69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import get_industry_performance, get_topic_news
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from tradingagents.agents.utils.tool_runner import run_tool_loop
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def create_industry_deep_dive(llm):
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def industry_deep_dive_node(state):
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scan_date = state["scan_date"]
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tools = [get_industry_performance, get_topic_news]
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# Inject Phase 1 context so the LLM can decide which sectors to drill into
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phase1_context = f"""## Phase 1 Scanner Reports (for your reference)
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### Geopolitical Report:
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{state.get("geopolitical_report", "Not available")}
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### Market Movers Report:
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{state.get("market_movers_report", "Not available")}
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### Sector Performance Report:
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{state.get("sector_performance_report", "Not available")}
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"""
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system_message = (
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"You are a senior research analyst performing an industry deep dive. "
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"You have received reports from three parallel scanners (geopolitical, market movers, sector performance). "
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"Review these reports and identify the 2-3 most promising sectors/industries to investigate further. "
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"Use get_industry_performance to drill into those sectors and get_topic_news for sector-specific news. "
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"Write a detailed report covering: "
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"(1) Why these industries were selected, "
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"(2) Top companies within each industry and their recent performance, "
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"(3) Industry-specific catalysts and risks, "
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"(4) Cross-references between geopolitical events and sector opportunities."
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f"\n\n{phase1_context}"
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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" For your reference, the current date is {current_date}.",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=scan_date)
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chain = prompt | llm.bind_tools(tools)
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result = run_tool_loop(chain, state["messages"], tools)
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report = result.content or ""
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return {
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"messages": [result],
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"industry_deep_dive_report": report,
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"sender": "industry_deep_dive",
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}
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return industry_deep_dive_node
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