diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py index a5e97193..5eed8ccc 100644 --- a/tradingagents/agents/managers/research_manager.py +++ b/tradingagents/agents/managers/research_manager.py @@ -46,14 +46,13 @@ def create_research_manager(llm, memory): return text return text[:max_chars] + "\n...(內容已截斷)" + # 為每個報告設置合理的字符限制 - # 模型 gpt-5-mini 的限制是 8192 tokens - # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值) - # 目標: 總字符數 < 3500 字符 (約 5250-7000 tokens，留足夠 tokens 給 completion) - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 600) - fundamentals_report = truncate_text(fundamentals_report, 600) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) # 整合當前情況 curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" @@ -71,8 +70,8 @@ def create_research_manager(llm, memory): past_memory_str += recommendation + "\n\n" # 截斷辯論歷史 - 這是最容易超過限制的部分 - # 限制辯論歷史在 1200 字符以內 - history = truncate_text(history, 1200) + # 增加限制以容納更長的辯論內容 + history = truncate_text(history, 3000) # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py index b98022b4..6af03d58 100644 --- a/tradingagents/agents/managers/risk_manager.py +++ b/tradingagents/agents/managers/risk_manager.py @@ -48,15 +48,14 @@ def create_risk_manager(llm, memory): return text return text[:max_chars] + "\n...(內容已截斷)" + # 為每個報告設置合理的字符限制 - # 模型 gpt-5-mini 的限制是 8192 tokens - # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值) - # 目標: 總字符數 < 3500 字符 (約 5250-7000 tokens，留足夠 tokens 給 completion) - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 600) - fundamentals_report = truncate_text(fundamentals_report, 600) - trader_plan = truncate_text(trader_plan, 800) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) + trader_plan = truncate_text(trader_plan, 2000) # 整合當前情況 curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" @@ -74,8 +73,8 @@ def create_risk_manager(llm, memory): past_memory_str += recommendation + "\n\n" # 截斷辯論歷史 - 這是最容易超過限制的部分 - # 限制辯論歷史在 1000 字符以內（風險辯論通常有3方，比投資辯論更長） - history = truncate_text(history, 1000) + # 增加限制以容納更長的辯論內容（風險辯論通常有3方，比投資辯論更長） + history = truncate_text(history, 3000) # 建立提示 (prompt) diff --git a/tradingagents/agents/researchers/bear_researcher.py b/tradingagents/agents/researchers/bear_researcher.py index a866fcd2..14d5f63b 100644 --- a/tradingagents/agents/researchers/bear_researcher.py +++ b/tradingagents/agents/researchers/bear_researcher.py @@ -58,14 +58,13 @@ def create_bear_researcher(llm, memory): return text[:last_pos + 1] + "\n\n...(為控制長度已精簡)" return truncated + "...(為控制長度已精簡)" + # 為每個報告設置合理的字符限制 - # 模型 gpt-4.1-mini 的限制是 8192 tokens - # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值) - # 目標: 總字符數 < 3000 字符 (約 4500-6000 tokens，留 2000+ tokens 給 completion) - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 800) # 新聞通常較長但也需要控制 - fundamentals_report = truncate_text(fundamentals_report, 600) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" diff --git a/tradingagents/agents/researchers/bull_researcher.py b/tradingagents/agents/researchers/bull_researcher.py index 19d9572c..77870b77 100644 --- a/tradingagents/agents/researchers/bull_researcher.py +++ b/tradingagents/agents/researchers/bull_researcher.py @@ -64,13 +64,11 @@ def create_bull_researcher(llm, memory): return truncated + "...(為控制長度已精簡)" # 為每個報告設置合理的字符限制 - # 模型 gpt-4.1-mini 的限制是 8192 tokens - # 混合中英文估算: 1 字符 ≈ 1.5-2 tokens (取保守值) - # 目標: 總字符數 < 3000 字符 (約 4500-6000 tokens，留 2000+ tokens 給 completion) - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 800) # 新聞通常較長但也需要控制 - fundamentals_report = truncate_text(fundamentals_report, 600) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" diff --git a/tradingagents/agents/risk_mgmt/aggresive_debator.py b/tradingagents/agents/risk_mgmt/aggresive_debator.py index 8fa413b7..0f8143b3 100644 --- a/tradingagents/agents/risk_mgmt/aggresive_debator.py +++ b/tradingagents/agents/risk_mgmt/aggresive_debator.py @@ -53,16 +53,17 @@ def create_risky_debator(llm): return text return text[:max_chars] + "\n...(內容已截斷)" + # 截斷各類輸入以控制 token 使用量 - # 模型限制: 8192 tokens，目標: < 3500 字符 - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 600) - fundamentals_report = truncate_text(fundamentals_report, 600) - trader_decision = truncate_text(trader_decision, 800) - history = truncate_text(history, 400) - current_safe_response = truncate_text(current_safe_response, 300) - current_neutral_response = truncate_text(current_neutral_response, 300) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) + trader_decision = truncate_text(trader_decision, 2000) + history = truncate_text(history, 1500) + current_safe_response = truncate_text(current_safe_response, 1000) + current_neutral_response = truncate_text(current_neutral_response, 1000) # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py index 57bb6101..3aec1911 100644 --- a/tradingagents/agents/risk_mgmt/conservative_debator.py +++ b/tradingagents/agents/risk_mgmt/conservative_debator.py @@ -54,16 +54,17 @@ def create_safe_debator(llm): return text return text[:max_chars] + "\n...(內容已截斷)" + # 截斷各類輸入以控制 token 使用量 - # 模型限制: 8192 tokens，目標: < 3500 字符 - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 600) - fundamentals_report = truncate_text(fundamentals_report, 600) - trader_decision = truncate_text(trader_decision, 800) - history = truncate_text(history, 400) - current_risky_response = truncate_text(current_risky_response, 300) - current_neutral_response = truncate_text(current_neutral_response, 300) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) + trader_decision = truncate_text(trader_decision, 2000) + history = truncate_text(history, 1500) + current_risky_response = truncate_text(current_risky_response, 1000) + current_neutral_response = truncate_text(current_neutral_response, 1000) # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py index 38272233..baf9d32e 100644 --- a/tradingagents/agents/risk_mgmt/neutral_debator.py +++ b/tradingagents/agents/risk_mgmt/neutral_debator.py @@ -53,16 +53,17 @@ def create_neutral_debator(llm): return text return text[:max_chars] + "\n...(內容已截斷)" + # 截斷各類輸入以控制 token 使用量 - # 模型限制: 8192 tokens，目標: < 3500 字符 - market_research_report = truncate_text(market_research_report, 500) - sentiment_report = truncate_text(sentiment_report, 500) - news_report = truncate_text(news_report, 600) - fundamentals_report = truncate_text(fundamentals_report, 600) - trader_decision = truncate_text(trader_decision, 800) - history = truncate_text(history, 400) - current_risky_response = truncate_text(current_risky_response, 300) - current_safe_response = truncate_text(current_safe_response, 300) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report = truncate_text(market_research_report, 2000) + sentiment_report = truncate_text(sentiment_report, 2000) + news_report = truncate_text(news_report, 2500) + fundamentals_report = truncate_text(fundamentals_report, 2000) + trader_decision = truncate_text(trader_decision, 2000) + history = truncate_text(history, 1500) + current_risky_response = truncate_text(current_risky_response, 1000) + current_safe_response = truncate_text(current_safe_response, 1000) # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py index f9b2ce04..0016b6a1 100644 --- a/tradingagents/agents/trader/trader.py +++ b/tradingagents/agents/trader/trader.py @@ -57,13 +57,14 @@ def create_trader(llm, memory): # 如果找不到合適的分隔符，直接在字符處截斷 return truncated + "...(為控制長度已精簡)" + # 截斷各類報告以控制 token 使用量 - # 這些報告將用於記憶檢索（embedding）和 LLM prompt - market_research_report_truncated = truncate_text(market_research_report, 500) - sentiment_report_truncated = truncate_text(sentiment_report, 500) - news_report_truncated = truncate_text(news_report, 600) - fundamentals_report_truncated = truncate_text(fundamentals_report, 600) - investment_plan_truncated = truncate_text(investment_plan, 800) + # 增加限制以確保 800+ 字的報告不被截斷 + market_research_report_truncated = truncate_text(market_research_report, 2000) + sentiment_report_truncated = truncate_text(sentiment_report, 2000) + news_report_truncated = truncate_text(news_report, 2500) + fundamentals_report_truncated = truncate_text(fundamentals_report, 2000) + investment_plan_truncated = truncate_text(investment_plan, 2000) # 整合當前情況（用於記憶檢索） curr_situation = f"{market_research_report_truncated}\n\n{sentiment_report_truncated}\n\n{news_report_truncated}\n\n{fundamentals_report_truncated}"