feat: PromptProvider 化 + $DEFLECTION_POOL 单一事实源注入 + runtime 必填化
- PromptProvider(app/engine/services/ai_service.py): 租户提示词四件套 from_dir 加载;build_system 把 system.md 的 $DEFLECTION_POOL 占位符替换为 wall.json deflections 话术池(按语言分组一行,EN/ZH/ES)再拼事实卡; extractor/evaluator 的 $existing_info/$messages/$profile/$recent_messages 替换逻辑自 AIService 原样搬入(行为等价);删模块级 import 时读文件的 PROMPTS_DIR 与四个常量,保留 DEFAULT_PROVIDER(jiancai 出厂,__file__ 锚定) - 两租户 system.md「三类请求的处理」第2条的迂回话术枚举 → $DEFLECTION_POOL 占位符(保留段落说明文字),话术只存 wall.json 一份,改一处两边同步变 - TenantRuntime 加 prompts 字段(加载期 from_dir(seed_dir/prompts)); ConversationManager 的 runtime 转必填(keyword-only),默认 AIService 用 runtime.prompts + compiled_wall.deflections 组装 - app/engine/prompts/ 删除(grep 无代码消费方,jiancai 出厂由 seed_profiles/jiancai/prompts 承接);test_company_facts/ test_system_prompt_dual_contact 的 cwd 相对路径改 Path(__file__) 锚定, test_extractor_evaluator_prompts 断言对象改 DEFAULT_PROVIDER(语义等价) - 新增 tests/test_prompt_provider.py(14 用例): 加载/注入/格式/占位符防漂移/ extractor-evaluator 组装/registry 接线/runtime 必填/池线过两墙 Co-Authored-By: Claude Code <noreply@anthropic.com>
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浅念
@@ -6,14 +6,96 @@ from pathlib import Path
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from app.engine.providers.base import AIProvider
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from app.engine.providers.glm import GLMProvider
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from app.engine.config import settings
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from app.engine.services.boundary_filter import DEFAULT_WALL
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logger = logging.getLogger(__name__)
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PROMPTS_DIR = Path(__file__).resolve().parent.parent / "prompts"
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def _format_messages(messages: list[dict]) -> str:
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lines = []
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for m in messages:
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role = m.get("role", "unknown")
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content = m.get("content", "")
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label = {"user": "客户", "assistant": "顾问"}.get(role, role)
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lines.append(f"{label}: {content}")
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return "\n".join(lines)
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def _load_prompt(filename: str) -> str:
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return (PROMPTS_DIR / filename).read_text(encoding="utf-8")
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# 话术池注入的语言行序(确定性输出;未列语言按字典序补位,不丢池)
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_DEFLECTION_LANG_ORDER = ("en", "zh", "es")
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def _format_deflection_pool(deflections) -> str:
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"""wall.json deflections 池 → system.md $DEFLECTION_POOL 注入块。
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按语言分组一行("- EN: …" / "- ZH: …"),条目引号包裹 " / " 分隔——
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紧凑不膨胀(设计 R1: 话术单一事实源,提示词侧只引用不复制)。
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只渲染主池(deflections);deflections_contact 是已留资态的墙替换池,
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本就不属于 system.md 教学内容,不注入。
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"""
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pools = dict(deflections or {})
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langs = [l for l in _DEFLECTION_LANG_ORDER if pools.get(l)]
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langs += sorted(l for l in pools
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if l not in _DEFLECTION_LANG_ORDER and pools.get(l))
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return "\n".join(
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f"- {lang.upper()}: " + " / ".join(f'"{line}"' for line in pools[lang])
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for lang in langs
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)
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class PromptProvider:
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"""租户提示词四件套持有者(Task 6, R1 话术单一事实源)。
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- from_dir(dir): 读 system/company_facts/extractor/evaluator 四个 md,
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存实例字段——提示词属于租户(seed_profiles/<slug>/prompts/),不再有
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代码级 app/engine/prompts 副本;
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- build_system(wall_deflections): system.md 的 $DEFLECTION_POOL 占位符
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替换为按语言枚举的话术清单 + "\\n\\n" + company_facts(行为 prompt
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在前、事实卡居中、当轮提醒最后拼接——拼接位在 AIService 内);
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- build_extractor/build_evaluator: $existing_info/$messages/$profile/
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$recent_messages 替换逻辑自 AIService 原样搬入(行为等价)。
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"""
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def __init__(self, system: str, company_facts: str,
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extractor: str, evaluator: str):
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self.system = system
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self.company_facts = company_facts
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self.extractor = extractor
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self.evaluator = evaluator
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@classmethod
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def from_dir(cls, prompts_dir) -> "PromptProvider":
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d = Path(prompts_dir)
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return cls(
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system=(d / "system.md").read_text(encoding="utf-8"),
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company_facts=(d / "company_facts.md").read_text(encoding="utf-8"),
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extractor=(d / "extractor.md").read_text(encoding="utf-8"),
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evaluator=(d / "evaluator.md").read_text(encoding="utf-8"),
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)
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def build_system(self, wall_deflections=None) -> str:
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system = self.system
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if "$DEFLECTION_POOL" in system:
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system = system.replace(
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"$DEFLECTION_POOL", _format_deflection_pool(wall_deflections))
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return system + "\n\n" + self.company_facts
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def build_extractor(self, existing_info: dict, messages: list[dict]) -> str:
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return self.extractor.replace(
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"$existing_info", json.dumps(existing_info or {}, ensure_ascii=False)
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).replace("$messages", _format_messages(messages))
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def build_evaluator(self, profile: dict, recent_messages: list[dict]) -> str:
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return self.evaluator.replace(
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"$profile", json.dumps(profile, ensure_ascii=False)
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).replace("$recent_messages",
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_format_messages(recent_messages[-6:]))
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# 代码级默认 = jiancai 出厂(路径锚定模块位置,与 cwd 无关): 旧测试与
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# 无租户上下文的默认路径用;租户路径一律经 TenantRuntime.prompts 注入。
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DEFAULT_PROVIDER = PromptProvider.from_dir(
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Path(__file__).resolve().parents[3] / "seed_profiles" / "jiancai" / "prompts")
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def create_provider() -> AIProvider:
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@@ -26,28 +108,28 @@ def create_provider() -> AIProvider:
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raise ValueError(f"Unknown AI provider: {settings.AI_PROVIDER}")
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SYSTEM_PROMPT = _load_prompt("system.md")
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COMPANY_FACTS = _load_prompt("company_facts.md") # 公司事实卡(知识与行为分离)
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EXTRACTOR_PROMPT = _load_prompt("extractor.md")
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EVALUATOR_PROMPT = _load_prompt("evaluator.md")
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# 对话基座: 行为 prompt 在前(红线显著性),事实卡居中(参考资料),
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# 当轮 context 提醒最后拼接(在 chat/stream_chat 内,保持最新鲜)
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_BASE_CHAT_PROMPT = SYSTEM_PROMPT + "\n\n" + COMPANY_FACTS
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class AIService:
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def __init__(self, provider: AIProvider | None = None):
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def __init__(self, provider: AIProvider | None = None,
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prompts: PromptProvider | None = None,
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wall_deflections: dict | None = None):
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self.provider = provider or create_provider()
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# Task 6: 提示词经 PromptProvider 注入(租户四件套);默认 = jiancai
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# 出厂 + 出厂墙话术池(与 DEFAULT_PROVIDER 同源,单一事实源 wall.json)
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self.prompts = prompts or DEFAULT_PROVIDER
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self.wall_deflections = (wall_deflections if wall_deflections is not None
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else DEFAULT_WALL.deflections)
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# 对话基座: 行为 prompt 在前(红线显著性),事实卡居中(参考资料),
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# 当轮 context 提醒最后拼接(在 chat/stream_chat 内,保持最新鲜)
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self._base_chat_prompt = self.prompts.build_system(self.wall_deflections)
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async def chat(self, messages: list[dict], context: str = "") -> str:
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"""Main conversation: get AI reply to user message."""
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prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
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prompt = self._base_chat_prompt + ("\n\n" + context if context else "")
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return await self.provider.chat(messages, prompt)
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async def stream_chat(self, messages: list[dict], context: str = "") -> AsyncGenerator[str, None]:
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"""Stream AI reply token by token."""
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prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
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prompt = self._base_chat_prompt + ("\n\n" + context if context else "")
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async for token in self.provider.stream_chat(messages, prompt):
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yield token
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@@ -55,9 +137,7 @@ class AIService:
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self, messages: list[dict], existing_info: dict
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) -> dict:
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"""Extract structured info from conversation history."""
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prompt = EXTRACTOR_PROMPT.replace(
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"$existing_info", json.dumps(existing_info or {}, ensure_ascii=False)
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).replace("$messages", self._format_messages(messages))
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prompt = self.prompts.build_extractor(existing_info, messages)
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_, extracted = await self.provider.chat_with_extraction(
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messages=[], system_prompt=prompt
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)
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@@ -67,24 +147,12 @@ class AIService:
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self, profile: dict, recent_messages: list[dict]
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) -> dict:
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"""Evaluate customer intent level."""
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prompt = EVALUATOR_PROMPT.replace(
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"$profile", json.dumps(profile, ensure_ascii=False)
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).replace("$recent_messages", self._format_messages(recent_messages[-6:]))
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prompt = self.prompts.build_evaluator(profile, recent_messages)
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_, result = await self.provider.chat_with_extraction(
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messages=[], system_prompt=prompt
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)
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return self._normalize_evaluation(result)
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@staticmethod
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def _format_messages(messages: list[dict]) -> str:
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lines = []
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for m in messages:
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role = m.get("role", "unknown")
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content = m.get("content", "")
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label = {"user": "客户", "assistant": "顾问"}.get(role, role)
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lines.append(f"{label}: {content}")
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return "\n".join(lines)
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@staticmethod
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def _normalize_evaluation(result: dict) -> dict:
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intent = result.get("intent_level", "pending")
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@@ -327,20 +327,24 @@ def _boundary_rotate() -> int:
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class ConversationManager:
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def __init__(self, db: AsyncSession, ai_service: AIService | None = None,
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runtime: "TenantRuntime | None" = None):
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def __init__(self, db: AsyncSession, *,
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runtime: "TenantRuntime",
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ai_service: AIService | None = None):
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self.db = db
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self.ai = ai_service or AIService()
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# Task 5b 装配线注入: 持有租户运行时引用(session_factory/compiled_wall/
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# config 就位;prompts 仍为模块级,Task 6 才 PromptProvider 化;email/push
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# 等 settings→config 业务接线是 Task 9)。None = 无租户上下文(直连测试)。
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# Task 6: runtime 必填(Task 5b 质量审查关门建议)——提示词/墙/会话
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# 工厂全部租户化,无租户上下文的构造不再合法;默认 AI 服务用该租户
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# 的 PromptProvider + compiled_wall.deflections 组装($DEFLECTION_POOL
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# 单一事实源注入)。email/push 等 settings→config 业务接线是 Task 9。
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self.runtime = runtime
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self.ai = ai_service or AIService(
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prompts=runtime.prompts,
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wall_deflections=runtime.compiled_wall.deflections)
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@property
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def session_factory(self):
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"""推送后台任务写结果用的工厂(写回派发租户的库);无 runtime 时
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None,lead_push_service 回退模块级兜底。"""
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return self.runtime.session_factory if self.runtime is not None else None
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"""推送后台任务写结果用的工厂(写回派发租户的库);runtime 未懒建
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engine 时 None,lead_push_service 回退模块级兜底。"""
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return self.runtime.session_factory
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async def create_conversation(
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self,
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