feat: add /v1/embeddings endpoint for mxbai-embed-large 1024 dim

Co-authored-by: Cursor <cursoragent@cursor.com>

config.py

@@ -158,6 +158,7 @@ MODEL_MAPPING = {
     "poweron-text-general": "qwen2.5:7b",
     "poweron-vision-general": "qwen2.5vl:7b",
     "poweron-vision-deep": "granite3.2-vision",
+    "poweron-embed": "mxbai-embed-large",
 }
 
 # Next-gen models (RTX PRO 6000 96 GB VRAM — prepared, activate after migration)
@@ -167,7 +168,7 @@ MODEL_MAPPING = {
 #     "poweron-text-reasoning": "deepseek-r1:70b",
 #     "poweron-vision-general": "llama4:scout",
 #     "poweron-vision-deep": "qwen2.5vl:72b",
-#     "poweron-embed": "nomic-embed-text",
+#     "poweron-embed": "mxbai-embed-large",
 #     "poweron-transcribe": "whisper-large-v3-turbo",
 # }
 
@@ -264,6 +265,30 @@ class OpenAiChatCompletionResponse(BaseModel):
     usage: OpenAiChatCompletionUsage
 
 
+class OpenAiEmbeddingRequest(BaseModel):
+    model: str
+    input: Any
+    dimensions: Optional[int] = None
+
+
+class OpenAiEmbeddingData(BaseModel):
+    object: str = "embedding"
+    embedding: List[float]
+    index: int
+
+
+class OpenAiEmbeddingUsage(BaseModel):
+    promptTokens: int = Field(default=0, alias="prompt_tokens")
+    totalTokens: int = Field(default=0, alias="total_tokens")
+
+
+class OpenAiEmbeddingResponse(BaseModel):
+    object: str = "list"
+    data: List[OpenAiEmbeddingData]
+    model: str
+    usage: OpenAiEmbeddingUsage
+
+
 # ============================================================================
 # Helper Functions
 # ============================================================================

docu/setupserver.md

@@ -62,6 +62,7 @@ Connect: ssh -i "C:\Users\pmots\Downloads\ollama-deploy-key.pem" ubuntu@83.228.2
 | `poweron-text-general` | `qwen2.5:7b` | Text-Neutralisierung |
 | `poweron-vision-general` | `qwen2.5vl:7b` | Handschrift, Dokumente |
 | `poweron-vision-deep` | `granite3.2-vision` | Rechnungen, Belege |
+| `poweron-embed` | `mxbai-embed-large` | Embedding (1024 dim, RAG failover) |
 
 ### URLs
 
@@ -581,6 +582,9 @@ ollama pull qwen2.5vl:7b
 
 # Vision: Rechnungen, Belege
 ollama pull granite3.2-vision
+
+# Embedding: RAG multi-provider failover (1024 dim)
+ollama pull mxbai-embed-large
 ```
 
 ### Modelle pruefen

routeOpenAi.py

@@ -1,13 +1,14 @@
 # Copyright (c) 2026 PowerOn AG
 # All rights reserved.
-"""OpenAI-compatible routes for Cursor integration (/v1/models, /v1/chat/completions)."""
+"""OpenAI-compatible routes (/v1/models, /v1/chat/completions, /v1/embeddings)."""
 
 import time
 import uuid
 import logging
+from typing import List, Optional
 
 import httpx
-from fastapi import APIRouter, HTTPException, Depends
+from fastapi import APIRouter, HTTPException, Depends, Header
 
 from config import (
     CONFIG, MODEL_MAPPING,
@@ -17,6 +18,8 @@ from config import (
     OpenAiChatCompletionRequest, OpenAiChatCompletionResponse,
     OpenAiChatCompletionChoice, OpenAiChatCompletionUsage,
     OpenAiChatMessage, OpenAiModelInfo, OpenAiModelsResponse,
+    OpenAiEmbeddingRequest, OpenAiEmbeddingResponse,
+    OpenAiEmbeddingData, OpenAiEmbeddingUsage,
 )
 
 logger = logging.getLogger(__name__)
@@ -143,3 +146,74 @@ async def _openAiChatCompletions(
         raise HTTPException(status_code=504, detail="Upstream timeout (Ollama)")
     except httpx.ConnectError:
         raise HTTPException(status_code=503, detail="Cannot connect to Ollama upstream")
+
+
+@router.post("/v1/embeddings", response_model=OpenAiEmbeddingResponse)
+async def _openAiEmbeddings(
+    request: OpenAiEmbeddingRequest,
+    xApiKey: Optional[str] = Header(None, alias="X-API-Key"),
+):
+    """OpenAI-compatible embeddings endpoint. Proxies to Ollama /api/embed."""
+    if xApiKey:
+        if CONFIG["apiKey"] and xApiKey != CONFIG["apiKey"]:
+            raise HTTPException(status_code=401, detail="Invalid API key")
+        allowed, info = rateLimiter.isAllowed(xApiKey)
+        if not allowed:
+            raise HTTPException(
+                status_code=429,
+                detail=f"Rate limit exceeded. Retry after {info['retryAfter']} seconds.",
+                headers={"Retry-After": str(int(info["retryAfter"]) + 1)},
+            )
+
+    internalModelName = _getInternalModelName(request.model)
+
+    texts = request.input if isinstance(request.input, list) else [request.input]
+
+    ollamaPayload = {"model": internalModelName, "input": texts}
+
+    try:
+        async with httpx.AsyncClient(timeout=120.0) as client:
+            response = await client.post(
+                f"{CONFIG['ollamaUrl']}/api/embed",
+                json=ollamaPayload,
+            )
+
+        if response.status_code == 404:
+            raise HTTPException(
+                status_code=404,
+                detail=f'Model "{internalModelName}" not found. Install with: ollama pull {internalModelName}',
+            )
+        if response.status_code != 200:
+            raise HTTPException(
+                status_code=response.status_code,
+                detail=f"Ollama API error: {response.status_code} - {response.text[:200]}",
+            )
+
+        responseData = response.json()
+        rawEmbeddings = responseData.get("embeddings", [])
+
+        totalTokens = responseData.get("prompt_eval_count", 0)
+
+        embeddingData = [
+            OpenAiEmbeddingData(embedding=vec, index=i)
+            for i, vec in enumerate(rawEmbeddings)
+        ]
+
+        return OpenAiEmbeddingResponse(
+            data=embeddingData,
+            model=request.model,
+            usage=OpenAiEmbeddingUsage(
+                promptTokens=totalTokens,
+                totalTokens=totalTokens,
+            ),
+        )
+
+    except httpx.TimeoutException:
+        raise HTTPException(status_code=504, detail="Upstream timeout (Ollama)")
+    except httpx.ConnectError:
+        raise HTTPException(status_code=503, detail="Cannot connect to Ollama upstream")
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in embedding endpoint: {e}")
+        raise HTTPException(status_code=500, detail=f"Embedding error: {str(e)}")