GoogleAPIを使用したときに発生したエラー修正
This commit is contained in:
TakumaNakao
parent
00a8d9978c
commit
b52d14f4be
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@ -1,2 +1,3 @@
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ALPHA_VANTAGE_API_KEY=alpha_vantage_api_key_placeholder
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OPENAI_API_KEY=openai_api_key_placeholder
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OPENAI_API_KEY=openai_api_key_placeholder
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GOOGLE_API_KEY=google_api_key_placeholder
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256
cli/main.py
256
cli/main.py
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@ -848,232 +848,80 @@ def run_analysis():
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# Stream the analysis
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trace = []
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for chunk in graph.graph.stream(init_agent_state, **args):
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if len(chunk["messages"]) > 0:
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# Get the last message from the chunk
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# Check for new messages and process them
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if chunk.get("messages") and (not trace or len(chunk["messages"]) > len(trace[-1].get("messages", []))):
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last_message = chunk["messages"][-1]
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# Extract message content and type
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if hasattr(last_message, "content"):
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content = extract_content_string(last_message.content) # Use the helper function
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content = extract_content_string(last_message.content)
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msg_type = "Reasoning"
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else:
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content = str(last_message)
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msg_type = "System"
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message_buffer.add_message(msg_type, content)
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# Add message to buffer
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message_buffer.add_message(msg_type, content)
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# If it's a tool call, add it to tool calls
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if hasattr(last_message, "tool_calls"):
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if hasattr(last_message, "tool_calls") and last_message.tool_calls:
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for tool_call in last_message.tool_calls:
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# Handle both dictionary and object tool calls
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if isinstance(tool_call, dict):
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message_buffer.add_tool_call(
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tool_call["name"], tool_call["args"]
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)
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message_buffer.add_tool_call(tool_call["name"], tool_call["args"])
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else:
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message_buffer.add_tool_call(tool_call.name, tool_call.args)
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# Update reports and agent status based on chunk content
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# Analyst Team Reports
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if "market_report" in chunk and chunk["market_report"]:
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message_buffer.update_report_section(
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"market_report", chunk["market_report"]
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)
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message_buffer.update_agent_status("Market Analyst", "completed")
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# Set next analyst to in_progress
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if "social" in selections["analysts"]:
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message_buffer.update_agent_status(
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"Social Analyst", "in_progress"
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)
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# Helper to check if a key was updated in the current chunk
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def was_updated(key):
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if key not in chunk or not chunk[key]:
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return False
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# Check if the value is new by comparing with the last trace
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if not trace or chunk[key] != trace[-1].get(key):
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return True
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return False
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if "sentiment_report" in chunk and chunk["sentiment_report"]:
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message_buffer.update_report_section(
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"sentiment_report", chunk["sentiment_report"]
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)
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message_buffer.update_agent_status("Social Analyst", "completed")
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# Set next analyst to in_progress
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if "news" in selections["analysts"]:
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message_buffer.update_agent_status(
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"News Analyst", "in_progress"
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)
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# Process report and status updates from any chunk
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if was_updated("market_report"):
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message_buffer.update_report_section("market_report", chunk["market_report"])
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message_buffer.update_agent_status("Market Analyst", "completed")
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if "social" in selections["analysts"]:
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message_buffer.update_agent_status("Social Analyst", "in_progress")
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if "news_report" in chunk and chunk["news_report"]:
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message_buffer.update_report_section(
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"news_report", chunk["news_report"]
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)
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message_buffer.update_agent_status("News Analyst", "completed")
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# Set next analyst to in_progress
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if "fundamentals" in selections["analysts"]:
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message_buffer.update_agent_status(
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"Fundamentals Analyst", "in_progress"
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)
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if was_updated("sentiment_report"):
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message_buffer.update_report_section("sentiment_report", chunk["sentiment_report"])
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message_buffer.update_agent_status("Social Analyst", "completed")
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if "news" in selections["analysts"]:
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message_buffer.update_agent_status("News Analyst", "in_progress")
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if "fundamentals_report" in chunk and chunk["fundamentals_report"]:
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message_buffer.update_report_section(
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"fundamentals_report", chunk["fundamentals_report"]
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)
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message_buffer.update_agent_status(
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"Fundamentals Analyst", "completed"
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)
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# Set all research team members to in_progress
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update_research_team_status("in_progress")
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if was_updated("news_report"):
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message_buffer.update_report_section("news_report", chunk["news_report"])
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message_buffer.update_agent_status("News Analyst", "completed")
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if "fundamentals" in selections["analysts"]:
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message_buffer.update_agent_status("Fundamentals Analyst", "in_progress")
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# Research Team - Handle Investment Debate State
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if (
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"investment_debate_state" in chunk
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and chunk["investment_debate_state"]
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):
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debate_state = chunk["investment_debate_state"]
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if was_updated("fundamentals_report"):
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message_buffer.update_report_section("fundamentals_report", chunk["fundamentals_report"])
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message_buffer.update_agent_status("Fundamentals Analyst", "completed")
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update_research_team_status("in_progress")
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# Update Bull Researcher status and report
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if "bull_history" in debate_state and debate_state["bull_history"]:
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# Keep all research team members in progress
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update_research_team_status("in_progress")
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# Extract latest bull response
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bull_responses = debate_state["bull_history"].split("\n")
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latest_bull = bull_responses[-1] if bull_responses else ""
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if latest_bull:
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message_buffer.add_message("Reasoning", latest_bull)
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# Update research report with bull's latest analysis
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message_buffer.update_report_section(
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"investment_plan",
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f"### Bull Researcher Analysis\n{latest_bull}",
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)
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if was_updated("investment_plan"):
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message_buffer.update_report_section("investment_plan", chunk["investment_plan"])
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update_research_team_status("completed")
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message_buffer.update_agent_status("Trader", "in_progress")
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# Update Bear Researcher status and report
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if "bear_history" in debate_state and debate_state["bear_history"]:
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# Keep all research team members in progress
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update_research_team_status("in_progress")
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# Extract latest bear response
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bear_responses = debate_state["bear_history"].split("\n")
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latest_bear = bear_responses[-1] if bear_responses else ""
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if latest_bear:
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message_buffer.add_message("Reasoning", latest_bear)
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# Update research report with bear's latest analysis
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message_buffer.update_report_section(
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"investment_plan",
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f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}",
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)
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if was_updated("trader_investment_plan"):
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message_buffer.update_report_section("trader_investment_plan", chunk["trader_investment_plan"])
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message_buffer.update_agent_status("Trader", "completed")
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message_buffer.update_agent_status("Risky Analyst", "in_progress")
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message_buffer.update_agent_status("Neutral Analyst", "in_progress")
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message_buffer.update_agent_status("Safe Analyst", "in_progress")
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# Update Research Manager status and final decision
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if (
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"judge_decision" in debate_state
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and debate_state["judge_decision"]
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):
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# Keep all research team members in progress until final decision
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update_research_team_status("in_progress")
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message_buffer.add_message(
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"Reasoning",
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f"Research Manager: {debate_state['judge_decision']}",
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)
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# Update research report with final decision
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message_buffer.update_report_section(
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"investment_plan",
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f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}",
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)
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# Mark all research team members as completed
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update_research_team_status("completed")
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# Set first risk analyst to in_progress
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message_buffer.update_agent_status(
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"Risky Analyst", "in_progress"
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)
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if was_updated("final_trade_decision"):
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message_buffer.update_report_section("final_trade_decision", chunk["final_trade_decision"])
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message_buffer.update_agent_status("Risky Analyst", "completed")
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message_buffer.update_agent_status("Neutral Analyst", "completed")
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message_buffer.update_agent_status("Safe Analyst", "completed")
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message_buffer.update_agent_status("Portfolio Manager", "completed")
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# Trading Team
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if (
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"trader_investment_plan" in chunk
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and chunk["trader_investment_plan"]
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):
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message_buffer.update_report_section(
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"trader_investment_plan", chunk["trader_investment_plan"]
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)
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# Set first risk analyst to in_progress
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message_buffer.update_agent_status("Risky Analyst", "in_progress")
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# Risk Management Team - Handle Risk Debate State
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if "risk_debate_state" in chunk and chunk["risk_debate_state"]:
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risk_state = chunk["risk_debate_state"]
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# Update Risky Analyst status and report
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if (
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"current_risky_response" in risk_state
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and risk_state["current_risky_response"]
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):
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message_buffer.update_agent_status(
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"Risky Analyst", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Risky Analyst: {risk_state['current_risky_response']}",
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)
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# Update risk report with risky analyst's latest analysis only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Risky Analyst Analysis\n{risk_state['current_risky_response']}",
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)
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# Update Safe Analyst status and report
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if (
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"current_safe_response" in risk_state
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and risk_state["current_safe_response"]
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):
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message_buffer.update_agent_status(
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"Safe Analyst", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Safe Analyst: {risk_state['current_safe_response']}",
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)
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# Update risk report with safe analyst's latest analysis only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Safe Analyst Analysis\n{risk_state['current_safe_response']}",
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)
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# Update Neutral Analyst status and report
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if (
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"current_neutral_response" in risk_state
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and risk_state["current_neutral_response"]
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):
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message_buffer.update_agent_status(
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"Neutral Analyst", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Neutral Analyst: {risk_state['current_neutral_response']}",
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)
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# Update risk report with neutral analyst's latest analysis only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Neutral Analyst Analysis\n{risk_state['current_neutral_response']}",
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)
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# Update Portfolio Manager status and final decision
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if "judge_decision" in risk_state and risk_state["judge_decision"]:
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message_buffer.update_agent_status(
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"Portfolio Manager", "in_progress"
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)
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message_buffer.add_message(
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"Reasoning",
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f"Portfolio Manager: {risk_state['judge_decision']}",
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)
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# Update risk report with final decision only
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message_buffer.update_report_section(
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"final_trade_decision",
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f"### Portfolio Manager Decision\n{risk_state['judge_decision']}",
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)
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# Mark risk analysts as completed
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message_buffer.update_agent_status("Risky Analyst", "completed")
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message_buffer.update_agent_status("Safe Analyst", "completed")
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message_buffer.update_agent_status(
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"Neutral Analyst", "completed"
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)
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message_buffer.update_agent_status(
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"Portfolio Manager", "completed"
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)
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# Update the display
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update_display(layout)
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# Always update the display to reflect any change
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update_display(layout)
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trace.append(chunk)
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@ -1,25 +1,33 @@
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import chromadb
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from chromadb.config import Settings
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from openai import OpenAI
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from langchain_google_genai import GoogleGenerativeAIEmbeddings
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class FinancialSituationMemory:
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def __init__(self, name, config):
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if config["backend_url"] == "http://localhost:11434/v1":
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self.embedding = "nomic-embed-text"
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def __init__(self, name, config, llm):
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self.config = config
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self.llm = llm
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if self.config["llm_provider"].lower() == "google":
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self.embedding_model = GoogleGenerativeAIEmbeddings(
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model="models/embedding-001"
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)
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else:
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self.embedding = "text-embedding-3-small"
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self.client = OpenAI(base_url=config["backend_url"])
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if config["backend_url"] == "http://localhost:11434/v1":
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self.embedding = "nomic-embed-text"
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else:
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self.embedding = "text-embedding-3-small"
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self.client = OpenAI(base_url=config["backend_url"])
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self.chroma_client = chromadb.Client(Settings(allow_reset=True))
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self.situation_collection = self.chroma_client.create_collection(name=name)
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def get_embedding(self, text):
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"""Get OpenAI embedding for a text"""
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response = self.client.embeddings.create(
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model=self.embedding, input=text
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)
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return response.data[0].embedding
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"""Get embedding for a text"""
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if self.config["llm_provider"].lower() == "google":
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return self.embedding_model.embed_query(text)
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else:
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response = self.client.embeddings.create(model=self.embedding, input=text)
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return response.data[0].embedding
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def add_situations(self, situations_and_advice):
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"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
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@ -45,8 +53,16 @@ class FinancialSituationMemory:
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)
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def get_memories(self, current_situation, n_matches=1):
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"""Find matching recommendations using OpenAI embeddings"""
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query_embedding = self.get_embedding(current_situation)
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"""Find matching recommendations using embeddings. Summarizes if text is too long."""
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# Check length and summarize if needed
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if len(current_situation) > 30000:
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prompt = f"Please summarize the following financial analysis text into a concise paragraph that captures the key insights and market sentiment. The summary should be suitable for use as a query to find similar past situations in a vector database:\n\n{current_situation}"
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summary_response = self.llm.invoke(prompt)
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query_text = summary_response.content
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else:
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query_text = current_situation
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query_embedding = self.get_embedding(query_text)
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results = self.situation_collection.query(
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query_embeddings=[query_embedding],
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@ -55,14 +71,15 @@ class FinancialSituationMemory:
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)
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matched_results = []
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for i in range(len(results["documents"][0])):
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matched_results.append(
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{
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"matched_situation": results["documents"][0][i],
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"recommendation": results["metadatas"][0][i]["recommendation"],
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"similarity_score": 1 - results["distances"][0][i],
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}
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)
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if results.get("documents") and results["documents"][0]:
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for i in range(len(results["documents"][0])):
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matched_results.append(
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{
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"matched_situation": results["documents"][0][i],
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"recommendation": results["metadatas"][0][i]["recommendation"],
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"similarity_score": 1 - results["distances"][0][i],
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}
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)
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return matched_results
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@ -85,11 +85,11 @@ class TradingAgentsGraph:
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raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
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# Initialize memories
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self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
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self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
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self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
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self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
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self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
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self.bull_memory = FinancialSituationMemory("bull_memory", self.config, self.quick_thinking_llm)
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self.bear_memory = FinancialSituationMemory("bear_memory", self.config, self.quick_thinking_llm)
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self.trader_memory = FinancialSituationMemory("trader_memory", self.config, self.quick_thinking_llm)
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self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config, self.deep_thinking_llm)
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self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config, self.deep_thinking_llm)
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# Create tool nodes
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self.tool_nodes = self._create_tool_nodes()
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