semvec/pypi-packages huggingface dataset:
rocket3 20241225.1
A multi-threaded WSGI compiliant and secure web server
Requires Python: >=3.7

# Rocket3

Rocket3 is the multi-threaded web server used by web2py and py4web, stripped of all the Python2 logic and dependencies. It now only supports Python3 and can be used without web2py or py4web.

Rocket was originally developed by Massimo Di Pierro, then rewritten much better by Timoty Ferrell, and then has minor refactorings made by Massimo and other web2py contributors.

## Example

```
from rocket3 import Rocket3 as Rocket

def demo_app(environ, start_response):
    """simple exmaple WSGI app"""
    start_response("200 OK", [("Content-Type", "text/html")])
    data = "<html><body><h1>Hello from Rocket Web Server</h1></body></html>"
    return [data]


server = Rocket(('0.0.0.0', 8080), "wsgi", {"wsgi_app": demo_app})
server.start()
```

## License

BSDv3
mcp-openai 0.2.0
MCP Client with OpenAI compatible API
Requires Python: >=3.11

<div align="center">
  <h1>𝔐&nbsp;&nbsp;mpc-openai&nbsp;&nbsp;✧</h1>
  <p><em>MCP Client with OpenAI compatible API</em></p>
</div>

> Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications.
>
> — [https://modelcontextprotocol.io](https://modelcontextprotocol.io)

______________________________________________________________________

> [!WARNING]
> This project is still in the early stages of development. Support is not planned.

This is a MCP **client** (not a server). It is meant to be used as a library for building LLMs UI that support MCP through an OpenAI compatible API. This opens the door to locally runnable inference engines ([vLLM](https://github.com/vllm-project/vllm), [Ollama](https://github.com/ollama/ollama), [TGI](https://github.com/huggingface/text-generation-inference), [llama.cpp](https://github.com/ggerganov/llama.cpp), [LMStudio](https://github.com/lmstudio-ai), ...) that support providing support for the OpenAI API ([text generation](https://platform.openai.com/docs/guides/text-generation), [function calling](https://platform.openai.com/docs/guides/function-calling), etc.).

## Usage

It is highly recommended to use [uv](https://docs.astral.sh/uv/) in your project based on mpc-openai:

- It manages python installation and virtual environment.
- It is an executable that can run self-contained python scripts (in our case MCP server)
- It is used for CI workflows.

Add mcp-openai to your project dependencies with:

```
uv add mcp-openai
```

or use classic pip install.

### Create a MCP client

Now you can create a MCP client by specifying your custom configuration.

```python
from mcp_openai import MCPClient
from mcp_openai import config

mcp_client_config = config.MCPClientConfig(
    mcpServers={
        "the-name-of-the-server": config.MCPServerConfig(
            command="uv",
            args=["run", "path/to/server/scripts.py/or/github/raw"],
        )
        # add here other servers ...
    }
)

llm_client_config = config.LLMClientConfig(
    api_key="api-key-for-auth",
    base_url="https://api.openai.com/v1",
)

llm_request_config = config.LLMRequestConfig(model=os.environ["MODEL_NAME"])

client = MCPClient(
    mcp_client_config,
    llm_client_config,
    llm_request_config,
)
```

### Connect and process messages with MCP client

```python
async def main():

    # Establish connection between the client and the server.
    await client.connect_to_server(server_name)

    # messages_in are coming from user interacting with the LLM
    # e.g. UI making use of this MCP client.
    messages_in = ...
    messages_out = await client.process_messages(messages_in)

    # messages_out contains the LLM response. If required, the LLM make use of
    # the available tools offered by the connected servers.
```
ai-search-api 0.0.8.2.9.1
search the future [test version]

# AI-Search

## What is it?

AI-Search is a powerful tool that allows you to perform intelligent searches on local files and services within the books folder. It uses artificial intelligence to select files and answers with greater ease and precision. It can also better understand your queries with a rich database of information.

## What can it do?

search with artificial intelligence all your files or services inside the books folder.

It is able to understand and select files and responses much more easily and precisely thanks to its new database full of information to use at any time.

By writing the URL of a web page you will see the text contained in it.

New API feature. Every data of our AI is loaded from an API in seconds, it also works offline.

## How to run the AI?

Running our application is very simple, you just need to run the main.py file 
or write the following code in the main folder of the project

### Using the PyPi library

> Install the library

```bash
pip3 install ai-search-api
```

> Import the library in your code

```python
import ai_search_api as ai
```

> Update the library

```python
ai.update()
```

> Ask a question

```python
while True:
	question = input("you: ")
	print("ai-search: " + str(ai.request(question)))
```

### Using the repository

> First import these two libraries that are needed to run the code

```python
    import update
    import ai_search.main as ai
```
> Now write this code that allows you to send questions and receive information from Ai-search
```python
    #infinite questions
    while True:
        # set the request
        ai.req = input('ai-search: ')
        ai.run()
        results = ai.result

        # print the results
        print('results: ')
        for result in results:
            print(result)
            print()
```

## Code Structure
main.py
> This is the entry point for the application. It handles user input and displays the results from the AI-powered search.

> ai_search/main.py
This module contains the core logic for the AI engine. It processes the search queries and retrieves the results based on local files or web scraping.

> update.py (Optional)
If the project includes an update mechanism, this file will manage it. If not needed, you can ignore it.

---

### Contributing
We welcome contributions to improve AI-Search! If you'd like to help, you can:

> Fork the repository

>Create a new branch for your changes

>Submit a pull request with a description of your modifications

>Please make sure to follow the project guidelines and write tests for any new features you add.

### License
This project is licensed under the MIT License. See the LICENSE file for details.

---

### Installation requirements

Make sure you have Python 3.x installed on your system.
```bash
Flask==3.0.0
requests==2.31.0
```

---
### Install Dependencies
Ensure that all necessary Python dependencies are installed. If a requirements.txt file is provided, you can install the dependencies using pip:

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

By @neopadAI
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