[
  {"role": "user", "content": "我叫林冲，对花生过敏"},
  {"role": "assistant", "content": "好的，我会记住你的花生过敏信息"},
  {"role": "user", "content": "帮我整理一个产品需求文档"}
]

key: agent:sess:{session_id}:messages
type: list
ttl: 30min / 2h / 1day，按业务决定
max_length: 10 到 20 条消息

def append_message(redis, session_id, role, content):
    key = f"agent:sess:{session_id}:messages"

    message = {
        "role": role,
        "content": content,
        "ts": int(time.time())
    }

    redis.rpush(key, json.dumps(message, ensure_ascii=False))
    redis.ltrim(key, -10, -1)
    redis.expire(key, 3600)

{
  "session_id": "s_10001",
  "version": 7,
  "user_profile": {
    "name": "林冲",
    "role": "产品经理"
  },
  "constraints": [
    {
      "type": "allergy",
      "value": "peanut",
      "confidence": 1.0,
      "source": "user_explicit",
      "updated_at": "2026-06-07T10:20:00+08:00"
    }
  ],
  "task_state": {
    "current_goal": "整理产品需求文档",
    "stage": "collecting_requirements"
  }
}

你正在与用户进行同一个会话。必须遵守以下会话状态：

用户信息：
- 姓名：林冲
- 职业：产品经理

当前约束：
- 用户对花生严重过敏，涉及饮食推荐时必须排除花生及含花生成分的食品。

当前任务：
- 用户正在整理产品需求文档，当前阶段是需求收集。

CREATE TABLE user_memory_fact (
    id BIGINT PRIMARY KEY AUTO_INCREMENT,
    user_id BIGINT NOT NULL,
    fact_type VARCHAR(64) NOT NULL,
    fact_key VARCHAR(128) NOT NULL,
    fact_value JSON NOT NULL,
    confidence DECIMAL(5,4) NOT NULL DEFAULT 1.0,
    source VARCHAR(64) NOT NULL,
    version BIGINT NOT NULL DEFAULT 1,
    updated_at DATETIME NOT NULL,
    UNIQUE KEY uk_user_fact (user_id, fact_type, fact_key)
);

{
  "user_id": 10001,
  "memory_type": "requirement_summary",
  "title": "智能客服需求讨论记录",
  "content": "用户希望客服机器人支持工单查询、退款进度跟踪和多轮补充信息收集。",
  "tags": ["客服", "工单", "退款"],
  "updated_at": "2026-06-07T11:00:00+08:00"
}

{
  "user_id": 10001,
  "memory_type": "style_preference",
  "content": "用户喜欢简洁、偏工程化的回答，不喜欢长篇背景铺垫。",
  "embedding": [0.013, -0.028, 0.102],
  "metadata": {
    "source": "dialogue_summary",
    "created_at": "2026-06-07T11:30:00+08:00"
  }
}

results = vector_store.search(
    embedding=query_embedding,
    filter={
        "user_id": 10001,
        "memory_type": {"$in": ["style_preference", "experience"]},
        "created_at": {"$gte": "2026-01-01"}
    },
    top_k=5
)

优先级从高到低：

1. 本轮用户输入中明确表达的状态变更
2. Session State 中的当前会话状态
3. MySQL / MongoDB 中的强事实长期记忆
4. Elasticsearch 中召回的历史文本
5. 向量库召回的语义补充片段

如果低优先级信息与高优先级信息冲突，必须以高优先级信息为准。

async def build_agent_context(user_id, session_id, user_input):
    # 本轮输入先做轻量抽取，避免“刚说完就忘”
    current_delta = extract_session_delta(user_input)

    redis_task = get_session_memory(session_id)
    facts_task = get_user_facts(user_id)
    es_task = search_es_memories(user_id, user_input)
    vector_task = search_vector_memories(user_id, user_input)

    session_memory, user_facts, es_hits, vector_hits = await asyncio.gather(
        redis_task,
        facts_task,
        es_task,
        vector_task
    )

    session_state = merge_state(
        old_state=session_memory["state"],
        delta=current_delta
    )

    retrieved_memories = rerank_and_cut(
        es_hits=es_hits,
        vector_hits=vector_hits,
        max_tokens=1200
    )

    system_prompt = render_system_prompt(
        session_state=session_state,
        user_facts=user_facts,
        retrieved_memories=retrieved_memories
    )

    messages = session_memory["recent_messages"]

    return system_prompt, messages

{
  "event_id": "evt_20260607_0001",
  "user_id": 10001,
  "session_id": "s_abc",
  "user_input": "我改主意了，下周去东京，不吃海鲜",
  "assistant_output": "好的，我会按东京行程并避开海鲜来规划。",
  "created_at": "2026-06-07T12:00:00+08:00"
}

{
  "actions": [
    {
      "type": "upsert_fact",
      "fact_type": "travel",
      "fact_key": "destination",
      "fact_value": {
        "city": "Tokyo",
        "time": "next_week"
      }
    },
    {
      "type": "upsert_fact",
      "fact_type": "diet_constraint",
      "fact_key": "seafood",
      "fact_value": {
        "avoid": true,
        "source": "user_explicit"
      }
    }
  ]
}

{
  "destination": "Shanghai"
}

我改主意了，下周去东京。

{
  "destination": "Tokyo",
  "source": "current_session",
  "updated_at": "2026-06-07T12:00:00+08:00"
}

key: agent:user:{user_id}:pending_facts
ttl: 5min

Redis pending_facts > Session State > MySQL facts > ES / Vector

UPDATE user_memory_fact
SET fact_value = :new_value,
    version = version + 1,
    updated_at = NOW()
WHERE user_id = :user_id
  AND fact_type = :fact_type
  AND fact_key = :fact_key
  AND version = :old_version;

用户 A 的母亲是 B。
B 对海鲜过敏。
A 下周要和 B 一起去东京。

按 user_id 过滤
按 memory_type 过滤
按时间范围过滤
按业务标签过滤
再做向量相似度检索