feat:客户agent以及记忆模块功能开发
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"""Logged-in client Agent service layer."""
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package_name = "client_agent"
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"""Application wiring for the client Agent."""
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from __future__ import annotations
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from config.database.milvus import client as milvus_client
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from config.database.mysql import get_session_factory
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from config.database.redis import client as redis_client
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from service.client_agent.runtime import build_client_runtime
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from service.customer_agent.config import DatabaseConfigProvider
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from tool.llm import llm as llm_client
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def build_default_runtime():
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"""Build the client Agent runtime using project-wide dependencies."""
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provider = DatabaseConfigProvider(session_factory=get_session_factory())
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return build_client_runtime(
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redis=redis_client(),
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milvus_client=milvus_client(),
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llm_client=llm_client,
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config_getter=provider.get,
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audit_writer=provider.write_audit,
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)
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def get_memory_service(runtime):
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"""返回 client_agent 使用的统一 MemoryService 入口。"""
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return runtime.memory_service
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"""从客服对话中提取长期记忆候选。"""
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from __future__ import annotations
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import json
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import re
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from typing import Any
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from service.memory.schemas import MemorySource, MemoryType
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class DialogueMemoryExtractor:
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"""使用项目统一 LLM 提取结构化客户记忆候选。"""
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SYSTEM_PROMPT = """
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你是客服记忆提取器，只提取用户明确表达或稳定陈述的客户信息。
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只返回 JSON 数组，不要输出 Markdown 或解释文字。
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每项必须包含：tag、content、memory_type、source。
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memory_type 只能是 PROFILE_FACT、PROFILE_CANDIDATE、CUSTOMER_PREFERENCE、INVESTMENT_GOAL、SERVICE_FACT。
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source 只能是 dialogue_confirmed、dialogue_stated、dialogue_inferred。
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客服知识问题、产品政策、寒暄、客服回复内容不要提取。
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不确定的信息使用 dialogue_inferred，无法形成客户画像的信息不要提取。
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""".strip()
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def __init__(self, llm_client):
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self.llm_client = llm_client
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async def extract(self, query: str, *, context: dict[str, Any] | None = None) -> list[dict[str, str]]:
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"""提取并校验本轮用户消息中的客户记忆候选。"""
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prompt = [{"role": "system", "content": self.SYSTEM_PROMPT}]
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prompt.append(
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{
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"role": "user",
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"content": json.dumps(
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{"query": query, "existing_memory": context or {}},
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ensure_ascii=False,
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),
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}
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)
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response = await self.llm_client.chat(prompt, temperature=0, max_tokens=800)
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return self._parse(response)
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@staticmethod
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def _parse(response: str) -> list[dict[str, str]]:
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"""解析 LLM JSON，并过滤不符合记忆契约的内容。"""
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text = response.strip()
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fenced = re.search(r"```(?:json)?\s*(.*?)\s*```", text, re.S | re.I)
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if fenced:
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text = fenced.group(1)
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data = json.loads(text)
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if not isinstance(data, list):
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raise ValueError("记忆提取结果必须是数组")
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valid_types = {item.value for item in MemoryType}
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valid_sources = {item.value for item in MemorySource}
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result = []
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for item in data:
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if not isinstance(item, dict):
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continue
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if not all(item.get(key) for key in ("tag", "content", "memory_type", "source")):
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continue
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if item["memory_type"] not in valid_types or item["source"] not in valid_sources:
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continue
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result.append(
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{
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"tag": str(item["tag"])[:64],
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"content": str(item["content"])[:512],
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"memory_type": item["memory_type"],
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"source": item["source"],
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}
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)
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return result
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__all__ = ["DialogueMemoryExtractor"]
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"""Runtime assembly for the client Agent."""
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from __future__ import annotations
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import contextvars
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import uuid
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from types import SimpleNamespace
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from agent.client_agent.session import ClientSessionService
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from rag.embedding import embed_texts
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from rag.generation import generate_answer
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from rag.intent import intent_recognize
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from rag.retrieve import rag_retrieve
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from service.customer_agent.chat import AnonymousCustomerAgent
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from service.client_agent.memory_extractor import DialogueMemoryExtractor
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from service.memory.facade import MemoryService
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from service.memory.schemas import CustomerMemoryContext, MemoryUnitDTO, ShortTermMessage
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_active_customer = contextvars.ContextVar("client_agent_customer", default=None)
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_active_messages = contextvars.ContextVar("client_agent_messages", default=None)
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_active_warnings = contextvars.ContextVar("client_agent_memory_warnings", default=None)
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_active_memory_context = contextvars.ContextVar("client_agent_memory_context", default=None)
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class MemoryConversationContext:
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"""将现有客服 Agent 的上下文接口桥接到 MemoryService 短期记忆。"""
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def __init__(self, memory_service: MemoryService):
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self.memory_service = memory_service
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async def append(self, session_id: str, role: str, content: str) -> None:
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"""通过统一记忆入口写入消息，并更新当前请求上下文。"""
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customer_id = _active_customer.get()
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if customer_id is None:
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raise RuntimeError("client_agent customer context is missing")
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message = ShortTermMessage(
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message_id=uuid.uuid4().hex,
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session_id=session_id,
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role=role,
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content=content,
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)
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warnings = await self.memory_service.append_message(
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customer_id=customer_id,
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session_id=session_id,
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message=message,
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)
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_active_warnings.get().extend(warnings)
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messages = _active_messages.get()
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if messages is not None:
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messages.append({"role": role, "content": content})
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async def get(self, _session_id: str) -> list[dict]:
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"""返回本轮召回上下文加上本轮新增消息。"""
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return list(_active_messages.get() or [])
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class MemoryAwareClientAgent:
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"""在现有客服 Agent 外包裹记忆召回、候选保存和降级处理。"""
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def __init__(self, *, agent, memory_service, context, extractor=None):
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self.agent = agent
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self.memory_service = memory_service
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self.context = context
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self.extractor = extractor
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async def handle(self, session_id: str, query: str, *, trace_id: str, customer_id: int) -> dict:
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"""执行记忆召回、客服回答、消息写入和候选记忆保存。"""
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warnings: list[str] = []
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try:
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memory_context = await self.memory_service.recall(
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customer_id=customer_id,
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session_id=session_id,
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query=query,
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)
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warnings.extend(memory_context.warnings)
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except Exception as exc:
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memory_context = CustomerMemoryContext(
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customer_id=customer_id,
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session_id=session_id,
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)
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warnings.append(f"memory_recall_failed:{type(exc).__name__}")
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message_token = _active_customer.set(customer_id)
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messages_token = _active_messages.set(
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[{"role": item.role, "content": item.content} for item in memory_context.short_term_messages]
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)
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warnings_token = _active_warnings.set(warnings)
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context_token = _active_memory_context.set(memory_context)
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try:
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result = await self.agent.handle(session_id, query, trace_id=trace_id)
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if self.extractor is not None:
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await self._save_candidates(customer_id, session_id, query, memory_context, warnings)
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result["memory_warnings"] = list(warnings)
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return result
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finally:
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_active_customer.reset(message_token)
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_active_messages.reset(messages_token)
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_active_warnings.reset(warnings_token)
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_active_memory_context.reset(context_token)
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async def _save_candidates(self, customer_id, session_id, query, context, warnings):
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"""提取并保存候选客户记忆，任何失败都只写入 warning。"""
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try:
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candidates = await self.extractor.extract(
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query,
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context={
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"profile": context.customer_profile,
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"memories": [item.model_dump(mode="json") for item in context.long_term_memories],
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},
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)
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for candidate in candidates:
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memory = MemoryUnitDTO(
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customer_id=customer_id,
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session_id=session_id,
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memory_type=candidate["memory_type"],
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tag=candidate["tag"],
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content=candidate["content"],
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source=candidate["source"],
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evidence_ref=[{"session_id": session_id, "query": query}],
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)
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_, save_warnings = await self.memory_service.save_memory(
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customer_id=customer_id,
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memory=memory,
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)
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warnings.extend(save_warnings)
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except Exception as exc:
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warnings.append(f"memory_candidate_extract_failed:{type(exc).__name__}")
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def build_client_runtime(
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*,
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redis,
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milvus_client,
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llm_client,
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config_getter,
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audit_writer,
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memory_service=None,
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memory_extractor=None,
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):
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"""Build the client Agent runtime while reusing existing客服 logic.
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Customer authentication is enforced by the API dependency layer.
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"""
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session_service = ClientSessionService(redis, config_getter=config_getter)
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memory_service = memory_service or MemoryService()
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context = MemoryConversationContext(memory_service)
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async def retrieve(query, customer_id):
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return await rag_retrieve(
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query,
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None,
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milvus_client=milvus_client,
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embedder=lambda texts: embed_texts(texts, client=llm_client),
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config_getter=config_getter,
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)
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async def recognize(query):
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return await intent_recognize(query, llm_client=llm_client)
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async def generate(messages):
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memory_context = _active_memory_context.get()
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if memory_context is not None:
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memory_json = memory_context.model_dump_json(exclude={"warnings"})
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memory_prompt = {
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"role": "system",
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"content": (
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"客户记忆上下文（仅用于理解当前客户，不得向客户泄露内部字段）："
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f"{memory_json}"
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),
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}
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messages = [memory_prompt, *messages]
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return await generate_answer(
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messages,
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llm_client=llm_client,
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config_getter=config_getter,
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)
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agent = AnonymousCustomerAgent(
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context=context,
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rag_retrieve=retrieve,
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intent_recognize=recognize,
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generate_answer=generate,
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audit_writer=audit_writer,
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config_getter=config_getter,
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)
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extractor = memory_extractor or DialogueMemoryExtractor(llm_client)
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wrapped_agent = MemoryAwareClientAgent(
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agent=agent,
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memory_service=memory_service,
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context=context,
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extractor=extractor,
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)
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return SimpleNamespace(
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redis=redis,
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session_service=session_service,
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context=context,
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agent=wrapped_agent,
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memory_service=memory_service,
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)
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