deep-fin-pre-commit-patch

.gitignore

@@ -174,3 +174,4 @@ werewolves_swarm
 .claude
 tensorboard_log
 tutorial/**/*.json
+node_modules

.pre-commit-config.yaml

@@ -5,6 +5,7 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-yaml
+        exclude: ^tutorial/example_deep_finance/
       - id: check-added-large-files
       - id: check-ast
       - id: check-json

tutorial/example_deep_finance/deep_finance_judge.py

@@ -200,7 +200,7 @@ def _load(path, key):
         _load(train_ref_ans_path, "train")
         _load(val_ref_ans_path, "val")
 
-    def _get_reference_data(self, task_id: str) -> Tuple[str, str]:
+    def _get_reference_data(self, task_id: str) -> Tuple[str, str | None]:
         """获取任务的参考答案和领域"""
         cache_key = "val" if task_id.startswith("val_") else "train"
         ans = DeepFinanceJudgeByOpenJudge._ref_answers_cache.get(cache_key, {}).get(task_id, "")
@@ -301,8 +301,8 @@ def compute_reward(self, workflow_task: WorkflowTask, workflow_output: WorkflowO
 
             # 1. 提取输入数据
             history = metadata.get("conversation_history", [])
-            query = metadata.get("query") or getattr(workflow_task.task, "main_query", "")
-            task_id = metadata.get("task_id") or getattr(workflow_task.task, "task_id", "")
+            query: str = metadata.get("query") or getattr(workflow_task.task, "main_query", "")
+            task_id: str = metadata.get("task_id") or getattr(workflow_task.task, "task_id", "")
             rubrics = metadata.get("rubrics")  # 可能是 None 或 list of dicts
             step_reward = metadata.get("reward_stats", {}).get("step_reward", 0.0)
             chat_date = metadata.get("chat_date") if metadata else datetime.now().strftime("%Y-%m-%d")
@@ -318,7 +318,7 @@ def compute_reward(self, workflow_task: WorkflowTask, workflow_output: WorkflowO
             # RM Gallery 耗时记录
             rm_start_time = time.time()
             if self._rm_enabled and self.rm_evaluator:
-                rm_raw = self._evaluate_with_rm_gallery(query, assistants[-1] if assistants else "", ref_ans, task_id, domain)
+                rm_raw = self._evaluate_with_rm_gallery(query, assistants[-1] if assistants else "", ref_ans, task_id, domain or "")
             else:
                 rm_raw = 0.0
             rm_time = time.time() - rm_start_time
@@ -788,19 +788,20 @@ def _save_evaluation_log(self, task_id: str, grader_results: Dict[str, List[Any]
         保存 OpenJudge 评估日志（可选）
         """
         try:
+            grader_results_log: Dict[str, List[Dict[str, Any]]] = {}
             log = {
                 "task_id": task_id,
                 "query": query,
                 "timestamp": datetime.now().isoformat(),
-                "grader_results": {}
+                "grader_results": grader_results_log
             }
 
             # 简化 grader_results 以便序列化
             for grader_name, score_list in grader_results.items():
-                log["grader_results"][grader_name] = []
+                grader_results_log[grader_name] = []
                 for score in score_list:
                     if hasattr(score, "score"):
-                        log["grader_results"][grader_name].append({
+                        grader_results_log[grader_name].append({
                             "score": score.score,
                             "reason": score.reason[:200] if hasattr(score, "reason") else "",
                         })

tutorial/example_deep_finance/judge/cgcv/json_utils.py

@@ -299,7 +299,7 @@ def validate_cgcv_schema(obj: Dict[str, Any]) -> Tuple[Optional[Dict[str, Any]],
         # 验证 status
         if normalized["status"] not in VALID_STATUSES:
             # 尝试模糊匹配
-            status_lower = normalized["status"]
+            status_lower: str = normalized["status"]
             matched = False
             for valid_status in VALID_STATUSES:
                 if valid_status in status_lower or status_lower in valid_status:

tutorial/opencode_build_openclaw_agent/README.md

@@ -75,8 +75,19 @@ In a new terminal:
 
 ```bash
 cd tutorial/opencode_build_openclaw_agent
+
+# Option 1: Use OpenJudge pointwise grading (default)
+export AJET_SWARM_URL="http://localhost:10086"
+export NUM_REPEAT=4
+export REWARD_MODE=pointwise
+export DASHSCOPE_API_KEY=your_api_key_here
+python fake_vllm_endpoint.py
+
+# Option 2: Use OpenJudge listwise ranking
 export AJET_SWARM_URL="http://localhost:10086"
 export NUM_REPEAT=4
+export REWARD_MODE=listwise
+export DASHSCOPE_API_KEY=your_api_key_here
 python fake_vllm_endpoint.py
 ```
 
@@ -113,13 +124,40 @@ Key parameters in `fake_vllm_endpoint.py`:
 - `num_repeat=4` - GRPO N parameter (responses per query)
 - `model` - Base model path
 
+Environment variables for reward computation:
+
+- `REWARD_MODE` - Reward computation mode: `pointwise` (default) or `listwise`
+- `DASHSCOPE_API_KEY` - API key for OpenJudge LLM grader
+- `JUDGE_BASE_URL` - Base URL for judge model API (default: DashScope)
+- `JUDGE_MODEL` - Judge model name (default: `qwen-plus`)
+
 ## Reward Function
 
-The `ExtraversionGrader` evaluates responses on a 1-10 scale:
-- 1 = Highly introverted (reserved, quiet)
-- 10 = Highly extraverted (energetic, enthusiastic)
+Two OpenJudge-based reward modes are available:
+
+### 1. Pointwise Mode (Default)
 
-Scores are normalized to [-1, 1] for GRPO training.
+Uses OpenJudge LLM grader to evaluate each response independently:
+- Evaluates extraversion traits on 1-10 scale
+- Provides detailed reasoning for each score
+- Scores normalized to [-1, 1] for GRPO training
+
+```bash
+export REWARD_MODE=pointwise
+export DASHSCOPE_API_KEY=your_api_key_here
+```
+
+### 2. Listwise Mode
+
+Uses OpenJudge to rank all responses together:
+- Compares responses directly against each other
+- Produces relative rankings
+- Best for capturing subtle differences
+
+```bash
+export REWARD_MODE=listwise
+export DASHSCOPE_API_KEY=your_api_key_here
+```
 
 ## Monitoring
 

tutorial/opencode_build_openclaw_agent/on_compute_relative_reward.py

@@ -1,41 +1,129 @@
 # -*- coding: utf-8 -*-
-"""Compute relative rewards based on extraversion personality alignment."""
+"""Compute relative rewards based on extraversion personality alignment using OpenJudge."""
 
+import os
 from typing import List, Dict
 from beast_logger import print_listofdict
+from openjudge.graders.base_grader import GraderMode, GraderScore, GraderRank
+from openjudge.graders.llm_grader import LLMGrader
+from openjudge.models import OpenAIChatModel
 
-def score_extraversion(response_text: str) -> float:
-    """Score response for extraversion traits (1-10 scale)."""
-    extraversion_keywords = [
-        'excited', 'love', 'amazing', 'awesome', 'fantastic', 'great',
-        'wonderful', 'thrilled', 'energetic', 'enthusiastic', 'fun',
-        'social', 'outgoing', 'active', 'lively', 'vibrant', 'happy',
-        'enjoy', 'delighted', 'cheerful', 'positive'
-    ]
+# Configuration
+REWARD_MODE = os.getenv("REWARD_MODE", "pointwise")  # Options: pointwise, listwise
+API_KEY = os.getenv("DASHSCOPE_API_KEY", "sk-xxx")
+BASE_URL = os.getenv("JUDGE_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
+JUDGE_MODEL = os.getenv("JUDGE_MODEL", "qwen-plus")
 
-    text_lower = response_text.lower()
-    score = 5.0
+# OpenJudge grader setup
+judge_model = OpenAIChatModel(
+    model=JUDGE_MODEL,
+    api_key=API_KEY,
+    base_url=BASE_URL,
+)
 
-    for keyword in extraversion_keywords:
-        if keyword in text_lower:
-            score += 0.5
+EXTRAVERSION_PROMPT = """You are evaluating responses for extraversion personality traits.
 
-    score += min(response_text.count('!') * 0.3, 2.0)
+Extraversion characteristics include:
+- Outgoing, energetic, enthusiastic tone
+- Social engagement and excitement
+- Positive, upbeat language
+- Action-oriented expressions
+- Use of exclamation marks and emotional words
 
-    if len(response_text) < 50:
-        score -= 1.0
+Rate the response on a scale of 0.0-1.0:
+0.0 = Highly introverted (reserved, quiet, minimal emotion)
+1.0 = Highly extraverted (energetic, enthusiastic, very expressive)
 
-    return max(1.0, min(10.0, score))
+Question: {question}
+Response: {response}
 
-async def on_compute_relative_reward(valid_results: List, all_answers: List[Dict]) -> List[float]:
-    """Compute relative rewards for extraversion alignment."""
+Return a json object with exactly two fields:
+- "score": float between 0.0 and 1.0
+- "reason": brief explanation"""
+
+def build_listwise_template(n: int) -> str:
+    """Build a listwise prompt template for n responses."""
+    answers_block = "\n".join([f"{i+1}. {{answer_{i+1}}}" for i in range(n)])
+    return f"""You are ranking multiple responses based on extraversion personality traits.
+
+Extraversion characteristics include:
+- Outgoing, energetic, enthusiastic tone
+- Social engagement and excitement
+- Positive, upbeat language
+- Action-oriented expressions
+
+Question: {{question}}
+
+Responses to rank:
+{answers_block}
+
+Rank these responses from most extraverted to least extraverted.
+Return a json object with exactly two fields:
+- "rank": list of integers (1-indexed) ordered from most to least extraverted, e.g. [2, 1, 3]
+- "reason": brief explanation of the ranking"""
+
+pointwise_grader = LLMGrader(
+    name="extraversion_pointwise",
+    mode=GraderMode.POINTWISE,
+    description="Evaluate extraversion traits",
+    model=judge_model,
+    template=EXTRAVERSION_PROMPT,
+)
+
+
+async def compute_pointwise_rewards(question: str, all_answers: List[Dict]) -> List[float]:
+    """Compute rewards using OpenJudge pointwise grading."""
     scores = []
     for answer in all_answers:
         content = answer.get("content", "")
-        raw_score = score_extraversion(content)
-        normalized = (raw_score - 5.5) / 4.5
-        scores.append(normalized)
-        answer["reward"] = normalized
+        result = await pointwise_grader.aevaluate(question=question, response=content)
+        if isinstance(result, GraderScore):
+            # score is already normalized 0-1 by OpenJudge
+            score = result.score
+        else:
+            score = 0.0
+        scores.append(score)
+        answer["reward"] = score
+    return scores
+
+
+async def compute_listwise_rewards(question: str, all_answers: List[Dict]) -> List[float]:
+    """Compute rewards using OpenJudge listwise ranking."""
+    n = len(all_answers)
+    template = build_listwise_template(n)
+    grader = LLMGrader(
+        name="extraversion_listwise",
+        mode=GraderMode.LISTWISE,
+        description="Rank responses by extraversion",
+        model=judge_model,
+        template=template,
+    )
+    kwargs = {"question": question}
+    for i, ans in enumerate(all_answers):
+        kwargs[f"answer_{i+1}"] = ans.get("content", "")
+
+    result = await grader.aevaluate(**kwargs)
+
+    scores = [0.0] * n
+    if isinstance(result, GraderRank):
+        # rank is a list of 1-indexed positions ordered best to worst
+        # convert to reward: rank 1 (best) -> 1.0, rank n (worst) -> 0.0
+        for position, idx in enumerate(result.rank):
+            scores[idx - 1] = 1.0 - (position / (n - 1)) if n > 1 else 0.5
+
+    for answer, score in zip(all_answers, scores):
+        answer["reward"] = score
+    return scores
+
+
+async def on_compute_relative_reward(valid_results: List, all_answers: List[Dict]) -> List[float]:
+    """Compute relative rewards for extraversion alignment."""
+    question = valid_results[0].get("question", "") if valid_results else ""
+
+    if REWARD_MODE == "listwise":
+        scores = await compute_listwise_rewards(question, all_answers)
+    else:  # pointwise (default)
+        scores = await compute_pointwise_rewards(question, all_answers)
 
-    print_listofdict(all_answers, header="on_compute_relative_reward")
+    print_listofdict(all_answers, header=f"on_compute_relative_reward (mode={REWARD_MODE})")
     return scores

tutorial/opencode_build_openclaw_agent/test_reward.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+"""Test script for on_compute_relative_reward.py using real OpenJudge API."""
+
+import asyncio
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(__file__))
+os.environ["DASHSCOPE_API_KEY"] = os.getenv("DASHSCOPE_API_KEY", "sk-xxx")
+
+
+async def test_pointwise():
+    """Test pointwise reward mode with real API."""
+    print("\n=== Testing Pointwise Mode (real API) ===")
+    os.environ["REWARD_MODE"] = "pointwise"
+
+    import importlib
+    import on_compute_relative_reward as mod
+    importlib.reload(mod)
+
+    valid_results = [{"question": "What are your thoughts on Paris?"}]
+    all_answers = [
+        {"content": "I'm so excited about Paris! It's amazing and wonderful!"},
+        {"content": "Paris is a city in France."},
+        {"content": "I absolutely love Paris! The energy is fantastic and vibrant!"},
+    ]
+
+    try:
+        scores = await mod.on_compute_relative_reward(valid_results, all_answers)
+        print(f"Scores: {scores}")
+        assert len(scores) == 3, f"Expected 3 scores, got {len(scores)}"
+        assert all(isinstance(s, float) for s in scores), "All scores should be floats"
+        # extraverted responses should score higher than neutral
+        assert scores[0] > scores[1], f"Extraverted response should score higher than neutral: {scores}"
+        assert scores[2] > scores[1], f"Extraverted response should score higher than neutral: {scores}"
+        print("✓ Pointwise mode test passed")
+        return True
+    except Exception as e:
+        print(f"✗ Pointwise mode test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+async def test_listwise():
+    """Test listwise reward mode with real API."""
+    print("\n=== Testing Listwise Mode (real API) ===")
+    os.environ["REWARD_MODE"] = "listwise"
+
+    import importlib
+    import on_compute_relative_reward as mod
+    importlib.reload(mod)
+
+    valid_results = [{"question": "What are your thoughts on Paris?"}]
+    all_answers = [
+        {"content": "I'm so excited about Paris! It's amazing and wonderful!"},
+        {"content": "Paris is a city in France."},
+        {"content": "I absolutely love Paris! The energy is fantastic and vibrant!"},
+    ]
+
+    try:
+        scores = await mod.on_compute_relative_reward(valid_results, all_answers)
+        print(f"Scores: {scores}")
+        assert len(scores) == 3, f"Expected 3 scores, got {len(scores)}"
+        assert all(isinstance(s, float) for s in scores), "All scores should be floats"
+        # neutral response should score lowest
+        assert scores[1] < scores[0] or scores[1] < scores[2], \
+            f"Neutral response should score lower than at least one extraverted response: {scores}"
+        print("✓ Listwise mode test passed")
+        return True
+    except Exception as e:
+        print(f"✗ Listwise mode test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+async def main():
+    print("Testing on_compute_relative_reward.py (real API)")
+    print("=" * 50)
+
+    results = []
+    results.append(await test_pointwise())
+    results.append(await test_listwise())
+
+    print("\n" + "=" * 50)
+    print(f"Tests passed: {sum(results)}/{len(results)}")
+    return all(results)
+
+
+if __name__ == "__main__":
+    success = asyncio.run(main())
+    sys.exit(0 if success else 1)