Initial commit (clean history)HEADmain

1 files changed, 73 insertions, 0 deletions

diff --git a/src/personalization/config/settings.py b/src/personalization/config/settings.py
new file mode 100644
index 0000000..1bb1bbe
--- /dev/null
+++ b/src/personalization/config/settings.py
@@ -0,0 +1,73 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Optional, Any, Dict
+
+import yaml
+from pydantic import BaseModel, Field
+
+
+class ModelSpec(BaseModel):
+    hf_id: str = Field(..., description="Hugging Face repository id")
+    local_path: str = Field(..., description="Local directory for model weights")
+    dtype: Optional[str] = Field(
+        default="bfloat16", description="Preferred torch dtype: bfloat16|float16|float32"
+    )
+    device_map: Optional[str] = Field(default="auto", description="Device map policy")
+
+
+class EmbeddingModelsConfig(BaseModel):
+    qwen3: Optional[ModelSpec] = None
+    nemotron: Optional[ModelSpec] = None
+
+
+class RerankerModelsConfig(BaseModel):
+    qwen3_8b: Optional[ModelSpec] = None
+
+
+class LocalModelsConfig(BaseModel):
+    llm: ModelSpec
+    preference_extractor: Any # Allow flexible dict or ModelSpec for now to support map
+    embedding: Optional[EmbeddingModelsConfig] = None
+    reranker: Optional[RerankerModelsConfig] = None
+
+
+def _resolve_config_path(env_key: str, default_rel: str) -> Path:
+    value = os.getenv(env_key)
+    if value:
+        return Path(value).expanduser().resolve()
+    return (Path.cwd() / default_rel).resolve()
+
+
+def load_local_models_config(path: Optional[str] = None) -> LocalModelsConfig:
+    config_path = Path(path) if path else _resolve_config_path(
+        "LOCAL_MODELS_CONFIG", "configs/local_models.yaml"
+    )
+    with open(config_path, "r", encoding="utf-8") as f:
+        raw = yaml.safe_load(f) or {}
+    models = raw.get("models", {})
+    embedding_cfg = None
+    if "embedding" in models:
+        emb = models["embedding"] or {}
+        # dtype/device_map are not necessary for embedders; ModelSpec still accepts them
+        embedding_cfg = EmbeddingModelsConfig(
+            qwen3=ModelSpec(**emb["qwen3"]) if "qwen3" in emb else None,
+            nemotron=ModelSpec(**emb["nemotron"]) if "nemotron" in emb else None,
+        )
+    
+    reranker_cfg = None
+    if "reranker" in models:
+        rer = models["reranker"] or {}
+        reranker_cfg = RerankerModelsConfig(
+            qwen3_8b=ModelSpec(**rer["qwen3_8b"]) if "qwen3_8b" in rer else None
+        )
+
+    return LocalModelsConfig(
+        llm=ModelSpec(**models["llm"]),
+        preference_extractor=models["preference_extractor"], # Pass raw dict/value
+        embedding=embedding_cfg,
+        reranker=reranker_cfg,
+    )
+
+