feat: 接入起卦后端流程并完善积分扣减链路

backend/src/core/agentscope/runtime/runner.py

@@ -13,11 +13,14 @@ from agentscope.message import Msg
 from agentscope.tool import Toolkit
 from agentscope.model import OpenAIChatModel
 from core.agentscope.prompts.system_prompt import build_system_prompt
+from core.agentscope.prompts.user_prompt import build_divination_user_prompt
 from core.agentscope.schemas.agui_input import extract_latest_user_payload
+from core.divination import derive_divination
 from core.agentscope.runtime.json_react_agent import JsonReActAgent
 from core.agentscope.runtime.model_tracking import TrackingChatModel
 from core.agentscope.runtime.stage_emitter import PipelineStageEmitter
 from core.agentscope.utils import patch_agentscope_json_repair_compat
+from core.agentscope.utils.json_finalize import finalize_json_response
 from core.config.settings import config
 from core.db.session import AsyncSessionLocal
 from models.llm import Llm
@@ -26,9 +29,11 @@ from models.system_agents import SystemAgents
 from schemas.agent.forwarded_props import (
     ClientTimeContext,
     RuntimeMode,
+    parse_forwarded_props_divination_payload,
     parse_forwarded_props_client_time,
     parse_forwarded_props_runtime_mode,
 )
+from schemas.domain.divination import DerivedDivinationData
 from schemas.agent.runtime_models import (
     WorkerAgentOutputLite,
     resolve_worker_output_model,
@@ -97,6 +102,21 @@ class AgentScopeRunner:
                 worker_toolkit = self._build_toolkit()
                 if cancel_checker is not None and await cancel_checker():
                     raise asyncio.CancelledError("run canceled by user")
+                derived_divination = self._resolve_derived_divination(
+                    run_input=run_input
+                )
+                await self._emit_step_event(
+                    pipeline=pipeline,
+                    run_input=run_input,
+                    step_name="divination",
+                    event_type="DIVINATION_DERIVED",
+                    runtime_mode=runtime_mode,
+                    extra_event={
+                        "divination": derived_divination.model_dump(
+                            mode="json", by_alias=True, exclude_none=True
+                        )
+                    },
+                )
                 worker_output = await self._execute_worker_step(
                     pipeline=pipeline,
                     run_input=run_input,
@@ -106,6 +126,7 @@ class AgentScopeRunner:
                     stage_config=worker_config,
                     runtime_client_time=runtime_client_time,
                     runtime_mode=runtime_mode,
+                    derived_divination=derived_divination,
                 )
                 return {
                     "worker": worker_output.model_dump(mode="json", exclude_none=True),
@@ -187,6 +208,7 @@ class AgentScopeRunner:
         stage_config: SystemAgentRuntimeConfig,
         runtime_client_time: ClientTimeContext | None,
         runtime_mode: RuntimeMode,
+        derived_divination: DerivedDivinationData,
     ) -> WorkerAgentOutputLite:
         worker_output_model = resolve_worker_output_model()
         await self._emit_step_event(
@@ -201,6 +223,7 @@ class AgentScopeRunner:
             input_messages=self._build_worker_input_messages(
                 context_messages=context_messages,
                 run_input=run_input,
+                derived_divination=derived_divination,
             ),
             toolkit=toolkit,
             run_input=run_input,
@@ -234,6 +257,7 @@ class AgentScopeRunner:
         runtime_mode: RuntimeMode,
     ) -> StageExecutionResult:
         tracking_model = self._build_model(stage_config=stage_config)
+        formatter = OpenAIChatFormatter()
         emitter = PipelineStageEmitter(
             pipeline=pipeline,
             session_id=run_input.thread_id,
@@ -243,32 +267,24 @@ class AgentScopeRunner:
             emit_text_events=True,
             emit_tool_events=False,
         )
-        agent = self._build_agent(
-            agent_name=stage_config.agent_type.value,
-            system_prompt=build_system_prompt(
-                agent_type=stage_config.agent_type,
-                llm_config=stage_config.llm_config,
-                user_context=user_context,
-                now_utc=datetime.now(timezone.utc),
-                runtime_client_time=runtime_client_time,
-                extra_context=stage_config.extra_context,
-                tools=None,
-            ),
-            toolkit=toolkit,
-            model=tracking_model,
-            emitter=emitter,
+        system_prompt = build_system_prompt(
+            agent_type=stage_config.agent_type,
+            llm_config=stage_config.llm_config,
+            user_context=user_context,
+            now_utc=datetime.now(timezone.utc),
+            runtime_client_time=runtime_client_time,
+            extra_context=stage_config.extra_context,
+            tools=None,
         )
-        async with self._active_agent_lock:
-            self._active_agent = agent
-        try:
-            response_msg = await agent.reply_json(
-                input_messages, output_model=worker_output_model
-            )
-        finally:
-            async with self._active_agent_lock:
-                if self._active_agent is agent:
-                    self._active_agent = None
-        worker_payload = worker_output_model.model_validate(response_msg.metadata or {})
+
+        _, worker_payload_raw = await finalize_json_response(
+            model=tracking_model,
+            formatter=formatter,
+            base_messages=[Msg("system", system_prompt, "system"), *input_messages],
+            output_model=worker_output_model,
+            retries=2,
+        )
+        worker_payload = worker_output_model.model_validate(worker_payload_raw)
         response_metadata = self._llm_pricing_service.build_usage_metadata(
             model=stage_config.model_code,
             usage_summary=tracking_model.usage_summary(),
@@ -278,7 +294,12 @@ class AgentScopeRunner:
             response_metadata=response_metadata,
         )
         return StageExecutionResult(
-            message=response_msg,
+            message=Msg(
+                name=stage_config.agent_type.value,
+                role="assistant",
+                content=worker_payload.answer,
+                metadata=worker_payload.model_dump(mode="json", exclude_none=True),
+            ),
             payload=worker_payload.model_dump(mode="json", exclude_none=True),
             response_metadata=response_metadata,
         )
@@ -288,13 +309,16 @@ class AgentScopeRunner:
         *,
         context_messages: list[Msg],
         run_input: RunAgentInput,
+        derived_divination: DerivedDivinationData,
     ) -> list[Msg]:
         if context_messages:
             last = context_messages[-1]
             if last.role == "user":
                 return context_messages
 
-        user_text, user_blocks = extract_latest_user_payload(run_input)
+        _, _ = extract_latest_user_payload(run_input)
+        user_text = build_divination_user_prompt(derived=derived_divination)
+        user_blocks = [{"type": "text", "text": user_text}]
         if (
             user_blocks
             and isinstance(user_blocks[0], dict)
@@ -307,6 +331,17 @@ class AgentScopeRunner:
         user_msg = Msg(name="user", role="user", content=content)
         return [*context_messages, user_msg]
 
+    @staticmethod
+    def _resolve_derived_divination(
+        *, run_input: RunAgentInput
+    ) -> DerivedDivinationData:
+        payload = parse_forwarded_props_divination_payload(
+            getattr(run_input, "forwarded_props", None)
+        )
+        if payload is None:
+            raise ValueError("forwardedProps.divinationPayload is required")
+        return derive_divination(payload)
+
     def _build_model(
         self, *, stage_config: SystemAgentRuntimeConfig
     ) -> TrackingChatModel: