deer-flow/backend/packages/harness/deerflow/models/factory.py

import logging

from langchain.chat_models import BaseChatModel
from langchain_openai.chat_models.base import BaseChatOpenAI

from deerflow.config import get_app_config
from deerflow.config.app_config import AppConfig
from deerflow.models.reasoning import ReasoningContract, ReasoningPolicyError, resolve_reasoning_contract, resolve_reasoning_request
from deerflow.reflection import resolve_class
from deerflow.tracing import build_tracing_callbacks

logger = logging.getLogger(__name__)

# ``ModelConfig`` fields that describe the profile rather than configure the
# provider client. They are stripped before the constructor sees the profile.
_MODEL_METADATA_FIELDS: frozenset[str] = frozenset(
    {
        "use",
        "name",
        "display_name",
        "description",
        "supports_thinking",
        "supports_reasoning_effort",
        # Declarative reasoning contract (issue #5073); consumed by the
        # normalization layer, never a provider kwarg.
        "reasoning",
        "when_thinking_enabled",
        "when_thinking_disabled",
        "thinking",
        "supports_vision",
        # Runtime/UI metadata used to size the context indicator. Provider
        # clients do not accept this as a model-constructor argument.
        "context_window",
        # Presentation-only metadata (consumed by the console's cost
        # display) — must never reach the provider client, which would
        # forward unknown kwargs into the completion request payload.
        "pricing",
        "request_admission",
    }
)


def _deep_merge_dicts(base: dict | None, override: dict) -> dict:
    """Recursively merge two dictionaries without mutating the inputs."""
    merged = dict(base or {})
    for key, value in override.items():
        if isinstance(value, dict) and isinstance(merged.get(key), dict):
            merged[key] = _deep_merge_dicts(merged[key], value)
        else:
            merged[key] = value
    return merged


def _vllm_disable_chat_template_kwargs(chat_template_kwargs: dict) -> dict:
    """Build the disable payload for vLLM/Qwen chat template kwargs."""
    disable_kwargs: dict[str, bool] = {}
    if "thinking" in chat_template_kwargs:
        disable_kwargs["thinking"] = False
    if "enable_thinking" in chat_template_kwargs:
        disable_kwargs["enable_thinking"] = False
    return disable_kwargs


def _merge_settings(settings: dict, payload: dict) -> None:
    """Deep-merge *payload* into the constructor settings in place.

    Nested dicts (``extra_body``, ``thinking``, ``chat_template_kwargs``) merge
    recursively so a synthesized thinking block never clobbers unrelated keys an
    operator put beside it (e.g. GLM's ``extra_body.tool_stream``).
    """
    for key, value in payload.items():
        if isinstance(value, dict) and isinstance(settings.get(key), dict):
            settings[key] = _deep_merge_dicts(settings[key], value)
        else:
            settings[key] = value


def _set_dotted_setting(settings: dict, path: str, value: object) -> None:
    """Write *value* at a dotted *path* (``reasoning_effort`` or ``extra_body.thinking.effort``)."""
    head, _, rest = path.partition(".")
    if not rest:
        settings[head] = value
        return
    nested: dict = {}
    cursor = nested
    parts = rest.split(".")
    for part in parts[:-1]:
        cursor[part] = {}
        cursor = cursor[part]
    cursor[parts[-1]] = value
    _merge_settings(settings, {head: nested})


def _infer_dialect(effective_wte: dict) -> str:
    """Infer the thinking payload dialect from a ``when_thinking_enabled`` template.

    Mirrors the historical disable-path heuristics so ``dialect: auto`` behaves
    like the legacy factory did for the same template shape.
    """
    extra_body = effective_wte.get("extra_body") or {}
    thinking = extra_body.get("thinking")
    if isinstance(thinking, dict) and thinking.get("type"):
        return "openai_extra_body"
    chat_template_kwargs = extra_body.get("chat_template_kwargs") or {}
    if isinstance(chat_template_kwargs, dict) and ("thinking" in chat_template_kwargs or "enable_thinking" in chat_template_kwargs):
        return "vllm_chat_template"
    native_thinking = effective_wte.get("thinking")
    if isinstance(native_thinking, dict) and native_thinking.get("type"):
        return "anthropic"
    if "reasoning" in effective_wte:
        return "ollama"
    return "none"


def _vllm_toggle_chat_template_kwargs(chat_template_kwargs: dict, *, enabled: bool) -> dict:
    """Mirror whichever vLLM/Qwen switch the template declares; default to ``enable_thinking``."""
    toggled = {key: enabled for key in ("thinking", "enable_thinking") if key in chat_template_kwargs}
    return toggled or {"enable_thinking": enabled}


def _dialect_payload(dialect: str, *, enabled: bool, chat_template_kwargs: dict | None = None) -> dict:
    """Constructor settings that switch thinking on or off for one payload dialect."""
    if dialect == "openai_extra_body":
        return {"extra_body": {"thinking": {"type": "enabled" if enabled else "disabled"}}}
    if dialect == "anthropic":
        return {"thinking": {"type": "enabled" if enabled else "disabled"}}
    if dialect == "vllm_chat_template":
        return {"extra_body": {"chat_template_kwargs": _vllm_toggle_chat_template_kwargs(chat_template_kwargs or {}, enabled=enabled)}}
    if dialect == "ollama":
        return {"reasoning": enabled}
    return {}


def _apply_legacy_thinking_settings(
    settings: dict,
    model_config,
    *,
    thinking_enabled: bool,
    has_thinking_settings: bool,
    effective_wte: dict,
    requested_reasoning_effort: str | None,
    is_codex_model: bool,
) -> None:
    """Historical thinking/effort payload path for profiles without a ``reasoning:`` contract.

    Kept byte-for-byte so existing configurations retain their behavior (issue
    #5073 acceptance criterion), including the synthesized ``reasoning_effort=minimal``
    on the OpenAI-compatible disable path that the contract path drops.
    """
    if requested_reasoning_effort is not None and not is_codex_model:
        settings["reasoning_effort"] = requested_reasoning_effort
    if thinking_enabled and has_thinking_settings and effective_wte:
        settings.update(effective_wte)
    if not thinking_enabled:
        if model_config.when_thinking_disabled is not None:
            # User-provided disable settings take full precedence
            settings.update(model_config.when_thinking_disabled)
        elif has_thinking_settings and effective_wte.get("extra_body", {}).get("thinking", {}).get("type"):
            # OpenAI-compatible gateway: thinking is nested under extra_body
            settings["extra_body"] = _deep_merge_dicts(
                settings.get("extra_body"),
                {"thinking": {"type": "disabled"}},
            )
            settings["reasoning_effort"] = "minimal"
        elif has_thinking_settings and (disable_chat_template_kwargs := _vllm_disable_chat_template_kwargs(effective_wte.get("extra_body", {}).get("chat_template_kwargs") or {})):
            # vLLM uses chat template kwargs to switch thinking on/off.
            settings["extra_body"] = _deep_merge_dicts(
                settings.get("extra_body"),
                {"chat_template_kwargs": disable_chat_template_kwargs},
            )
        elif has_thinking_settings and effective_wte.get("thinking", {}).get("type"):
            # Native langchain_anthropic: thinking is a direct constructor parameter
            settings["thinking"] = {"type": "disabled"}


def _apply_contract_thinking_settings(
    settings: dict,
    contract: ReasoningContract,
    model_config,
    *,
    thinking_enabled: bool,
    effective_wte: dict,
    requested_reasoning_effort: str | None,
    codex_handles_generic_effort: bool,
) -> None:
    """Thinking/effort payload path for profiles that declare a ``reasoning:`` contract.

    ``thinking_enabled`` and ``requested_reasoning_effort`` are the *resolved*
    values from :func:`resolve_reasoning_request`, so a required-thinking model
    never enters the disable branch and the effort value is already one the
    provider accepts.

    * Enabled: the dialect's enable payload deep-merged under the operator's
      ``when_thinking_enabled`` / ``thinking`` template (template wins), plus
      ``clear_thinking`` when the contract states a history requirement and the
      dialect nests thinking under ``extra_body``.
    * Disabled: ``when_thinking_disabled`` when present, else the dialect's
      disable payload. No effort value is ever synthesized here.
    * Effort: written last at ``effort.path`` so a validated request wins over
      a template value; ``None`` leaves any profile/template value in place. A
      model without an effort contract never forwards ``reasoning_effort``.
    """
    dialect = contract.dialect if contract.dialect != "auto" else _infer_dialect(effective_wte)
    # Both directions mirror whichever vLLM switch the template declares, so a
    # template using the older ``thinking`` key never also grows ``enable_thinking``.
    chat_template_kwargs = (effective_wte.get("extra_body") or {}).get("chat_template_kwargs") or {}
    if thinking_enabled:
        payload = _deep_merge_dicts(_dialect_payload(dialect, enabled=True, chat_template_kwargs=chat_template_kwargs), effective_wte)
        if contract.history is not None and dialect == "openai_extra_body":
            payload = _deep_merge_dicts(payload, {"extra_body": {"thinking": {"clear_thinking": contract.history == "clear"}}})
        _merge_settings(settings, payload)
    elif model_config.when_thinking_disabled is not None:
        _merge_settings(settings, model_config.when_thinking_disabled)
    else:
        _merge_settings(settings, _dialect_payload(dialect, enabled=False, chat_template_kwargs=chat_template_kwargs))
    if contract.effort is None or contract.effort.path != "reasoning_effort":
        # A custom path is the only effort wire format for this contract.
        # Drop a generic key supplied by model_overrides as well as stale
        # settings on a ModelConfig mutated after load-time validation.
        settings.pop("reasoning_effort", None)
    if contract.effort is not None and requested_reasoning_effort is not None and not codex_handles_generic_effort:
        _set_dotted_setting(settings, contract.effort.path, requested_reasoning_effort)


def _declares_api_base(model_class: type) -> bool:
    """Whether *model_class* declares ``api_base`` as its own constructor field.

    ``langchain_deepseek:ChatDeepSeek`` (and therefore ``PatchedChatDeepSeek``) does, so for it
    ``api_base`` is the canonical endpoint key and must be passed through untouched. Every other
    ``BaseChatOpenAI`` subclass inherits only ``openai_api_base`` (alias ``base_url``).
    """
    return "api_base" in getattr(model_class, "model_fields", {})


def _normalize_openai_base_url(model_class: type, model_settings_from_config: dict) -> None:
    """Map the common ``api_base`` alias to ``base_url`` for OpenAI-compatible clients.

    ``BaseChatOpenAI`` subclasses accept the OpenAI endpoint override as ``base_url`` (with
    ``openai_api_base`` as a legacy alias). Several providers in ``config.example.yaml`` use
    ``api_base`` for *other* model classes, so users frequently copy ``api_base`` onto such a model
    by mistake. Because ``ModelConfig`` is ``extra="allow"``, the bad key is not caught at
    config-load time — it is forwarded to the constructor, which does not reject it but transfers it
    into ``model_kwargs``; that is then spread into every ``Completions.create()`` call and rejected
    by the OpenAI SDK at *request* time with an opaque ``unexpected keyword argument 'api_base'``
    error (and the endpoint override is silently dropped). Rename it here so the model works as the
    user intended.

    Gated on ``issubclass(model_class, BaseChatOpenAI)`` rather than a class-path allowlist, so any
    OpenAI-compatible subclass is covered automatically — the divert-and-crash behaviour is a
    property of the base class, not of the two paths that used to be listed. Classes that declare
    ``api_base`` themselves are skipped: there the key is canonical, not a typo.
    """
    if not issubclass(model_class, BaseChatOpenAI) or _declares_api_base(model_class):
        return
    if "api_base" not in model_settings_from_config:
        return
    if "base_url" in model_settings_from_config or "openai_api_base" in model_settings_from_config:
        # Canonical key already present; drop the alias to avoid a duplicate-intent kwarg.
        model_settings_from_config.pop("api_base", None)
        logger.warning("Model config sets both an endpoint key (base_url/openai_api_base) and 'api_base'; using the former and ignoring 'api_base'.")
        return
    model_settings_from_config["base_url"] = model_settings_from_config.pop("api_base")
    logger.debug("Normalized model config key 'api_base' -> 'base_url' for OpenAI-compatible client.")


def _warn_unknown_model_settings(model_class, model_name: str, model_settings_from_config: dict) -> None:
    """Warn about config keys the OpenAI client will silently divert into ``model_kwargs``.

    ``ModelConfig`` is ``extra="allow"``, so a typo'd key (e.g. ``maxx_tokens``) is not caught at
    config-load time. LangChain's OpenAI client does not reject an unknown constructor kwarg — it
    emits a ``UserWarning`` and transfers the key into ``model_kwargs``, which is then spread into
    every ``Completions.create()`` call and rejected by the OpenAI SDK at *request* time with an
    opaque ``unexpected keyword argument`` error that is very hard to trace back to a config typo.

    This turns that latent failure into an explicit, actionable log line at model-build time. It is
    **scoped to the OpenAI-compatible family** — that is where the ``model_kwargs``
    divert-and-crash behavior occurs and where the known field/alias set is accurate. The family is
    ``issubclass(model_class, BaseChatOpenAI)``: the divert is implemented in that base class, so
    every subclass inherits it. Other providers (e.g. ``ChatAnthropic``) route extra kwargs
    differently and would false-positive against this allow-list, so they are intentionally left
    alone. Best-effort and non-fatal: it only fires when the class exposes a pydantic
    ``model_fields`` schema, treats both field names and their aliases as valid, and allow-lists the
    standard passthrough kwargs the factory injects and the OpenAI client accepts.
    """
    if not issubclass(model_class, BaseChatOpenAI):
        return
    known = getattr(model_class, "model_fields", None)
    if not known:
        return
    valid_names = set(known.keys())
    for field in known.values():
        alias = getattr(field, "alias", None)
        if alias:
            valid_names.add(alias)
    # Standard kwargs the factory injects or the OpenAI client accepts beyond declared fields.
    valid_names |= {
        "model",
        "model_kwargs",
        "extra_body",
        "default_headers",
        "default_query",
        "stream_usage",
        "stream_chunk_timeout",
        "reasoning_effort",
    }
    unknown = sorted(k for k in model_settings_from_config if k not in valid_names)
    if unknown:
        logger.warning(
            "Model '%s' (%s): config key(s) %s are not recognized parameters of the model class and will be forwarded as-is; this may raise at request time. Check for typos (e.g. 'maxx_tokens' -> 'max_tokens').",
            model_name,
            getattr(model_class, "__name__", "?"),
            unknown,
        )


# Default chunk-gap budget for OpenAI-compatible streaming responses.
#
# langchain-openai raises ``StreamChunkTimeoutError`` after this many seconds
# without receiving a chunk. Its own default is 120s, which is too aggressive for
# reasoning models (DeepSeek-R1, Doubao-thinking, GPT-5) whose first chunk can
# legitimately take 90~150s. We default to 240s so the streaming layer rarely
# trips on long thinking pauses; the LLMErrorHandlingMiddleware still retries
# (budget=2) if a real stall happens. Users can override per-model in config.yaml.
_DEFAULT_STREAM_CHUNK_TIMEOUT_SECONDS: float = 240.0


def _apply_stream_chunk_timeout_default(model_class: type, model_settings_from_config: dict) -> None:
    """Inject a generous ``stream_chunk_timeout`` for OpenAI-compatible clients.

    ``stream_chunk_timeout`` is a field of langchain-openai's ``BaseChatOpenAI``, so
    it is accepted by ``ChatOpenAI`` and by every DeerFlow provider that subclasses
    it: ``PatchedChatOpenAI`` plus the self-hosted / reasoning adapters
    ``VllmChatModel``, ``MindIEChatModel``, ``PatchedChatDeepSeek``,
    ``PatchedChatMiMo``, ``PatchedChatStepFun`` and ``PatchedChatMiniMax``. We gate on
    ``issubclass(model_class, BaseChatOpenAI)`` rather than an explicit class-path
    allowlist so any OpenAI-compatible subclass inherits the default (and honors an
    explicit override) automatically. Issue #3189 was reported against ``mimo-v2.5``
    (``PatchedChatMiMo``); the original fix (#3195) matched only ``ChatOpenAI`` /
    ``PatchedChatOpenAI``, so those subclasses kept langchain-openai's aggressive
    built-in chunk-gap timeout and — worse — silently discarded a user's explicit
    ``stream_chunk_timeout``.

    Behaviour:

    * ``BaseChatOpenAI`` subclass: an explicit value in ``config.yaml`` is preserved.
      An explicit ``null`` is dropped upstream by ``model_dump(exclude_none=True)``
      and therefore treated as "unset", so the default is injected.
    * Any other client (e.g. ``ChatAnthropic``): drop the key so it is never
      forwarded to a constructor that does not declare it. The kwarg is not a
      declared field of these clients: depending on the client it is either
      silently dropped (``ChatAnthropic`` declares ``extra="ignore"``) or, for
      other OpenAI-style clients, diverted into ``model_kwargs`` and rejected
      at request time. Either way the user's intent is lost, so we drop it
      proactively instead.
    """
    if not issubclass(model_class, BaseChatOpenAI):
        model_settings_from_config.pop("stream_chunk_timeout", None)
        return
    if "stream_chunk_timeout" in model_settings_from_config:
        return
    model_settings_from_config["stream_chunk_timeout"] = _DEFAULT_STREAM_CHUNK_TIMEOUT_SECONDS


def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *, app_config: AppConfig | None = None, attach_tracing: bool = True, model_overrides: dict | None = None, **kwargs) -> BaseChatModel:
    """Create a chat model instance from the config.

    Args:
        name: The name of the model to create. If None, the first model in the config will be used.
        thinking_enabled: Enable the model's extended-thinking mode when supported.
        app_config: Explicit application config; falls back to the cached global if omitted.
        model_overrides: Optional per-caller sampling overrides (e.g. a custom
            agent's ``temperature`` / ``max_tokens``) layered on top of the
            model profile. ``None`` values are ignored so an unset override
            never clobbers a profile value. Applied before the thinking / Codex
            transforms so provider-specific normalization (e.g. Codex dropping
            ``max_tokens``) still governs an overridden value.
        attach_tracing: When True (default), attach tracing callbacks (Langfuse,
            LangSmith) directly to the model instance. Standalone callers — anything
            that invokes the model outside a LangGraph run that already wires tracing
            at the invocation root (``MemoryUpdater``, ad-hoc utilities, etc.) — keep
            this default so the model-level callback still produces traces. Callers
            that already attach tracing at the graph root (``make_lead_agent``, the
            in-graph ``TitleMiddleware``) MUST pass ``attach_tracing=False``; otherwise
            the same LLM call emits duplicate spans (one rooted at the graph, one at
            the model) and ``session_id`` / ``user_id`` metadata never reach the trace
            because the model becomes a nested observation whose ``langfuse_*`` keys
            get stripped.

    Returns:
        A chat model instance.
    """
    config = app_config or get_app_config()
    if name is None:
        name = config.models[0].name
    model_config = config.get_model_config(name)
    if model_config is None:
        raise ValueError(f"Model {name} not found in config") from None
    model_class = resolve_class(model_config.use, BaseChatModel)
    model_settings_from_config = model_config.model_dump(exclude_none=True, exclude=set(_MODEL_METADATA_FIELDS))
    if isinstance(model_config.reasoning, bool | str):
        # Before the capability contract, ``reasoning`` was an unrestricted
        # provider kwarg. ChatOllama uses the boolean form and, for gpt-oss
        # style models, the ``low|medium|high`` level string; keep forwarding
        # both on the legacy path even though contract metadata is excluded
        # above.
        model_settings_from_config["reasoning"] = model_config.reasoning
    # Layer per-caller sampling overrides (e.g. a custom agent's temperature /
    # max_tokens) on top of the profile. Ignore None so an unset override never
    # clobbers a configured profile value. Applied here — before the thinking
    # and Codex transforms below — so provider-specific normalization (Codex
    # dropping max_tokens, thinking disable-paths) still governs the merged
    # value exactly as it would a profile-native one.
    if model_overrides:
        model_settings_from_config.update({key: value for key, value in model_overrides.items() if value is not None})
    # The per-request reasoning effort layers the same way. The regular lead-agent
    # build forwards the key even when None (neither the request nor the custom
    # agent chose one), so it must leave kwargs: a profile that also yields
    # reasoning_effort would otherwise hand the constructor the keyword twice.
    # Codex validates and maps the requested value itself below.
    from deerflow.models.openai_codex_provider import CodexChatModel

    is_codex_model = issubclass(model_class, CodexChatModel)
    requested_reasoning_effort = kwargs.pop("reasoning_effort", None)
    # Compute effective when_thinking_enabled by merging in the `thinking` shortcut field.
    # The `thinking` shortcut is equivalent to setting when_thinking_enabled["thinking"].
    has_thinking_settings = (model_config.when_thinking_enabled is not None) or (model_config.thinking is not None)
    effective_wte: dict = dict(model_config.when_thinking_enabled) if model_config.when_thinking_enabled else {}
    if model_config.thinking is not None:
        merged_thinking = {**(effective_wte.get("thinking") or {}), **model_config.thinking}
        effective_wte = {**effective_wte, "thinking": merged_thinking}

    # One normalized reasoning policy for every caller (issue #5073): the lead
    # agent, subagents, summarization, title generation and one-shot utilities
    # all land here, so required thinking, unsupported thinking and restricted
    # effort vocabularies are enforced in exactly one place.
    contract = resolve_reasoning_contract(model_config)
    # Codex's legacy/default-path adapter writes the generic constructor key.
    # A declared custom path takes precedence even for a Codex subclass.
    codex_handles_generic_effort = is_codex_model and (contract.source == "legacy" or contract.effort is None or contract.effort.path == "reasoning_effort")
    if contract.source == "legacy" and thinking_enabled and has_thinking_settings and not contract.supports_thinking:
        raise ValueError(f"Model {name} does not support thinking. Set `supports_thinking` to true in the `config.yaml` to enable thinking.") from None
    try:
        resolved_reasoning = resolve_reasoning_request(contract, thinking_enabled=thinking_enabled, reasoning_effort=requested_reasoning_effort)
    except ReasoningPolicyError as exc:
        raise ValueError(f"Model {name}: {exc}") from exc
    if resolved_reasoning.adjustments:
        logger.info("Model '%s': reasoning request adjusted by its capability contract (%s)", name, ", ".join(resolved_reasoning.adjustments))
    requested_reasoning_effort = resolved_reasoning.reasoning_effort
    if contract.source == "legacy":
        # Profiles without a ``reasoning:`` block keep the historical payload
        # path byte-for-byte, driven by the caller's raw thinking flag.
        _apply_legacy_thinking_settings(
            model_settings_from_config,
            model_config,
            thinking_enabled=thinking_enabled,
            has_thinking_settings=has_thinking_settings,
            effective_wte=effective_wte,
            requested_reasoning_effort=requested_reasoning_effort,
            is_codex_model=is_codex_model,
        )
        if not contract.supports_reasoning_effort:
            requested_reasoning_effort = None
            model_settings_from_config.pop("reasoning_effort", None)
    else:
        thinking_enabled = resolved_reasoning.thinking_enabled
        _apply_contract_thinking_settings(
            model_settings_from_config,
            contract,
            model_config,
            thinking_enabled=thinking_enabled,
            effective_wte=effective_wte,
            requested_reasoning_effort=requested_reasoning_effort,
            codex_handles_generic_effort=codex_handles_generic_effort,
        )

    # Normalize the api_base -> base_url alias FIRST, so the downstream OpenAI-compatible
    # heuristics (stream_usage default below / stream_chunk_timeout) see the canonical endpoint key.
    _normalize_openai_base_url(model_class, model_settings_from_config)
    _apply_stream_chunk_timeout_default(model_class, model_settings_from_config)

    # For Codex Responses API models: map thinking mode to reasoning_effort
    if is_codex_model:
        # The ChatGPT Codex endpoint currently rejects max_tokens/max_output_tokens.
        model_settings_from_config.pop("max_tokens", None)

        if codex_handles_generic_effort:
            # Use explicit reasoning_effort from frontend if provided (low/medium/high)
            if not thinking_enabled:
                model_settings_from_config["reasoning_effort"] = "none"
            elif requested_reasoning_effort in ("low", "medium", "high", "xhigh"):
                model_settings_from_config["reasoning_effort"] = requested_reasoning_effort
            elif "reasoning_effort" not in model_settings_from_config:
                model_settings_from_config["reasoning_effort"] = "medium"

    # For MindIE models: enforce conservative retry defaults.
    # Timeout normalization is handled inside MindIEChatModel itself.
    if getattr(model_class, "__name__", "") == "MindIEChatModel":
        # Enforce max_retries constraint to prevent cascading timeouts.
        model_settings_from_config["max_retries"] = model_settings_from_config.get("max_retries", 1)

    # Ensure stream_usage is enabled so that token usage metadata is available
    # in streaming responses.  LangChain's BaseChatOpenAI only defaults
    # stream_usage=True when no custom base_url/api_base is set, so models
    # hitting third-party endpoints (e.g. doubao, deepseek) silently lose
    # usage data.  We default it to True unless explicitly configured.
    if "stream_usage" not in model_settings_from_config and "stream_usage" not in kwargs:
        if "stream_usage" in getattr(model_class, "model_fields", {}):
            model_settings_from_config["stream_usage"] = True

    # Translate the declared context window into the langchain profile so
    # profile-dependent features (e.g. SummarizationMiddleware fraction triggers,
    # which resolve thresholds from profile["max_input_tokens"]) work for
    # third-party OpenAI-compatible models whose SDK ships no profile of its
    # own (#3103). ``profile`` is a metadata-only BaseChatModel field
    # (exclude=True) and never reaches the provider request payload. An
    # explicit profile from a caller or model_overrides is never clobbered.
    translate_context_window = bool(model_config.context_window) and "profile" not in kwargs and "profile" not in model_settings_from_config

    if model_config.request_admission is not None:
        from deerflow.models.request_admission import get_request_admission

        if "rate_limiter" in kwargs or "rate_limiter" in model_settings_from_config:
            raise ValueError("request_admission cannot be combined with a custom rate_limiter")
        model_settings_from_config["rate_limiter"] = get_request_admission(name, model_config.request_admission)
        # SDK-internal retries do not re-enter BaseChatModel's admission hook.
        # Keep retries at the middleware layer where each attempt is paced.
        if "max_retries" in model_class.model_fields:
            kwargs.pop("max_retries", None)
            model_settings_from_config["max_retries"] = 0

    _warn_unknown_model_settings(model_class, name, model_settings_from_config)

    # 配置提供默认值，调用方显式传入的非空参数统一覆盖配置。
    # 先合并再展开，避免同名字段通过两个 **dict 传入时触发 TypeError。
    effective_model_settings = dict(model_settings_from_config)
    effective_model_settings.update({key: value for key, value in kwargs.items() if value is not None})
    model_instance = model_class(**effective_model_settings)

    if translate_context_window:
        # Applied *after* construction and merged into the provider's inferred
        # profile: passing ``profile`` to the constructor would REPLACE the whole
        # inferred metadata (tool_calling, structured_output, io capabilities,
        # output limits) with just this one key. The declared window wins on
        # max_input_tokens itself — the operator set it precisely because the
        # inferred (or absent) value doesn't match their gateway.
        inferred_profile = getattr(model_instance, "profile", None)
        model_instance.profile = {**(inferred_profile or {}), "max_input_tokens": model_config.context_window}

    if attach_tracing:
        callbacks = build_tracing_callbacks()
        if callbacks:
            existing_callbacks = model_instance.callbacks or []
            model_instance.callbacks = [*existing_callbacks, *callbacks]
            logger.debug(f"Tracing attached to model '{name}' with providers={len(callbacks)}")
    return model_instance