Use Prime CLI config for v1 eval

verifiers/v1/clients/config.py

@@ -1,43 +1,59 @@
 """Client configs: describe an OpenAI-compatible endpoint and resolve it to a Client.
 
 A `BaseClientConfig` is an OpenAI-compatible endpoint (base_url + API-key env var
-+ extra headers) that `resolve_client` turns into a `Client`. Prime team-billing
-is baked in via a validator, so it's handled in one place. Both the eval entrypoint
-(its model client) and in-env LLM calls (e.g. a judge reward) build clients from
-these — inherit `BaseClientConfig` to get the endpoint/header handling for free.
++ extra headers) that `resolve_client` turns into a `Client`. The default Prime
+endpoint, API key, and team fall back to the active Prime CLI config, so direct
+`uv run eval` calls behave like `prime eval`. Both the eval entrypoint (its model
+client) and in-env LLM calls (e.g. a judge reward) build clients from these.
 `ClientConfig` is the CLI-selectable discriminated union (eval | train).
 """
 
 import os
 from typing import Annotated, Literal
+from urllib.parse import urlparse
 
 from openai import AsyncOpenAI
 from pydantic import Field, model_validator
 from pydantic_config import BaseConfig
 from renderers import RendererConfig
 
+from verifiers.utils.client_utils import load_prime_config
 from verifiers.v1.clients.client import Client
 from verifiers.v1.clients.eval import EvalClient
 from verifiers.v1.clients.train import TrainClient
 
+DEFAULT_PRIME_INFERENCE_URL = "https://api.pinference.ai/api/v1"
 PRIME_INFERENCE_HOST = "pinference.ai"
 PRIME_TEAM_ID_HEADER = "X-Prime-Team-ID"
 
 
 class BaseClientConfig(BaseConfig):
     """An OpenAI-compatible endpoint. The API key is read from an env var."""
 
-    base_url: str = "https://api.pinference.ai/api/v1"
+    base_url: str = DEFAULT_PRIME_INFERENCE_URL
     api_key_var: str = "PRIME_API_KEY"
     headers: dict[str, str] = Field(default_factory=dict)
     """Extra HTTP headers sent on every request."""
 
     @model_validator(mode="after")
-    def add_prime_team_id(self) -> "BaseClientConfig":
-        # Prime inference bills the personal balance unless a team is named; on
-        # that endpoint, route billing to PRIME_TEAM_ID when set (explicit wins).
-        team_id = os.environ.get("PRIME_TEAM_ID")
-        if PRIME_INFERENCE_HOST in self.base_url and team_id:
+    def apply_prime_config(self) -> "BaseClientConfig":
+        if self.api_key_var != "PRIME_API_KEY":
+            return self
+        prime_config = load_prime_config()
+        prime_base_url = (
+            os.environ.get("PRIME_INFERENCE_URL")
+            or prime_config.get("inference_url")
+            or DEFAULT_PRIME_INFERENCE_URL
+        )
+        if "base_url" not in self.model_fields_set:
+            self.base_url = prime_base_url
+        host = urlparse(self.base_url).hostname or ""
+        if host != PRIME_INFERENCE_HOST and not host.endswith(
+            f".{PRIME_INFERENCE_HOST}"
+        ):
+            return self
+        team_id = os.environ.get("PRIME_TEAM_ID") or prime_config.get("team_id")
+        if team_id:
             self.headers.setdefault(PRIME_TEAM_ID_HEADER, team_id)
         return self
 
@@ -73,7 +89,15 @@ class TrainClientConfig(BaseClientConfig):
 
 
 def resolve_client(config: BaseClientConfig) -> Client:
-    api_key = os.environ.get(config.api_key_var, "EMPTY")
+    api_key = os.environ.get(config.api_key_var)
+    host = urlparse(config.base_url).hostname or ""
+    if (
+        not api_key
+        and config.api_key_var == "PRIME_API_KEY"
+        and (host == PRIME_INFERENCE_HOST or host.endswith(f".{PRIME_INFERENCE_HOST}"))
+    ):
+        api_key = load_prime_config().get("api_key")
+    api_key = api_key or "EMPTY"
     if isinstance(config, TrainClientConfig):
         # The renderer calls a vLLM `/inference/v1/generate` engine through the OpenAI SDK.
         openai = AsyncOpenAI(