"""
  Conversational Shopping Agent with LangGraph

  
  Architecture:
  - ReAct-style agent: plan → search → evaluate → re-plan or respond
  - search_products is session-bound, writing curated results to SearchResultRegistry
  - Final AI message references results via [SEARCH_REF:xxx] tokens instead of
    re-listing product details; the UI renders product cards from the registry

  """
  

  import json

  import logging
  from pathlib import Path

  from typing import Any, Optional, Sequence

  
  from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
  from langchain_openai import ChatOpenAI
  from langgraph.checkpoint.memory import MemorySaver
  from langgraph.graph import END, START, StateGraph
  from langgraph.graph.message import add_messages
  from langgraph.prebuilt import ToolNode
  from typing_extensions import Annotated, TypedDict
  
  from app.config import settings

  from app.search_registry import global_registry

  from app.tools.search_tools import get_all_tools
  
  logger = logging.getLogger(__name__)
  

  # ── System prompt ──────────────────────────────────────────────────────────────
  # Universal: works for any e-commerce vertical (fashion, electronics, home, etc.)
  # Key design decisions:
  #   1. Guides multi-query search planning with explicit evaluate-and-decide loop
  #   2. Forbids re-listing product details in the final response
  #   3. Mandates [SEARCH_REF:xxx] inline citation as the only product presentation mechanism

  SYSTEM_PROMPT = """  角色定义
    你是我们店铺的一名专业的电商导购，是一个善于倾听、主动引导、懂得搭配的“时尚顾问”，通过有温度的对话，给用户提供有价值的信息，包括需求引导、方案推荐、搜索结果推荐，最终促成满意的购物决策或转化行为。
    作为我们店铺的一名专业的销售，除了本店铺的商品的推荐，你可以给用户提供有帮助的信息，但是不要虚构商品、提供本商店搜索结果以外的商品。
    
    一些原则：
    1. 价值提供与信息收集的原则：
      1. 优先价值提供：适时的提供有价值的信息，如商品推荐、穿搭建议、趋势信息，在推荐方向上有需求缺口、需要明确的重要信息时，要适时的做“信息收集”，引导式的澄清需求、提高商品发现的效率，形成“提供-反馈”的良性循环。
      2. 缺口大（比如品类或者使用人群都不能确定）→ 给出方案推荐 + 1-2个关键问题让用户选择；缺口小→直接检索+方案呈现，根据情况，可以考虑该方向下重要的决策因素，进行提议和问题收集，让用户既得到相关信息、又得到下一步的方向引导、同时也有机会修正或者细化诉求。
      3. 选项驱动式澄清：推荐几个清晰的方向，呈现方案或商品搜索结果，再做澄清
      4. 单轮对话最好只提一个问题，最多两个，禁止多问题堆叠。
      5. 站在用户立场思考：比如询问用户期待的效果或感觉、使用的场合、想解决的问题，而不是询问具体的款式、参数，你需要将用户表达的需求翻译为具体可检索的商品特征（版型、材质、设计元素、风格标签等），并据此筛选商品、组织推荐逻辑。
    2. 如何使用make_search_products_tool：
      1. 可以生成多个query进行搜索：在需要搜索商品的时候，可以将需求分解为 2-4 个搜索查询，每个 query 聚焦一个明确的商品子类或搜索角度。
      2. 可以根据搜索结果调整搜索策略：每次调用 search_products 后，工具会返回搜索结果的相关性的判断、以及搜索结果的topN的title，你需要决策是否要调整搜索策略，比如结果质量太差，可能需要调整搜索词、或者加大试探的query数量（不要超过3-5个）。
      3. 使用 [SEARCH_REF:xxx] 内联引用搜索结果：搜索工具会返回一个结果引用标识[SEARCH_REF:xxx]，撰写最终答复的时候可以直接引用将 [SEARCH_REF:xxx] ，系统会自动在该位置渲染对应的商品卡片列表，无需复述搜索结果。

  """
  
  
  # ── Agent state ────────────────────────────────────────────────────────────────
  
  class AgentState(TypedDict):
      messages: Annotated[Sequence[BaseMessage], add_messages]
      current_image_path: Optional[str]
  
  
  # ── Helper ─────────────────────────────────────────────────────────────────────

  # Max length for logging single content field (avoid huge logs)
  _LOG_CONTENT_MAX = 8000
  _LOG_TOOL_RESULT_MAX = 4000
  
  

  def _extract_message_text(msg) -> str:

      """Extract plain text from a LangChain message (handles str or content_blocks)."""

      content = getattr(msg, "content", "")
      if isinstance(content, str):
          return content
      if isinstance(content, list):
          parts = []
          for block in content:
              if isinstance(block, dict):

                  parts.append(block.get("text") or block.get("content") or "")

              else:
                  parts.append(str(block))
          return "".join(str(p) for p in parts)
      return str(content) if content else ""
  
  

  def _message_for_log(msg: BaseMessage) -> dict:
      """Serialize a message for structured logging (content truncated)."""
      text = _extract_message_text(msg)
      if len(text) > _LOG_CONTENT_MAX:
          text = text[:_LOG_CONTENT_MAX] + f"... [truncated, total {len(text)} chars]"
      out: dict[str, Any] = {
          "type": getattr(msg, "type", "unknown"),
          "content": text,
      }
      if hasattr(msg, "tool_calls") and msg.tool_calls:
          out["tool_calls"] = [
              {"name": tc.get("name"), "args": tc.get("args", {})}
              for tc in msg.tool_calls
          ]
      return out
  
  

  # ── Agent class ────────────────────────────────────────────────────────────────

  class ShoppingAgent:

      """ReAct shopping agent with search-evaluate-decide loop and registry-based result referencing."""

  
      def __init__(self, session_id: Optional[str] = None):
          self.session_id = session_id or "default"
  

          llm_kwargs = dict(

              model=settings.openai_model,
              temperature=settings.openai_temperature,
              api_key=settings.openai_api_key,
          )

          if settings.openai_api_base_url:
              llm_kwargs["base_url"] = settings.openai_api_base_url

          self.llm = ChatOpenAI(**llm_kwargs)

          # Tools are session-bound so search_products writes to the right registry partition
          self.tools = get_all_tools(session_id=self.session_id, registry=global_registry)

          self.llm_with_tools = self.llm.bind_tools(self.tools)
  

          self.graph = self._build_graph()

          logger.info(f"ShoppingAgent ready — session={self.session_id}")

  
      def _build_graph(self):

          def agent_node(state: AgentState):

              messages = state["messages"]

              if not any(isinstance(m, SystemMessage) for m in messages):

                  messages = [SystemMessage(content=SYSTEM_PROMPT)] + list(messages)

              request_log = [_message_for_log(m) for m in messages]
              req_json = json.dumps(request_log, ensure_ascii=False)
              if len(req_json) > _LOG_CONTENT_MAX:
                  req_json = req_json[:_LOG_CONTENT_MAX] + f"... [truncated total {len(req_json)}]"
              logger.info("[%s] LLM_REQUEST messages=%s", self.session_id, req_json)

              response = self.llm_with_tools.invoke(messages)

              response_log = _message_for_log(response)
              logger.info(
                  "[%s] LLM_RESPONSE %s",
                  self.session_id,
                  json.dumps(response_log, ensure_ascii=False),
              )

              return {"messages": [response]}
  

          def should_continue(state: AgentState):

              last = state["messages"][-1]
              if hasattr(last, "tool_calls") and last.tool_calls:

                  return "tools"

              return END
  

          tool_node = ToolNode(self.tools)

          workflow = StateGraph(AgentState)

          workflow.add_node("agent", agent_node)
          workflow.add_node("tools", tool_node)

          workflow.add_edge(START, "agent")
          workflow.add_conditional_edges("agent", should_continue, ["tools", END])
          workflow.add_edge("tools", "agent")
  

          return workflow.compile(checkpointer=MemorySaver())

  
      def chat(self, query: str, image_path: Optional[str] = None) -> dict:

          """
          Process a user query and return the agent response with metadata.

  
          Returns:

              dict with keys:
                response      – final AI message text (may contain [SEARCH_REF:xxx] tokens)
                tool_calls    – list of {name, args, result_preview}
                debug_steps   – detailed per-node step log
                search_refs   – dict[ref_id → SearchResult] for all searches this turn
                error         – bool

          """
          try:

              logger.info(f"[{self.session_id}] chat: {query!r} image={bool(image_path)}")

              if image_path and not Path(image_path).exists():
                  return {

                      "response": f"错误：图片文件不存在：{image_path}",

                      "error": True,
                  }
  

              # Snapshot registry before the turn so we can report new additions
              registry_before = set(global_registry.get_all(self.session_id).keys())
  

              message_content = query
              if image_path:

                  message_content = f"{query}\n[用户上传了图片：{image_path}]"

              config = {"configurable": {"thread_id": self.session_id}}
              input_state = {
                  "messages": [HumanMessage(content=message_content)],
                  "current_image_path": image_path,
              }
  

              tool_calls: list[dict] = []
              debug_steps: list[dict] = []
  

              for event in self.graph.stream(input_state, config=config):

                  logger.debug(f"[{self.session_id}] event keys: {list(event.keys())}")

                  if "agent" in event:

                      agent_out = event["agent"]
                      step_msgs: list[dict] = []
                      step_tcs: list[dict] = []

                      for msg in agent_out.get("messages", []):
                          text = _extract_message_text(msg)
                          step_msgs.append({

                              "type": getattr(msg, "type", "assistant"),

                              "content": text[:500],
                          })

                          if hasattr(msg, "tool_calls") and msg.tool_calls:
                              for tc in msg.tool_calls:

                                  entry = {"name": tc.get("name"), "args": tc.get("args", {})}
                                  tool_calls.append(entry)
                                  step_tcs.append(entry)

                      debug_steps.append({"node": "agent", "messages": step_msgs, "tool_calls": step_tcs})

                  if "tools" in event:
                      tools_out = event["tools"]
                      step_results: list[dict] = []
                      msgs = tools_out.get("messages", [])

                      # Match results back to tool_calls by position within this event
                      unresolved = [tc for tc in tool_calls if "result" not in tc]
                      for i, msg in enumerate(msgs):
                          text = _extract_message_text(msg)
                          preview = text[:600] + ("…" if len(text) > 600 else "")
                          if i < len(unresolved):
                              unresolved[i]["result"] = preview

                              tc_name = unresolved[i].get("name", "")
                              tc_args = unresolved[i].get("args", {})
                              result_log = text if len(text) <= _LOG_TOOL_RESULT_MAX else text[:_LOG_TOOL_RESULT_MAX] + f"... [truncated total {len(text)}]"
                              logger.info(
                                  "[%s] TOOL_CALL_RESULT name=%s args=%s result=%s",
                                  self.session_id,
                                  tc_name,
                                  json.dumps(tc_args, ensure_ascii=False),
                                  result_log,
                              )

                          step_results.append({"content": preview})

                      debug_steps.append({"node": "tools", "results": step_results})

              final_state = self.graph.get_state(config)

              final_msg = final_state.values["messages"][-1]
              response_text = _extract_message_text(final_msg)
  
              # Collect new SearchResults added during this turn
              registry_after = global_registry.get_all(self.session_id)
              new_refs = {
                  ref_id: result
                  for ref_id, result in registry_after.items()
                  if ref_id not in registry_before
              }

              logger.info(
                  f"[{self.session_id}] done — tool_calls={len(tool_calls)}, new_refs={list(new_refs.keys())}"
              )

  
              return {
                  "response": response_text,
                  "tool_calls": tool_calls,

                  "debug_steps": debug_steps,

                  "search_refs": new_refs,

                  "error": False,
              }
  
          except Exception as e:

              logger.error(f"[{self.session_id}] chat error: {e}", exc_info=True)

              return {

                  "response": f"抱歉，处理您的请求时遇到错误：{e}",
                  "tool_calls": [],
                  "debug_steps": [],
                  "search_refs": {},

                  "error": True,
              }
  
      def get_conversation_history(self) -> list:

          try:
              config = {"configurable": {"thread_id": self.session_id}}
              state = self.graph.get_state(config)

              if not state or not state.values.get("messages"):
                  return []
  

              result = []

              for msg in state.values["messages"]:

                  if isinstance(msg, SystemMessage):
                      continue

                  if getattr(msg, "type", None) in ("system", "tool"):

                      continue

                  role = "user" if msg.type == "human" else "assistant"
                  result.append({"role": role, "content": _extract_message_text(msg)})

              return result

          except Exception as e:

              logger.error(f"get_conversation_history error: {e}")

              return []
  
      def clear_history(self):

          logger.info(f"[{self.session_id}] clear requested (use new session_id to fully reset)")

  
  
  def create_shopping_agent(session_id: Optional[str] = None) -> ShoppingAgent:

      return ShoppingAgent(session_id=session_id)