diff --git a/Dockerfile b/Dockerfile
index 3aae3d6..de84bdc 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -53,7 +53,15 @@ mapping = {
 choice = os.environ.get("RAG_EMBEDDINGS","gte-multilingual").lower()
 hf_id = mapping.get(choice, "BAAI/bge-small-en-v1.5")
 # cache_folder respecteert SENTENCE_TRANSFORMERS_HOME/HF_HOME, maar we forceren expliciet:
-SentenceTransformer(hf_id, cache_folder=os.environ.get("SENTENCE_TRANSFORMERS_HOME","/opt/sentence-transformers"))
+cache_root = os.environ.get("SENTENCE_TRANSFORMERS_HOME", "/opt/sentence-transformers")
+local_dir = os.path.join(cache_root, "embedder")
+os.makedirs(cache_root, exist_ok = True)
+
+print("Downloading SentenceTransformer:", hf_id)
+# Laat SentenceTransformer zelf hun cache doen (HF_HOME etc)
+# wij saven het eindresultaat daarna naar local_dir
+model = SentenceTransformer(hf_id, cache_folder=os.environ.get("SENTENCE_TRANSFORMERS_HOME","/opt/sentence-transformers"))
+model.save(local_dir)
 print("Prefetched SentenceTransformer:", hf_id)
 PY
 
diff --git a/app.py b/app.py
index de778ed..d2ca986 100644
--- a/app.py
+++ b/app.py
@@ -190,10 +190,21 @@ def _extract_tool_calls_from_text(txt: str):
         raw = obj["call_tool"]
         if isinstance(raw, dict):
             raw = [raw]
+        if isinstance(raw, str):
+            print("Invalid toolcall:",str(raw))
+            raw=[]
         tc = []
         for it in (raw or []):
-            name = (it or {}).get("name")
-            args = (it or {}).get("arguments") or {}
+            try:
+                name = (it or {}).get("name")
+            except Exception as e:
+                print("Error:",str(e),"raw=",str(raw),"it=",str(it))
+                continue
+            try:
+                args = (it or {}).get("arguments") or {}
+            except Exception as e:
+                print("Error:",str(e),"raw=",str(raw),"it=",str(it))
+                continue
             if isinstance(args, str):
                 try: args = json.loads(args)
                 except Exception: pass
@@ -540,11 +551,23 @@ def _build_embedder() -> _Embedder:
             model_name, family, slug = mapping["bge-small"]
         else:
             model_name, family, slug = mapping[choice]
+        cache_dir = os.environ.get("SENTENCE_TRANSFORMERS_HOME", "/opt/sentence-transformers")
+        local_dir = os.path.join(cache_dir, "embedder")
         st_kwargs = {"device": "cpu"}
+        if os.path.isdir(local_dir):
+            # Prefetched model in image → gebruik dat
+            model_source = local_dir
+            print(f"Loading SentenceTransformer from local dir: {local_dir}")
+        else:
+            # Geen lokale dir gevonden → terugvallen op HF-ID + cache_folder
+            model_source = model_name
+            st_kwargs["cache_folder"] = cache_dir
+            print(f"Local dir {local_dir} not found, falling back to HF model: {model_name}")
+
         try:
             if "trust_remote_code" in inspect.signature(SentenceTransformer).parameters:
                 st_kwargs["trust_remote_code"] = True
-            model = SentenceTransformer(model_name, **st_kwargs)
+            model = SentenceTransformer(model_source, **st_kwargs)
             # optioneel: CPU thread-telling forceren
             try:
                 thr = int(os.getenv("RAG_TORCH_THREADS", "0"))
@@ -554,8 +577,8 @@ def _build_embedder() -> _Embedder:
             except Exception:
                 pass
             return _Embedder(slug=slug, family=family, model=model, device="cpu")
-        except Exception:
-            pass
+        except Exception as e:
+            print("ERROR building embedder:",str(e))
 
     # Fallback via Chroma embedding functions
     from chromadb.utils import embedding_functions as ef
@@ -2630,6 +2653,8 @@ async def openai_chat_completions(body: dict = Body(...), request: Request = Non
                 })
 
             call = (obj.get("call_tool") or {})
+            if isinstance(call, str):
+                call={"name": "Invalid_toolcall", "arguments": {}}
             tname = call.get("name")
             if tname not in TOOLS_REGISTRY:
                 return JSONResponse(_openai_chat_response(model, f"Onbekende tool: {tname}", messages))