semvec/pypi-packages huggingface dataset:
chatcsvai 0.2.2
A chatbot for interacting with CSV files using AI.
Requires Python: >=3.8

# ChatCSV AI

ChatCSV AI is a Python application that allows users to interact with CSV files using natural language queries. It leverages AI models to provide insights and perform operations on the data contained within CSV files.

## Features

- Query CSV data using SQL-like syntax.
- Retrieve data semantically using natural language.
- Execute Python code for calculations and data manipulations.
- Interactive command-line interface for user-friendly interactions.

## Installation

To install the project, clone the repository and navigate to the project directory. Then, run the following command:

```bash
pip install chatcsvai
```

## Usage

To start the application, use the following command:

```bash
chatcsvai --csv <csv-file-location> --desc "<file-description" --groq-api-key <api-key> --model-name <model-name>

```

### Command-Line Arguments

- `--csv`: Path to the CSV file (required).
- `--desc`: Description of your data (required).
- `--groq-api-key`: Your GROQ API Key (required).
- `--model-name`: (Optional) Specify the GROQ model name. Defaults to `meta-llama/llama-4-scout-17b-16e-instruct`.

## Example

```bash
python main.py --csv data.csv --desc "Sales data for 2023" --groq-api-key your_api_key --model-name your_model_name
```

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.
foxbobbyMCP 0.1.0
支持多种大模型对话/生图/音频的MCP服务器
Requires Python: >=3.7

# foxbobbyMCP

多模态大模型 MCP 服务器，支持对话、生图、音频等能力，自动调用 cloudAPI 支持的多种大模型。

## 功能
- fox_chat：对话（支持 OpenAI/Deepseek 等）
- fox_image：生图（支持 OpenAI/Midjourney 等）
- fox_audio：文本转音频（支持 OpenAI TTS）

## 安装

```bash
pip install foxbobbyMCP
```

## .env 配置

在项目根目录下创建 `.env` 文件，内容如下：

```
CLOUD_API_URL=https://你的cloudapi服务器地址
CLOUD_API_KEY=你的API密钥
PYPI_TOKEN=你的pypi token
```

## 启动 MCP 服务器

```bash
# 使用 uvx 启动
uvx foxbobbyMCP

# 或直接运行
python -m foxbobbyMCP
```

## MCP 工具说明

- `fox_chat(prompt: str, model: str = "gpt-4.1")`：对话
- `fox_image(prompt: str, model: str = "dall-e-3", size: str = "1024x1024")`：生图
- `fox_audio(text: str, model: str = "tts-1", voice: str = "alloy")`：文本转音频

## 平台集成（如阿里云百炼）

MCP服务配置示例：

```json
{
  "mcpServers": {
    "foxbobby-mcp": {
      "command": "uvx",
      "args": ["foxbobbyMCP"]
    }
  }
}
```

## 许可证

MIT License
biogeme-optimization 0.0.11
Various optimization algorithms for teaching and research

# biogeme-optimization
[![PyPi](https://img.shields.io/pypi/v/biogeme_optimization.svg)](https://pypi.python.org/pypi/biogeme_optimization)

Various optimization algorithms used for teaching and research. In particular, they are used by Biogeme.

The package contains the following modules:

## algebra
It contains functions dealing with linear algebra:

- A modified Cholesky factorization introduced by Schnabel and Eskow (1999)
- The calculation of a descent direction based on this factorization.

## bfgs
The functions in this module calculate 

- the BFGS update of the hessian approximation (see Eq. (13.12) in Bierlaire (2015)),
- the inverse BFGS update of the hessian approximation (see Eq. (13.13) in Bierlaire (2015)).

## bounds
This module mainly defines the class `Bounds`that manages the bound constraints.

## diagnostics
This module defines the diagnostic of some optimization subproblems
(dogleg, and comjugate gradient).

## exceptions
It defines the `OptimizationError` exception.

## format
It defines the class `FormattedColumns` that formats the information
reported at each iteration of an algorithm.

## function
It defines the abstract class `FunctionToMinimize` that encapsulate
the calculation of the objective function and its derivatives.

## hybrid_function
It defines the class `HybridFunction` that calculates the objective
function and its derivatives, where the second derivative can be
either the analytical hessian, or a BFGS approximation.

## linesearch
This module implements the line search algorithms (see Chapter 11 in Bierlaire, 2015).

## simple_bounds
This module implements the minimization algorithm under bound
constraints proposed by Conn et al. (1988).

## trust_region
This module implements the trust region algorithms (see Chapter 12 in
Bierlaire, 2015).

## References

- Bierlaire, M. (2015). [Optimization: Principles and
  Algortihms](https://transp-or.epfl.ch/books/optimization/html/OptimizationPrinciplesAlgorithms2018.pdf). EPFL
  Press.
- Conn, A. R., Gould, N. I. M and Toint, Ph. L. (1988) [Testing a
  Class of Methods for Solving Minimization Problems with Simple
  Bounds on the
  Variables](https://www.ams.org/journals/mcom/1988-50-182/S0025-5718-1988-0929544-3/S0025-5718-1988-0929544-3.pdf). Mathematics
  of Computation, 50(182), 399-430.
- Schnabel, R. B. and Eskow, E. (1999) [A Revised Modified Cholesky Factorization Algorithm](https://doi.org/10.1137/s105262349833266x). SIAM Journal on Optimization, 9(4), 1135-1148.
brainminer 0.1.2
worldquant brain alpha miner
Requires Python: <4.0,>=3.10

# Config.yaml Format



# Changelog

## 0.1.0 (2025-04-25)

- Support basic operations for worldquant brain: login, alpha simulation, alpha query, dataset query

## 0.1.1(2025-04-27)

- Add more utility functions for alpha mining

## 0.1.2(2025-05-20)

- Support basic alpha miner functionality

# Commands

+ Local debug installation：`pip install -e .`
+ Publish to pypi: `poetry publish --build`
dappier 0.3.6
The Dappier Python SDK provides an easy-to-use interface for interacting with the Dappier API.
Requires Python: <4.0,>=3.9

# Dappier Python SDK

The Dappier Python SDK provides an easy-to-use interface for interacting with the Dappier API.

---

## Installation

Install the SDK using pip:

```bash
pip install dappier
```

---

## Initialization

Below is an example of how to use the Dappier SDK:

```python
import os
from dappier import Dappier

# Set your API key as an environment variable
os.environ["DAPPIER_API_KEY"] = "<YOUR_API_KEY>"

# Initialize the Dappier SDK
app = Dappier()
```

Replace `<YOUR_API_KEY>` with actual Dappier API key which you can get from your [Dappier account](https://platform.dappier.com/home).

---

## Real-Time Search

You can perform a real-time search by providing a query. This will search for real-time data related to your query. You can pick a specific aimodel from [marketplace](https://marketplace.dappier.com/).

```python
response = app.search_real_time_data(query="What is the stock price of Apple ?", ai_model_id="am_01j06ytn18ejftedz6dyhz2b15")
```

---

## AI Recommendations

The AI Recommendations feature allows you to query for articles and other content using a specific data model. You can pick a specific datamodel from [marketplace](https://marketplace.dappier.com/).

```python
response = app.get_ai_recommendations(
    query="latest tech news",
    data_model_id="dm_02hr75e8ate6adr15hjrf3ikol",
    similarity_top_k=5,
    ref="techcrunch.com",
    num_articles_ref=2,
    search_algorithm="most_recent"
)
```

For detailed documentation and advanced features, refer to the official [Dappier documentation](https://docs.dappier.com/quickstart).
news-server 0.1.0
A MCP server for news search and sentiment analysis
Requires Python: >=3.8

# News Server

A MCP server for news search and sentiment analysis with email reporting capabilities.

## Features

- Search Google News by keywords
- Analyze sentiment of news content
- Generate reports and send via email

## Installation

```bash
pip install news-server
```

## Usage

1. Create a `.env` file with required API keys:
   ```
   SERPER_API_KEY=your_serper_key
   DASHSCOPE_API_KEY=your_dashscope_key
   SMTP_SERVER=your_smtp_server
   EMAIL_USER=your_email
   EMAIL_PASS=your_password
   ```

2. Run the server:
```bash
python -m news_server.server
```

## Requirements

- Python 3.8+
- See pyproject.toml for dependencies