brain-of-reese/tests/unit/test_llm_client.py

"""Unit tests: LLMClient embeddings (batching, order, loud dim failure).

The fakes stand in at the httpx-transport layer — that is where LLMClient
actually talks to the endpoint (see ``LLMClient._embed_batch`` in
``app/rag/llm.py`` for why the openai SDK's own ``embeddings.create`` is
bypassed: it injects ``encoding_format``, which aipi's litellm proxy
rejects).
"""
from __future__ import annotations

import asyncio
import json
from types import SimpleNamespace
from typing import Any, cast

import pytest

from app.config import Settings
from app.rag.llm import (
    EmbeddingDimensionError,
    EmbeddingError,
    LLMClient,
    LLMError,
    StreamPiece,
    ToolCallPiece,
)


def _settings(**kwargs: Any) -> Settings:
    kwargs.setdefault("_env_file", None)
    return Settings(**kwargs)  # pyright: ignore[reportCallIssue]  (kwarg exists at runtime)


class _Row:
    def __init__(self, index: int, embedding: list[float]) -> None:
        self.index = index
        self.embedding = embedding


class _Response:
    def __init__(self, rows: list[_Row]) -> None:
        self.data = rows


class _FakeEmbeddingsService:
    """Simulates the /embeddings endpoint; records calls; can fail."""

    def __init__(
        self,
        dim: int = 768,
        fail: Exception | None = None,
        drop_index: int = -1,
        http_error: int | None = None,
        too_large_min: int | None = None,
    ) -> None:
        self.dim = dim
        self.fail = fail
        self.drop_index = drop_index
        self.http_error = http_error
        self.too_large_min = too_large_min
        self.calls: list[list[str]] = []

    async def create(self, *, model: str, input: list[str]) -> _Response:
        self.calls.append(list(input))
        if self.fail is not None:
            raise self.fail
        return _Response(
            [
                _Row(i, [0.5] * self.dim)
                for i in range(len(input))
                if i != self.drop_index
            ]
        )


class _FakeHttpResponse:
    def __init__(
        self, status_code: int, payload: dict[str, Any] | None = None, text: str = ""
    ) -> None:
        self.status_code = status_code
        self._payload = payload
        self.text = text or (json.dumps(payload) if payload is not None else "boom")

    def json(self) -> Any:
        if self._payload is None:
            raise ValueError("no json body")
        return self._payload


#: The endpoint's real error phrasing (litellm) — the client keys off it.
_TOO_LARGE_TEXT = 'input (9999 tokens) is too large to process. increase the physical batch size'


class _FakeHttp:
    """Stands in for the httpx transport the openai client owns."""

    def __init__(self, service: _FakeEmbeddingsService) -> None:
        self.service = service
        self.bodies: list[dict[str, Any]] = []

    async def post(
        self, url: str, *, json: dict[str, Any], headers: dict[str, str] | None = None
    ) -> _FakeHttpResponse:
        self.bodies.append(json)
        assert "Authorization" in (headers or {})
        if self.service.http_error is not None:
            return _FakeHttpResponse(self.service.http_error)
        if (
            self.service.too_large_min is not None
            and len(json["input"]) >= self.service.too_large_min
        ):
            return _FakeHttpResponse(500, None, _TOO_LARGE_TEXT)
        rows = await self.service.create(model=json["model"], input=json["input"])
        payload = {"data": [{"index": r.index, "embedding": r.embedding} for r in rows.data]}
        return _FakeHttpResponse(200, payload)


class _FakeClient:
    """Stands in for the openai AsyncOpenAI object (only its transport is used)."""

    def __init__(self, http: _FakeHttp) -> None:
        self._client = http


def _make_client(service: _FakeEmbeddingsService, **kwargs: Any) -> tuple[LLMClient, _FakeHttp]:
    kwargs.setdefault("embed_batch_size", 2)
    llm = LLMClient(_settings(**kwargs))
    http = _FakeHttp(service)
    llm._client = _FakeClient(http)  # pyright: ignore[reportAttributeAccessIssue]
    return llm, http


def test_embed_batches_by_batch_size_and_keeps_order() -> None:
    service = _FakeEmbeddingsService()
    llm, http = _make_client(service)
    texts = [f"t{i}" for i in range(5)]
    vecs = asyncio.run(llm.embed(texts))

    assert [len(c) for c in service.calls] == [2, 2, 1]
    assert [t for call in service.calls for t in call] == texts
    assert len(vecs) == 5
    assert all(len(v) == 768 for v in vecs)
    assert llm.embed_batches == 3
    # aipi (litellm) rejects the SDK's injected "encoding_format" — the
    # payload must stay a minimal {model, input} body.
    assert all(set(b) == {"model", "input"} for b in http.bodies)


def test_embed_empty_returns_empty_without_calling_endpoint() -> None:
    service = _FakeEmbeddingsService()
    llm, http = _make_client(service)
    assert asyncio.run(llm.embed([])) == []
    assert http.bodies == []
    assert service.calls == []
    assert llm.embed_batches == 0


def test_embed_one_returns_single_vector() -> None:
    llm, _ = _make_client(_FakeEmbeddingsService())
    vec = asyncio.run(llm.embed_one("hello"))
    assert len(vec) == 768


def test_dim_mismatch_fails_loudly_with_actionable_message() -> None:
    llm, _ = _make_client(_FakeEmbeddingsService(dim=512))
    with pytest.raises(EmbeddingDimensionError) as exc:
        asyncio.run(llm.embed(["hello"]))
    msg = str(exc.value)
    assert "512" in msg and "768" in msg
    assert "BOR_EMBEDDING_DIM" in msg
    assert "llm_probe" in msg


def test_endpoint_error_is_wrapped() -> None:
    llm, _ = _make_client(_FakeEmbeddingsService(fail=RuntimeError("connection refused")))
    with pytest.raises(EmbeddingError, match="connection refused"):
        asyncio.run(llm.embed(["hello"]))
    assert llm.embed_batches == 0


def test_http_error_surfaces_status() -> None:
    llm, _ = _make_client(_FakeEmbeddingsService(http_error=502))
    with pytest.raises(EmbeddingError, match="HTTP 502"):
        asyncio.run(llm.embed(["hello"]))
    assert llm.embed_batches == 0


def test_missing_vector_row_is_rejected() -> None:
    llm, _ = _make_client(_FakeEmbeddingsService(drop_index=1))
    with pytest.raises(EmbeddingError, match="returned 1 vectors for 2 inputs"):
        asyncio.run(llm.embed(["a", "b"]))


def test_batch_size_one_forces_one_call_per_text() -> None:
    service = _FakeEmbeddingsService()
    llm, _ = _make_client(service, embed_batch_size=1)
    asyncio.run(llm.embed(["a", "b", "c"]))
    assert [len(c) for c in service.calls] == [1, 1, 1]


def test_token_budget_limits_texts_per_request() -> None:
    """~2000-char chunks must not stack up past aipi's ~1024-token cap."""
    service = _FakeEmbeddingsService()
    llm, _ = _make_client(service, embed_batch_size=16)  # high count cap
    texts = ["x" * 2000 for _ in range(4)]
    vecs = asyncio.run(llm.embed(texts))
    # 2000 + 2000 chars > 3600-char (≈900-token) budget ⇒ one chunk per request
    assert [len(c) for c in service.calls] == [1, 1, 1, 1]
    assert len(vecs) == 4


def test_small_chunks_pack_up_to_count_cap() -> None:
    service = _FakeEmbeddingsService()
    llm, _ = _make_client(service, embed_batch_size=4)  # count cap binds
    texts = ["short text" for _ in range(9)]
    vecs = asyncio.run(llm.embed(texts))
    assert [len(c) for c in service.calls] == [4, 4, 1]
    assert len(vecs) == 9


def test_too_large_response_halves_batch_until_it_fits() -> None:
    """The tokenizer estimate can be wrong for dense content — the client
    must halve an over-large request and preserve order."""
    service = _FakeEmbeddingsService(too_large_min=3)
    llm, http = _make_client(service, embed_batch_size=16)  # all 8 fit one request
    texts = [f"t{i}" for i in range(8)]
    vecs = asyncio.run(llm.embed(texts))
    # http.bodies sees every request (including the rejected ones); the
    # left-half recursion completes before the right half starts.
    assert [len(b["input"]) for b in http.bodies] == [8, 4, 2, 2, 4, 2, 2]
    assert len(vecs) == 8
    assert all(len(v) == 768 for v in vecs)


def test_single_oversized_text_fails_actionably() -> None:
    service = _FakeEmbeddingsService(too_large_min=1)
    llm, _ = _make_client(service)
    with pytest.raises(EmbeddingError, match="token cap"):
        asyncio.run(llm.embed(["x" * 3000]))
    assert llm.embed_batches == 0


# ---------- chat streaming (phase 03) ----------


def _tool_call(
    index: int,
    id: str | None = None,
    name: str | None = None,
    arguments: str | None = None,
):
    """One fake ``delta.tool_calls[]`` partial (openai SDK shape, phase 37).

    ``function`` is None when neither *name* nor *arguments* is given —
    mirroring the real wire, where id-only fragments carry no function.
    """
    fn = None
    if name is not None or arguments is not None:
        fn = SimpleNamespace(name=name, arguments=arguments)
    return SimpleNamespace(index=index, id=id, function=fn)


def _chunk(
    content: str | None = "text",
    empty: bool = False,
    reasoning: str | None = None,
    tool_calls: list | None = None,
    finish_reason: str | None = None,
):
    """One fake ChatCompletionChunk (``choices[].delta`` shape).

    ``reasoning_content``, ``tool_calls`` and ``finish_reason`` are
    present only when provided — mirroring the real wire, where the
    fields exist only when the model sends them.
    """
    if empty:
        return SimpleNamespace(choices=[])
    delta: SimpleNamespace = SimpleNamespace(content=content)
    if reasoning is not None:
        delta.reasoning_content = reasoning
    if tool_calls is not None:
        delta.tool_calls = tool_calls
    choice = SimpleNamespace(delta=delta)
    if finish_reason is not None:
        choice.finish_reason = finish_reason
    return SimpleNamespace(choices=[choice])


class _FakeChatStream:
    def __init__(self, chunks: list) -> None:
        self._chunks = list(chunks)

    def __aiter__(self):
        self._i = 0
        return self

    async def __anext__(self):
        if self._i >= len(self._chunks):
            raise StopAsyncIteration
        chunk = self._chunks[self._i]
        self._i += 1
        return chunk


class _FakeCompletion:
    """One fake non-streaming ChatCompletion (``choices[].message`` shape).

    ``content=None`` mirrors the real wire where the field can be absent or
    empty (reasoning-only replies, provider quirks).
    """

    def __init__(self, content: str | None, empty_choices: bool = False) -> None:
        if empty_choices:
            self.choices = []
        else:
            self.choices = [SimpleNamespace(message=SimpleNamespace(content=content))]


class _FakeCompletions:
    def __init__(
        self,
        chunks: list | None = None,
        fail: Exception | None = None,
        completion: _FakeCompletion | None = None,
    ) -> None:
        self.chunks = chunks or []
        self.fail = fail
        self.completion = completion
        self.kwargs: dict | None = None
        self.chat_kwargs: dict | None = None

    async def create(self, **kwargs) -> _FakeChatStream | _FakeCompletion:
        self.kwargs = kwargs
        if self.fail is not None:
            raise self.fail
        if kwargs.get("stream"):
            return _FakeChatStream(self.chunks)
        self.chat_kwargs = kwargs
        assert self.completion is not None
        return self.completion


def _make_stream_client(
    chunks: list | None = None,
    fail: Exception | None = None,
    **settings_kwargs: Any,
) -> tuple[LLMClient, _FakeCompletions]:
    completions = _FakeCompletions(chunks, fail)
    fake_openai = SimpleNamespace(chat=SimpleNamespace(completions=completions))
    llm = LLMClient(_settings(**settings_kwargs))
    llm._client = fake_openai  # pyright: ignore[reportAttributeAccessIssue]
    return llm, completions


async def _collect(llm: LLMClient, messages: list[dict[str, str]]) -> list[StreamPiece]:
    """Collect pieces from a tools-less stream (phase 37 task 02, test (a):
    without tools, no ToolCallPiece can appear)."""
    pieces = [p async for p in llm.chat_stream(messages)]
    assert all(isinstance(p, StreamPiece) for p in pieces)
    return cast("list[StreamPiece]", pieces)


def test_chat_stream_yields_deltas_in_order() -> None:
    llm, completions = _make_stream_client(
        [_chunk("Hey "), _chunk("you've "), _chunk("got this! 🧠")]
    )
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    # Content-only chunks yield content pieces in wire order.
    assert [(p.kind, p.text) for p in pieces] == [
        ("content", "Hey "),
        ("content", "you've "),
        ("content", "got this! 🧠"),
    ]
    assert all(isinstance(p, StreamPiece) for p in pieces)


def test_chat_stream_uses_locked_generation_params() -> None:
    llm, completions = _make_stream_client([_chunk("x")])
    messages = [{"role": "system", "content": "s"}, {"role": "user", "content": "u"}]
    asyncio.run(_collect(llm, messages))
    assert completions.kwargs is not None
    assert completions.kwargs["model"] == "turbo"
    assert completions.kwargs["stream"] is True
    assert completions.kwargs["temperature"] == 0.4
    # Phase 11: the old hard 700-token cap is gone — answers may run up to
    # BOR_MAX_OUTPUT_TOKENS (default 32 768) so they are not cut off.
    assert completions.kwargs["max_tokens"] == 32_768
    assert completions.kwargs["messages"] == messages
    # Phase 37: no tools passed ⇒ no `tools` key at all (byte-identical
    # request to pre-phase-37).
    assert "tools" not in completions.kwargs


def test_chat_stream_max_tokens_comes_from_settings() -> None:
    """The output cap is operator-configurable, not a client constant."""
    llm, completions = _make_stream_client(
        [_chunk("x")], max_output_tokens=1234  # pyright: ignore[reportArgumentType]
    )
    asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert completions.kwargs is not None
    assert completions.kwargs["max_tokens"] == 1234


def test_chat_stream_skips_empty_deltas_and_choiceless_chunks() -> None:
    llm, _ = _make_stream_client([_chunk("a"), _chunk(empty=True), _chunk(None), _chunk("b")])
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert [(p.kind, p.text) for p in pieces] == [("content", "a"), ("content", "b")]


def test_chat_stream_maps_reasoning_content_to_thinking_pieces() -> None:
    """The verified aipi wire field (``delta.reasoning_content``) maps to
    ``thinking`` pieces; content chunks are untouched by the presence of
    reasoning elsewhere in the stream."""
    llm, _ = _make_stream_client(
        [
            _chunk("", reasoning="Step 1: parse the question."),
            _chunk("", reasoning="Step 2: cite the doc."),
            _chunk("Talos."),
        ]
    )
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert [(p.kind, p.text) for p in pieces] == [
        ("thinking", "Step 1: parse the question."),
        ("thinking", "Step 2: cite the doc."),
        ("content", "Talos."),
    ]


def test_chat_stream_falls_back_to_reasoning_field() -> None:
    """Future-proofing: a bare ``delta.reasoning`` field (no
    ``reasoning_content``) is picked up by the fallback getattr."""
    chunk = SimpleNamespace(
        choices=[
            SimpleNamespace(delta=SimpleNamespace(content="ans", reasoning="why not"))
        ]
    )
    llm, _ = _make_stream_client([chunk])
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert [(p.kind, p.text) for p in pieces] == [
        ("thinking", "why not"),
        ("content", "ans"),
    ]


def test_chat_stream_thinking_yields_before_content_in_chunk() -> None:
    """One chunk carrying both fields yields the thinking piece first."""
    llm, _ = _make_stream_client([_chunk("answer", reasoning="hmm")])
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert [(p.kind, p.text) for p in pieces] == [
        ("thinking", "hmm"),
        ("content", "answer"),
    ]


def test_chat_stream_interleaved_thinking_and_content_order_preserved() -> None:
    """The piece sequence must match the chunk sequence exactly — a late
    or interleaved thinking chunk is emitted at its wire position."""
    llm, _ = _make_stream_client(
        [
            _chunk("", reasoning="t1"),
            _chunk("c1"),
            _chunk("", reasoning="t2"),
            _chunk("c2"),
        ]
    )
    pieces = asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))
    assert [(p.kind, p.text) for p in pieces] == [
        ("thinking", "t1"),
        ("content", "c1"),
        ("thinking", "t2"),
        ("content", "c2"),
    ]


def test_chat_stream_wraps_failures_as_llm_error() -> None:
    llm, _ = _make_stream_client(fail=RuntimeError("connection reset by peer"))

    async def drain() -> None:
        async for _ in llm.chat_stream([{"role": "user", "content": "q"}]):
            pass

    with pytest.raises(LLMError, match="connection reset by peer"):
        asyncio.run(drain())


def test_chat_stream_llm_error_passes_through_unwrapped() -> None:
    llm, _ = _make_stream_client(fail=LLMError("already wrapped"))
    with pytest.raises(LLMError, match="already wrapped"):
        asyncio.run(_collect(llm, [{"role": "user", "content": "q"}]))


# ---------- tool-call streaming (phase 37, task 02) ----------

#: The agent's tool list (phase 37) — the exact wire shape AGENT_TOOLS will
#: pass through (the names are whatever the caller's tools list names).
_AGENT_TOOLS: list[dict[str, Any]] = [
    {
        "type": "function",
        "function": {
            "name": "list_documents",
            "description": "List the indexed documents.",
            "parameters": {"type": "object", "properties": {}},
        },
    },
    {
        "type": "function",
        "function": {
            "name": "read_document",
            "description": "Add one indexed document's full text to the context.",
            "parameters": {
                "type": "object",
                "properties": {
                    "source": {"type": "string"},
                    "path": {"type": "string"},
                },
                "required": ["source", "path"],
            },
        },
    },
]


def _collect_with_tools(
    llm: LLMClient, messages: list[dict[str, str]], tools: list[dict[str, Any]]
) -> list[StreamPiece | ToolCallPiece]:
    async def run() -> list[StreamPiece | ToolCallPiece]:
        return [p async for p in llm.chat_stream(messages, tools=tools)]

    return asyncio.run(run())


def test_chat_stream_passes_tools_when_given() -> None:
    """(e) A non-None tools list is forwarded verbatim to create()."""
    llm, completions = _make_stream_client([_chunk("ok")], llm_chat_model="turbo")
    _collect_with_tools(llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS)
    assert completions.kwargs is not None
    assert completions.kwargs["tools"] == _AGENT_TOOLS


def test_chat_stream_accumulates_tool_call_across_chunk_partials() -> None:
    """(b) name on the first partial, arguments in fragments — merged into
    one ToolCallPiece with the concatenated JSON, at finish_reason."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[
                    _tool_call(
                        0,
                        id="call_abc",
                        name="read_document",
                        arguments='{"source": "Homelab", "pa',
                    )
                ],
            ),
            _chunk(None, tool_calls=[_tool_call(0, arguments='th": "kubernetes.md"}')]),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )
    pieces = _collect_with_tools(
        llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS
    )
    assert pieces == [
        ToolCallPiece(
            id="call_abc",
            name="read_document",
            arguments={"source": "Homelab", "path": "kubernetes.md"},
        )
    ]


def test_chat_stream_two_tool_calls_yielded_in_index_order() -> None:
    """(c) Indices 0 and 1, interleaved partials (index 1 seen first) —
    both calls, in index order, each merged from its own fragments."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[
                    _tool_call(1, id="call_b", name="read_document", arguments='{"sou')
                ],
            ),
            _chunk(
                None,
                tool_calls=[
                    _tool_call(0, id="call_a", name="list_documents"),
                    _tool_call(1, arguments='rce": "Homelab", "path": "a.md"}')
                ],
            ),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )
    pieces = _collect_with_tools(
        llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS
    )
    assert pieces == [
        ToolCallPiece(id="call_a", name="list_documents", arguments={}),
        ToolCallPiece(
            id="call_b",
            name="read_document",
            arguments={"source": "Homelab", "path": "a.md"},
        ),
    ]


def test_chat_stream_tool_calls_yielded_at_stream_end_without_finish_reason() -> None:
    """The spec's other emission point: stream ends without a
    finish_reason="tool_calls" chunk — pieces still materialize."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[_tool_call(0, id="call_z", name="list_documents")],
            )
        ]
    )
    pieces = _collect_with_tools(
        llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS
    )
    assert pieces == [ToolCallPiece(id="call_z", name="list_documents", arguments={})]


def test_chat_stream_synthesizes_call_id_when_absent() -> None:
    """Wire never carried the call id ⇒ synthesized "call_<index>"."""
    llm, _ = _make_stream_client(
        [
            _chunk(None, tool_calls=[_tool_call(2, name="read_document", arguments="{}")]),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )
    pieces = _collect_with_tools(
        llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS
    )
    assert pieces == [
        ToolCallPiece(
            id="call_2",
            name="read_document",
            arguments={},
        )
    ]


def test_chat_stream_null_arguments_become_empty_dict() -> None:
    """JSON "null" (and, by the same branch, absent arguments) ⇒ {}."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[
                    _tool_call(0, id="call_n", name="list_documents", arguments="null")
                ],
            ),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )
    pieces = _collect_with_tools(
        llm, [{"role": "user", "content": "q"}], _AGENT_TOOLS
    )
    assert pieces == [ToolCallPiece(id="call_n", name="list_documents", arguments={})]


def test_chat_stream_malformed_tool_arguments_raise_llm_error() -> None:
    """(d) A silently dropped tool call would corrupt the loop — malformed
    arguments JSON must fail loudly."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[
                    _tool_call(
                        0,
                        id="call_x",
                        name="read_document",
                        arguments='{"source": "Homelab",',
                    )
                ],
            ),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )

    async def drain() -> None:
        async for _ in llm.chat_stream(
            [{"role": "user", "content": "q"}], tools=_AGENT_TOOLS
        ):
            pass

    with pytest.raises(LLMError, match="malformed tool-call arguments"):
        asyncio.run(drain())


def test_chat_stream_non_object_tool_arguments_raise_llm_error() -> None:
    """The OpenAI contract says arguments is a JSON *object* — a bare array
    is malformed too."""
    llm, _ = _make_stream_client(
        [
            _chunk(
                None,
                tool_calls=[
                    _tool_call(0, id="call_y", name="read_document", arguments='[1, 2]')
                ],
            ),
            _chunk(None, finish_reason="tool_calls"),
        ]
    )

    async def drain() -> None:
        async for _ in llm.chat_stream(
            [{"role": "user", "content": "q"}], tools=_AGENT_TOOLS
        ):
            pass

    with pytest.raises(LLMError, match="non-object tool-call arguments"):
        asyncio.run(drain())


# ---------- one-shot chat: LLMClient.chat (phase 30, task 01) ----------


def _make_chat_client(
    completion: _FakeCompletion | None = None,
    fail: Exception | None = None,
    **settings_kwargs: Any,
) -> tuple[LLMClient, _FakeCompletions]:
    completions = _FakeCompletions(fail=fail, completion=completion)
    fake_openai = SimpleNamespace(chat=SimpleNamespace(completions=completions))
    llm = LLMClient(_settings(**settings_kwargs))
    llm._client = fake_openai  # pyright: ignore[reportAttributeAccessIssue]
    return llm, completions


def test_chat_returns_trimmed_content_with_locked_params() -> None:
    """Default model is ``lite`` (BOR_LLM_SUMMARY_MODEL), non-streaming,
    low temperature, fixed 2048-token budget — summaries are short."""
    llm, completions = _make_chat_client(_FakeCompletion("  Summary text.\n"))
    messages = [{"role": "system", "content": "s"}, {"role": "user", "content": "u"}]
    out = asyncio.run(llm.chat(messages))
    assert out == "Summary text."
    assert completions.chat_kwargs is not None
    assert completions.chat_kwargs["model"] == "lite"
    assert completions.chat_kwargs["stream"] is False
    assert completions.chat_kwargs["temperature"] == 0.2
    assert completions.chat_kwargs["max_tokens"] == 2048
    assert completions.chat_kwargs["messages"] == messages


def test_chat_default_model_comes_from_llm_summary_model_setting() -> None:
    llm, completions = _make_chat_client(
        _FakeCompletion("x"), llm_summary_model="tiny"
    )
    asyncio.run(llm.chat([{"role": "user", "content": "q"}]))
    assert completions.chat_kwargs is not None
    assert completions.chat_kwargs["model"] == "tiny"


def test_chat_explicit_model_overrides_the_default() -> None:
    llm, completions = _make_chat_client(
        _FakeCompletion("x"), llm_summary_model="tiny"
    )
    asyncio.run(llm.chat([{"role": "user", "content": "q"}], model="special"))
    assert completions.chat_kwargs is not None
    assert completions.chat_kwargs["model"] == "special"


def test_chat_transport_failure_wrapped_as_llm_error_with_base_url() -> None:
    """HTTP/transport failures (incl. >=400 surfaced by the SDK) are wrapped
    with the base URL in the message — same style as chat_stream."""
    llm, _ = _make_chat_client(fail=RuntimeError("HTTP 502 Bad Gateway"))
    with pytest.raises(LLMError, match="HTTP 502") as exc:
        asyncio.run(llm.chat([{"role": "user", "content": "q"}]))
    assert "aipi.reeseapps.com" in str(exc.value)


def test_chat_llm_error_passes_through_unwrapped() -> None:
    llm, _ = _make_chat_client(fail=LLMError("already wrapped"))
    with pytest.raises(LLMError, match="already wrapped"):
        asyncio.run(llm.chat([{"role": "user", "content": "q"}]))


def test_chat_empty_choices_raises_llm_error() -> None:
    llm, _ = _make_chat_client(_FakeCompletion(None, empty_choices=True))
    with pytest.raises(LLMError, match="no choices"):
        asyncio.run(llm.chat([{"role": "user", "content": "q"}]))


def test_chat_missing_content_raises_llm_error() -> None:
    """A silent empty summary must never be stored — None content fails."""
    llm, _ = _make_chat_client(_FakeCompletion(None))
    with pytest.raises(LLMError, match="empty content"):
        asyncio.run(llm.chat([{"role": "user", "content": "q"}]))


def test_chat_whitespace_only_content_raises_llm_error() -> None:
    llm, _ = _make_chat_client(_FakeCompletion("   \n\t "))
    with pytest.raises(LLMError, match="empty content"):
        asyncio.run(llm.chat([{"role": "user", "content": "q"}]))