68 lines
3.1 KiB
Python
68 lines
3.1 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_language_instruction,
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get_social_sentiment,
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)
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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.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_social_sentiment,
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get_company_news,
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]
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system_message = (
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"You are a company sentiment analyst. "
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"Your job is to assess public narrative, sentiment, and crowd positioning around the company without claiming direct social-media coverage unless a tool explicitly provides it. "
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"Use `get_social_sentiment(symbol, start_date, end_date)` for dedicated or clearly labeled news-derived sentiment context, and `get_company_news(symbol, start_date, end_date)` for direct company-news evidence. "
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"If the sentiment tool says a dedicated social provider is unavailable, explicitly state that you are working from news-derived sentiment instead of pretending you saw social posts. "
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"Write a detailed report covering sentiment drivers, tone shifts, narrative concentration, what is improving, what is deteriorating, and the main trading implications."
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" End with a Markdown table that summarizes key signals, evidence, and confidence."
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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 sentiment 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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"sentiment_report": report,
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}
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return social_media_analyst_node
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