dashAI (unofficial)

Returns everything needed to configure a training run over a dataset. Gathers into a single call what the raw API splits into four (`/{id}`, `/info`, `/types` and `/sample`), because deciding which columns are input and which is output requires seeing them together. Args: params (DescribeDataset): contains: - dataset_id (int): dataset id - include_sample (bool): include sample rows (default True) Returns: str: JSON {"dataset": {...}, "info": {...}, "column_types": {...}, "sample": [...]} If one part is unavailable it comes back as null instead of failing whole.

Returns class counts for a finished prediction — never the rows. dashAI stores predictions as an Arrow dataset on disk and GET /predict/ only returns the SQL row (id, status, paths). This tool reads that row and, when the job is finished, aggregates the output column. The label list never leaves the function. Args: params (GetPrediction): contains prediction_id from dashai_predict. Returns: str: JSON {prediction_id, run_id, dataset_id, status, finished, n, n_classes, class_counts}. Paths and row lists are stripped.

Returns the configuration and metrics of a training run. This is where results are read once dashai_job_status says `finished`. If the run did not finish, the metrics will come back empty — that is not an error. Args: params (GetRun): contains: - run_id (int): run id Returns: str: JSON with the full run: model parameters, status and metrics per split (train / validation / test).

Polls the status of an enqueued job (training, prediction, explanation). dashAI's statuses: `not_started` (queued), `started` (running), `finished` (done) and `error` (failed). Telling `started` from `error` matters: the first is worth waiting on, the second does not improve by polling again. Args: params (JobStatus): contains: - job_id (str): id returned when enqueuing Returns: str: JSON {"job_id": str, "status": str, "finished": bool, "failed": bool, "raw": {...}}

Lists the registered components: models, metrics, tasks and optimizers. ALWAYS use this before dashai_train_model. The names dashAI expects are exact and case-sensitive, and the catalogue changes with the plugins that instance has installed — they cannot be guessed. Args: params (ListComponents): contains: - types (Optional[List[str]]): filter by 'Model', 'Metric', 'Task', 'Optimizer' Returns: str: JSON {"count": int, "components": [{"name": str, "type": str, "schema": {...}}]} The `schema` field describes the hyperparameters that component accepts.

Lists the datasets loaded in dashAI. Returns only id, name, date and status — just enough to pick one. To see columns and types use dashai_describe_dataset with the id. Args: params (ListDatasets): contains: - limit (int): maximum to return, 1-200 (default 50) Returns: str: JSON {"count": int, "datasets": [{"id", "name", "created", "status"}]} If there are none: a message explaining how to load data from the GUI.

Lists the recorded training runs, with their status. Useful for comparing models trained within the same experiment. Args: params (ListRuns): contains: - model_session_id (Optional[int]): filter by experiment - limit (int): maximum to return, 1-200 (default 50) Returns: str: JSON {"count": int, "runs": [{"id", "name", "model_name", "status", "goal_metric"}]}

Enqueues a prediction using the model of an already finished run. Like training, it is asynchronous: it returns a job_id and the result is followed with dashai_job_status. Collapses the two calls the GUI makes (read from DatasetPredictionPanel + createPrediction + enqueuePredictionJob, not from the docs): 1. POST /predict/ creates the Prediction row ({run_id, dataset_id}) 2. POST /job/ PredictJob enqueues with {prediction_id} — NOT run_id Passing only run_id to the job raises KeyError 'prediction_id' inside PredictJob.run. The Prediction row must exist first. Args: params (Predict): contains: - run_id (int): id of a finished run - dataset_id (Optional[int]): dataset to score; defaults to the run's training dataset (from its model session) Returns: str: JSON {"job_id", "run_id", "prediction_id", "dataset_id", "status": "enqueued", "next_step"}

Checks that dashAI is running and summarizes what is loaded. Call this FIRST when something fails or when you do not know whether the backend is up: it tells "dashAI is down" apart from "that id does not exist", which are two problems with different fixes. Args: params (NoArgs): no parameters. Returns: str: JSON with the following schema: { "base_url": str, # which instance is being targeted "reachable": bool, # whether it responded "datasets": int, # number of loaded datasets "runs": int, # number of recorded runs "queue_empty": bool, # whether the job queue is empty "compatibility": { # live API vs the release verified end to end "verified_against": str, # e.g. "dashAI 0.9.7.post1" "status": str, # "ok" | "mismatch" | "unknown" "warnings": [str], # only on mismatch: what differs "note": str # only on mismatch/unknown } } On failure: "Error: <what happened and what to do>".

Trains a model on a dataset and returns the id of the enqueued job. It does NOT wait for it to finish. Training can take minutes or hours, so dashAI enqueues it and this tool returns immediately; progress is polled with dashai_job_status. Collapses the three calls the raw API demands: 1. POST /model-session/ creates the experiment (dataset, task, columns, metrics) 2. POST /run/ creates the run (model, hyperparameters) 3. POST /job/ enqueues the ModelJob Args: params (TrainModel): contains: - dataset_id (int), task_name (str), model_name (str) - input_columns / output_columns (List[str]) - metrics (List[str]), goal_metric (str) - parameters (Dict): model hyperparameters - splits (Dict[str, float]): proportions adding up to 1.0 - optimizer_name (str), optimizer_parameters (Dict) - run_name (Optional[str]) Returns: str: JSON {"job_id": str, "run_id": int, "model_session_id": int, "status": "enqueued", "next_step": str} On failure: "Error: ..." stating which parameter dashAI rejected. Examples: - "Train a random forest on dataset 3 predicting 'species'" - Do not use it to read results: that is dashai_get_run, with the run_id.