73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
import time
|
|
import json
|
|
from tradingagents.agents.utils.agent_utils import get_news, get_global_news
|
|
from tradingagents.agents.utils.prompts import get_news_analyst_prompt, get_agent_role_instruction, get_context_message
|
|
from tradingagents.dataflows.config import get_config
|
|
|
|
|
|
def create_news_analyst(llm, language: str = "zh-TW"):
|
|
"""
|
|
建立一個新聞分析師節點。
|
|
|
|
Args:
|
|
llm: 用於分析的語言模型。
|
|
language: 報告語言 ('en' 或 'zh-TW')
|
|
|
|
Returns:
|
|
一個處理新聞分析的節點函式。
|
|
"""
|
|
def news_analyst_node(state):
|
|
"""
|
|
分析最近的新聞和趨勢。
|
|
|
|
Args:
|
|
state: 當前的代理狀態。
|
|
|
|
Returns:
|
|
更新後的代理狀態，包含新聞分析報告和訊息。
|
|
"""
|
|
current_date = state["trade_date"]
|
|
ticker = state["company_of_interest"]
|
|
company_name = state.get("company_name", ticker)
|
|
|
|
tools = [
|
|
get_news,
|
|
get_global_news,
|
|
]
|
|
|
|
# Get language-specific prompts
|
|
system_message = get_news_analyst_prompt(language)
|
|
role_instruction = get_agent_role_instruction(language)
|
|
context_msg = get_context_message(language, current_date, company_name, ticker)
|
|
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
f"{role_instruction}"
|
|
" 您可以使用以下工具：{tool_names}。\n{system_message}"
|
|
f" {context_msg}",
|
|
),
|
|
MessagesPlaceholder(variable_name="messages"),
|
|
]
|
|
)
|
|
|
|
prompt = prompt.partial(system_message=system_message)
|
|
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
|
|
|
|
chain = prompt | llm.bind_tools(tools)
|
|
result = chain.invoke(state["messages"])
|
|
|
|
# Report logic: only save report when LLM gives final response
|
|
report = state.get("news_report", "")
|
|
|
|
if len(result.tool_calls) == 0:
|
|
report = result.content
|
|
|
|
return {
|
|
"messages": [result],
|
|
"news_report": report,
|
|
}
|
|
|
|
return news_analyst_node |