feat: add streaming output

backend/app/core/streaming_provider.py

@@ -0,0 +1,76 @@
+import contextvars
+import json
+from typing import Any, Dict, List, Optional
+from loguru import logger
+from nanobot.providers.litellm_provider import LiteLLMProvider
+from nanobot.providers.base import LLMResponse
+from litellm import acompletion, stream_chunk_builder
+
+streaming_queue_var = contextvars.ContextVar("streaming_queue", default=None)
+
+class StreamingLiteLLMProvider(LiteLLMProvider):
+    async def chat(
+        self,
+        messages: List[Dict[str, Any]],
+        tools: Optional[List[Dict[str, Any]]] = None,
+        model: Optional[str] = None,
+        temperature: float = 0.7,
+        max_tokens: int = 4000,
+        reasoning_effort: Optional[str] = None,
+        request_timeout: Optional[int] = None,
+        num_retries: Optional[int] = None,
+    ) -> LLMResponse:
+        original_model = model or self.default_model
+        model_name = self._resolve_model(original_model)
+
+        kwargs: Dict[str, Any] = {
+            "model": model_name,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "stream": True,  # 强制开启流式
+        }
+        
+        if self.api_key and self.api_key != "no-key":
+            kwargs["api_key"] = self.api_key
+        if self.api_base:
+            kwargs["api_base"] = self.api_base
+        if self.extra_headers:
+            kwargs["extra_headers"] = self.extra_headers
+        if tools:
+            kwargs["tools"] = tools
+        if request_timeout is not None:
+            kwargs["timeout"] = request_timeout
+        if num_retries is not None:
+            kwargs["num_retries"] = max(0, int(num_retries))
+
+        if reasoning_effort and self._supports_reasoning_effort(model_name):
+            kwargs["reasoning_effort"] = reasoning_effort
+
+        try:
+            response_stream = await acompletion(**kwargs)
+            chunks = []
+            queue = streaming_queue_var.get()
+            
+            async for chunk in response_stream:
+                chunks.append(chunk)
+                
+                if queue is not None:
+                    # 提取普通内容或 think 内容
+                    delta = chunk.choices[0].delta if chunk.choices else None
+                    if delta:
+                        content = getattr(delta, "content", None)
+                        reasoning_content = getattr(delta, "reasoning_content", None)
+                        
+                        if content:
+                            await queue.put({"type": "delta", "content": content})
+                        if reasoning_content:
+                            await queue.put({"type": "progress", "content": reasoning_content, "is_reasoning": True})
+                            
+            # 还原为完整的 response 对象供 nanobot 处理
+            full_response = stream_chunk_builder(chunks, messages=messages)
+            return self._parse_response(full_response)
+            
+        except Exception as e:
+            logger.error("StreamingLiteLLMProvider failed: {}", e)
+            raise