197 lines
10 KiB
Python
197 lines
10 KiB
Python
import logging
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from langchain.chat_models import BaseChatModel
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from deerflow.config import get_app_config
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from deerflow.config.app_config import AppConfig
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from deerflow.reflection import resolve_class
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from deerflow.tracing import build_tracing_callbacks
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logger = logging.getLogger(__name__)
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def _deep_merge_dicts(base: dict | None, override: dict) -> dict:
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"""Recursively merge two dictionaries without mutating the inputs."""
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merged = dict(base or {})
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for key, value in override.items():
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if isinstance(value, dict) and isinstance(merged.get(key), dict):
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merged[key] = _deep_merge_dicts(merged[key], value)
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else:
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merged[key] = value
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return merged
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def _vllm_disable_chat_template_kwargs(chat_template_kwargs: dict) -> dict:
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"""Build the disable payload for vLLM/Qwen chat template kwargs."""
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disable_kwargs: dict[str, bool] = {}
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if "thinking" in chat_template_kwargs:
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disable_kwargs["thinking"] = False
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if "enable_thinking" in chat_template_kwargs:
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disable_kwargs["enable_thinking"] = False
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return disable_kwargs
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def _enable_stream_usage_by_default(model_use_path: str, model_settings_from_config: dict) -> None:
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"""Enable stream usage for OpenAI-compatible models unless explicitly configured.
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LangChain only auto-enables ``stream_usage`` for OpenAI models when no custom
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base URL or client is configured. DeerFlow frequently uses OpenAI-compatible
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gateways, so token usage tracking would otherwise stay empty and the
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TokenUsageMiddleware would have nothing to log.
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"""
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if model_use_path != "langchain_openai:ChatOpenAI":
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return
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if "stream_usage" in model_settings_from_config:
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return
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if "base_url" in model_settings_from_config or "openai_api_base" in model_settings_from_config:
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model_settings_from_config["stream_usage"] = True
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def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *, app_config: AppConfig | None = None, force_disable_thinking: bool = False, **kwargs) -> BaseChatModel:
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"""Create a chat model instance from the config.
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Args:
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name: The name of the model to create. If None, the first model in the config will be used.
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thinking_enabled: Turn the model's extended thinking / reasoning on.
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force_disable_thinking: Hard override that disables thinking even when the
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per-model config declares no ``when_thinking_*`` shape. Many internal
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vLLM / Qwen models only set ``supports_thinking: true`` (no disable
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shape), so passing ``thinking_enabled=False`` alone injects nothing and
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the model keeps reasoning on. When this flag is set (and thinking is
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off) we additionally inject both common OpenAI-compatible disable shapes
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— vLLM/Qwen ``chat_template_kwargs.enable_thinking=False`` and Zhipu GLM
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``thinking.type="disabled"`` — mirroring
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``TitleMiddleware._force_disable_thinking``. No effect when thinking is
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enabled, and skipped for model classes that don't accept ``extra_body``.
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Returns:
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A chat model instance.
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"""
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config = app_config or get_app_config()
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if name is None:
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name = config.models[0].name
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model_config = config.get_model_config(name)
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if model_config is None:
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raise ValueError(f"Model {name} not found in config") from None
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model_class = resolve_class(model_config.use, BaseChatModel)
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# Transparently apply DeerFlow's OpenAI-compatible patch layer so users
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# don't need to switch every `use: langchain_openai:ChatOpenAI` entry.
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if model_config.use == "langchain_openai:ChatOpenAI":
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from deerflow.models.patched_openai import PatchedChatOpenAI
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model_class = PatchedChatOpenAI
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model_settings_from_config = model_config.model_dump(
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exclude_none=True,
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exclude={
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"use",
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"name",
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"display_name",
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"description",
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"supports_thinking",
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"supports_reasoning_effort",
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"when_thinking_enabled",
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"when_thinking_disabled",
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"thinking",
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"supports_vision",
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},
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)
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# Compute effective when_thinking_enabled by merging in the `thinking` shortcut field.
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# The `thinking` shortcut is equivalent to setting when_thinking_enabled["thinking"].
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has_thinking_settings = (model_config.when_thinking_enabled is not None) or (model_config.thinking is not None)
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effective_wte: dict = dict(model_config.when_thinking_enabled) if model_config.when_thinking_enabled else {}
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if model_config.thinking is not None:
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merged_thinking = {**(effective_wte.get("thinking") or {}), **model_config.thinking}
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effective_wte = {**effective_wte, "thinking": merged_thinking}
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if thinking_enabled and has_thinking_settings:
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if not model_config.supports_thinking:
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raise ValueError(f"Model {name} does not support thinking. Set `supports_thinking` to true in the `config.yaml` to enable thinking.") from None
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if effective_wte:
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model_settings_from_config.update(effective_wte)
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if not thinking_enabled:
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if model_config.when_thinking_disabled is not None:
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# User-provided disable settings take full precedence
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model_settings_from_config.update(model_config.when_thinking_disabled)
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elif has_thinking_settings and effective_wte.get("extra_body", {}).get("thinking", {}).get("type"):
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# OpenAI-compatible gateway: thinking is nested under extra_body
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model_settings_from_config["extra_body"] = _deep_merge_dicts(
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model_settings_from_config.get("extra_body"),
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{"thinking": {"type": "disabled"}},
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)
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model_settings_from_config["reasoning_effort"] = "minimal"
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elif has_thinking_settings and (disable_chat_template_kwargs := _vllm_disable_chat_template_kwargs(effective_wte.get("extra_body", {}).get("chat_template_kwargs") or {})):
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# vLLM uses chat template kwargs to switch thinking on/off.
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model_settings_from_config["extra_body"] = _deep_merge_dicts(
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model_settings_from_config.get("extra_body"),
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{"chat_template_kwargs": disable_chat_template_kwargs},
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)
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elif has_thinking_settings and effective_wte.get("thinking", {}).get("type"):
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# Native langchain_anthropic: thinking is a direct constructor parameter
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model_settings_from_config["thinking"] = {"type": "disabled"}
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if not model_config.supports_reasoning_effort:
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kwargs.pop("reasoning_effort", None)
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model_settings_from_config.pop("reasoning_effort", None)
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# Hard override: force-disable thinking regardless of the per-model config shape.
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# Injected only for OpenAI-compatible classes (those declaring `extra_body`);
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# other providers would reject the kwarg, so they are skipped (they ignore it).
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# Three disable shapes are injected so the common OpenAI-compatible gateways are
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# all covered: SiliconFlow / DashScope-style **top-level** ``enable_thinking``
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# (DeepSeek-V3.1+/Qwen3/GLM hybrid models on api.siliconflow.cn — the knob the
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# nested ``chat_template_kwargs`` shape does NOT reach), vLLM/Qwen native
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# ``chat_template_kwargs.enable_thinking``, and Zhipu GLM ``thinking.type``.
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# Only models that explicitly opt into thinking should receive vendor-specific
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# disable payloads. Generic OpenAI-compatible gateways (including LiteLLM)
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# may reject these otherwise-unknown fields instead of forwarding them.
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if (
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force_disable_thinking
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and not thinking_enabled
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and model_config.supports_thinking
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and "extra_body" in getattr(model_class, "model_fields", {})
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):
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model_settings_from_config["extra_body"] = _deep_merge_dicts(
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model_settings_from_config.get("extra_body"),
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{"enable_thinking": False, "chat_template_kwargs": {"enable_thinking": False}, "thinking": {"type": "disabled"}},
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)
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_enable_stream_usage_by_default(model_config.use, model_settings_from_config)
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# For Codex Responses API models: map thinking mode to reasoning_effort
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from deerflow.models.openai_codex_provider import CodexChatModel
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if issubclass(model_class, CodexChatModel):
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# The ChatGPT Codex endpoint currently rejects max_tokens/max_output_tokens.
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model_settings_from_config.pop("max_tokens", None)
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# Use explicit reasoning_effort from frontend if provided (low/medium/high)
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explicit_effort = kwargs.pop("reasoning_effort", None)
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if not thinking_enabled:
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model_settings_from_config["reasoning_effort"] = "none"
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elif explicit_effort and explicit_effort in ("low", "medium", "high", "xhigh"):
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model_settings_from_config["reasoning_effort"] = explicit_effort
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elif "reasoning_effort" not in model_settings_from_config:
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model_settings_from_config["reasoning_effort"] = "medium"
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# For MindIE models: enforce conservative retry defaults.
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# Timeout normalization is handled inside MindIEChatModel itself.
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if getattr(model_class, "__name__", "") == "MindIEChatModel":
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# Enforce max_retries constraint to prevent cascading timeouts.
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model_settings_from_config["max_retries"] = model_settings_from_config.get("max_retries", 1)
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# Ensure stream_usage is enabled so that token usage metadata is available
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# in streaming responses. LangChain's BaseChatOpenAI only defaults
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# stream_usage=True when no custom base_url/api_base is set, so models
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# hitting third-party endpoints (e.g. doubao, deepseek) silently lose
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# usage data. We default it to True unless explicitly configured.
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if "stream_usage" not in model_settings_from_config and "stream_usage" not in kwargs:
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if "stream_usage" in getattr(model_class, "model_fields", {}):
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model_settings_from_config["stream_usage"] = True
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model_instance = model_class(**kwargs, **model_settings_from_config)
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callbacks = build_tracing_callbacks()
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if callbacks:
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existing_callbacks = model_instance.callbacks or []
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model_instance.callbacks = [*existing_callbacks, *callbacks]
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logger.debug(f"Tracing attached to model '{name}' with providers={len(callbacks)}")
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return model_instance
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