fix: empty response handling, /no_think model gating, per-model profiles

- Detect empty LLM responses (no content, no tool calls) instead of
  silently treating them as task completion. Retries once without tools
  before warning the user.
- Gate /no_think system message and chat_template_kwargs to Qwen/QwQ
  models only — sending /no_think to llama3.x caused empty responses.
- Add model_profiles config section for per-model overrides (token
  budget, thinking, temperature, max_tokens) matched by name prefix.
  Applied at startup and on /model switch.
- Update SessionManager on /model switch so session files record the
  correct model.
- Add NDJSON fallback in SSE stream parser for Ollama compatibility.
- Improve read_file error to suggest find_files on FileNotFoundError.
- Add diagnostic logging for empty streams and empty results.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

app/services/llm.py

@@ -151,8 +151,9 @@ class LLMClient:
         if tools:
             payload["tools"] = tools
 
-        # When thinking is disabled, inject chat_template_kwargs for backends that support it
-        if not self._config.thinking:
+        # When thinking is disabled, inject chat_template_kwargs for backends
+        # that support it (Qwen 3.x thinking models).
+        if not self._config.thinking and self._config.model.lower().startswith(("qwen", "qwq")):
             payload.setdefault("chat_template_kwargs", {})["enable_thinking"] = False
 
         # Merge model-specific extra parameters (e.g., reasoning_effort)
@@ -170,20 +171,32 @@ class LLMClient:
                         status_code=response.status_code,
                     )
 
+                chunk_count = 0
                 async for line in response.aiter_lines():
-                    if not line.startswith("data: "):
+                    line = line.strip()
+                    if not line:
                         continue
 
-                    data = line[6:]  # strip "data: " prefix
-
-                    if data.strip() == "[DONE]":
-                        return
+                    # SSE format: "data: {json}" or "data: [DONE]"
+                    if line.startswith("data: "):
+                        data = line[6:]
+                        if data.strip() == "[DONE]":
+                            break
+                    elif line.startswith("{"):
+                        # Plain NDJSON fallback (some Ollama versions)
+                        data = line
+                    else:
+                        continue
 
                     try:
                         yield json.loads(data)
+                        chunk_count += 1
                     except json.JSONDecodeError:
                         logger.warning("malformed_sse_chunk", data=data[:200])
 
+                if chunk_count == 0:
+                    logger.warning("empty_stream", model=self._config.model)
+
         except httpx.ConnectError as e:
             raise LLMConnectionError(f"Cannot connect to LLM endpoint: {e}") from e
         except httpx.TimeoutException as e:

app/services/session.py

@@ -52,6 +52,10 @@ class SessionManager:
         self._session_dir = workspace_root / config.session_dir
         self._session_id = f"{self._workspace_hash}_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}"
 
+    def update_model(self, model: str) -> None:
+        """Update the model name for session metadata."""
+        self._model = model
+
     def save(self, ctx: "SessionContext") -> Path:
         """Save session state to a JSON file via atomic write.
 

app/services/streaming.py

@@ -60,8 +60,10 @@ class StreamHandler:
         """
         thinking_notified = False
         last_update_time = 0.0
+        chunk_count = 0
 
         async for chunk in chunk_iter:
+            chunk_count += 1
             self._process_chunk(chunk)
 
             if not self._display_config.stream_output:
@@ -96,6 +98,14 @@ class StreamHandler:
             self._on_done()
 
         tool_calls = self._build_tool_calls() or None
+
+        if chunk_count > 0 and not self._accumulated_content and not tool_calls:
+            logger.debug(
+                "stream_empty_result",
+                chunks_received=chunk_count,
+                had_reasoning=bool(self._accumulated_reasoning),
+            )
+
         return Message(
             role="assistant",
             content=self._accumulated_content or None,