chore: layout optimize

backend/app/api/embedding_models.py

@@ -0,0 +1,96 @@
+from typing import List, Optional
+from fastapi import APIRouter, HTTPException, Depends
+from openai import OpenAI
+
+from app.schemas.embedding_model import (
+    EmbeddingModelConfig,
+    EmbeddingModelConfigCreate,
+    EmbeddingModelConfigUpdate,
+    EmbeddingModelConnectionTestRequest
+)
+from app.services.embedding_model_store import embedding_model_store
+from app.services.openai_compat import normalize_openai_base_url
+from app.api.llm import get_admin_user, get_current_user, CurrentUser
+
+router = APIRouter()
+
+def _mask_api_key(value: Optional[str]) -> Optional[str]:
+    if not value:
+        return None
+    if len(value) <= 8:
+        return "*" * len(value)
+    return f"{value[:4]}{'*' * (len(value) - 8)}{value[-4:]}"
+
+@router.get("/embedding-models", response_model=List[EmbeddingModelConfig])
+def list_embedding_models(current_user: CurrentUser = Depends(get_current_user)):
+    models = embedding_model_store.list_models()
+    for m in models:
+        if not current_user.is_admin:
+            m["api_key"] = None
+    return models
+
+@router.post("/embedding-models", response_model=EmbeddingModelConfig)
+def create_embedding_model(payload: EmbeddingModelConfigCreate, _: CurrentUser = Depends(get_admin_user)):
+    return embedding_model_store.create_model(payload.model_dump())
+
+@router.get("/embedding-models/{model_id}", response_model=EmbeddingModelConfig)
+def get_embedding_model(model_id: str, current_user: CurrentUser = Depends(get_current_user)):
+    model = embedding_model_store.get_model(model_id)
+    if not model:
+        raise HTTPException(status_code=404, detail="Embedding model not found")
+    if not current_user.is_admin:
+        model["api_key"] = None
+    return model
+
+@router.put("/embedding-models/{model_id}", response_model=EmbeddingModelConfig)
+def update_embedding_model(model_id: str, payload: EmbeddingModelConfigUpdate, _: CurrentUser = Depends(get_admin_user)):
+    model = embedding_model_store.update_model(model_id, payload.model_dump(exclude_unset=True))
+    if not model:
+        raise HTTPException(status_code=404, detail="Embedding model not found")
+    return model
+
+@router.delete("/embedding-models/{model_id}")
+def delete_embedding_model(model_id: str, _: CurrentUser = Depends(get_admin_user)):
+    deleted = embedding_model_store.delete_model(model_id)
+    if not deleted:
+        raise HTTPException(status_code=404, detail="Embedding model not found")
+    return {"status": "success"}
+
+@router.post("/embedding-models/test")
+def test_embedding_model_connection(payload: EmbeddingModelConnectionTestRequest, _: CurrentUser = Depends(get_admin_user)):
+    api_base = normalize_openai_base_url(payload.api_base or "")
+    api_key = payload.api_key
+    model_name = (payload.model or "").strip()
+
+    if not api_base:
+        raise HTTPException(status_code=400, detail="API Base is required")
+    if not api_key:
+        raise HTTPException(status_code=400, detail="API Key is required")
+    if not model_name:
+        raise HTTPException(status_code=400, detail="Model name is required")
+
+    try:
+        client = OpenAI(
+            api_key=api_key,
+            base_url=api_base,
+        )
+        embedding_resp = client.embeddings.create(
+            model=model_name,
+            input="connection test",
+        )
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=f"Embedding call failed: {exc}")
+
+    dimension = None
+    if getattr(embedding_resp, "data", None):
+        first = embedding_resp.data[0]
+        vector = getattr(first, "embedding", None)
+        if isinstance(vector, list):
+            dimension = len(vector)
+
+    return {
+        "success": True,
+        "message": "Connection successful",
+        "model_name": model_name,
+        "embedding_dimension": dimension,
+    }