Phase 3：Flows 接管网页文字路径（默认仍 legacy）
按 P1-Pipecat Flows Agent化与记忆改造交接.md 的 Phase 3 完成。 **语音路径保持 legacy**，文字路径可由 AI_AGENT_RUNTIME=flows 切换。 服务器实测通过工具、姓名、记忆三项验收，随后按文档要求改回 legacy。 新增 app/agent/flows_runtime.py - FlowsBridge：LLM 文本帧 → RTVI 消息 + 分句 TTS，产出与 legacy 的 _run_turn 一致（bot-llm-started → 逐 token bot-llm-text → stopped → bot-transcription / bot-output） - UserTextRecorder：截下用户原话供策略层判定语义等级 - build_flows_parts / make_flow_manager：拆成两段，解决 "FlowManager 要 worker、worker 要等管线" 的先后依赖 - 节点只用 task_messages，**不用 role_message**（避免两条 system 消息） - respond_immediately=False（不自动开口） - handler 用闭包捕获 ctx_factory，不用模块级全局（并发会话会串身份） 新增 app/agent/llm_service.py - 固化 Phase 1 spike 的选型：复用 DeepSeekLLMService，显式覆盖模型名 （Pipecat 默认的 deepseek-v4-flash 不存在） 配置 app/config.py - 新增 AgentConfig + AI_AGENT_RUNTIME，**默认 legacy** 改造 services/bot_server.py - EngineProcessor 加 owns_text：flows 模式下语音仍归它，文字帧往下传 - runtime_deps() 暴露 memory/identity/session/tts，两条路径共用同一份资源 （避免各建一份 SQLite 连接或各加载一次 TTS） - **_run_task 改用 PipelineRunner**：直接 task.run() 时 runner 没挂上， Flows 的工具执行会失败（"runner is not set; call attach() first"）， 异常被转成给 LLM 的通用错误串，默认日志看不到。这正是 Phase 1 spike 记录的坑 2，集成时又踩了一次。 新增 app/tests/test_flows_runtime.py（22 项，不依赖网络） 新增 deploy.sh - **不用 --delete**、一次只同步一个目录、显式排除清单。 起因见下。 客户端 client/test_client.py 加 --text 模式（RTVI send-text） - 信封字段实测校正两次：**是 id 不是 msg_id**，**data.content 不是 text**； 写错会被服务端以 "Invalid RTVI transport message" 拒掉且客户端无感知 - 等待条件改为"等到非静音音频"，不能数音频块——WebRTC 空闲时也在推静音 服务器实测（2026-09-16） - legacy 文字：11.2s 音频 / 25 个有声段 - flows 文字：9.3s 音频 / 18 个有声段，首音约 2.8s - flows 记忆闭环：只说"记住我不吃香菜" → 工具写入 confirmed 记忆 → 数据库确认 #4 [confirmed] preference 饮食偏好=不吃香菜 - 称呼"林"从档案恢复 已知问题（留给 Phase 4/7） - Pipecat 报 "Both system_instruction and an initial system message in context are set" 警告。来源未查明（我只用了 task_messages）。 切回本地 vLLM 时可能 400，因为该后端拒绝多条 system 消息。 - Flows 路径暂不暴露 web_search（交接文档要求分阶段，搜索仍走 legacy）
This commit is contained in:
ylhan
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"""Pipecat Flows 运行时装配（Phase 3）。
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**只接管网页文字路径。** 语音仍走 legacy 的 `EngineProcessor`——交接文档
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明确要求 "语音路径暂时保持 legacy"，等 Phase 3、4 稳定后再迁。
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管线形态：
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transport.input()
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→ EngineProcessor(owns_text=False) 语音仍由它处理；文字帧往下传
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→ flows 的 context aggregator.user()
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→ DeepSeekLLMService Flows 驱动
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→ FlowsBridge LLM 文本 → RTVI 消息 + 分句 TTS
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→ flows 的 context aggregator.assistant()
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→ transport.output()
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两个关键设计：
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* **只用 `task_messages`，不用 `role_message`。** Phase 1 spike 实测：
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两者会各产生一条 system 消息进请求。只用一种。这里把人格与任务都写进
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`task_messages`，与 legacy"每轮重建单条 system"的做法一致。
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* **工具执行复用 `app/agent/tools.py`**，不另写一套。身份由服务端注入，
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模型不得传。
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"""
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from __future__ import annotations
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import asyncio
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import logging
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from datetime import UTC, datetime
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from typing import Any, AsyncIterator, Callable
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import numpy as np
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from pipecat.frames.frames import (
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Frame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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TextFrame,
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)
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from ..config import AppConfig
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from ..memory.base import MemoryStore
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from ..prompts import PERSONA_RULES
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from ..voice.tts import split_sentences
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from . import tools as agent_tools
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log = logging.getLogger(__name__)
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# ---------------------------------------------------------------- 本轮用户原话
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#: 本轮用户原话。策略层要它区分"用户明确要求记住"和"模型自己推断"，
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#: 而 Flows 的工具 handler 只看得到工具参数，看不到原话。
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#: 用单元素列表做可变闭包——handler 是闭包，拿不到外层的非局部变量。
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#:
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#: 注意：这是**单用户在用的原型**。多用户并发会话时这里会互相覆盖，
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#: 要支持并发得改成按 session 分键（Phase 5 迁移语音路径时一并处理）。
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_LAST_USER_TEXT: list[str] = [""]
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def set_last_user_text(text: str) -> None:
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_LAST_USER_TEXT[0] = text or ""
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# ---------------------------------------------------------------- 节点
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#: 会话控制状态。**不要把长期记忆复制进来**——Flows 的 state 不是数据库，
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#: 长期数据一律走 MemoryStore（交接文档 §七）。
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def _conversation_node(*, display_name: str, tool_schemas: list[dict],
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ctx_factory: Callable[[], agent_tools.ToolContext]) -> dict:
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"""默认节点：普通陪伴对话 + 可用工具。
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人格与任务都放在 task_messages 里——**不用 role_message**，
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避免两条 system 消息（见模块文档）。
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"""
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persona = PERSONA_RULES
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if display_name:
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persona += f"\n\n当前用户希望被称为：{display_name}。"
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from pipecat.flows import FlowsFunctionSchema
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functions = []
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for schema in tool_schemas:
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fn = schema["function"]
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functions.append(FlowsFunctionSchema(
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name=fn["name"],
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description=fn["description"],
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properties=fn["parameters"].get("properties", {}),
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required=fn["parameters"].get("required", []),
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handler=_make_handler(fn["name"], ctx_factory),
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))
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return {
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"name": "conversation",
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"task_messages": [
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{"role": "system", "content": persona},
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],
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"functions": functions,
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# 进入节点不自动开口：文字路径要等用户先说话。
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# （spike 实测默认 True 会在 initialize 时立刻触发一轮回复，
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# 那条回复与用户输入无关，会污染归因，也会让网页莫名多一段话。）
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"respond_immediately": False,
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}
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def _make_handler(name: str, ctx_factory: Callable[[], agent_tools.ToolContext]):
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"""把工具层的执行器包成 Flows 的 handler。
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**用闭包捕获 ctx_factory，不用模块级全局**——全局在并发连接时会串
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（两个会话各自的 owner_user_id 会互相覆盖）。
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"""
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async def handler(args, flow_manager=None):
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outcome = await agent_tools.execute(name, dict(args or {}), ctx_factory())
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return outcome.content
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return handler
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# ---------------------------------------------------------------- 输入记录
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class UserTextRecorder(FrameProcessor):
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"""记录本轮用户原话，供策略层判定语义等级。
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策略层要区分"用户明确要求记住"和"模型自己推断"，依据是**用户原话**，
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而 Flows 的工具 handler 只看得到工具参数。所以这里把原话截下来。
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"""
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def __init__(self, extractor):
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super().__init__()
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self._extract = extractor
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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from pipecat.frames.frames import LLMMessagesAppendFrame
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMMessagesAppendFrame):
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try:
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text = self._extract(frame.messages)
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if text:
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set_last_user_text(text)
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except Exception: # noqa: BLE001 — 记录失败不该影响对话
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log.debug("记录用户原话失败", exc_info=True)
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await self.push_frame(frame, direction)
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# ---------------------------------------------------------------- 输出桥
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class FlowsBridge(FrameProcessor):
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"""把 Flows/LLM 的文本帧转成 RTVI 消息 + 分句 TTS。
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产出**与 legacy 的 `_run_turn` 保持一致**（网页依赖这些消息渲染）：
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bot-llm-started → 逐 token 的 bot-llm-text → bot-llm-stopped
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→ bot-transcription / bot-output（完整文本）
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"""
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def __init__(self, *, rtvi, tts, cfg: AppConfig, audio_enabled: bool = True):
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super().__init__()
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self.rtvi = rtvi
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self.tts = tts
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self.cfg = cfg
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self.audio_enabled = audio_enabled
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self._buffer = ""
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self._reply: list[str] = []
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self._started = False
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self._speak_tasks: list[asyncio.Task] = []
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMFullResponseStartFrame):
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self._started = True
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self._buffer = ""
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self._reply = []
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await self.push_frame(frame, direction)
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await self.rtvi.push_transport_message(_rtvi_bot_started())
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elif isinstance(frame, TextFrame) and frame.text:
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self._buffer += frame.text
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self._reply.append(frame.text)
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# 逐 token 给网页（Playground 的 Conversation 视图需要）
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await self.rtvi.push_transport_message(_rtvi_bot_text(frame.text))
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await self.push_frame(frame, direction)
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# 攒到句子边界就送去合成——这是提前出声的唯一手段
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segs = split_sentences(self._buffer)
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if len(segs) > 1:
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*ready, self._buffer = segs
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for seg in ready:
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await self._speak(seg)
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elif isinstance(frame, LLMFullResponseEndFrame):
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if self._buffer.strip():
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await self._speak(self._buffer)
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self._buffer = ""
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reply = "".join(self._reply).strip()
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if reply:
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# 完整文本单发一次：Playground 1.7 的 Conversation 视图
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# 主要消费完整转写/bot-output，只给 token 级不会稳定渲染。
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await self.rtvi.push_transport_message(_rtvi_bot_transcription(reply))
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await self.rtvi.push_transport_message(
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_rtvi_bot_output(reply, spoken=self.audio_enabled))
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if self._started:
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await self.rtvi.push_transport_message(_rtvi_bot_stopped())
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self._started = False
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await self.push_frame(frame, direction)
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else:
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await self.push_frame(frame, direction)
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async def _speak(self, text: str) -> None:
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text = (text or "").strip()
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if not text or not self.audio_enabled:
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return
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try:
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audio = await asyncio.to_thread(self.tts.synthesize, text)
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except Exception as e: # noqa: BLE001 — TTS 失败不该中断文字显示
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log.warning("Flows 路径 TTS 失败（文字仍显示）：%s", e)
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return
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if audio is None or not len(audio):
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return
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await self._push_audio(np.asarray(audio, dtype=np.float32),
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getattr(self.tts, "sample_rate", 24000))
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async def _push_audio(self, samples: np.ndarray, sample_rate: int) -> None:
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from pipecat.frames.frames import OutputAudioRawFrame
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out_rate = self.cfg.voice.output_sample_rate if hasattr(
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self.cfg.voice, "output_sample_rate") else 24000
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if sample_rate != out_rate:
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n = int(len(samples) * out_rate / sample_rate)
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samples = np.interp(np.linspace(0, len(samples) - 1, n),
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np.arange(len(samples)), samples).astype(np.float32)
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pcm = (np.clip(samples, -1, 1) * 32767).astype(np.int16)
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step = out_rate // 50 # 20ms 一块
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for i in range(0, len(pcm), step):
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await self.push_frame(OutputAudioRawFrame(
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audio=pcm[i:i + step].tobytes(), sample_rate=out_rate,
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num_channels=1))
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# ---------------------------------------------------------------- RTVI 消息
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def _rtvi_bot_started():
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import pipecat.processors.frameworks.rtvi.models as R
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return R.BotLLMStartedMessage()
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def _rtvi_bot_stopped():
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import pipecat.processors.frameworks.rtvi.models as R
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return R.BotLLMStoppedMessage()
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def _rtvi_bot_text(text: str):
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import pipecat.processors.frameworks.rtvi.models as R
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return R.BotLLMTextMessage(data=R.TextMessageData(text=text))
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def _rtvi_bot_transcription(text: str):
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import pipecat.processors.frameworks.rtvi.models as R
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return R.BotTranscriptionMessage(data=R.TextMessageData(text=text))
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def _rtvi_bot_output(text: str, *, spoken: bool):
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import pipecat.processors.frameworks.rtvi.models as R
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return R.BotOutputMessage(data=R.BotOutputMessageData(
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text=text, aggregated_by="sentence", spoken=spoken))
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# ---------------------------------------------------------------- 装配
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async def build_flows_parts(
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*,
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cfg: AppConfig,
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memory: MemoryStore,
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identity,
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session_id: str,
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tts,
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rtvi,
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audio_enabled: bool = True,
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):
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"""造出 Flows 路径需要的**零件**，不含 FlowManager。
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为什么不在这里建 FlowManager：它要求传 `worker`（PipelineWorker），
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而 worker 又必须在管线建好之后才能建，是个先后依赖。
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所以顺序是：零件 → 管线 → worker → FlowManager → initialize。
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返回 (llm, aggregator, bridge, ctx_factory, tool_schemas)
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"""
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from .llm_service import create_llm_service
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llm = create_llm_service(cfg.llm)
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if llm is None:
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raise RuntimeError(
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f"Flows 路径不支持后端 {cfg.llm.name!r}（只支持 deepseek）。"
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f"请把 AI_AGENT_RUNTIME 改回 legacy。")
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context = LLMContext()
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aggregator = LLMContextAggregatorPair(context)
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def ctx_factory() -> agent_tools.ToolContext:
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"""每次工具调用现算身份——用户可能在会话中途切换。"""
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d = identity.current()
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return agent_tools.ToolContext(
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owner_user_id=d.user_id, # ← 服务端注入，模型不得传
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session_id=session_id,
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turn_id=f"t_{datetime.now(UTC).strftime('%H%M%S%f')}",
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identity_confirmed=d.confirmed,
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user_text=_LAST_USER_TEXT[0],
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store=memory,
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# Phase 3 先不暴露 web_search：搜索仍走 legacy 路径，
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# 等 flows 文字路径验收通过后再接（交接文档要求分阶段）。
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search=None,
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memory_limit=cfg.context.max_memories,
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memory_timeout_s=cfg.context.memory_timeout_s,
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)
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bridge = FlowsBridge(rtvi=rtvi, tts=tts, cfg=cfg, audio_enabled=audio_enabled)
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tool_schemas = agent_tools.available_schemas(ctx_factory())
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return llm, aggregator, bridge, ctx_factory, tool_schemas
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def make_flow_manager(*, worker, llm, aggregator):
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"""worker 建好之后再建 FlowManager。"""
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from pipecat.flows import FlowManager
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return FlowManager(worker=worker, llm=llm, context_aggregator=aggregator)
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async def initialize_conversation_node(
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flow, *, display_name: str, tool_schemas: list[dict],
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ctx_factory: Callable[[], agent_tools.ToolContext],
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) -> None:
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"""设置初始节点。
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**必须 await。** `FlowManager.initialize` 是协程，但签名显示 `-> None`，
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漏 await 不报错，只是人格/任务/工具**全部静默失效**——
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Phase 1 spike 在这里栽过一次，表现极具误导性：
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模型会正常回复（因为 LLM 照样被调用），但没有人格、没有工具。
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"""
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node = _conversation_node(display_name=display_name,
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tool_schemas=tool_schemas,
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ctx_factory=ctx_factory)
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await flow.initialize(node)
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__all__ = [
|
||||
"FlowsBridge", "UserTextRecorder", "build_flows_parts", "make_flow_manager",
|
||||
"initialize_conversation_node",
|
||||
"set_last_user_text", "PERSONA_RULES",
|
||||
]
|
||||
@@ -212,6 +212,23 @@ class MemoryConfig:
|
||||
|
||||
# ---------------------------------------------------------------- 汇总
|
||||
|
||||
@dataclass
|
||||
class AgentConfig:
|
||||
"""Agent 运行时的选择。
|
||||
|
||||
Phase 3 起有两条路径，**默认必须是 legacy**——交接文档要求
|
||||
"flows 默认值只有在线上验收后才能从 legacy 改为 flows"。
|
||||
"""
|
||||
|
||||
#: "legacy" = ConversationEngine 手写工具循环（当前线上行为）
|
||||
#: "flows" = Pipecat Flows 驱动文字路径（Phase 3 新路径）
|
||||
runtime: str = field(default_factory=lambda: _env("AI_AGENT_RUNTIME", "legacy"))
|
||||
|
||||
@property
|
||||
def use_flows(self) -> bool:
|
||||
return self.runtime.strip().lower() == "flows"
|
||||
|
||||
|
||||
@dataclass
|
||||
class AppConfig:
|
||||
# 单用户原型的稳定内部档案 ID。它只用于数据隔离，绝不是用户可见姓名。
|
||||
@@ -228,6 +245,7 @@ class AppConfig:
|
||||
search: SearchConfig = field(default_factory=SearchConfig)
|
||||
voice: VoiceConfig = field(default_factory=VoiceConfig)
|
||||
memory: MemoryConfig = field(default_factory=MemoryConfig)
|
||||
agent: AgentConfig = field(default_factory=AgentConfig)
|
||||
|
||||
@classmethod
|
||||
def load(cls) -> "AppConfig":
|
||||
|
||||
@@ -0,0 +1,223 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Phase 3 Flows 运行时装配测试。
|
||||
|
||||
**不依赖网络、不启动 WebRTC**。验证的是装配正确性：
|
||||
|
||||
- 节点配置形态（只用 task_messages、不自动开口）
|
||||
- 工具 schema 随身份变化
|
||||
- handler 桥接到工具层，且身份由服务端注入
|
||||
- 运行时开关默认是 legacy
|
||||
|
||||
真正跑起来的行为（RTVI 消息、TTS、延迟）由现场测试和真人网页验收覆盖。
|
||||
|
||||
用法：
|
||||
~/venvs/ai_assistant/bin/python app/tests/test_flows_runtime.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent.parent
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
||||
from app.agent import flows_runtime as FR # noqa: E402
|
||||
from app.agent.tools import ToolContext # noqa: E402
|
||||
from app.config import AgentConfig, AppConfig # noqa: E402
|
||||
from app.identity import ManualIdentityProvider # noqa: E402
|
||||
from app.memory.base import ConfirmStatus, Memory, MemoryKind # noqa: E402
|
||||
|
||||
OK, FAIL = [], []
|
||||
|
||||
|
||||
def check(name: str, cond: bool, detail: str = "") -> None:
|
||||
(OK if cond else FAIL).append(name)
|
||||
print(f" [{'PASS' if cond else 'FAIL'}] {name}" + (f" — {detail}" if detail else ""))
|
||||
|
||||
|
||||
class FakeStore:
|
||||
"""最小实现，只够装配与 handler 调用。"""
|
||||
def __init__(self):
|
||||
self.rows: list[Memory] = []
|
||||
self._n = 1
|
||||
|
||||
async def retrieve(self, owner_user_id, query, **kw):
|
||||
return [r for r in self.rows if r.owner_user_id == owner_user_id][:5]
|
||||
|
||||
async def remember(self, mem: Memory) -> int:
|
||||
m = Memory(**{**mem.__dict__, "memory_id": self._n})
|
||||
self._n += 1
|
||||
self.rows.append(m)
|
||||
return m.memory_id
|
||||
|
||||
async def forget(self, owner_user_id, memory_id):
|
||||
before = len(self.rows)
|
||||
self.rows = [r for r in self.rows
|
||||
if not (r.memory_id == memory_id
|
||||
and r.owner_user_id == owner_user_id)]
|
||||
return len(self.rows) < before
|
||||
|
||||
|
||||
class FakeTTS:
|
||||
sample_rate = 24000
|
||||
def synthesize(self, text):
|
||||
import numpy as np
|
||||
return np.zeros(2400, dtype=np.float32)
|
||||
|
||||
|
||||
class FakeRTVI:
|
||||
def __init__(self): self.msgs = []
|
||||
async def push_transport_message(self, m): self.msgs.append(type(m).__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 配置开关
|
||||
|
||||
def test_runtime_switch():
|
||||
print("\n=== 1. 运行时开关 ===")
|
||||
check("默认是 legacy", AgentConfig(runtime="legacy").use_flows is False)
|
||||
check("显式 flows 才启用", AgentConfig(runtime="flows").use_flows is True)
|
||||
check("大小写不敏感", AgentConfig(runtime="FLOWS").use_flows is True)
|
||||
check("未知值当 legacy", AgentConfig(runtime="xyz").use_flows is False)
|
||||
|
||||
saved = os.environ.pop("AI_AGENT_RUNTIME", None)
|
||||
try:
|
||||
from app.config import load_env
|
||||
import importlib
|
||||
import app.config as C
|
||||
C._loaded = False # 重新读一次环境
|
||||
check("未设环境变量时默认 legacy",
|
||||
AppConfig.load().agent.runtime == "legacy")
|
||||
finally:
|
||||
if saved is not None:
|
||||
os.environ["AI_AGENT_RUNTIME"] = saved
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 节点形态
|
||||
|
||||
def test_node_shape():
|
||||
print("\n=== 2. 节点配置形态 ===")
|
||||
identity = ManualIdentityProvider("u_a")
|
||||
store = FakeStore()
|
||||
bridge = FR.FlowsBridge(rtvi=FakeRTVI(), tts=FakeTTS(),
|
||||
cfg=AppConfig.load())
|
||||
|
||||
def ctx_factory():
|
||||
d = identity.current()
|
||||
return ToolContext(owner_user_id=d.user_id, session_id="s1",
|
||||
turn_id="t1", identity_confirmed=d.confirmed,
|
||||
store=store)
|
||||
|
||||
schemas = [
|
||||
{"type": "function", "function": {
|
||||
"name": "memory_search", "description": "x",
|
||||
"parameters": {"type": "object",
|
||||
"properties": {"query": {"type": "string"}},
|
||||
"required": ["query"]}}},
|
||||
]
|
||||
node = FR._conversation_node(display_name="林", tool_schemas=schemas,
|
||||
ctx_factory=ctx_factory)
|
||||
|
||||
check("节点名是 conversation", node["name"] == "conversation")
|
||||
check("**不用 role_message**", "role_message" not in node,
|
||||
"避免两条 system 消息")
|
||||
check("用 task_messages 承载人格",
|
||||
len(node["task_messages"]) == 1
|
||||
and node["task_messages"][0]["role"] == "system")
|
||||
check("人格里含称呼", "林" in node["task_messages"][0]["content"])
|
||||
check("respond_immediately=False（不自动开口）",
|
||||
node["respond_immediately"] is False)
|
||||
check("工具已注册", len(node["functions"]) == 1)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- handler 桥接
|
||||
|
||||
async def test_handler_bridge():
|
||||
print("\n=== 3. handler 桥接到工具层 ===")
|
||||
identity = ManualIdentityProvider("u_a")
|
||||
store = FakeStore()
|
||||
FR.set_last_user_text("记住我不吃香菜")
|
||||
|
||||
def ctx_factory():
|
||||
d = identity.current()
|
||||
return ToolContext(owner_user_id=d.user_id, session_id="s1",
|
||||
turn_id="t1", identity_confirmed=d.confirmed,
|
||||
user_text=FR._LAST_USER_TEXT[0], store=store)
|
||||
|
||||
handler = FR._make_handler("memory_remember", ctx_factory)
|
||||
out = await handler({"kind": "preference", "key": "饮食偏好",
|
||||
"value": "不吃香菜"})
|
||||
check("handler 返回文本", isinstance(out, str) and "已记住" in out, out[:40])
|
||||
check("写入的 owner 来自服务端",
|
||||
store.rows and store.rows[0].owner_user_id == "u_a")
|
||||
|
||||
# 身份未确认时，handler 应拒绝（策略层兜底）
|
||||
identity.reset()
|
||||
out2 = await handler({"kind": "preference", "value": "x"})
|
||||
# 策略层给模型的话是"现在还不能记录私人信息。请先确认身份。"，
|
||||
# 断言要匹配实际措辞（早先写死"身份未确认"导致误判成 FAIL）。
|
||||
check("身份未确认时 handler 拒绝",
|
||||
"不能记录" in out2 or "确认身份" in out2, out2[:40])
|
||||
|
||||
# 越权参数应被工具层拦下
|
||||
identity.switch("u_a")
|
||||
out3 = await handler({"kind": "fact", "value": "x", "owner_user_id": "u_b"})
|
||||
check("越权参数被拦", "不合法" in out3, out3[:40])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 零件装配
|
||||
|
||||
async def test_build_parts():
|
||||
print("\n=== 4. build_flows_parts 装配 ===")
|
||||
cfg = AppConfig.load()
|
||||
# 强制用 deepseek 配置（本地 env 可能没设 AI_LLM_PRIMARY）
|
||||
from app.config import deepseek_config
|
||||
cfg.llm = deepseek_config()
|
||||
|
||||
if not cfg.llm.api_key:
|
||||
check("跳过（无 DeepSeek 密钥）", True)
|
||||
return
|
||||
|
||||
llm, aggregator, bridge, ctx_factory, schemas = await FR.build_flows_parts(
|
||||
cfg=cfg, memory=FakeStore(), identity=ManualIdentityProvider("u_a"),
|
||||
session_id="s1", tts=FakeTTS(), rtvi=FakeRTVI())
|
||||
|
||||
check("返回 LLM service", llm is not None and type(llm).__name__ == "DeepSeekLLMService")
|
||||
check("模型名被覆盖为 deepseek-flash",
|
||||
llm._settings.model == cfg.llm.model, llm._settings.model)
|
||||
check("thinking 关闭",
|
||||
getattr(llm._settings.thinking, "type", None) == "disabled")
|
||||
check("aggregator 有 user/assistant",
|
||||
aggregator.user() is not None and aggregator.assistant() is not None)
|
||||
check("工具 schema 随身份给出（含记忆工具）",
|
||||
any(s["function"]["name"].startswith("memory_") for s in schemas),
|
||||
str([s["function"]["name"] for s in schemas]))
|
||||
check("Phase 3 不含 web_search",
|
||||
not any(s["function"]["name"] == "web_search" for s in schemas),
|
||||
"搜索仍走 legacy 路径")
|
||||
|
||||
# 身份未确认时不暴露记忆工具
|
||||
_, _, _, _, schemas2 = await FR.build_flows_parts(
|
||||
cfg=cfg, memory=FakeStore(), identity=ManualIdentityProvider(),
|
||||
session_id="s1", tts=FakeTTS(), rtvi=FakeRTVI())
|
||||
check("身份未确认时不给记忆工具",
|
||||
not any(s["function"]["name"].startswith("memory_") for s in schemas2))
|
||||
|
||||
|
||||
def main() -> int:
|
||||
test_runtime_switch()
|
||||
test_node_shape()
|
||||
asyncio.run(test_handler_bridge())
|
||||
asyncio.run(test_build_parts())
|
||||
|
||||
print("\n" + "=" * 62)
|
||||
print(f"通过 {len(OK)} / {len(OK) + len(FAIL)}")
|
||||
for n in FAIL:
|
||||
print(f" 未通过: {n}")
|
||||
return 0 if not FAIL else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user