"""The LangGraph agent and the streaming turn runner.

A deliberately plain ReAct loop: the model calls campus tools until it has enough
to answer, capped so a confused turn can't spin. The interesting parts are around
the edges rather than in the graph shape:

* **No checkpointer.** The durable transcript is the thread JSON in the dataset
  repo (see `threads.py`), which the request loads and passes in. A Space sleeps
  and restarts, so an in-process checkpointer would be a second, less reliable
  source of truth. `compile(checkpointer=...)` is a one-line change if a future
  feature needs mid-turn resumption.
* **Citations are derived, not trusted.** Tools record every document they return.
  After the turn, the links the model actually wrote are matched back against that
  set by URL, so a source chip means "retrieval returned this", not "the model said
  so". Anything it linked that retrieval never saw is reported as a web result.
* **Sub-agents are a seam.** The team wants a "plan my next semesters" course
  planner later. It becomes another node here; nothing about this file has to
  change to accommodate it.
"""
from __future__ import annotations

import logging
import os
import re
from typing import Annotated, TypedDict

from langchain_core.messages import (AIMessage, AIMessageChunk, HumanMessage,
                                     SystemMessage, ToolMessage)
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages

from .. import kb
from . import prompts, tools as t

log = logging.getLogger("foresight.agent")

DEFAULT_MODEL = "gpt-5.6-sol"
MAX_TOOL_LOOPS = 6
# Long conversations get expensive and drift. Keep the most recent exchanges;
# the full transcript is always on disk.
MAX_HISTORY_MESSAGES = 24

_MARKDOWN_LINK = re.compile(r"\[[^\]]*\]\((https?://[^\s)]+)\)")


def model_name() -> str:
    return os.environ.get("FORESIGHT_CHAT_MODEL", DEFAULT_MODEL)


def enabled() -> bool:
    return bool(os.environ.get("OPENAI_API_KEY"))


class State(TypedDict):
    messages: Annotated[list, add_messages]
    loops: int


def _chat_model():
    """Bound chat model. Imported lazily so the app boots without the SDK or a key."""
    from langchain_openai import ChatOpenAI

    llm = ChatOpenAI(model=model_name(), use_responses_api=True, streaming=True)
    # `web_search` is OpenAI-hosted: it runs server-side, so there's no local
    # execution branch for it and results come back already attributed.
    return llm.bind_tools([*t.KB_TOOLS, {"type": "web_search"}])


_BY_NAME = {tool.name: tool for tool in t.KB_TOOLS}


def _build():
    def agent(state: State):
        return {"messages": [_chat_model().invoke(state["messages"])]}

    def run_tools(state: State):
        last = state["messages"][-1]
        out = []
        for call in getattr(last, "tool_calls", []) or []:
            tool = _BY_NAME.get(call["name"])
            if tool is None:
                # Hosted tools (web_search) never reach here; anything else is a
                # model mistake and should be reported back rather than crash.
                out.append(ToolMessage(content=f"Unknown tool: {call['name']}",
                                       tool_call_id=call["id"], name=call["name"]))
                continue
            try:
                result = tool.invoke(call["args"])
            except Exception as err:
                log.warning("tool %s failed: %s", call["name"], err)
                result = {"error": f"{call['name']} failed: {err}"}
            out.append(ToolMessage(content=str(result), tool_call_id=call["id"],
                                   name=call["name"]))
        return {"messages": out, "loops": state.get("loops", 0) + 1}

    def next_step(state: State):
        last = state["messages"][-1]
        if not getattr(last, "tool_calls", None):
            return END
        if state.get("loops", 0) >= MAX_TOOL_LOOPS:
            log.info("agent: hit the %d-loop cap — answering with what it has", MAX_TOOL_LOOPS)
            return END
        return "tools"

    graph = StateGraph(State)
    graph.add_node("agent", agent)
    graph.add_node("tools", run_tools)
    graph.add_edge(START, "agent")
    graph.add_conditional_edges("agent", next_step, {"tools": "tools", END: END})
    graph.add_edge("tools", "agent")
    return graph.compile()


_graph = None


def get_graph():
    global _graph
    if _graph is None:
        _graph = _build()
    return _graph


def _history(messages: list[dict]) -> list:
    out = []
    for m in messages[-MAX_HISTORY_MESSAGES:]:
        text = m.get("text") or ""
        if not text:
            continue
        out.append(HumanMessage(text) if m.get("role") == "user" else AIMessage(text))
    return out


def _citations(answer: str, retrieved: dict) -> tuple[list[dict], list[str]]:
    """Split the answer's links into knowledge-base citations and web links.

    A source chip means "a tool returned this document during this turn" — so the
    only thing matched against is `retrieved`. Deliberately *not* the whole index:
    looking a URL up there would hand a chip to a model that guessed a real
    vanderbilt.edu address without ever searching for it, which is exactly the
    failure the chip is supposed to rule out.
    """
    by_url = {d.url: d for d in retrieved.values() if d.url}
    cites, web, seen = [], [], set()
    for url in _MARKDOWN_LINK.findall(answer):
        if url in seen:
            continue
        seen.add(url)
        doc = by_url.get(url)
        if doc is not None:
            cites.append(doc.cite())
        else:
            web.append(url)
    return cites, web


async def run_turn(question: str, history: list[dict], profile: dict | None,
                   first_name: str = ""):
    """Run one turn, yielding SSE-shaped events.

    Yields dicts: {"type": "tool"|"token"|"sources"|"suggestion"|"error"}.
    The caller owns thread ids and persistence.
    """
    retrieved, suggestion = t.start_turn()
    answer_parts: list[str] = []
    announced: set[str] = set()

    state = {
        "messages": [SystemMessage(prompts.system_prompt(profile, first_name)),
                     *_history(history), HumanMessage(question)],
        "loops": 0,
    }

    try:
        async for kind, payload in get_graph().astream(
                state, stream_mode=["messages", "updates"]):
            if kind == "messages":
                chunk, _meta = payload
                # This stream carries every message a node produces, including
                # ToolMessages. Only the model's own output is the answer — without
                # this check the student watches raw tool JSON scroll past.
                if not isinstance(chunk, (AIMessage, AIMessageChunk)):
                    continue
                text = _text_of(chunk)
                if text:
                    answer_parts.append(text)
                    yield {"type": "token", "text": text}
                for call in getattr(chunk, "tool_call_chunks", None) or []:
                    name = call.get("name")
                    if name and name not in announced:
                        announced.add(name)
                        label = t.TOOL_LABELS.get(name)
                        if label:
                            yield {"type": "tool", "name": name, "status": "running",
                                   "label": label}
            elif kind == "updates":
                for node, update in (payload or {}).items():
                    if node != "tools":
                        continue
                    for msg in update.get("messages", []):
                        if getattr(msg, "name", None):
                            yield {"type": "tool", "name": msg.name, "status": "done"}
    except Exception as err:
        log.exception("agent: turn failed")
        yield {"type": "error", "message": str(err)}
        return

    answer = "".join(answer_parts)
    cites, web = _citations(answer, retrieved)
    if cites:
        yield {"type": "sources", "items": cites}
    if web:
        yield {"type": "web", "items": web}
    if suggestion:
        yield {"type": "suggestion", **suggestion}
    yield {"type": "final", "text": answer, "sources": cites,
           "suggestion": suggestion or None, "tools": sorted(announced)}


def _text_of(chunk) -> str:
    """Text out of a streamed chunk, whose content may be a string or a list of
    typed blocks depending on which tools are bound."""
    content = getattr(chunk, "content", "")
    if isinstance(content, str):
        return content
    out = []
    for block in content or []:
        if isinstance(block, str):
            out.append(block)
        elif isinstance(block, dict) and block.get("type") in ("text", "output_text"):
            out.append(block.get("text") or "")
    return "".join(out)