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.reflection import resolve_class from deerflow.tracing import build_tracing_callbacks logger = logging.getLogger(__name__) 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 _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={ "use", "name", "display_name", "description", "supports_thinking", "supports_reasoning_effort", "when_thinking_enabled", "when_thinking_disabled", "thinking", "supports_vision", # 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", }, ) # 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}) # 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} if thinking_enabled and has_thinking_settings: if not model_config.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 if effective_wte: model_settings_from_config.update(effective_wte) if not thinking_enabled: if model_config.when_thinking_disabled is not None: # User-provided disable settings take full precedence model_settings_from_config.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 model_settings_from_config["extra_body"] = _deep_merge_dicts( model_settings_from_config.get("extra_body"), {"thinking": {"type": "disabled"}}, ) model_settings_from_config["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. model_settings_from_config["extra_body"] = _deep_merge_dicts( model_settings_from_config.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 model_settings_from_config["thinking"] = {"type": "disabled"} if not model_config.supports_reasoning_effort: kwargs.pop("reasoning_effort", None) model_settings_from_config.pop("reasoning_effort", None) # 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 from deerflow.models.openai_codex_provider import CodexChatModel if issubclass(model_class, CodexChatModel): # The ChatGPT Codex endpoint currently rejects max_tokens/max_output_tokens. model_settings_from_config.pop("max_tokens", None) # Use explicit reasoning_effort from frontend if provided (low/medium/high) explicit_effort = kwargs.pop("reasoning_effort", None) if not thinking_enabled: model_settings_from_config["reasoning_effort"] = "none" elif explicit_effort and explicit_effort in ("low", "medium", "high", "xhigh"): model_settings_from_config["reasoning_effort"] = explicit_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 _warn_unknown_model_settings(model_class, name, model_settings_from_config) model_instance = model_class(**kwargs, **model_settings_from_config) 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