semvec/pypi-packages huggingface dataset:
tablestore-agent-storage 1.0.3
A Python SDK for agent storage
Requires Python: >=3.8

# Aliyun Tablestore Agent Storage Python SDK

A Python SDK for agent storage with support for Aliyun OSS and OTS, featuring knowledge base and document management capabilities.

## Installation

```bash
pip install -e .
```

## Quick Start

```python
from tablestore_agent_storage import AgentStorageClient

# Initialize client with unified credentials
client = AgentStorageClient(
    access_key_id='your_access_key_id',
    access_key_secret='your_access_key_secret',
    oss_endpoint='https://oss-cn-hangzhou.aliyuncs.com',
    oss_bucket_name='your_bucket_name',
    ots_endpoint='https://your_instance.cn-hangzhou.ots.aliyuncs.com',
    ots_instance_name='your_instance_name'
)

# Create a knowledge base
response = client.create_knowledge_base({
    'name': 'my_knowledge_base',
    'description': 'My first knowledge base'
})

# Add a document with OSS key
client.add_documents({
    'knowledgeBaseName': 'my_knowledge_base',
    'documents': [{
        'ossKey': 'oss://your-bucket/path/to/file.pdf',
        'metadata': {'author': 'aliyun'}
    }]
})

# Or add a document by uploading a local file
client.upload_documents({
    'knowledgeBaseName': 'my_knowledge_base',
    'documents': [{
        'filePath': '/path/to/local/file.pdf',
        'metadata': {'author': 'aliyun'}
    }]
})

# Search/Retrieve
results = client.retrieve({
    'knowledgeBaseName': 'knowledgeBaseName',
    "retrievalQuery": {
        "text": "text to search",
        "type": "TEXT"
    }
})
```

## API Reference

### Knowledge Base Operations

- `create_knowledge_base(request)` - Create new knowledge base
- `list_knowledge_base(request)` - List all knowledge bases
- `describe_knowledge_base(request)` - Get knowledge base details
- `delete_knowledge_base(request)` - Delete knowledge base

### Document Operations

- `add_documents(request)` - Add a document to a knowledge base (requires OSS key)
- `upload_documents(request)` - Add a document by uploading a local file (automatically uploads to OSS)
- `list_documents(request)` - List documents in knowledge base
- `get_document(request)` - Get document details
- `delete_documents(request)` - Delete a document

### Retrieval Operations

- `retrieve(request)` - Perform vector search/retrieval

## Configuration

The SDK requires the following configuration parameters:

- `access_key_id`: Your Aliyun access key ID (shared by OSS and OTS)
- `access_key_secret`: Your Aliyun access key secret (shared by OSS and OTS)
- `oss_endpoint`: OSS service endpoint
- `oss_bucket_name`: OSS bucket name
- `ots_endpoint`: OTS service endpoint (optional)
- `ots_instance_name`: OTS instance name (optional)

## Examples

See the `examples/` directory for more detailed usage examples.

## Dependencies

- oss2 >= 2.18.0
- tablestore >=6.3.9

# Contact

- [阿里云 Tablestore 官方网站](http://www.aliyun.com/product/ots)
- [阿里云官网联系方式](https://help.aliyun.com/document_detail/61890.html)
- [阿里云 Tablestore 官方文档](https://help.aliyun.com/zh/tablestore/product-overview)
dblpcli 0.2.0
CLI for DBLP, for humans and agents alike
Requires Python: >=3.10
Keywords: academic, bibliography, bibtex, cli, dblp

# dblpcli

CLI for DBLP, for humans and agents alike.

## Installation

```bash
pip install dblpcli
```

Or with [uv](https://github.com/astral-sh/uv):

```bash
uv pip install dblpcli
```

## Quick Start

Run directly without installing using [uvx](https://docs.astral.sh/uv/guides/tools/):

```bash
uvx dblpcli search "transformer attention"
```

Or after installing:

## Usage

### Search publications

```bash
dblpcli search "transformer attention"
dblpcli search "deep learning" --year 2020-2024 --limit 10
dblpcli search "author:Vaswani" --format json
```

### Get publication details

```bash
dblpcli pub conf/nips/VaswaniSPUJGKP17
dblpcli pub conf/nips/VaswaniSPUJGKP17 --format json
```

### Export BibTeX

```bash
dblpcli bibtex conf/nips/VaswaniSPUJGKP17
dblpcli bibtex conf/nips/VaswaniSPUJGKP17 --key vaswani2017attention
dblpcli bibtex conf/nips/VaswaniSPUJGKP17 journals/jmlr/KingmaB14 --output refs.bib
```

### Author commands

```bash
dblpcli author search "Geoffrey Hinton"
dblpcli author get h/GeoffreyEHinton
dblpcli author pubs h/GeoffreyEHinton --year 2020-2024
dblpcli author bibtex h/GeoffreyEHinton
```

### Venue commands

```bash
dblpcli venue search "NeurIPS"
dblpcli venue get conf/nips
dblpcli venue pubs conf/nips --year 2023
```

## Output Formats

All commands support the `--format` / `-f` flag:

- `table` (default) - Human-readable Rich table
- `json` - Structured JSON for agents/scripts
- `bibtex` - BibTeX entries

```bash
dblpcli search "transformers" --format json
dblpcli search "transformers" -f json
```

## License

MIT
mwgencode-mcp 0.1.5
A MCP-based code generation tool for web frameworks
Requires Python: >=3.11

# MWGenCode-MCP

一个基于 MCP 的 Web 框架代码生成工具。

## 功能特点

- 支持多种 Web 框架的代码生成（Flask、AioHTTP、FastAPI）
- UML 模型到 Swagger 类的转换
- 项目初始化和配置文件生成
- 支持自动升级到 K8s 部署

## 安装

```bash
pip install mwgencode-mcp
```

## 使用方法

1. 初始化项目：
```bash
初始化以myproject为项目名的flask项目
```

2. 生成 Swagger 类：
```bash
把myclass生成 Swagger 类
```

3. 添加操作：
```bash
给myclassmng添加添加myopration 操作
```

更多使用说明请参考文档。

## 依赖要求

- Python >= 3.11
- MCP >= 0.1.0
- PyYAML >= 5.1

## mcp server json
```json
{
  "mcpServers": {
    "mwgencode_pypi": {
      "command": "python",
      "args": [
        "-m",
        "mwgencode_mcp.main"
      ]
    }
  }
}
```

## 构建包命令:
```bash
python -m build
pip install -e .p #  本地安装包
python -m twine upload dist/*
```