Initial implementation

src/va_evaluator/config.py

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+from pydantic import BaseModel
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class IndexSettings(BaseModel):
+    name: str | None = None
+    endpoint: str | None = None
+    deployment: str | None = None
+
+    @property
+    def require_endpoint(self) -> str:
+        if not self.endpoint:
+            raise ValueError("INDEX__ENDPOINT environment variable must be set")
+        return self.endpoint
+
+    @property
+    def require_deployment(self) -> str:
+        if not self.deployment:
+            raise ValueError("INDEX__DEPLOYMENT environment variable must be set")
+        return self.deployment
+
+
+class AgentSettings(BaseModel):
+    name: str = "default"
+    language_model: str = "gemini-2.0-flash"
+    embedding_model: str = "text-embedding-004"
+
+
+class BigQuerySettings(BaseModel):
+    project_id: str | None = None
+    dataset_id: str | None = None
+    synth_gen_table: str = "synthetic_questions"
+    search_eval_table: str = "search_eval_results"
+    keypoint_eval_table: str = "keypoint_eval_results"
+
+    @property
+    def require_dataset_id(self) -> str:
+        if not self.dataset_id:
+            raise ValueError("BIGQUERY__DATASET_ID environment variable must be set")
+        return self.dataset_id
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_nested_delimiter="__")
+
+    project_id: str
+    location: str = "us-central1"
+    bucket: str | None = None
+    index: IndexSettings = IndexSettings()
+    agent: AgentSettings = AgentSettings()
+    bigquery: BigQuerySettings = BigQuerySettings()
+
+    @property
+    def require_bucket(self) -> str:
+        if not self.bucket:
+            raise ValueError("BUCKET environment variable must be set")
+        return self.bucket