Fix: normalize the response in QA mode

main.py

@@ -715,6 +715,31 @@ def ensure_async_embeddings(embedder):
         return embedder
     return AsyncEmbeddingAdapter(embedder)
 
+
+def llm_content_to_text(content: Any) -> str:
+    """
+    Normalize LLM content payloads (string/list/dict) into plain text.
+    Some providers (e.g., Responses API) may return structured blocks.
+    """
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts: List[str] = []
+        for item in content:
+            text = llm_content_to_text(item)
+            if text:
+                parts.append(text)
+        return "\n".join(parts)
+    if isinstance(content, dict):
+        # Common structured message shapes from provider SDKs.
+        for key in ("text", "content", "output_text"):
+            if key in content:
+                return llm_content_to_text(content.get(key))
+        return json.dumps(content, ensure_ascii=False)
+    return str(content)
+
 @app.post("/set_api_key")
 async def set_api_key(
     api_key_input: Optional[str] = Form(None),
@@ -1813,7 +1838,7 @@ async def process_qa_async(
             response = await llm_QA.ainvoke(
                 [HumanMessage(content=f"Summarise this in relation to '{question}':\n{doc.page_content}")]
             )
-            summary = getattr(response, "content", str(response))
+            summary = llm_content_to_text(getattr(response, "content", response))
             # update progress *after* the chunk is done
             done = EMBEDDING_PROGRESS[progress_id]["processed_chunks"] + 1
             EMBEDDING_PROGRESS[progress_id]["processed_chunks"] = done
@@ -1830,7 +1855,7 @@ async def process_qa_async(
         final_response = await llm_QA.ainvoke(
             [HumanMessage(content="Combine the following partial answers into one:\n" + "\n".join(summaries))]
         )
-        final = getattr(final_response, "content", str(final_response))
+        final = llm_content_to_text(getattr(final_response, "content", final_response))
         EMBEDDING_PROGRESS[progress_id] = {
             "status": "completed",
             "progress": 100,