44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
|
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
|
||
|
|
from .state import AgentState
|
||
|
|
from services.prompt_manager import PromptManager
|
||
|
|
from services.template_matcher import TemplateMatcher
|
||
|
|
|
||
|
|
|
||
|
|
def process_input(state: AgentState) -> AgentState:
|
||
|
|
"""处理用户输入"""
|
||
|
|
state.current_step = "processed"
|
||
|
|
return state
|
||
|
|
|
||
|
|
|
||
|
|
def generate_response(state: AgentState, model) -> AgentState:
|
||
|
|
"""使用 LLM 生成回复"""
|
||
|
|
if state.messages:
|
||
|
|
response = model.invoke(state.messages)
|
||
|
|
state.messages.append(response)
|
||
|
|
return state
|
||
|
|
|
||
|
|
|
||
|
|
def normalize_input(state: AgentState, model) -> AgentState:
|
||
|
|
"""将用户输入规范化为标准英文语句"""
|
||
|
|
if not state.messages:
|
||
|
|
return state
|
||
|
|
|
||
|
|
last_message = state.messages[-1]
|
||
|
|
if not isinstance(last_message, HumanMessage):
|
||
|
|
return state
|
||
|
|
|
||
|
|
prompt_manager = PromptManager()
|
||
|
|
system_prompt = SystemMessage(
|
||
|
|
content=prompt_manager.get("system", "english_normalizer")
|
||
|
|
)
|
||
|
|
|
||
|
|
response = model.invoke([system_prompt, HumanMessage(content=last_message.content)])
|
||
|
|
normalized = response.content if hasattr(response, "content") else str(response)
|
||
|
|
|
||
|
|
state.context["original_input"] = last_message.content
|
||
|
|
state.context["normalized_input"] = normalized
|
||
|
|
|
||
|
|
matcher = TemplateMatcher()
|
||
|
|
state.context["table_match"] = matcher.match(normalized)
|
||
|
|
return state
|