61 lines
2.6 KiB
Python
61 lines
2.6 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.polymarket_tools import get_social_sentiment, get_whale_activity
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def create_social_media_analyst(llm):
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def social_media_analyst_node(state):
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current_date = state["trade_date"]
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event_id = state["event_id"]
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event_question = state["event_question"]
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tools = [
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get_social_sentiment,
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get_whale_activity,
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]
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system_message = (
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"You are a social sentiment analyst for prediction markets. "
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"Analyze social media opinion and whale/top trader positions for the event. "
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"Use get_social_sentiment(query) to gather Twitter and Reddit discussions related to the prediction market event. "
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"Use get_whale_activity(market_id) to identify what large holders are doing with their positions. "
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"Write a comprehensive report on public sentiment and large trader behavior. "
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"Do not simply state the trends are mixed — provide detailed and fine-grained analysis and insights that may help traders make decisions."
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" Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
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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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" 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}. The event we are analyzing: {event_question} (Event ID: {event_id})",
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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(event_id=event_id)
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prompt = prompt.partial(event_question=event_question)
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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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"sentiment_report": report,
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}
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return social_media_analyst_node
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