"""Probe the self-hosted LLM endpoint (aipi).

Lists available models and verifies the embedding dimension of the
configured ``embed`` model against ``BOR_EMBEDDING_DIM`` (default 768).
With ``--tools`` it additionally probes the configured chat model's
OpenAI-style tool-calling support (phase 37, task 01): a trivial
no-parameter ``get_time`` function is offered in a non-streaming and a
streaming ``chat/completions`` request, and a supported / not-supported
verdict is printed for each — the agent loop (``app/rag/agent.py``) is
built against that verdict. Run this before the first import if the LLM
backend ever changes:

    uv run python -m scripts.llm_probe
    uv run python -m scripts.llm_probe --tools
"""
from __future__ import annotations

import argparse
import json
import os
import sys
from collections.abc import Iterable
from datetime import date

import httpx
from dotenv import load_dotenv

#: Trivial probe function (phase 37, task 01): no parameters, so a
#: compliant tool call carries arguments of exactly ``{}``.
_PROBE_TOOL: dict = {
    "type": "function",
    "function": {
        "name": "get_time",
        "description": "Get the current time.",
        "parameters": {"type": "object", "properties": {}},
    },
}

#: A question for which calling ``get_time`` is the natural move.
_PROBE_MESSAGES: list[dict] = [{"role": "user", "content": "What time is it right now?"}]


def parse_tool_response_nonstreaming(payload: dict | None) -> dict:
    """Extract tool-call facts from one non-streaming chat.completions body.

    Returns ``{"finish_reason": str | None, "calls": [(name, arguments)]}``
    — empty/None when the reply carries no tool calls (a plain content
    answer) or is malformed (including a non-dict body).
    """
    choices = (payload or {}).get("choices") or []
    if not isinstance(choices, list) or not choices:
        return {"finish_reason": None, "calls": []}
    first = choices[0]
    message = first.get("message") or {}
    calls: list[tuple[str, str]] = []
    for tc in message.get("tool_calls") or []:
        fn = tc.get("function") or {}
        calls.append((str(fn.get("name") or ""), str(fn.get("arguments") or "")))
    return {"finish_reason": first.get("finish_reason"), "calls": calls}


def parse_tool_response_streaming(lines: Iterable[str]) -> dict:
    """Accumulate ``delta.tool_calls`` fragments across SSE ``data:`` lines.

    Wire convention (OpenAI): the first fragment of a call carries
    ``index`` + ``id`` + ``function.name`` and (possibly partial)
    ``function.arguments``; later fragments carry ``index`` + further
    ``arguments`` pieces; the final chunk carries ``finish_reason``.
    Parsing stops at ``data: [DONE]``; malformed ``data:`` lines are
    skipped (aipi sometimes interleaves keep-alive noise). Returns::

        {
          "finish_reason": str | None,
          "calls": [(name, arguments)],  # accumulated per index
          "delta_chunks": int,           # chunks carrying tool_calls
          "indexed": bool,               # every such chunk had int "index"
          "had_id": bool,                # some chunk carried the call "id"
          "arguments_in_deltas": bool,   # some fragment carried arguments
        }
    """
    finish_reason: str | None = None
    by_index: dict[int, dict[str, str]] = {}
    delta_chunks = 0
    indexed = True
    had_id = False
    arguments_in_deltas = False
    for raw in lines:
        line = raw.strip()
        if not line.startswith("data:"):
            continue
        data = line[5:].strip()
        if data == "[DONE]":
            break
        try:
            payload = json.loads(data)
        except json.JSONDecodeError:
            continue
        choices = payload.get("choices") or []
        if not isinstance(choices, list):
            continue
        for choice in choices:
            fr = choice.get("finish_reason")
            if fr:
                finish_reason = fr
            delta = choice.get("delta") or {}
            for tc in delta.get("tool_calls") or []:
                idx = tc.get("index")
                if isinstance(idx, int):
                    key = idx
                else:
                    indexed = False
                    key = 0 if not by_index else max(by_index) + 1
                slot = by_index.setdefault(key, {"name": "", "arguments": ""})
                delta_chunks += 1
                if tc.get("id"):
                    had_id = True
                fn = tc.get("function") or {}
                if fn.get("name"):
                    slot["name"] += str(fn["name"])
                if fn.get("arguments"):
                    arguments_in_deltas = True
                    slot["arguments"] += str(fn["arguments"])
    calls = [(slot["name"], slot["arguments"]) for _, slot in sorted(by_index.items())]
    return {
        "finish_reason": finish_reason,
        "calls": calls,
        "delta_chunks": delta_chunks,
        "indexed": indexed,
        "had_id": had_id,
        "arguments_in_deltas": arguments_in_deltas,
    }


def _called(result: dict, expected: str) -> bool:
    """True when the request finished with tool_calls invoking *expected*."""
    return (
        result["finish_reason"] == "tool_calls"
        and any(name == expected for name, _ in result["calls"])
    )


def classify_tool_calling(nonstream: dict, stream: dict, expected: str = "get_time") -> str:
    """Phase-37 verdict: ``"supported"`` or ``"not-supported"``.

    Supported requires *both* requests to finish with
    ``finish_reason="tool_calls"`` calling *expected*, and the streaming
    request to deliver the calls as indexed ``delta.tool_calls`` chunks
    with a call ``id`` (the OpenAI wire convention). Anything less —
    including an intermittent split outcome — is ``"not-supported"``
    (fail-loud house style); the phase then uses the documented
    prompt-based structured-call fallback.
    """
    if (
        _called(nonstream, expected)
        and _called(stream, expected)
        and stream["delta_chunks"] > 0
        and stream["indexed"]
        and stream["had_id"]
    ):
        return "supported"
    return "not-supported"


def _no_stream_result() -> dict:
    return {
        "finish_reason": None,
        "calls": [],
        "delta_chunks": 0,
        "indexed": False,
        "had_id": False,
        "arguments_in_deltas": False,
    }


def probe_tools(client: httpx.Client, chat_model: str) -> int:
    """Run the ``--tools`` probe (phase 37, task 01) and print the verdicts.

    Returns 0 when both requests were made and classified (either verdict
    is a valid, recorded outcome) and 1 when the endpoint is unreachable
    (the probe is inconclusive, not a "not supported" signal).
    """
    base_payload: dict = {
        "model": chat_model,
        "messages": _PROBE_MESSAGES,
        "tools": [_PROBE_TOOL],
    }

    # (a) non-streaming request
    ns_raw: dict | None = None
    try:
        resp = client.post("/chat/completions", json={**base_payload, "stream": False})
        resp.raise_for_status()
        ns_raw = resp.json()
        nonstream = parse_tool_response_nonstreaming(ns_raw)
    except httpx.HTTPStatusError as e:
        print(f"[probe] tools(non-stream) HTTP {e.response.status_code}: {e.response.text[:200]}")
        nonstream = {"finish_reason": None, "calls": []}
    except httpx.TransportError as e:
        print(f"[probe] tools(non-stream) transport error: {e}")
        return 1
    ns_ok = _called(nonstream, "get_time")
    print(f"[probe] tools(non-stream) verdict : {'supported' if ns_ok else 'not supported'}")
    print(
        f"[probe] tools(non-stream) finish_reason={nonstream['finish_reason']!r} "
        f"calls={nonstream['calls']!r}"
    )
    if not ns_ok and ns_raw:
        content = ((ns_raw.get("choices") or [{}])[0].get("message") or {}).get("content") or ""
        if content:
            print(f"[probe] tools(non-stream) answered in content instead: {content[:160]!r}")

    # (b) streaming request
    lines: list[str] = []
    try:
        with client.stream(
            "POST", "/chat/completions", json={**base_payload, "stream": True}
        ) as resp:
            resp.raise_for_status()
            for line in resp.iter_lines():
                lines.append(line)
                if line.startswith("data:") and line[5:].strip() == "[DONE]":
                    break
        stream = parse_tool_response_streaming(lines)
    except httpx.HTTPStatusError as e:
        print(f"[probe] tools(stream) HTTP {e.response.status_code}: {e.response.text[:200]}")
        stream = _no_stream_result()
    except httpx.TransportError as e:
        print(f"[probe] tools(stream) transport error: {e}")
        return 1
    st_ok = (
        _called(stream, "get_time")
        and stream["delta_chunks"] > 0
        and stream["indexed"]
        and stream["had_id"]
    )
    print(f"[probe] tools(stream) verdict      : {'supported' if st_ok else 'not supported'}")
    print(
        f"[probe] tools(stream) finish_reason={stream['finish_reason']!r} "
        f"calls={stream['calls']!r}"
    )
    print(
        f"[probe] tools(stream) delta.tool_calls: chunks={stream['delta_chunks']} "
        f"indexed={stream['indexed']} had_id={stream['had_id']} "
        f"arguments_in_deltas={stream['arguments_in_deltas']}"
    )

    verdict = classify_tool_calling(nonstream, stream)
    today = date.today().isoformat()
    detail = (
        "non-streaming + streaming tool_calls follow the OpenAI wire convention"
        if verdict == "supported"
        else "phase falls back to the documented prompt-based structured-call path"
    )
    print(f"[probe] TOOLS VERDICT ({today})   : {verdict} — {detail}")
    print(f"[probe] summary                   : probe: {chat_model} tool_calls={verdict} {today}")
    return 0


def main(argv: list[str] | None = None) -> int:
    # CLI-only: pick up .env without side effects on import (the parse/
    # classify functions above are imported from unit tests, where the
    # ambient environment must stay pristine).
    load_dotenv()
    parser = argparse.ArgumentParser(
        description=(
            "Probe the aipi endpoint: models, embedding dimension, and "
            "(with --tools) the chat model's tool-calling support."
        )
    )
    parser.add_argument(
        "--tools",
        action="store_true",
        help=(
            "also probe the chat model's OpenAI-style tools/tool_calls support "
            "(non-streaming + streaming) and print a supported / not-supported verdict"
        ),
    )
    args = parser.parse_args(argv)

    base_url = os.environ.get("BOR_LLM_BASE_URL", "https://aipi.reeseapps.com/v1").rstrip("/")
    api_key = (
        os.environ.get("BOR_LLM_API_KEY")
        or os.environ.get("AIPI_KEY")
        or "not-needed"
    )
    embed_model = os.environ.get("BOR_LLM_EMBED_MODEL", "embed")
    chat_model = os.environ.get("BOR_LLM_CHAT_MODEL", "turbo")
    expected_dim = int(os.environ.get("BOR_EMBEDDING_DIM", "768"))

    headers = {"Authorization": f"Bearer {api_key}"}
    tools_rc = 0
    with httpx.Client(base_url=base_url, headers=headers, timeout=60.0) as client:
        r = client.get("/models")
        r.raise_for_status()
        models = [m["id"] for m in r.json()["data"]]
        print(f"[probe] base_url : {base_url}")
        print(f"[probe] models   : {', '.join(models)}")

        for needed in (chat_model, embed_model):
            if needed not in models:
                print(f"[probe] ERROR: required model '{needed}' not available")
                return 1

        r = client.post(
            "/embeddings",
            json={"model": embed_model, "input": "brain of reese dimension probe"},
        )
        r.raise_for_status()
        dims = sorted({len(d["embedding"]) for d in r.json()["data"]})
        print(f"[probe] dims({embed_model}): {dims}")

        if args.tools:
            tools_rc = probe_tools(client, chat_model)

    if dims != [expected_dim]:
        print(
            f"[probe] MISMATCH: expected {expected_dim}, got {dims}. "
            "Update BOR_EMBEDDING_DIM and recreate the chunks table (see README)."
        )
        return 1
    print("[probe] OK — models present, embedding dimension matches configuration.")
    return tools_rc


if __name__ == "__main__":
    sys.exit(main())