reporeformat V2 (#23)

* reformat factor implement process * move some code to more reasonable place * fix the bug * add test function in factor_extract_and_implement.py * change select factor number to ratio , add some factor implement setting and fix some bug while using knowledgebase * change evoagent * add abstract class EvoAgent * add benchmark workflow * fix some bug in llm_utils * run wenjun's code * fix the knowledgebase instance check --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
2026-07-28 07:57:44 +00:00 · 2024-06-14 12:59:44 +08:00
parent 9e82da243b
commit ebb659a018
32 changed files with 2227 additions and 1141 deletions
@@ -0,0 +1,50 @@
+from rdagent.core.evolving_framework import Feedback, EvolvableSubjects, Evaluator, EvoStep
+from rdagent.core.evolving_framework import EvoAgent
+from tqdm import tqdm
+
+class RAGEvoAgent(EvoAgent):
+    def __init__(self, max_loop, evolving_strategy, rag) -> None:
+        super().__init__(max_loop, evolving_strategy)
+        self.rag = rag
+        self.evolving_trace = []
+
+    def multistep_evolve(
+        self, 
+        evo: EvolvableSubjects,
+        eva: Evaluator | Feedback, 
+        *,
+        with_knowledge: bool = False,
+        with_feedback: bool = True,
+        knowledge_self_gen: bool = False) -> EvolvableSubjects:
+
+        for _ in tqdm(range(self.max_loop), "Implementing factors"):
+            # 1. knowledge self-evolving
+            if knowledge_self_gen and self.rag is not None:
+                self.rag.generate_knowledge(self.evolving_trace)
+            # 2. 检索需要的Knowledge
+            queried_knowledge = None
+            if with_knowledge and self.rag is not None:
+                # TODO: 这里放了evolving_trace实际上没有作用
+                queried_knowledge = self.rag.query(evo, self.evolving_trace)
+
+            # 3. evolve
+            evo = self.evolving_strategy.evolve(
+                    evo=evo,
+                    evolving_trace=self.evolving_trace,
+                    queried_knowledge=queried_knowledge,
+                )
+            
+            # 4. 封装Evolve结果
+            es = EvoStep(evo, queried_knowledge)
+
+            # 5. 环境评测反馈
+            if with_feedback:
+                es.feedback = eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
+
+            # 6. 更新trace
+            self.evolving_trace.append(es)
+            for index, feedback in enumerate(es.feedback):
+                if feedback is not None:
+                    evo.evolve_trace[evo.target_factor_tasks[index].factor_name][-1].feedback = feedback
+
+        return evo