Merge #104 into develop from feature/20251219_myh

feat(workflow): add support for question classifier in graph construction

* feature/20251219_myh: (11 commits)
  feat(workflow): support variable types(TODO)
  fix(workflow): fix passing of loop variable termination condition
  feat(workflow): add support for passing workspace ID
  feat(workflow): support retrieving variables wrapped in {{}} from variable pool
  feat(prompt_opt): support streaming output for prompt optimization API
  feat(workflow): update workflow conditional logic
  feat(workflow): enable front-end to cover pre-rendered non-variable values
  fix(workflow): ensure default values are properly retrieved in HTTP nodes
  refactor(workflow): refactor graph construction to support subgraph building
  Merge branch 'develop' into feature/20251219_myh
  feat(workflow): add support for question classifier in graph construction

Signed-off-by: Eternity <1533512157@qq.com>
Reviewed-by: zhuwenhui5566@163.com <zhuwenhui5566@163.com>
Merged-by: zhuwenhui5566@163.com <zhuwenhui5566@163.com>

CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/104

api/app/services/prompt_optimizer_service.py

@@ -1,5 +1,6 @@
 import re
 import uuid
+from typing import Any, AsyncGenerator
 
 import json_repair
 from langchain_core.prompts import ChatPromptTemplate
@@ -123,7 +124,7 @@ class PromptOptimizerService:
             user_id: uuid.UUID,
             current_prompt: str,
             user_require: str
-    ) -> OptimizePromptResult:
+    ) -> AsyncGenerator[dict[str, str | Any], Any]:
         """
         Optimize a user-provided prompt using a configured prompt optimizer LLM.
 
@@ -161,6 +162,7 @@ class PromptOptimizerService:
             BusinessException: If the LLM response cannot be parsed as valid JSON
             or does not conform to the expected output format.
         """
+        self.create_message(tenant_id, session_id, user_id, role=RoleType.USER, content=user_require)
         model_config = self.get_model_config(tenant_id, model_id)
         session_history = self.get_session_message_history(session_id=session_id, user_id=user_id)
 
@@ -202,17 +204,54 @@ class PromptOptimizerService:
         messages.extend(session_history[:-1])  # last message is current message
         messages.extend([(RoleType.USER.value, rendered_user_message)])
         logger.info(f"Prompt optimization message: {messages}")
-        optim_resp = await llm.ainvoke(messages)
-        logger.info(optim_resp.content)
-        optim_result = json_repair.repair_json(optim_resp.content, return_objects=True)
-        prompt = optim_result.get("prompt")
-        desc = optim_result.get("desc")
+        buffer = ""
+        prompt_started = False
+        prompt_finished = False
+        idx = 0
 
-        return OptimizePromptResult(
-            prompt=prompt,
-            desc=desc
+        async for chunk in llm.astream(messages):
+            content = getattr(chunk, "content", chunk)
+            if not content:
+                continue
+            buffer += content
+            cache = buffer[:-20]
+
+            # 尝试找到 "prompt": " 开始位置
+            if prompt_finished:
+                continue
+
+            if not prompt_started:
+                m = re.search(r'"prompt"\s*:\s*"', cache)
+                if m:
+                    prompt_started = True
+                    prompt_index = m.end()
+                    idx = prompt_index
+            else:
+                m = re.search(r'"\s*,\s*\\?n?\s*"desc"\s*:\s*"', buffer)
+                if m:
+                    prompt_index = m.start()
+                    prompt_finished = True
+                    yield {"type": "delta", "content": buffer[idx:prompt_index]}
+                else:
+                    yield {"type": "delta", "content": cache[idx:]}
+                    if len(cache) != 0:
+                        idx = len(cache)
+
+        # optim_resp = await llm.astream(messages)
+        logger.info(buffer)
+        optim_result = json_repair.repair_json(buffer, return_objects=True)
+        # prompt = optim_result.get("prompt")
+        desc = optim_result.get("desc")
+        self.create_message(
+            tenant_id=tenant_id,
+            session_id=session_id,
+            user_id=user_id,
+            role=RoleType.ASSISTANT,
+            content=desc
         )
 
+        yield {"type": "done", "desc": optim_result.get("desc")}
+
     @staticmethod
     def parser_prompt_variables(prompt: str):
         try:

api/app/services/workflow_service.py

@@ -410,7 +410,8 @@ class WorkflowService:
             self,
             app_id: uuid.UUID,
             payload: DraftRunRequest,
-            config: WorkflowConfig
+            config: WorkflowConfig,
+            workspace_id: uuid.UUID,
     ):
         """运行工作流
 
@@ -484,7 +485,7 @@ class WorkflowService:
                 workflow_config=workflow_config_dict,
                 input_data=input_data,
                 execution_id=execution.execution_id,
-                workspace_id="",
+                workspace_id=str(workspace_id),
                 user_id=payload.user_id
             )
 
@@ -530,7 +531,8 @@ class WorkflowService:
             self,
             app_id: uuid.UUID,
             payload: DraftRunRequest,
-            config: WorkflowConfig
+            config: WorkflowConfig,
+            workspace_id: uuid.UUID,
     ):
         """运行工作流（流式）
 
@@ -603,7 +605,7 @@ class WorkflowService:
                     workflow_config=workflow_config_dict,
                     input_data=input_data,
                     execution_id=execution.execution_id,
-                    workspace_id="",
+                    workspace_id=str(workspace_id),
                     user_id=payload.user_id
             ):
                 # 直接转发 executor 的事件（已经是正确的格式）