Class: MlflowClient

A thin client over the Databricks workspace REST API, owning the host + bearer token so callers (eval-run creation, assessment writes, the judge's serving endpoint) don't each re-derive URLs or re-attach auth. The host is normalized once at construction.

Constructors

Constructor

new MlflowClient(host: string, token: string): MlflowClient;

Parameters

Returns

MlflowClient

Properties

baseUrl

readonly baseUrl: string;

Normalized workspace base URL (scheme guaranteed, no trailing slash).

Methods

post()

post<T>(path: string, body: unknown): Promise<T>;

POST JSON to an MLflow REST endpoint. Returns the parsed JSON body, or throws with the status + response text so callers can surface a precise error. Use for calls whose failure should abort (e.g. runs/create).

The thrown message embeds up to 500 chars of the upstream response body to aid debugging. That is fine for the dev-facing eval CLI, but do NOT relay it into an end-user HTTP response if this client is reused in a request handler — the body can carry workspace-internal detail.

Type Parameters

Parameters

Returns

Promise<T>

postResult()

postResult(path: string, body: unknown): Promise<PostResult>;

POST JSON without throwing: returns { ok, status, error } so best-effort writes (e.g. per-trace assessments) can be collected and reported without aborting the run.

error embeds up to 500 chars of the upstream body — same caveat as post: fine to log for the dev CLI, don't relay it to end users.

Parameters

Returns

Promise<PostResult>

servingEndpointsUrl()

servingEndpointsUrl(): string;

OpenAI-compatible base URL for Databricks Model Serving, used as the judge's OPENAI_BASE_URL. Same workspace host + token as the MLflow REST calls.

Returns

string