Metadata-Version: 2.4
Name: timem-ai
Version: 0.1.3
Summary: TiMEM Python SDK - Time-based Memory Management and Experience Learning toolkit | TiMEM Python SDK - 时间记忆管理与经验学习工具包
Home-page: https://github.com/AIGility-Cloud-Innovation/timem-python
Author: AIGility Cloud Innovation
Author-email: AIGility Cloud Innovation <contact@aigility.com>
Maintainer-email: AIGility Cloud Innovation <contact@aigility.com>
License: MIT
Project-URL: Homepage, https://github.com/AIGility-Cloud-Innovation/timem-python
Project-URL: Documentation, https://timem-python.readthedocs.io
Project-URL: Repository, https://github.com/AIGility-Cloud-Innovation/timem-python
Project-URL: Issues, https://github.com/AIGility-Cloud-Innovation/timem-python/issues
Project-URL: Changelog, https://github.com/AIGility-Cloud-Innovation/timem-python/blob/main/CHANGELOG.md
Keywords: timem,memory,experience-learning,rule-engine,user-profile,ai,machine-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Database :: Database Engines/Servers
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
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Requires-Dist: pydantic>=2.0.0
Requires-Dist: typing-extensions>=4.0.0
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# TiMEM Python SDK

**Time-based Memory Management and Experience Learning Toolkit**

TiMEM SDK 是一个强大的 Python 客户端库，用于与 TiMEM Engine API 交互，提供记忆管理、经验学习和用户画像计算等功能。

## 特性

- ✅ **经验学习引擎**：从反馈数据中智能学习生成规则（直接使用 LLM，无需聚类）
- ✅ **规则召回**：基于上下文智能召回相关经验规则
- ✅ **记忆管理**：支持 L1-L5 分层记忆的增删改查
- ✅ **用户画像**：从 L5 级别记忆计算用户画像
- ✅ **同步/异步支持**：完整的同步和异步 API 封装
- ✅ **批量操作**：支持并发批量处理提升效率
- ✅ **自动重试**：内置请求重试机制保证可靠性

## 安装

```bash
pip install timem-python
```

或从源码安装：

```bash
git clone https://github.com/aigility/timem-python.git
cd timem-python
pip install -e .
```

## 快速开始

### 同步客户端

```python
from timem import TiMEMClient

# 初始化客户端
client = TiMEMClient(
    api_key="your-api-key",
    base_url="http://localhost:8001"
)

# 1. 学习：从反馈生成经验规则（可选提供反馈案例）
result = client.learn(
    domain="aicv",
    feedback_cases=[  # 可选：提供新的反馈案例
        {
            "case_id": "case_001",
            "user_id": 12345,
            "domain": "aicv",
            "suggestion_type": "项目经验优化",
            "feedback_action": "adopt",
            "feedback_score": 5.0
        }
    ],
    min_case_count=3,
    min_adoption_rate=0.6,
    strategy="adaptive"
)
print(f"Generated {result['data']['generated_rule_count']} rules")

# 或者直接从系统中已有的反馈学习
result = client.learn(domain="aicv", strategy="adaptive")

# 2. 召回：根据上下文召回相关规则
rules = client.recall(
    context={
        "job_title": "Python开发工程师",
        "issue_type": "项目经验",
        "section": "工作经历"
    },
    domain="aicv",
    top_k=5
)
print(f"Recalled {len(rules['data']['rules'])} rules")

# 3. 添加记忆
memory = client.add_memory(
    user_id=12345,
    domain="aicv",
    content={
        "type": "interaction",
        "action": "resume_analysis",
        "context": {"job": "软件工程师"}
    },
    layer_type="L1",
    tags=["resume", "analysis"]
)

# 4. 搜索记忆
memories = client.search_memory(
    user_id=12345,
    domain="aicv",
    tags=["resume"],
    limit=10
)

# 5. 计算用户画像
profile = client.compute_profile(
    user_id=12345,
    domain="aicv"
)

# 关闭客户端
client.close()
```

### 异步客户端

```python
import asyncio
from timem import AsyncTiMEMClient

async def main():
    # 使用上下文管理器自动管理连接
    async with AsyncTiMEMClient(
        api_key="your-api-key",
        base_url="http://localhost:8001"
    ) as client:
        
        # 1. 异步学习
        result = await client.learn(
            domain="aicv",
            strategy="adaptive"
        )
        
        # 2. 异步召回
        rules = await client.recall(
            context={"job_title": "数据科学家"},
            domain="aicv"
        )
        
        # 3. 批量学习（多个领域并发）
        batch_results = await client.batch_learn(
            domains=["aicv", "education", "consulting"]
        )
        
        # 4. 批量添加记忆（并发）
        memories = [
            {
                "user_id": 12345,
                "domain": "aicv",
                "content": {"action": "view_job", "job_id": 1}
            },
            {
                "user_id": 12345,
                "domain": "aicv",
                "content": {"action": "apply_job", "job_id": 1}
            }
        ]
        results = await client.batch_add_memories(memories)

# 运行异步函数
asyncio.run(main())
```

### 便捷函数

```python
import asyncio
from timem import learn_async, recall_async

# 快速异步学习
async def quick_learn():
    result = await learn_async(
        api_key="your-api-key",
        domain="aicv",
        strategy="adaptive"
    )
    return result

# 快速异步召回
async def quick_recall():
    rules = await recall_async(
        api_key="your-api-key",
        context={"job_title": "产品经理"},
        domain="aicv"
    )
    return rules

asyncio.run(quick_learn())
asyncio.run(quick_recall())
```

## API 文档

### 经验学习引擎

#### learn()

从反馈案例中学习生成经验规则（使用 LLM 直接分析，无需聚类）

**参数：**
- `domain` (str): 业务领域，默认 "general"
- `feedback_cases` (List[Dict], optional): 可选的反馈案例列表，如果提供则先收集再学习
- `min_case_count` (int): 最小案例数量，默认 3
- `min_adoption_rate` (float): 最小采纳率阈值，默认 0.6
- `min_confidence_score` (float): 最小置信度阈值，默认 0.5
- `strategy` (str): 总结策略，可选 "single", "cluster", "adaptive"（推荐）

**返回：** 学习结果包含生成的规则数量和规则ID列表

**使用场景：**
- 提供 feedback_cases：实时收集反馈并立即学习
- 不提供 feedback_cases：从系统中已有的未处理反馈学习

#### recall()

根据上下文召回相关经验规则

**参数：**
- `context` (Dict): 上下文信息（如 job_title, issue_type, section）
- `domain` (str): 业务领域
- `top_k` (int): 返回前 K 条规则，默认 5
- `min_confidence` (float): 最小置信度，默认 0.5

**返回：** 召回的规则列表，包含相关性评分

### 记忆管理

#### add_memory()

添加记忆到 TiMEM 系统

**参数：**
- `user_id` (int): 用户ID
- `domain` (str): 业务领域
- `content` (Dict): 记忆内容
- `layer_type` (str): 记忆层级（L1-L5），默认 "L1"
- `tags` (List[str], optional): 标签
- `keywords` (List[str], optional): 关键词

**返回：** 创建的记忆信息

#### search_memory()

搜索记忆

**参数：**
- `user_id` (int, optional): 用户ID过滤
- `domain` (str, optional): 领域过滤
- `layer_type` (str, optional): 层级过滤
- `tags` (List[str], optional): 标签过滤
- `keywords` (List[str], optional): 关键词过滤
- `limit` (int): 返回数量，默认 20
- `offset` (int): 分页偏移，默认 0

**返回：** 搜索结果和记忆列表

### 用户画像

#### compute_profile()

从 L5 级别记忆计算用户画像

**参数：**
- `user_id` (int): 用户ID
- `domain` (str): 业务领域
- `source_memory_ids` (List[str], optional): 指定记忆ID

**返回：** 计算的画像信息

#### get_profile()

获取用户画像

**参数：**
- `user_id` (int): 用户ID
- `domain` (str): 业务领域

**返回：** 用户画像信息

#### search_users()

根据画像特征搜索用户

**参数：**
- `criteria` (Dict): 搜索条件（如偏好、行为模式）
- `domain` (str, optional): 领域过滤
- `limit` (int): 返回数量，默认 20

**返回：** 匹配的用户列表

## 异步批量操作

### batch_learn()

并发学习多个领域的反馈

```python
async with AsyncTiMEMClient(api_key="your-key") as client:
    results = await client.batch_learn(
        domains=["aicv", "education", "consulting"],
        strategy="adaptive"
    )
```

### batch_add_memories()

并发添加多条记忆

```python
async with AsyncTiMEMClient(api_key="your-key") as client:
    memories = [
        {"user_id": 1, "domain": "aicv", "content": {...}},
        {"user_id": 2, "domain": "aicv", "content": {...}}
    ]
    results = await client.batch_add_memories(memories)
```

## 异常处理

```python
from timem import TiMEMClient, TiMEMError, AuthenticationError, APIError, ValidationError

try:
    client = TiMEMClient(api_key="your-key")
    result = client.learn(domain="aicv")
except ValidationError as e:
    print(f"验证错误: {e}")
except AuthenticationError as e:
    print(f"认证失败: {e}")
except APIError as e:
    print(f"API错误 [{e.status_code}]: {e}")
except TiMEMError as e:
    print(f"TiMEM错误: {e}")
```

## 配置选项

```python
client = TiMEMClient(
    api_key="your-api-key",
    base_url="http://localhost:8001",  # TiMEM Engine URL
    timeout=60.0,                       # 请求超时（秒）
)

async_client = AsyncTiMEMClient(
    api_key="your-api-key",
    base_url="http://localhost:8001",
    timeout=60.0,
    max_retries=3,                      # 最大重试次数
    retry_delay=1.0,                    # 重试延迟（秒）
)
```

## 开发指南

### 安装开发依赖

```bash
pip install -r requirements-dev.txt
```

### 运行测试

```bash
pytest tests/
```

### 构建发布

```bash
python -m build
python -m twine upload dist/*
```

## 架构说明

TiMEM SDK 遵循以下设计原则：

1. **简洁接口**：Learn 算子直接使用 LLM，无需手动聚类
2. **智能召回**：基于标签和上下文的相关性评分
3. **分层记忆**：支持 L1-L5 五层记忆管理
4. **异步优先**：充分利用异步编程提升性能
5. **容错设计**：自动重试和异常处理

## 许可证

MIT License

## 联系我们

- Email: contact@aigility.com
- GitHub: https://github.com/aigility/timem-python
- Documentation: https://docs.timem.aigility.com

## 更新日志

### v0.1.0 (2025-10-18)

- ✨ 初始版本发布
- ✨ 支持经验学习引擎（Learn/Recall）
- ✨ 支持记忆管理（Add/Search/Update/Delete）
- ✨ 支持用户画像计算
- ✨ 完整的同步/异步 API
- ✨ 批量操作支持
