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

# Proscia

> Generate H-Optimus embeddings from slides in your Concentriq® LS workspace with Proscia's Concentriq® Embeddings service.

[Proscia Concentriq® Embeddings](https://github.com/Proscia/proscia-ai-tools) extracts [H-Optimus](/documentation/models/h-optimus) embeddings from slides in a [Concentriq® LS](https://www.proscia.com/) workspace — no downloading, tiling, or self-hosted server required. It's the best fit when your slides already live in Concentriq® LS: Proscia tiles each slide, runs the model, and returns one safetensor file of embeddings per slide.

<Note>
  This integration is operated by Proscia. Bioptimus provides the H-Optimus foundation model; Proscia provides the Concentriq® platform, the embeddings service, and the [`proscia-ai-tools`](https://github.com/Proscia/proscia-ai-tools) client. For platform access, workspace provisioning, and support, contact Proscia.
</Note>

## How it works

<Steps>
  <Step title="Slides live in Concentriq® LS">
    Your whole-slide images are stored in your Concentriq® LS workspace. Nothing is downloaded locally.
  </Step>

  <Step title="Select H-Optimus and request embeddings">
    Using the `proscia-ai-tools` Python client, you point at one or more image repositories, pick the H-Optimus model tag, and submit an embedding job. The call returns a ticket ID.
  </Step>

  <Step title="Proscia runs inference">
    Concentriq® Embeddings tiles each slide at your chosen magnification and runs H-Optimus on Proscia's infrastructure.
  </Step>

  <Step title="Pull embeddings back">
    Fetch the results by ticket ID. Each slide comes back as a compact safetensor file of feature vectors that you load into memory for downstream tasks.
  </Step>
</Steps>

## 1. Install the client

Install the client directly from GitHub:

```bash theme={null}
pip install git+https://github.com/Proscia/proscia-ai-tools.git
```

## 2. Authenticate

The client connects to your Concentriq® workspace endpoint. It supports three authentication methods — provide exactly one:

<CodeGroup>
  ```python Email + password theme={null}
  from proscia_ai_tools.client import ClientWrapper as Client

  client = Client(
      url="https://<your-workspace>.concentriq.proscia.com",
      email="<your-email>",
      password="<your-password>",
  )
  ```

  ```python API key theme={null}
  from proscia_ai_tools.client import ClientWrapper as Client

  client = Client(
      url="https://<your-workspace>.concentriq.proscia.com",
      api_key="<your-concentriq-api-key>",
  )
  ```

  ```python JWT token theme={null}
  from proscia_ai_tools.client import ClientWrapper as Client

  client = Client(
      url="https://<your-workspace>.concentriq.proscia.com",
      token="<your-jwt-token>",
  )
  ```
</CodeGroup>

<Info>
  Your workspace URL and credentials are issued by Proscia. Keep them out of source control — load them from environment variables or a secrets manager rather than hard-coding them.
</Info>

## 3. Generate embeddings with H-Optimus

Submit an embedding job against one or more Concentriq® image repositories, selecting the H-Optimus model tag and the microns-per-pixel (`mpp`) resolution. The call returns a ticket ID for retrieving the results.

```python theme={null}
# Submit an embedding job for one or more repositories.
ticket_id = client.embed_repos(
    ids=[1234],                      # Concentriq® repository IDs
    model="bioptimus/H-optimus-1",   # H-Optimus foundation model tag
    mpp=0.5,                           # target resolution (microns per pixel)
)

# Retrieve the embeddings once the job completes.
embeddings = client.get_embeddings(ticket_id)
```

The client also exposes `embed_images` (for specific image IDs) and `embed_roi` (for annotation- or region-based embeddings). See the [`proscia-ai-tools`](https://github.com/Proscia/proscia-ai-tools) client for the full API.

<Note>
  The model tag selects the foundation model Concentriq® Embeddings runs. Use the H-Optimus tag published by Proscia for your workspace; the exact set of available models is controlled by Proscia and may expand over time.
</Note>

## 4. Explore end-to-end examples

Proscia maintains runnable notebooks for complete workflows — embedding generation, regions of interest, annotations, clustering, segmentation, and zero-shot classification:

<Card title="Proscia example notebooks" icon="github" href="https://github.com/Proscia/proscia-ai-tools/tree/main/notebooks">
  Browse the `notebooks/` folder in `proscia-ai-tools` for end-to-end Concentriq® Embeddings workflows using H-Optimus.
</Card>

## When to use a different option

<CardGroup cols={2}>
  <Card title="Slides not in Concentriq®?" href="/deployment/overview">
    If your slides live outside Concentriq® LS, deploy H-Optimus or M-Optimus via AWS, on-premise, or Hugging Face.
  </Card>

  <Card title="Need whole-slide tooling?" href="/guides/get-started/sdk">
    The Bioptimus SDK handles tiling, tissue masking, and output formats against a Bioptimus-deployed server.
  </Card>
</CardGroup>