semvec/pypi-packages huggingface dataset:
deeplexer 0.0.4
Free DeepL Pro even Quota Exceeded
License: MIT
Requires Python: >=3.10
Keywords: machine translation, translate, translation, deepl, deeplx

# DeepLexer

Working DeepL Pro even Quota Exceeded.

Use your own risks.

## Usage
```
pip install deeplexer
```

```python
import asyncio
from deeplexer import Deeplexer

async def main():
    session_file = './sessions.json'  # Required. Path to the session be stored.
    username = '<deepl username>'  # Optional if session file is valid
    password = '<deepl password>'  # Optional if session file is valid
    async with Deeplexer(session_file, 
                         username=username, 
                         password=password) as deeplex:
        query = 'Hello World!'
        translation = await deeplex.translate(query, 'EN', 'KO')
        print('Translation:', translation.text)
    print('All jobs have been finished')

if __name__ == '__main__':
    event_loop = asyncio.get_event_loop()
    event_loop.run_until_complete(main())
    event_loop.close()
```

## License

Licensed under the [MIT license](https://github.com/OrigamiDream/deeplexer/blob/main/LICENSE).
pineconeutils 0.0.4
PineconeUtils is a Python module designed to handle and process data for embedding and indexing using Pinecone, Cohere, and OpenAI services. This utility module makes it easy to load, chunk, prepare, and upsert data into a Pinecone index, making it ideal for applications involving text embedding and retrieval augmented systems(RAG)
License: MIT
Requires Python: >=3.7

# PineconeUtils

PineconeUtils is a Python module designed to handle and process data for embedding and indexing using Pinecone, Cohere, and OpenAI services. This utility module makes it easy to load, chunk, prepare, and upsert data into a Pinecone index, making it ideal for applications involving text embedding and retrieval systems(RAG).

## Features

- Load text data from `.txt`, `.docx`, and `.pdf` files.
- Chunk text data for processing.
- Prepare embeddings using either Cohere or OpenAI models.
- Upsert prepared data into a Pinecone index.

## Installation

To install PineconeUtils, you can use pip:

```bash
pip install pineconeutils
```

# Usage

Here's a quick example of how to use PineconeUtils:

## Setup

First, ensure you have the necessary API keys and setup information:

```bash
pinecone_api_key = "your_pinecone_api_key"
cohere_api_key = "your_cohere_api_key"
openai_api_key = "your_openai_api_key"
index_name = "your_index_name"
namespace_id = "your_namespace_id"
```

# Load Data

Load data from a supported file format:

```bash
from pineconeutils import PineconeUtils

# Create instance of PineconeUtils
pinecone = PineconeUtils(pinecone_api_key=pinecone_api_key, openai_api_key=openai_api_key,cohere_api_key =cohere_api_key, index_name=index_name, namespace_id=namespace_id)

path = "path_to_your_file.docx"
data = pinecone.load_data(path)
print("Loaded Data:", data)
```

# Process Data

## Chunk and prepare data for embedding:


## For openai
```bash
chunks = pinecone.chunk_data(data, chunk_size=100, chunk_overlap=10)
print("Data Chunks:", chunks)

prepared_data = pinecone.prepare_data(chunks, model="text-embedding-ada-002", service="openai")
```

## For cohere

```bash
chunks = pinecone.chunk_data(data, chunk_size=100, chunk_overlap=10)
print("Data Chunks:", chunks)

prepared_data = pinecone.prepare_data(chunks, model="embed-english-v3.0", service="cohere",input_type="search_document")
```
For more about Cohere Embeddings: [Cohere Embeddings](https://docs.cohere.com/docs/embeddings)

# Upsert Data

## Upsert data into Pinecone index:

```bash
successful = pinecone.upsert_data(prepared_data)
print("Data upsertion was", "successful" if successful else "unsuccessful")
```

# Development

To contribute to the development of PineconeUtils, you can clone the repository and submit pull requests.

# Support

If you encounter any issues or have questions, please file an issue on the GitHub repository.

# License

This project is licensed under the MIT License - see the LICENSE file for details.