semvec/pypi-packages huggingface dataset:
flowity 0.0.1
Workflow language designed for LLM.

# Flowity

Workflow language designed for LLM.

## Installation

```bash
pip install flowity
```

## Write flowity code

```flowity
Hello, who are you?

$resp = $query()
$end($resp)
```

## Run a workflow

```python
import os

# === Setup Langchain ===

from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = "sk-xxxx" # Your OpenAI api key
# Set OPENAI_API_BASE if you're using a reverse proxy
# os.environ["OPENAI_API_BASE"] = "https://api.openai.com/v1"

model = ChatOpenAI()

# === Write Flowity Code ===

code = """
Hello, who are you?

$str = $query()
$end($str)
"""

# === Run Flowity Code ===

from flowity.rt import rtime

rt = rtime.FlowityRuntime()

ret = rt.run(
    code=code,
    model=model
)

print(ret)
```
good-coder 0.1.0
The Data Engineer Toolkit
Requires Python: <4.0,>=3.11

# Good Coder [NL] -- The Data Engineer Toolkit

---

**Documentation**: Work In Progress

---

## Installation

```console
$ pip install good-coder
```


### Example Usage

Here's how you can use this package:

```python
from good_coder import GoodCoder

gdc = GoodCoder()
gdc.greet()
```
agent-bdi 2.5.2
Agent BDI framework
Requires Python: >=3.9

# Physiologically Inspired Framework for Complex System Integration


## Introduction
In this study, inspired by the physiological operation of the human body, an efficient and resilient software framework was developed to aid in the integration of mature AI technologies, allowing them to coordinate with one another to accomplish more advanced goals. A navigation system for the visually impaired was then developed to validate the framework, with promising experimental results. This framework can be applied to other types of AI systems.


## Instruction
1. Install the required packages.
````
pip install -r requirements.txt
````
2. Create config.py, rewite the MQTT settings. 
````
cp config.sample.py config.py
````
3. Start the framework.
````
python start.py
````


## Class Diagram
The class diagram of the integration of agents and coordination. HolonicAgent represents the core, and HeadAgents and BodyAgents represent the collection of head agents and subagents, respectively. MQTT is the fundamental communication protocol for agents in the global circulation system, and MqttClient is a private member of HolonicAgent, which allows the agent to have built-in MQTT connection and reception capabilities.

All agents inherit from HolonicAgent to form a hierarchical structure, and they use the DDS to achieve neural message transmission depending on their specific behavior. Each super-agent is a DDS domain that publishes or subscribes to related topics with the required QoS, such as the DEADLINE policy to confirm the date of the data or the TRANSPORT_PRIORITY policy to define the transmission priority order, in order to achieve the purpose of a specific agent.
<figure>
    <img src="https://i.imgur.com/9vIa7JH.png" height=500>
    <figcaption>Class diagram of integration</figcaption>
</figure>


## Sequence Diagram
According to the sequence diagram depicted in below, the DDS and MQTT serve to transmit messages for the agents. Action 1 entails generating an independent process immediately after the root agent is initialized. Action 2 entails subscribing to or publishing relevant topics within the QoS constraints. Action 3 entails recursively calling all the subagents to initiate the action. The agent main action is performed in a separate process of Action 2 until it is notified of its termination. Finally, Action 4 entails generating a global broadcast with MQTT, with a system termination notification serving as an example in this study.
<figure>
    <img src="https://i.imgur.com/6lA3w1X.png" height=500>
    <figcaption>Sequence Diagram of Integration</figcaption>
</figure>
turing-planet 0.1.4
Python Extension Framework For Turing Planet
Requires Python: <3.10.0,>=3.9.0

# turing-planet星球扩展组件
+ _版本v0.0.10_

此模块是星球服务的python扩展组件程序，实现`langchain`和`llamaIndex`的大模型和向量提取接口。
安装本组件后，用户可以按照`langchain`和`llamaIndex`的api调用星球API服务，完成注入会话，文档问答等功能

## 主要模块
- turing_planet.langchain

langchain 模块扩展，集成方式参照`langchain-说明文档`

- turing_planet.llama_index

llama_index 模块扩展，集成方式参照`llamaIndex-说明文档`

## 环境准备

- 本机安装python3，要求3.9
- 部署好星球服务, 版本要求1.2.0_build1026+

```shell
pip install turing-planet
# 如果通过清华镜像无法拉取，尝试以下命令
pip install turing-planet --index-url https://pypi.org/simple
```



## 快速开始
安装`turing-planet`类库后，开始编写你的应用

### 环境变量设置
支持两种环境变量设置
- .env 文件

在你的项目下创建.env文件，文件内容为环境变量设置，如：
```properties
TURING_PLANET_ENDPOINT=127.0.0.1:9980
```

程序中，加载env文件
```python
from dotenv import load_dotenv

# 加载环境变量
load_dotenv()

```


- 系统环境变量

建议通过环境变量的方式设置，星球api服务地址。优先级高于.env 文件

```shell
# 星球api服务
export TURING_PLANET_ENDPOINT=127.0.0.1:9980
```

- 参数传递

当然，如果不设置环境变量，也可以通过参数传入。例如
```python
sparkChat = ChatSparkAI(endpoint="127.0.0.1:9980", domain="turing-general")
```

**domain说明**

    公有云
    xfyun-generalv3.5: V3.5协议
    xfyun-generalv3: V3.0协议
    xfyun-generalv2: V2.0协议
    xfyun-general: V1.5协议
    私有化
    turing-general：通用版本
    turing-iflycode.ge: 私有化代码大模型
    AIUI
    aiui-rewrite-v1: 改写
    aiui-slot-v1: 抽槽