Eric Wang
ca73b1e8c5
Closes the error-code gap between LangGraph and FastGPT backends: the endpoint's 401/429 branches previously could never trigger under the LangGraph backend because all provider failures collapsed into LLMAPIError (500). - agent/llm: LLMAuthError / LLMRateLimitError as LLMAPIError subclasses; OpenAILLMService raises them from openai AuthenticationError (401, no retry) and RateLimitError (429, after bounded retries exhausted) - backends/langgraph: _complete_turn translates LLMAuthError -> ChatBackendAuthenticationError and LLMRateLimitError -> ChatBackendRateLimitError; stream reuses complete() so the mapping applies to both paths; other errors (timeout etc.) propagate unchanged - endpoint untouched: existing 401/429 response branches now fire for both backends Verified: 212 unit tests (7 new error-mapping tests)
108 lines
4.4 KiB
Python
108 lines
4.4 KiB
Python
"""LangGraph implementation of the backend-neutral chat contract（作业 2）。
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实现 ``ChatBackend`` Protocol，把图执行结果翻译为公共契约：
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- ``complete()`` 返回 ``ChatResult``，``content`` 保留
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``<state>XXXX</state>`` 前缀（API 层负责解析）；
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- ``stream()`` 产生 ``TextDelta`` / ``FormUpdate``；初期允许把固定完整答案
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切成若干片段，真实增量输出在作业 8 接入。
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通过 ``build_graph()`` 编译的图构造；会话用
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``configurable.thread_id = chat_input.session_id`` 绑定。
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对应测试：test/backends/test_langgraph_backend.py
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"""
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from collections.abc import AsyncIterator
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from ..agent.graph import GraphDeps, build_graph, workflow_fake_llm
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from ..agent.llm import LLMAuthError, LLMRateLimitError
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from .chat import (
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ChatBackend,
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ChatBackendAuthenticationError,
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ChatBackendRateLimitError,
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ChatInput,
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ChatResult,
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ChatStreamEvent,
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FormUpdate,
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TextDelta,
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)
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class LangGraphBackend(ChatBackend):
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"""Adapt a compiled LangGraph to the neutral chat contract."""
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def __init__(self, graph=None, llm=None):
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"""参数:
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graph: 编译后的图；缺省调用 ``build_graph()``。
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llm: LLM 服务（运行时依赖）；缺省为离线 workflow fake。
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生产环境由 lifespan 注入 ``build_llm_service()`` 的实例。
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"""
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self._graph = graph if graph is not None else build_graph()
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self._llm = llm if llm is not None else workflow_fake_llm()
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async def complete(self, chat_input: ChatInput) -> ChatResult:
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"""非流式：执行一轮图并翻译结果。
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TODO(作业 2): 实现该方法。要求：
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- 用 ``ainvoke`` 执行图，``thread_id`` 取
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``chat_input.session_id``；
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- 初始 state 至少包含 ``session_id``、``input_text``；
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- 返回 ``ChatResult(content=<响应文本（含 state 前缀）>,
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status_code=None, form_update={})``；
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- 本阶段不处理 ``need_form_update`` 的真实表单差量
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（作业 6 起实现）。
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"""
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result = await self._complete_turn(chat_input)
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return ChatResult(
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content = result["response_text"],
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status_code = None,
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form_update = result.get("turn_patch") or {}
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)
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async def _complete_turn(self, chat_input: ChatInput) -> dict:
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"""执行一轮图，把 LLM 层错误翻译为后端中立错误。
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错误码等价（对照 FastGPT 后端）：
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LLMAuthError -> ChatBackendAuthenticationError（API 层 401）；
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LLMRateLimitError -> ChatBackendRateLimitError（API 层 429）。
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"""
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graph_input: dict = {
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"session_id": chat_input.session_id,
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"input_text": chat_input.text,
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"client_mode": chat_input.client_mode,
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}
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# 作业 6：应用服务注入的最新业务字段覆盖本轮输入，
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# 使 set_info 写入后本轮 chat 立即可见（输入覆盖 checkpoint 同键）。
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if chat_input.injected_fields:
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graph_input.update(chat_input.injected_fields)
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try:
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return await self._graph.ainvoke(
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graph_input,
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config={"configurable": {"thread_id": chat_input.session_id}},
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context=GraphDeps(llm=self._llm),
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)
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except LLMAuthError as exc:
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raise ChatBackendAuthenticationError(str(exc)) from exc
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except LLMRateLimitError as exc:
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raise ChatBackendRateLimitError(str(exc)) from exc
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async def stream(self, chat_input: ChatInput) -> AsyncIterator[ChatStreamEvent]:
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"""流式：产生中立流事件。
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TODO(作业 2): 实现该方法。最低要求：
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- 先产出含完整 ``<state>...</state>`` 前缀的 ``TextDelta``，
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再产出正文片段（保证 API 层的 stage 先于正文约束）；
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- 本阶段允许“完整答案切片段”，提交说明须标注
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“尚未接入模型增量输出”（作业 8 改为真实增量）。
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"""
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result = await self.complete(chat_input)
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prefix, sep, body = result.content.partition("</state>")
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if not sep:
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yield TextDelta(result.content)
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return
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yield TextDelta(prefix + "</state>")
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if result.form_update:
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yield FormUpdate(result.form_update)
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if body:
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yield TextDelta(body)
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