feat(models): support reasoning_content streaming
This commit is contained in:
Timebomb2018
@@ -458,7 +458,7 @@ class AgentRunService:
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statement = opening["statement"]
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suggested_questions = opening["suggested_questions"]
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# 如果有变量，进行替换（仅支持 {{var_name}} 格式）
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if variables:
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for var_name, var_value in variables.items():
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@@ -595,6 +595,9 @@ class AgentRunService:
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max_tokens=effective_params.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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deep_thinking=effective_params.get("deep_thinking", False),
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thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
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capability=api_key_config.get("capability", []),
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)
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# 5. 处理会话ID（创建或验证），新会话时写入开场白
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@@ -689,7 +692,8 @@ class AgentRunService:
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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})
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}),
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"reasoning_content": result.get("reasoning_content")
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},
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files=files,
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processed_files=processed_files,
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@@ -701,6 +705,7 @@ class AgentRunService:
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response = {
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"message": result["content"],
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"reasoning_content": result.get("reasoning_content"),
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"conversation_id": conversation_id,
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"usage": result.get("usage", {
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"prompt_tokens": 0,
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@@ -838,7 +843,10 @@ class AgentRunService:
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max_tokens=effective_params.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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streaming=True
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streaming=True,
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deep_thinking=effective_params.get("deep_thinking", False),
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thinking_budget_tokens=effective_params.get("thinking_budget_tokens"),
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capability=api_key_config.get("capability", []),
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)
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# 5. 处理会话ID（创建或验证），新会话时写入开场白
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@@ -898,6 +906,7 @@ class AgentRunService:
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# 9. 流式调用 Agent（支持多模态），同时并行启动 TTS
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full_content = ""
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full_reasoning = ""
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total_tokens = 0
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# 启动流式 TTS（文本边输出边合成）
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@@ -916,6 +925,9 @@ class AgentRunService:
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):
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if isinstance(chunk, int):
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total_tokens = chunk
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elif isinstance(chunk, dict) and chunk.get("type") == "reasoning":
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full_reasoning += chunk["content"]
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yield self._format_sse_event("reasoning", {"content": chunk["content"]})
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else:
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full_content += chunk
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yield self._format_sse_event("message", {"content": chunk})
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@@ -944,7 +956,8 @@ class AgentRunService:
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app_id=agent_config.app_id,
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user_id=user_id,
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meta_data={
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"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens}
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"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens},
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"reasoning_content": full_reasoning or None
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},
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files=files,
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processed_files=processed_files,
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@@ -1665,7 +1678,7 @@ class AgentRunService:
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"""从 text_queue 取文本按句子切分后喂给 synthesizer"""
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import re
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buf = ""
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sentence_end = re.compile(r'[\u3002\uff01\uff1f\.!?\n]')
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sentence_end = re.compile(r'[\u3002\uff01\uff1f.!?\n]')
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while True:
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chunk = await text_queue.get()
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if chunk is None:
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@@ -1894,6 +1907,7 @@ class AgentRunService:
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"conversation_id": result['conversation_id'],
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"parameters_used": model_info["parameters"],
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"message": result.get("message"),
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"reasoning_content": result.get("reasoning_content"),
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"usage": usage,
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"elapsed_time": elapsed,
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"tokens_per_second": (
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@@ -2012,7 +2026,7 @@ class AgentRunService:
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# 需要从 ModelApiKey 获取实际的模型名称，或者在 ModelConfig 中添加 model 字段
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return None
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def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> AgentConfig:
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def _with_parameters(self, agent_config: AgentConfig, parameters: Dict[str, Any]) -> tuple[AgentConfig, Any]:
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"""创建一个带有覆盖参数的 agent_config（浅拷贝，只修改 model_parameters）
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Args:
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@@ -2110,6 +2124,7 @@ class AgentRunService:
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start_time = time.time()
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full_content = ""
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full_reasoning = ""
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returned_conversation_id = model_conversation_id
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audio_url = None
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audio_status = None
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@@ -2168,6 +2183,18 @@ class AgentRunService:
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"content": chunk
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}))
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# 转发深度思考事件（带模型标识）
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if event_type == "reasoning" and event_data:
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reasoning_chunk = event_data.get("content", "")
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full_reasoning += reasoning_chunk
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await event_queue.put(self._format_sse_event("model_reasoning", {
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"model_index": idx,
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"model_config_id": model_config_id,
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"label": model_label,
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"conversation_id": returned_conversation_id,
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"content": event_data.get("content", "")
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}))
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# 从 end 事件中提取 features 输出字段
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if event_type == "end" and event_data:
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audio_url = event_data.get("audio_url")
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@@ -2199,6 +2226,7 @@ class AgentRunService:
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"conversation_id": returned_conversation_id,
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"parameters_used": model_info["parameters"],
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"message": full_content,
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"reasoning_content": full_reasoning or None,
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"elapsed_time": elapsed,
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"audio_url": audio_url,
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"audio_status": audio_status,
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@@ -2351,6 +2379,7 @@ class AgentRunService:
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"label": r["label"],
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"conversation_id": r.get("conversation_id"),
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"message": r.get("message"),
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"reasoning_content": r.get("reasoning_content"),
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"elapsed_time": r.get("elapsed_time", 0),
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"audio_url": r.get("audio_url"),
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"audio_status": r.get("audio_status"),
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