semvec/pypi-packages huggingface dataset:
utils-rag 1.0.6
Here is an enhanced version of the LocalEmbeddings class that provides similar functionality to the OpenAI embedding model and includes more features and detailed comments in English and Chinese
Requires Python: >=3.8

# Utils

Mimic langchain to customize some tools for working with local models

## [Utils-RAG](./utils_rag/)

### [EnhancedLocalEmbeddings.py](./utils_rag/EnhancedLocalEmbeddings.py)

Here is an enhanced version of the LocalEmbeddings class that provides similar functionality to the OpenAI embedding model and includes more features and detailed comments in English and Chinese: | 以下是增强版的 LocalEmbeddings 类，它提供了与 OpenAI 嵌入模型类似的功能，并且包括更多特性和详细的中英文注释：

* **Initializing the Local Model | 初始化本地嵌入模型**

```python
# 1. 初始化本地嵌入模型 | Initialize local embedding model
# 使用 SentenceTransformer 模型
# Use a SentenceTransformer model
local_embeddings = EnhancedLocalEmbeddings(model_path="sentence-transformers/all-MiniLM-L6-v2")

# 使用 Hugging Face 模型（需要指定分词器路径）
# Use a Hugging Face model (requires tokenizer path)
local_embeddings_hf = EnhancedLocalEmbeddings(
    model_path="bert-base-uncased",
    tokenizer_path="bert-base-uncased"
)
```

* **Embedding Single Text | 嵌入单个文本**

```python
embedding = local_embeddings.embed_text("The quick brown fox jumps over the lazy dog.")
print("单文本嵌入结果 | Single text embedding:", embedding[:10])  # 打印前 10 个维度
```

* **Embedding Multiple Texts | 嵌入多个文本**

```python
texts = ["Text 1", "Text 2"]
embeddings = local_embeddings.embed_documents(texts)
print("多文本嵌入结果 | Multiple text embeddings:", [embedding[:10] for embedding in embeddings])
```

* **Embedding Single Texts Tsynchronously | 异步嵌入单个文本**

```python
import asyncio
async def async_example():
    embedding = await local_embeddings.aembed_text("Async embedding example.")
    print("异步单文本嵌入结果 | Async single text embedding:", embedding[:10])

asyncio.run(async_example())

```

* **Embedding Multipes Texts Tsynchronously | 异步嵌入单个文本**

```python
import asyncio

# 异步嵌入多个文本
async def async_example():
    texts = ["Text 1 for async.", "Text 2 for async."]
    embeddings = await local_embeddings.aembed_documents(texts)
    print("异步多文本嵌入结果 | Async multiple text embeddings:", [embedding[:10] for embedding in embeddings])

asyncio.run(async_example())

```

* **Embedded Query Text | 嵌入查询文本**

```python
query = "What is the meaning of life?"
query_embedding = local_embeddings.embed_query(query)
print("查询嵌入结果 | Query embedding:", query_embedding[:10])
```

* **Batch Embedding of Multiple Text | 批量嵌入多个文本**

```python
large_texts = ["Sample text " + str(i) for i in range(100)]
batch_embeddings = local_embeddings.embed_batch(large_texts, batch_size=16)
print("批量嵌入结果 | Batch embeddings:", [embedding[:10] for embedding in batch_embeddings[:3]])
```

* **Direct Call Example | 直接调用实例**

```python
texts = ["Direct call example 1.", "Direct call example 2."]
embeddings = local_embeddings(texts)
print("直接调用嵌入结果 | Direct call embeddings:", [embedding[:10] for embedding in embeddings])
```
pro-search 0.2.3
An advanced web search package combining AI and web scraping
Requires Python: >=3.7

# ProSearch

A powerful web search and content synthesis tool that leverages GROQ AI to provide comprehensive, context-rich answers to your queries.

## Features

Advanced web searching with filtering of sponsored content
Intelligent query refinement
Content scraping and summarization
Multi-source context synthesis

## Installation

```bash
pip install pro-search
```

## Usage
Basic usage example:

```python
from pro_search import ProSearch

# Initialize with your GROQ API key
searcher = ProSearch(api_key="your_groq_api_key")

# Run a search query
query = "What are the latest developments in quantum computing?"
result = searcher.run(query)

print(result)
```

## Configuration

The ProSearch class accepts the following parameters:
`api_key`: Your GROQ API key (required)
`num_results`: Number of search results to process (default: 5)
`num_retries`: Number of retry attempts for failed searches (default: 5)
Kala-Quantum 1.0.1
A framework for hybrid quantum-classical AI models
Requires Python: >=3.8

# Kala_Quantum

**Kala_Quantum** is a Python framework designed for building and training hybrid quantum-classical AI models. This framework integrates quantum neural networks (QNNs) with classical neural networks (NNs) for advanced AI applications, such as conversational chatbots, predictive modeling, and more.

## Features

- **Quantum Neural Network (QNN):** Implements a multi-layer parameterized QNN for quantum processing.
- **Classical Neural Network (NN):** Provides classical deep learning capabilities using PyTorch.
- **Hybrid Quantum-Classical Model:** Combines quantum and classical components for integrated learning.
- **Custom Quantum Gates:** Includes predefined single-qubit gates and supports constructing tensor product gates for multi-qubit systems.
- **Extensible:** Modular design for easy customization and integration.

## Installation
```bash
   pip install Kala-Quantum
```
## Usage

### Quantum and Classical Integration
The framework allows defining a hybrid quantum-classical model:

```python
from kala_quantum import QuantumAI

# Initialize the model
quantum_ai = QuantumAI(input_size=4, n_qubits=4, n_layers=2, hidden_size=8, output_size=4)

# Example inputs
quantum_inputs = [0.5, 0.8, 1.2, 0.3]  # Quantum embeddings
classical_inputs = [0.1, 0.4, 0.7, 0.9]  # Classical features

# Predict output
outputs = quantum_ai.forward(quantum_inputs, classical_inputs)
print("Model Outputs:", outputs)
```

### Chatbot Example
Train a quantum-classical chatbot with conversational capabilities:

```python
from kala_quantum import QuantumChatBot

# Initialize chatbot model
chatbot = QuantumChatBot(input_size=4, n_qubits=4, n_layers=2, hidden_size=8, output_size=4)

# Train the model (example data required)
# chatbot.train(data, labels)

# Interact with the chatbot
response = chatbot.generate_response("Hello, how are you?")
print("Chatbot Response:", response)
```

## Contributing
We welcome contributions! Please submit issues or pull requests for improvements and fixes.

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

## Contact
For inquiries or support, contact [saikamesh.y@gmail.com].
Utils-RAG 1.0.4
Here is an enhanced version of the LocalEmbeddings class that provides similar functionality to the OpenAI embedding model and includes more features and detailed comments in English and Chinese
Requires Python: >=3.8

# Utils

Mimic langchain to customize some tools for working with local models

## [Utils-RAG](./Utils-RAG/)

### [EnhancedLocalEmbeddings.py](./Utils-RAG/EnhancedLocalEmbeddings.py)

Here is an enhanced version of the LocalEmbeddings class that provides similar functionality to the OpenAI embedding model and includes more features and detailed comments in English and Chinese: | 以下是增强版的 LocalEmbeddings 类，它提供了与 OpenAI 嵌入模型类似的功能，并且包括更多特性和详细的中英文注释：

* **Initializing the Local Model | 初始化本地嵌入模型**

```python
# 1. 初始化本地嵌入模型 | Initialize local embedding model
# 使用 SentenceTransformer 模型
# Use a SentenceTransformer model
local_embeddings = EnhancedLocalEmbeddings(model_path="sentence-transformers/all-MiniLM-L6-v2")

# 使用 Hugging Face 模型（需要指定分词器路径）
# Use a Hugging Face model (requires tokenizer path)
local_embeddings_hf = EnhancedLocalEmbeddings(
    model_path="bert-base-uncased",
    tokenizer_path="bert-base-uncased"
)
```

* **Embedding Single Text | 嵌入单个文本**

```python
embedding = local_embeddings.embed_text("The quick brown fox jumps over the lazy dog.")
print("单文本嵌入结果 | Single text embedding:", embedding[:10])  # 打印前 10 个维度
```

* **Embedding Multiple Texts | 嵌入多个文本**

```python
texts = ["Text 1", "Text 2"]
embeddings = local_embeddings.embed_documents(texts)
print("多文本嵌入结果 | Multiple text embeddings:", [embedding[:10] for embedding in embeddings])
```

* **Embedding Single Texts Tsynchronously | 异步嵌入单个文本**

```python
import asyncio
async def async_example():
    embedding = await local_embeddings.aembed_text("Async embedding example.")
    print("异步单文本嵌入结果 | Async single text embedding:", embedding[:10])

asyncio.run(async_example())

```

* **Embedding Multipes Texts Tsynchronously | 异步嵌入单个文本**

```python
import asyncio

# 异步嵌入多个文本
async def async_example():
    texts = ["Text 1 for async.", "Text 2 for async."]
    embeddings = await local_embeddings.aembed_documents(texts)
    print("异步多文本嵌入结果 | Async multiple text embeddings:", [embedding[:10] for embedding in embeddings])

asyncio.run(async_example())

```

* **Embedded Query Text | 嵌入查询文本**

```python
query = "What is the meaning of life?"
query_embedding = local_embeddings.embed_query(query)
print("查询嵌入结果 | Query embedding:", query_embedding[:10])
```

* **Batch Embedding of Multiple Text | 批量嵌入多个文本**

```python
large_texts = ["Sample text " + str(i) for i in range(100)]
batch_embeddings = local_embeddings.embed_batch(large_texts, batch_size=16)
print("批量嵌入结果 | Batch embeddings:", [embedding[:10] for embedding in batch_embeddings[:3]])
```

* **Direct Call Example | 直接调用实例**

```python
texts = ["Direct call example 1.", "Direct call example 2."]
embeddings = local_embeddings(texts)
print("直接调用嵌入结果 | Direct call embeddings:", [embedding[:10] for embedding in embeddings])
```
libspectrum 0.0.2
Library for communication with VMK spectrometer through USB interface
Requires Python: >=3.11
Keywords: USB, library, spectrometer

# Libspectrum

<div align="center">

![PyPI Version](https://img.shields.io/pypi/v/libspectrum)
![Build Status](https://img.shields.io/github/actions/workflow/status/routybor/libspectrum/release.yml)
[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://routybor.github.io/libspectrum/)

Python библиотека для работы со спектрометрами производства VMK через USB интерфейс.

🔍 [Документация](https://routybor.github.io/libspectrum/)
📦 [PyPI](https://pypi.org/project/libspectrum/)

</div>

## ✨ Возможности

- 📈 Получение спектральных данных
- 🎯 Калибровка по темновому сигналу
- ⚙️ Возможность неблокирующего и непрерывного чтения
- 💾 Сохранение и загрузка измерений
- ➗ Математические операции со спектрами

## 📈 Демо приложение

В рамках проекта для демонстрации возможностей неблокирующего чтения было написанно небольшое
демо приложение, для визуализации спектра в реальном времени.

<p align="center">
  <img width="70%" src="/docs/gui_short.gif" alt="Banner">
</p>

## 💻 Поддерживаемые платформы

| ОС | Статус | Реализация |
|----|--------|------------|
| Windows | ✅ | Python + ftd2xx |
| Linux (Ubuntu) | ⚠️ | C++ + libftdi |
| macOS | ❌ | Не поддерживается |