the OpenAI Python SDK, but you can use any OpenAI SDK or call the OpenAI-compatible HTTP API. See Call any LLM for an example using the OpenAI Node SDK.The TensorZero Python SDK doesn’t have an independent embedding endpoint at the moment.
The TensorZero Python SDK integrates with the OpenAI Python SDK to provide a unified API for calling any LLM.
1

Set up the credentials for your LLM provider

For example, if you’re using OpenAI, you can set the OPENAI_API_KEY environment variable with your API key.
export OPENAI_API_KEY="sk-..."
See the Integrations page to learn how to set up credentials for other LLM providers.
2

Install the OpenAI and TensorZero Python SDKs

You can install the OpenAI and TensorZero SDKs with a Python package manager like pip.
pip install openai tensorzero
3

Initialize the OpenAI client

Let’s initialize the TensorZero Gateway and patch the OpenAI client to use it. For simplicity, we’ll use an embedded gateway without observability or custom configuration.
from openai import OpenAI
from tensorzero import patch_openai_client

client = OpenAI()
patch_openai_client(client, async_setup=False)
The TensorZero Python SDK supports both the synchronous OpenAI client and the asynchronous AsyncOpenAI client. Both options support running the gateway embedded in your application with patch_openai_client or connecting to a standalone gateway with base_url. The embedded gateway supports synchronous initialization with async_setup=False or asynchronous initialization with async_setup=True. See Clients for more details.
4

Call the LLM

result = client.embeddings.create(
    input="Hello, world!",
    model="tensorzero::embedding_model_name::openai::text-embedding-3-small",
    # or: Azure, any OpenAI-compatible endpoint (e.g. Ollama, Voyager)
)

CreateEmbeddingResponse(
    data=[
        Embedding(
            embedding=[
                -0.019143931567668915,
                # ...
            ],
            index=0,
            object='embedding'
        )
    ],
    model='tensorzero::embedding_model_name::openai::text-embedding-3-small',
    object='list',
    usage=Usage(prompt_tokens=4, total_tokens=4)
)

​
Define a custom embedding model

You can define a custom embedding model in your TensorZero configuration file. For example, let’s define a custom embedding model for nomic-embed-text served locally by Ollama.
1

Deploy the Ollama embedding model

Download the embedding model and launch the Ollama server:
ollama pull nomic-embed-text
ollama serve
We assume that Ollama is available on http://localhost:11434.
2

Define your custom embedding model

Add your custom model and model provider to your configuration file:
tensorzero.toml
[embedding_models.nomic-embed-text]
routing = ["ollama"]

[embedding_models.nomic-embed-text.providers.ollama]
type = "openai"
api_base = "http://localhost:11434/v1"
model_name = "nomic-embed-text"
api_key_location = "none"
See the Configuration Reference for details on configuring your embedding models.
3

Call your custom embedding model

Use your custom model by referencing it with tensorzero::embedding_model_name::nomic-embed-text.For example, using the OpenAI Python SDK:
from openai import OpenAI
from tensorzero import patch_openai_client

client = OpenAI()

patch_openai_client(
    client,
    config_file="config/tensorzero.toml",
    async_setup=False,
)

result = client.embeddings.create(
    input="Hello, world!",
    model="tensorzero::embedding_model_name::nomic-embed-text",
)

CreateEmbeddingResponse(
    data=[
        Embedding(
            embedding=[
                -0.019143931567668915,
                # ...
            ],
            index=0,
            object='embedding'
        )
    ],
    model='tensorzero::embedding_model_name::nomic-embed-text',
    object='list',
    usage=Usage(prompt_tokens=4, total_tokens=4)
)