72 lines
3.0 KiB
Python
72 lines
3.0 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_company_news,
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get_disclosures,
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get_language_instruction,
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get_macro_news,
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)
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def create_news_analyst(llm):
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def news_analyst_node(state):
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current_date = state.get("analysis_date") or state["trade_date"]
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instrument_context = build_instrument_context(
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state["company_of_interest"],
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state.get("instrument_profile"),
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)
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tools = [
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get_company_news,
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get_macro_news,
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get_disclosures,
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]
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system_message = (
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"You are a news and event analyst. "
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"Build the report from three evidence blocks: company news, macro news, and disclosures. "
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"Use `get_company_news(symbol, start_date, end_date)` for company-specific coverage, "
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"`get_macro_news(curr_date, look_back_days, limit, region, language)` for broader market context, "
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"and `get_disclosures(symbol, start_date, end_date)` for filing or disclosure events when available. "
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"Do not describe unsupported tool signatures or imaginary search capabilities. "
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"Present 3 to 5 key events with event type, source, why it matters, bullish implication, bearish implication, and confidence. "
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"Finish with a concise Markdown table summarizing the evidence."
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+ get_language_instruction()
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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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" Return the completed news report directly once you have enough evidence."
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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}. {instrument_context}",
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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=current_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"news_report": report,
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}
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return news_analyst_node
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