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

# Adaptive Embeddings

> How our embeddings automatically adapt to your model's complexity without losing predictive power.

Avra delivers embeddings as 1024-dimensional vectors from the Graph Foundation Model. Each vector is **adaptive**: you can slice it client-side to smaller dimensions (512, 256, 128, 64, 32, 16) while preserving semantic meaning and predictive power. One API call, one storage footprint, every dimension you need — no retraining, no separate endpoints.

## Why adaptive embeddings

* **One training pass, many footprints** — derive lighter embeddings for simpler models without retraining.
* **Consistent semantics** — lower-dimensional slices preserve the ordering and relative distances of the full vector.
* **Compose with your RFM** — workspace-specific representations update every slice simultaneously.

## Selecting a dimensionality

<Columns>
  <Column>
    <Card title="1024 dimensions">
      Maximum fidelity for deep learning, semantic search, or graph analytics where
      latency and storage are secondary.
    </Card>
  </Column>

  <Column>
    <Card title="512 dimensions">
      Balanced trade-off between richness and compute for most production
      workloads.
    </Card>
  </Column>

  <Column>
    <Card title="256 dimensions">
      Optimized for real-time scoring and streaming analytics on CPU-bound services.
    </Card>
  </Column>
</Columns>

<Columns>
  <Column>
    <Card title="128 dimensions">
      Ideal for dashboards, feature stores, and propensity models where storage is a
      concern but accuracy must remain high.
    </Card>
  </Column>

  <Column>
    <Card title="64 / 32 / 16 dimensions">
      Reserve for edge deployments or extreme latency constraints. Treat the size as
      a hyperparameter—step down gradually while monitoring KPIs.
    </Card>
  </Column>
</Columns>

<Callout type="info">
  All Avra API calls return the full 1024-dimensional embedding. Refer to the [API Reference](/api-reference/overview) for examples of slicing these vectors client-side to your desired dimensionality.
</Callout>

## Best practices

* Treat dimensionality as a hyperparameter: start at 256-d for tree-based models (XGBoost, LightGBM), evaluate performance, then adjust up or down based on validation metrics.
* Normalize vectors before computing cosine similarity or feeding them into distance-based models.
* Cache embeddings alongside the model snapshot ID so experiments remain reproducible.
* Avoid applying additional dimensionality reduction for production workloads—it degrades the learned semantics. Reserve techniques like PCA or t-SNE for visualization only.

## <a id="trajectory-signals" />Trajectory signals (temporal dynamics)

Embeddings aren’t static. As new signals arrive, an entity’s position moves—its “trajectory.” Reading this movement adds foresight to your models without changing your integration.

* Direction of change: is the entity moving toward patterns historically linked to risk or growth?
* Magnitude vs. noise: we compare movement to typical volatility to avoid false alarms.
* Aggregation: roll up movements at portfolio, sector, or region level to spot drift early.

How to use

* Credit: trigger reviews when customers move toward risky clusters before KPIs deteriorate.
* Growth: prioritize accounts whose movement matches your best customers’ pre‑expansion patterns.
* Ops: watch suppliers whose trajectories resemble prior disruption events.

Access

* Returned as movement indicators with relevant products (e.g., Credit Score) and accessible via embeddings over time for custom analytics.