more_dots/agent/nodes.py

import json

from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage, AIMessage
from .state import AgentState
from services.prompt_manager import get_prompt_manager
from services.template_matcher import get_template_matcher
from services.sql_prompt_manager import get_sql_prompt_manager
from tools.sr_api_tool import SrApiQueryTool


def _short(value, max_len: int = 500) -> str:
    text = str(value)
    return text if len(text) <= max_len else text[:max_len] + "..."


def process_input(state: AgentState) -> AgentState:
    """处理用户输入"""
    print("[process_input][in] messages=", _short(state.messages))
    state.current_step = "processed"
    print("[process_input][out] current_step=", state.current_step)
    return state


def generate_response(state: AgentState, model) -> AgentState:
    """使用 LLM 生成回复"""
    print("[generate_response][in] context_keys=", list((state.context or {}).keys()))
    sr_api_result = state.context.get("sr_api_result")
    if sr_api_result:
        state.messages.append(AIMessage(content=str(sr_api_result)))
        print("[generate_response][out] source=sr_api_result")
        return state
    final_sql = state.context.get("final_sql")
    if final_sql:
        state.messages.append(AIMessage(content=final_sql))
        print("[generate_response][out] source=final_sql")
        return state
    if state.messages:
        response = model.invoke(state.messages)
        state.messages.append(response)
        print("[generate_response][out] source=model_invoke")
    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

    print("[normalize_input][in] user_input=", _short(last_message.content))

    prompt_manager = get_prompt_manager()
    normalizer_prompt = (
        prompt_manager.get("system", "english_normalizer")
        or prompt_manager.get("user", "english_normalizer")
    )
    system_prompt = SystemMessage(content=normalizer_prompt)

    response = model.invoke([system_prompt, HumanMessage(content=last_message.content)])
    normalized = response.content if hasattr(response, "content") else str(response)
    print("[normalize_input][out] normalized=", _short(normalized))

    state.context["original_input"] = last_message.content
    state.context["normalized_input"] = normalized

    matcher = get_template_matcher()
    state.context["table_match"] = matcher.match(normalized)
    print("[normalize_input][out] table_match=", _short(state.context.get("table_match")))
    return state


def generate_sql(state: AgentState, model) -> AgentState:
    """根据表名与提示词生成 SQL"""
    table_match = state.context.get("table_match") or {}
    table_name = table_match.get("table_name")
    normalized = state.context.get("normalized_input")

    if not table_name or not normalized:
        print("[generate_sql][skip] missing table_name or normalized")
        return state

    print("[generate_sql][in] table_name=", table_name)
    print("[generate_sql][in] normalized=", _short(normalized))

    prompt_manager = get_sql_prompt_manager()
    prompt_data = prompt_manager.get_prompt(table_name)
    if not prompt_data:
        print("[generate_sql][skip] prompt not found for table=", table_name)
        return state

    prompt_text = json.dumps(prompt_data, ensure_ascii=False, indent=2)
    system_template = get_prompt_manager().get("system", "sql_mysql_select_only")
    system_content = system_template.format(table_prompt_json=prompt_text)
    user_content = f"User question (normalized English): {normalized}"
    response = model.invoke([SystemMessage(content=system_content), HumanMessage(content=user_content)])
    sql_text = response.content if hasattr(response, "content") else str(response)
    print("[generate_sql][out] sql=", _short(sql_text))

    state.context["final_sql"] = sql_text
    if not state.context.get("skip_sr_api"):
        tool = SrApiQueryTool()
        state.context["sr_api_result"] = tool.run(json.dumps({"sql": sql_text}, ensure_ascii=False))
        print("[generate_sql][out] sr_api_result=", _short(state.context.get("sr_api_result")))
    return state