Native V8 Connector for AI agents.

Yes, it works with any array of labels, whether you're doing binary classification or multiple categories.

LLMs are probabilistic, not deterministic. They often hallucinate decimals on large datasets, while this Connector uses a local runtime for perfect math.

Yes, it processes the arrays you provide to your agent locally, ensuring your data stays private and the math stays exact.

It generates a full breakdown of hits and misses, making it easy to see if the model is favoring one category over another.

It calculates Accuracy, Precision, Recall, F1-Score, and the full confusion matrix breakdown.

It's designed for classification tasks where you have a list of actual results and a list of predicted results.

LLMs operate on tokens and probability distributions. If you give them 500 predictions, they might summarize or estimate the F1-score rather than calculating it exactly. This engine ensures 100% mathematical precision.

Yes, the engine automatically detects unique labels from both arrays and constructs an N-by-N confusion matrix, handling both binary and multiclass evaluations flawlessly.

The only limit is the standard Context Window limit for transmitting the JSON arrays. For arrays exceeding 100k items, consider chunking or local CSV aggregators.