tradingagents/agents/managers/risk_manager.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 import time
 import json
+from tradingagents.agents.utils.output_filter import fix_common_llm_errors, validate_and_warn
 
 
 def create_risk_manager(llm, memory):
@@ -49,13 +50,10 @@ def create_risk_manager(llm, memory):
         # 從記憶體中獲取過去相似情況的經驗
         past_memories = memory.get_memories(curr_situation, n_matches=2)
 
-        # 將過去的經驗格式化為字串（限制長度）
+        # 將過去的經驗格式化為字串
         past_memory_str = ""
         for i, rec in enumerate(past_memories, 1):
             recommendation = rec["recommendation"]
-            # 限制每條記憶的長度
-            if len(recommendation) > 200:
-                recommendation = recommendation[:200] + "...(已截斷)"
             past_memory_str += recommendation + "\n\n"
         
         # 截斷辯論歷史 - 這是最容易超過限制的部分
@@ -108,6 +106,10 @@ def create_risk_manager(llm, memory):
 
         # 呼叫 LLM 生成決策
         response = llm.invoke(prompt)
+        
+        # CRITICAL FIX: Apply output filtering to fix common LLM errors
+        response.content = fix_common_llm_errors(response.content)
+        validate_and_warn(response.content, "Risk_Manager")
 
         # 更新風險辯論狀態
         new_risk_debate_state = {

tradingagents/agents/utils/output_filter.py

@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+"""
+LLM Output Post-Processing Filter
+Fixes common LLM output errors including character corruption and format issues
+"""
+import re
+
+
+def count_chinese_words(text: str) -> int:
+    """
+    Count Chinese characters in text (excluding markdown and tables)
+    
+    Args:
+        text: Input text
+        
+    Returns:
+        Number of Chinese characters
+    """
+    # Remove code blocks
+    clean_text = re.sub(r'```.*?```', '', text, flags=re.DOTALL)
+    # Remove tables
+    clean_text = re.sub(r'\|.*?\|', '', clean_text, flags=re.MULTILINE)
+    # Remove markdown formatting
+    clean_text = re.sub(r'[#\*_`~\[\]\(\)]', '', clean_text)
+    
+    # Count Chinese characters (CJK Unified Ideographs)
+    return len([c for c in clean_text if '\u4e00' <= c <= '\u9fff'])
+
+
+def fix_common_llm_errors(text: str) -> str:
+    """
+    Fix common LLM character selection errors
+    
+    Args:
+        text: LLM output text
+        
+    Returns:
+        Corrected text
+    """
+    # Common character misuse patterns
+    replacements = {
+        # '煉' misuse - should be '練' (practice/train) in most contexts
+        '煉習': '練習',
+        '訓煉': '訓練',
+        '**煉**': '**練**',
+        '（煉': '（練',
+        '煉）': '練）',
+        
+        # Other common errors (add as discovered)
+        '絓驗': '經驗',  # We saw this corruption before
+    }
+    
+    for wrong, correct in replacements.items():
+        text = text.replace(wrong, correct)
+    
+    return text
+
+
+def validate_and_warn(content: str, agent_name: str) -> list:
+    """
+    Validate report content and return list of warnings
+    
+    Args:
+        content: Report content
+        agent_name: Name of the agent
+        
+    Returns:
+        List of warning messages
+    """
+    warnings = []
+    
+    # Check for suspicious '煉' character
+    # Only flag if not in proper contexts like "冶煉", "提煉", "鍛煉"
+    if '煉' in content:
+        proper_contexts = ['冶煉', '提煉', '鍛煉', '精煉', '修煉']
+        is_proper = any(ctx in content for ctx in proper_contexts)
+        if not is_proper:
+            # Find context around '煉'
+            idx = content.find('煉')
+            context = content[max(0, idx-15):min(len(content), idx+15)]
+            warnings.append(f"Suspicious '煉' character found. Context: ...{context}...")
+    
+    # Check word count
+    word_count = count_chinese_words(content)
+    if word_count < 800:
+        warnings.append(f"Too short: {word_count} words (expected 800-1500)")
+    elif word_count > 1500:
+        warnings.append(f"Too long: {word_count} words (expected 800-1500)")
+    
+    # Check for truncation markers that shouldn't be there
+    truncation_markers = ['...(已截斷)', '...(內容已截斷)', '...(為控制長度已精簡)']
+    for marker in truncation_markers:
+        if marker in content:
+            warnings.append(f"Found truncation marker: '{marker}'")
+    
+    if warnings:
+        print(f"\n⚠️  {agent_name} Report Warnings:")
+        for warning in warnings:
+            print(f"   - {warning}")
+    
+    return warnings
+
+
+def post_process_agent_output(content: str, agent_name: str, retry_callback=None) -> str:
+    """
+    Complete post-processing pipeline for agent output
+    
+    Args:
+        content: Raw agent output
+        agent_name: Name of the agent
+        retry_callback: Optional function to call if validation fails
+        
+    Returns:
+        Processed and validated content
+    """
+    # Step 1: Fix common errors
+    content = fix_common_llm_errors(content)
+    
+    # Step 2: Validate and warn
+    warnings = validate_and_warn(content, agent_name)
+    
+    # Step 3: Critical validation - retry if needed
+    word_count = count_chinese_words(content)
+    if (word_count < 800 or word_count > 1500) and retry_callback:
+        print(f"\n🔄 {agent_name}: Word count {word_count} out of range, triggering retry...")
+        # Callback should regenerate the content
+        # This is optional and should be implemented in the calling code
+    
+    return content

tradingagents/dataflows/openai.py

@@ -42,7 +42,7 @@ def get_stock_news_openai(query, start_date, end_date):
                 "search_context_size": "low",
             }
         ],
-        temperature=1,
+        temperature=0.7,  # Reduced from 1.0 to prevent character errors like '煉' and stabilize output
         max_output_tokens=4096,
         top_p=1,
         store=True,
@@ -90,7 +90,7 @@ def get_global_news_openai(curr_date, look_back_days=7, limit=5):
                 "search_context_size": "low",
             }
         ],
-        temperature=1,
+        temperature=0.7,  # Reduced from 1.0 to prevent character errors and stabilize output
         max_output_tokens=4096,
         top_p=1,
         store=True,
@@ -137,7 +137,7 @@ def get_fundamentals_openai(ticker, curr_date):
                 "search_context_size": "low",
             }
         ],
-        temperature=1,
+        temperature=0.7,  # Reduced from 1.0 to prevent character errors and stabilize output
         max_output_tokens=4096,
         top_p=1,
         store=True,