54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.scanner_tools import get_topic_news
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from tradingagents.agents.utils.tool_runner import run_tool_loop
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def create_geopolitical_scanner(llm):
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def geopolitical_scanner_node(state):
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scan_date = state["scan_date"]
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tools = [get_topic_news]
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system_message = (
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"You are a geopolitical analyst scanning global news for risks and opportunities affecting financial markets. "
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"Use get_topic_news to search for news on: geopolitics, trade policy, sanctions, central bank decisions, "
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"energy markets, and military conflicts. Analyze the results and write a concise report covering: "
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"(1) Major geopolitical events and their market impact, "
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"(2) Central bank policy signals, "
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"(3) Trade/sanctions developments, "
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"(4) Energy and commodity supply risks. "
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"Include a risk assessment table at the end."
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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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"geopolitical_report": report,
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"sender": "geopolitical_scanner",
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}
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return geopolitical_scanner_node
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