feat: run fixed research pipelines within durable budgets

backend/tests/ai_fake.py

@@ -5,7 +5,7 @@ import json
 from contextlib import asynccontextmanager
 from uuid import uuid4
 
-from pydantic_ai.messages import ToolReturnPart, UserPromptPart
+from pydantic_ai.messages import ModelResponse, TextPart, ToolCallPart, ToolReturnPart, UserPromptPart
 from pydantic_ai.models.function import DeltaToolCall, FunctionModel
 
 from tests.research_fake import research_step
@@ -95,6 +95,39 @@ async def fake_stream(messages, info):
     yield {0: DeltaToolCall(name=name, json_args=json.dumps(args), tool_call_id=uuid4().hex)}
 
 
+def fake_structured(messages, info):
+    if not info.output_tools:
+        return ModelResponse(parts=[TextPart("READY")])
+    tool = info.output_tools[0]
+    context = next(
+        (json.loads(p.content) for m in reversed(messages) for p in m.parts if isinstance(p, UserPromptPart)),
+        {},
+    )
+    fields = [name for name, kind in context.get("fields", {}).items() if kind == "MATRIX"][:2]
+    template = {
+        "name": "合成流水线模板",
+        "description": "合成模型研究假设",
+        "expression": "rank({field})",
+        "variables": {
+            "field": {"kind": "field", "field_type": "MATRIX", "values": fields or ["TEST_FIN_001"]}
+        },
+    }
+    properties = tool.parameters_json_schema.get("properties", {})
+    if "summary" in properties:
+        data = {"summary": "合成评估建议", "risks": ["仅供验收"], "suggestions": ["继续核实缺失证据"]}
+    elif "input_ids" in properties:
+        data = {
+            "name": "合成特征方案",
+            "hypothesis": context.get("hypothesis", "合成假设"),
+            "input_ids": [i["id"] for i in context.get("inputs", [])],
+            "steps": [],
+            "template": template,
+        }
+    else:
+        data = template
+    return ModelResponse(parts=[ToolCallPart(tool.name, data)])
+
+
 @asynccontextmanager
 async def fake_model(config, settings):
-    yield FunctionModel(stream_function=fake_stream, model_name="test-model")
+    yield FunctionModel(function=fake_structured, stream_function=fake_stream, model_name="test-model")