"""
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_RESULTS_REF:ref_id] tokens instead of
  re-listing product details; the UI renders product cards from the registry
"""

import json
import logging
import re
from urllib.parse import urlparse
from datetime import datetime
from pathlib import Path
from typing import Any, Optional, Sequence

from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from langchain_core.outputs import ChatResult
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_RESULTS_REF:ref_id] inline citation as the only product presentation mechanism
SYSTEM_PROMPT = f"""  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. 兼顾价值提供和需求澄清：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. 意图判断-缺口大：当无法从对话中确定关键变量（如使用对象不明确、无法判断男性或女性使用、品类细分不清等）时，从“使用对象”、“品类细分”、“使用场景”、“风格效果”等高层意图维度切入，提供方向性选项 + 1–2个关键问题，引导用户做选择（以下仅提供参考思路，具体话术不要照搬）：
    1. 人群不明确时（如果从对话中无法确认用使用人群，比如搜索意图是男女都可以消费的品类比如T恤、裤子）：男款、女款，还是中性风都可以？
    2. 确定是女性、但是风格不明确时：你想穿出哪种感觉？职场干练 松弛自在 活力元气 温柔知性
    3. 使用场景不明确时：平时通勤场合多吗？还是更喜欢生活化穿搭？
    4. 如上此类，存在大的需求缺口，则需要先进行澄清、不要调用商品搜索。
  3. 意图判断-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（思考哪些维度最可能影响推荐结果），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。
  4. 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
  5. 单轮对话最好只提1-2个问题。
  6. 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search_products：
  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）。结果太差的原因有可能是你生成的query不合理、请根据你看到的商品名称的构成组织搜索关键词。
3. 在最终回复中使用 [SEARCH_RESULTS_REF:ref_id] 内联引用搜索结果：
  1. 搜索工具会返回一个结果引用标识[SEARCH_RESULTS_REF:ref_id]，撰写最终答复的时候请直接引用 [SEARCH_RESULTS_REF:ref_id] ，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。
  2. 因为系统会自动将[SEARCH_RESULTS_REF:ref_id]渲染为搜索结果，所以[SEARCH_RESULTS_REF:ref_id]必须独占一行，且只在需要渲染该query完整的搜索结果时才进行引用，同一个结果不要重复引用。
   """

SYSTEM_PROMPT___2 = """  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_RESULTS_REF:ref_id] 内联引用搜索结果：搜索工具会返回一个结果引用标识[SEARCH_RESULTS_REF:ref_id]，撰写最终答复的时候可以直接引用将 [SEARCH_RESULTS_REF:ref_id] ，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。
    4. 因为系统会自动将[SEARCH_RESULTS_REF:ref_id]渲染为搜索结果，所以只在需要渲染该query完整的搜索结果时才进行引用，同一个结果不要重复引用。
"""


# ── 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 ""


# 部分 API（如 DeepSeek）在 content 中返回 think 标签块，需去掉后只保留正式回复
_RE_THINK_TAGS = re.compile(r"<think>.*?<\/think>", re.DOTALL | re.IGNORECASE)
# 仅提取 <think> 标签内正文（用于日志打印 thinking）
_RE_THINK_INNER = re.compile(r"<think>(.*?)<\/think>", re.DOTALL | re.IGNORECASE)


def _normalize_base_url(base_url: Optional[str]) -> str:
    return (base_url or "").strip().rstrip("/")


def _is_openai_official_base_url(base_url: Optional[str]) -> bool:
    normalized = _normalize_base_url(base_url)
    if not normalized:
        return False
    hostname = (urlparse(normalized).hostname or "").lower()
    return hostname.endswith("api.openai.com")


def _is_dashscope_base_url(base_url: Optional[str]) -> bool:
    normalized = _normalize_base_url(base_url)
    if not normalized:
        return False
    hostname = (urlparse(normalized).hostname or "").lower()
    return "dashscope" in hostname


def _coerce_reasoning_text(value: Any) -> str:
    """Best-effort conversion from reasoning payload to plain text."""
    if value is None:
        return ""
    if isinstance(value, str):
        return value.strip()
    if isinstance(value, dict):
        parts: list[str] = []
        for key in ("content", "summary", "text", "reasoning_content"):
            item = value.get(key)
            if isinstance(item, str) and item.strip():
                parts.append(item.strip())
            elif isinstance(item, list):
                for sub in item:
                    s = _coerce_reasoning_text(sub)
                    if s:
                        parts.append(s)
        if parts:
            return "\n".join(parts).strip()
        try:
            return json.dumps(value, ensure_ascii=False)
        except Exception:
            return str(value).strip()
    if isinstance(value, list):
        parts = [_coerce_reasoning_text(v) for v in value]
        joined = "\n".join(p for p in parts if p)
        if joined:
            return joined.strip()
        try:
            return json.dumps(value, ensure_ascii=False)
        except Exception:
            return str(value).strip()
    return str(value).strip()


def _extract_formal_reply(msg) -> str:
    """
    只截取大模型回复中的「正式结果」，去掉 thinking/reasoning 内容。
    - 若 content 为 list（如 Responses API）：只取 type 为 output_text/text 的块，跳过 reasoning。
    - 若 content 为 str：去掉 think 标签及其内容。
    """
    content = getattr(msg, "content", "")
    if isinstance(content, list):
        parts = []
        for block in content:
            if not isinstance(block, dict):
                continue
            block_type = (block.get("type") or "").lower()
            if block_type in ("reasoning",):
                continue
            text = block.get("text") or block.get("content") or ""
            if text:
                parts.append(text)
        return "".join(str(p) for p in parts).strip()
    if isinstance(content, str):
        return _RE_THINK_TAGS.sub("", content).strip()
    return str(content).strip() if content else ""


def _extract_thinking(msg) -> str:
    """提取大模型回复中的 thinking/reasoning 内容（仅用于日志）。"""
    kwargs = getattr(msg, "additional_kwargs", None) or {}
    # DashScope 等兼容接口返回的 reasoning_content（由 ChatOpenAIWithReasoningContent 注入）
    rc = _coerce_reasoning_text(kwargs.get("reasoning_content"))
    if rc:
        return rc
    # Responses API 等返回的 reasoning 字段
    reasoning = _coerce_reasoning_text(kwargs.get("reasoning"))
    if reasoning:
        return reasoning
    content = getattr(msg, "content", "")
    if isinstance(content, list):
        parts = []
        for block in content:
            if not isinstance(block, dict):
                continue
            block_type = (block.get("type") or "").lower()
            if block_type not in ("reasoning", "reasoning_content", "thinking"):
                continue
            text = _coerce_reasoning_text(block.get("text") or block.get("content") or block)
            if text:
                parts.append(text)
        if parts:
            return "".join(str(p) for p in parts).strip()
    if isinstance(content, str):
        m = _RE_THINK_INNER.search(content)
        if m:
            return m.group(1).strip()
    return ""


def _message_for_log(msg: BaseMessage, include_thinking: bool = False) -> dict:
    """Serialize a message for structured logging (content truncated)."""
    msg_kwargs = getattr(msg, "additional_kwargs", None) or {}
    if msg_kwargs and any(k in msg_kwargs for k in ("reasoning", "reasoning_content")):
        text = _extract_formal_reply(msg) or _extract_message_text(msg)
    else:
        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 include_thinking:
        thinking = _extract_thinking(msg)
        if thinking:
            if len(thinking) > _LOG_CONTENT_MAX:
                thinking = thinking[:_LOG_CONTENT_MAX] + f"... [truncated, total {len(thinking)} chars]"
            out["thinking"] = thinking
    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


# ── DashScope thinking 支持 ─────────────────────────────────────────────────────
# LangChain 解析 chat completion 时不会把 API 返回的 reasoning_content 写入 message，
# 子类在 _create_chat_result 中把 reasoning_content 注入到 additional_kwargs，便于日志打印。

class ChatOpenAIWithReasoningContent(ChatOpenAI):
    """ChatOpenAI 子类：将 API 返回的 reasoning_content 注入到 message.additional_kwargs。"""

    def _create_chat_result(
        self,
        response: Any,
        generation_info: Optional[dict] = None,
    ) -> ChatResult:
        result = super()._create_chat_result(response, generation_info)
        if isinstance(response, dict):
            response_dict = response
        else:
            response_dict = getattr(response, "model_dump", None)
            response_dict = response_dict() if callable(response_dict) else {}
        if not response_dict:
            return result
        choices = response_dict.get("choices") or []
        for i, res in enumerate(choices):
            if i >= len(result.generations):
                break
            msg_dict = res.get("message") or {}
            if isinstance(msg_dict, dict) and "reasoning_content" in msg_dict:
                rc = msg_dict["reasoning_content"]
                if rc and isinstance(result.generations[i].message, BaseMessage):
                    result.generations[i].message.additional_kwargs["reasoning_content"] = rc
        return result


# ── 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[str, Any] = dict(
            model=settings.openai_model,
            temperature=settings.openai_temperature,
            api_key=settings.openai_api_key,
        )
        base_url = _normalize_base_url(settings.openai_api_base_url)
        if base_url:
            llm_kwargs["base_url"] = base_url

        use_reasoning = getattr(settings, "openai_use_reasoning", False)
        if use_reasoning and (not base_url or _is_openai_official_base_url(base_url)):
            # OpenAI 官方 endpoint：使用 Responses API 的 reasoning 参数。
            llm_kwargs["use_responses_api"] = True
            effort = getattr(settings, "openai_reasoning_effort", "medium") or "medium"
            llm_kwargs["model_kwargs"] = {"reasoning": {"effort": effort, "summary": "none"}}
        elif use_reasoning and _is_dashscope_base_url(base_url):
            # DashScope 兼容 endpoint：通过 extra_body 开启思考，返回 reasoning_content。
            extra = llm_kwargs.get("extra_body") or {}
            llm_kwargs["extra_body"] = {**extra, "enable_thinking": True}
        elif use_reasoning and base_url:
            logger.info(
                "Reasoning requested but base_url is non-OpenAI/non-DashScope; "
                "skipping provider-specific reasoning params. base_url=%s",
                base_url,
            )

        llm_class = (
            ChatOpenAIWithReasoningContent
            if base_url and not _is_openai_official_base_url(base_url)
            else ChatOpenAI
        )
        self.llm = llm_class(**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, include_thinking=True)
            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_RESULTS_REF:ref_id] 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_formal_reply(final_msg) or _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"
                content = _extract_formal_reply(msg) or _extract_message_text(msg) if role == "assistant" else _extract_message_text(msg)
                result.append({"role": role, "content": content})
            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)