feat: enhance logging and error handling in backend and editor components

backend/llm.py

@@ -2,6 +2,7 @@ import os
 from typing import AsyncGenerator
 from openai import AsyncOpenAI
 import json
+import time
 
 api_key = os.getenv('OPENAI_API_KEY', 'ollama')
 base_url = os.getenv('OLLAMA_BASE_URL', 'http://192.168.0.120:11434/v1/')
@@ -18,9 +19,13 @@ async def stream_openai(prompt: str) -> AsyncGenerator[str, None]:
     调用 OpenAI/Ollama API 并流式返回补全内容。
     参考 completions-sample-code 的 streaming 逻辑。
     """
-    print(f"[LLM] Calling API with prompt length: {len(prompt)}")
+    start_time = time.time()
+    print(f"[LLM] ========== API Call Start ==========")
+    print(f"[LLM] Prompt length: {len(prompt)}")
+    print(f"[LLM] Model: {model}")
     
     try:
+        print(f"[LLM] Creating streaming chat completion...")
         stream = await client.chat.completions.create(
             model=model,
             messages=[{"role": "user", "content": prompt}],
@@ -29,16 +34,36 @@ async def stream_openai(prompt: str) -> AsyncGenerator[str, None]:
             temperature=0.2,
         )
 
+        print(f"[LLM] Stream created successfully, iterating...")
         chunk_count = 0
-        async for chunk in stream:
-            if chunk.choices[0].delta.content:
-                content = chunk.choices[0].delta.content
-                chunk_count += 1
-                print(f"[LLM] Chunk {chunk_count}: {content}")
-                yield json.dumps({"content": content})
+        first_chunk_time = None
         
-        print(f"[LLM] Stream complete, total chunks: {chunk_count}")
+        async for chunk in stream:
+            current_time = time.time()
+            if first_chunk_time is None:
+                first_chunk_time = current_time - start_time
+            
+            chunk_count += 1
+            choice = chunk.choices[0] if chunk.choices else None
+            
+            if choice and choice.delta.content:
+                content = choice.delta.content
+                print(f"[LLM] Chunk {chunk_count}: '{content}' (latency: {current_time - start_time:.3f}s)")
+                yield json.dumps({"content": content})
+            elif chunk.choices and hasattr(chunk.choices[0], 'finish_reason'):
+                finish_reason = chunk.choices[0].finish_reason
+                print(f"[LLM] Chunk {chunk_count}: finish_reason={finish_reason}")
+                if finish_reason:
+                    break
+            else:
+                print(f"[LLM] Chunk {chunk_count}: empty or no content")
+        
+        total_time = time.time() - start_time
+        print(f"[LLM] Stream complete - chunks: {chunk_count}, first chunk latency: {first_chunk_time:.3f}s, total time: {total_time:.3f}s")
+        print(f"[LLM] ========== API Call End ==========")
     except Exception as e:
         error_msg = f"Error: {str(e)}"
         print(f"[LLM] Error: {error_msg}")
-        yield json.dumps({"error": str(e)})
+        import traceback
+        traceback.print_exc()
+        yield json.dumps({"error": str(e), "type": type(e).__name__})