a8a933d3dc
- 版本号 0.7.0.beta3 → 0.7.0（pyproject ×2 / package.json / docker-compose ×2 / uv.lock ×2 / README ×2 / quick-start / 文档首页） - changelog 标题更新为 v0.7.0 (2026-06-13)，修正版本号描述 - quick-start 版本表更新为当前稳定版 - 修复 package import 排序，同步前端 prettier 格式化
85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
from langchain.chat_models import BaseChatModel
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from pydantic import SecretStr
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from yuxi import config as sys_config
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from yuxi.models.providers.cache import model_cache
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from yuxi.utils import get_docker_safe_url
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from yuxi.utils.logging_config import logger
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# 这些提供商的 OpenAI 兼容流式接口，在 LangGraph v3 事件流累积工具调用时会丢失
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# tool_call 的关键字段（siliconflow MiniMax 丢 name、alibaba 百炼丢 id），空值会被写入
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# checkpoint，导致工具结果无法按 tool_call_id 关联、工具状态永远停留在“进行中”。
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# 对这些提供商在工具调用阶段禁用流式（正文回答仍流式）以拿到完整 tool_call。
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# 该缺陷属 LangChain v3 流式协议上游问题（langchain#37420 / langchainjs#10937 /
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# langgraphjs#2496），截至 langchain-core 1.4.4 仍未修复，待上游修复后可移除本处理。
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_NON_STREAMING_TOOL_CALL_PROVIDERS = ("siliconflow", "alibaba")
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def _requires_non_streaming_tool_calls(provider_id: str, model_id: str) -> bool:
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return provider_id.startswith(_NON_STREAMING_TOOL_CALL_PROVIDERS)
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def resolve_chat_model_spec(model_spec: str | None, *, fallback: str | None = None) -> str:
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"""解析空模型配置，不吞掉已经配置但无效的模型值。
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这里仅处理模型为空时的优先级：请求或配置值、调用方 fallback、系统默认模型；
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具体模型是否存在、是否为聊天模型仍由 model_cache 校验。
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"""
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for candidate in (model_spec, fallback, sys_config.default_model):
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if isinstance(candidate, str) and candidate.strip():
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return candidate.strip()
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raise ValueError("model spec 不能为空")
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def load_chat_model(fully_specified_name: str | None, **kwargs) -> BaseChatModel:
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fully_specified_name = resolve_chat_model_spec(fully_specified_name)
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info = model_cache.get_model_info(fully_specified_name)
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if not info:
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available_specs = model_cache.get_all_specs("chat")
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available_ids = [item.spec for item in available_specs[:10]]
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raise ValueError(
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f"Unknown model spec: '{fully_specified_name}'. "
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f"Available chat models ({len(available_specs)}): {available_ids}"
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)
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if info.model_type != "chat":
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raise ValueError(f"Model {fully_specified_name} is not a chat model (type={info.model_type})")
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api_key = info.api_key
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base_url = get_docker_safe_url(info.base_url)
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logger.debug(f"Loading model {fully_specified_name} with provider_type={info.provider_type}")
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if info.provider_type == "anthropic":
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from langchain_anthropic import ChatAnthropic
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return ChatAnthropic(
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model=info.model_id,
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api_key=SecretStr(api_key),
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base_url=base_url,
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**kwargs,
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)
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if info.provider_type == "gemini":
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from langchain_google_genai import ChatGoogleGenerativeAI
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return ChatGoogleGenerativeAI(
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model=info.model_id,
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google_api_key=SecretStr(api_key),
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**kwargs,
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)
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from langchain_openai import ChatOpenAI
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openai_kwargs = dict(kwargs)
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if _requires_non_streaming_tool_calls(info.provider_id, info.model_id):
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openai_kwargs.setdefault("disable_streaming", "tool_calling")
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return ChatOpenAI(
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model=info.model_id,
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api_key=SecretStr(api_key),
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base_url=base_url,
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stream_usage=True,
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**openai_kwargs,
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)
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