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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.

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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.

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Selecting a dimensionality

1024 dimensions

Maximum fidelity for deep learning, semantic search, or graph analytics where latency and storage are secondary.

512 dimensions

Balanced trade-off between richness and compute for most production workloads.

256 dimensions

Optimized for real-time scoring and streaming analytics on CPU-bound services.

128 dimensions

Ideal for dashboards, feature stores, and propensity models where storage is a concern but accuracy must remain high.

64 / 32 / 16 dimensions

Reserve for edge deployments or extreme latency constraints. Treat the size as a hyperparameter—step down gradually while monitoring KPIs.
All Avra API calls return the full 1024-dimensional embedding. Refer to the API Reference for examples of slicing these vectors client-side to your desired dimensionality.

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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.

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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.