Add YAML config support and Compose deployment example

app/config.py

@@ -2,26 +2,66 @@ from __future__ import annotations
 
 import os
 from functools import lru_cache
-from typing import Literal
+from pathlib import Path
+from typing import Any, Literal
 
-from pydantic import BaseModel, Field
+import yaml
+from pydantic import BaseModel
 
 Provider = Literal["openai", "anthropic"]
+DEFAULT_CONFIG_PATHS = ["config.yml", "config.yaml", "/config/config.yml", "/config/config.yaml"]
 
 
 class LLMSettings(BaseModel):
-    provider: Provider = Field(default=os.getenv("LLM_PROVIDER", "openai"))
-    api_key: str = Field(default=os.getenv("LLM_API_KEY", "none"))
-    model: str = Field(default=os.getenv("LLM_MODEL", "qwen2.5-7b-instruct.q4_k_m"))
-    base_url: str = Field(default=os.getenv("LLM_BASE_URL", "http://ollama.internal.henryhosted.com:9292/v1"))
-    temperature: float = Field(default=float(os.getenv("LLM_TEMPERATURE", "0.1")))
-    timeout_seconds: float = Field(default=float(os.getenv("LLM_TIMEOUT_SECONDS", "60")))
-    max_retries: int = Field(default=int(os.getenv("LLM_MAX_RETRIES", "3")))
+    provider: Provider = "openai"
+    api_key: str = "none"
+    model: str = "qwen2.5-7b-instruct.q4_k_m"
+    base_url: str = "http://ollama.internal.henryhosted.com:9292/v1"
+    temperature: float = 0.1
+    timeout_seconds: float = 60
+    max_retries: int = 3
+
+
+def _load_yaml_config() -> dict[str, Any]:
+    explicit = os.getenv("EMAIL_CLASSIFIER_CONFIG") or os.getenv("APP_CONFIG_FILE")
+    candidates = [explicit] if explicit else DEFAULT_CONFIG_PATHS
+    for candidate in candidates:
+        if not candidate:
+            continue
+        path = Path(candidate)
+        if not path.exists() or not path.is_file():
+            continue
+        data = yaml.safe_load(path.read_text()) or {}
+        if not isinstance(data, dict):
+            raise ValueError(f"Config file must contain a mapping/object: {path}")
+        llm = data.get("llm", data)
+        if not isinstance(llm, dict):
+            raise ValueError(f"LLM config must be a mapping/object: {path}")
+        return llm
+    return {}
+
+
+def _env_or_yaml(env_name: str, yaml_data: dict[str, Any], yaml_key: str, default: Any) -> Any:
+    value = os.getenv(env_name)
+    if value is not None:
+        return value
+    if yaml_key in yaml_data and yaml_data[yaml_key] is not None:
+        return yaml_data[yaml_key]
+    return default
 
 
 @lru_cache(maxsize=1)
 def get_settings() -> LLMSettings:
-    return LLMSettings()
+    yaml_data = _load_yaml_config()
+    return LLMSettings(
+        provider=_env_or_yaml("LLM_PROVIDER", yaml_data, "provider", "openai"),
+        api_key=_env_or_yaml("LLM_API_KEY", yaml_data, "api_key", "none"),
+        model=_env_or_yaml("LLM_MODEL", yaml_data, "model", "qwen2.5-7b-instruct.q4_k_m"),
+        base_url=_env_or_yaml("LLM_BASE_URL", yaml_data, "base_url", "http://ollama.internal.henryhosted.com:9292/v1"),
+        temperature=float(_env_or_yaml("LLM_TEMPERATURE", yaml_data, "temperature", 0.1)),
+        timeout_seconds=float(_env_or_yaml("LLM_TIMEOUT_SECONDS", yaml_data, "timeout_seconds", 60)),
+        max_retries=int(_env_or_yaml("LLM_MAX_RETRIES", yaml_data, "max_retries", 3)),
+    )
 
 
 def get_request_settings(