leofratu.github.io

{
  "name": "get_training_state",
  "kind": "answer",
  "impl": "imperative",
  "description": "Read the Label Loop task, online Naive Bayes model, label queue, current configuration, and aggregate holdout metrics. Hidden holdout labels and unresolved human labels are excluded.",
  "inputSchema": {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "inspect_uncertain_samples",
  "kind": "answer",
  "impl": "imperative",
  "description": "Return unlabeled support tickets ranked by normalized predictive entropy. Use this to select the most informative samples for human review.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "minimum": 1,
        "maximum": 8,
        "description": "Number of uncertain samples to return."
      }
    },
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "queue_label_review",
  "kind": "transact",
  "impl": "imperative",
  "description": "Queue one to three unlabeled samples for the person. This tool cannot assign labels. The review appears on the shared page and must be completed by the person.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "sampleIds": {
        "type": "array",
        "minItems": 1,
        "maxItems": 3,
        "uniqueItems": true,
        "items": {
          "type": "string",
          "pattern": "^ticket-[0-9]{3}$"
        },
        "description": "Unlabeled sample IDs returned by inspect_uncertain_samples."
      },
      "note": {
        "type": "string",
        "minLength": 1,
        "maxLength": 180,
        "description": "Why these labels would improve the model."
      }
    },
    "required": [
      "sampleIds",
      "note"
    ],
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "train_confirmed_batch",
  "kind": "act",
  "impl": "imperative",
  "description": "Incrementally update the in-browser classifier with up to eight labels already confirmed by the person. Unreviewed and agent-predicted labels are never trained.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "maximum": {
        "type": "integer",
        "minimum": 1,
        "maximum": 8,
        "description": "Maximum confirmed examples to consume."
      }
    },
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "evaluate_model",
  "kind": "act",
  "impl": "imperative",
  "description": "Return aggregate accuracy, macro F1, confidence, log loss, and per-label metrics on a fixed hidden holdout set. Individual holdout examples and answers are never returned.",
  "inputSchema": {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "propose_model_config",
  "kind": "act",
  "impl": "imperative",
  "description": "Stage new smoothing and low-confidence review settings with a rationale. The proposal does not change the classifier until the person accepts it on the page.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "alpha": {
        "type": "number",
        "minimum": 0.1,
        "maximum": 3,
        "description": "Laplace smoothing strength."
      },
      "reviewThreshold": {
        "type": "number",
        "minimum": 0.5,
        "maximum": 0.95,
        "description": "Predictions below this confidence require human review."
      },
      "rationale": {
        "type": "string",
        "minLength": 1,
        "maxLength": 220,
        "description": "Why the proposed settings fit the current evidence."
      }
    },
    "required": [
      "alpha",
      "reviewThreshold",
      "rationale"
    ],
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "inspect_training_history",
  "kind": "answer",
  "impl": "imperative",
  "description": "Read metric checkpoints and the provenance of examples trained during this session. This never reveals hidden holdout answers.",
  "inputSchema": {
    "type": "object",
    "properties": {},
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "predict_ticket",
  "kind": "act",
  "impl": "imperative",
  "description": "Classify new support-ticket text with the current in-browser model. Returns probabilities, normalized entropy, the approved review threshold, and a route-or-review decision.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "text": {
        "type": "string",
        "minLength": 3,
        "maxLength": 300,
        "description": "Support-ticket text to classify."
      }
    },
    "required": [
      "text"
    ],
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}

{
  "name": "inspect_model_diagnostics",
  "kind": "act",
  "impl": "imperative",
  "description": "Return one-vs-rest feature weights, aggregate confusion counts, calibration gap, and log loss for the current model without exposing individual holdout rows.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "featureLimit": {
        "type": "integer",
        "minimum": 3,
        "maximum": 10,
        "description": "Top learned tokens per class."
      }
    },
    "additionalProperties": false
  },
  "page": "/oai-hackathon/"
}