semvec/pypi-packages huggingface dataset:
xfao-core 0.0.1
test
Requires Python: >=3.13

# xfao-core

[![](https://img.shields.io/pypi/v/xfao-core.svg?label=PyPI&style=flat-square)](https://pypi.org/pypi/xfao-core/)
[![](https://img.shields.io/pypi/pyversions/xfao-core.svg?label=Python&color=yellow&style=flat-square)](https://pypi.org/pypi/xfao-core/)
[![Downloads](https://static.pepy.tech/personalized-badge/xfao-core?period=total&units=international_system&left_color=black&right_color=brightgreen&left_text=Downloads&style=flat-square)]
wanyun-JitSdk 1.0.7
Official Jit API Authorization SDK
Keywords: python, jit, sdk, apiAuth

# JitSDK

Jit officially provides built-in API authorization components, enabling your applications to easily implement external API interface services. Additionally, we offer a comprehensive SDK to help you quickly and conveniently integrate and use these authorized API interfaces.

## Usage
 1. Install Dependencies

~~~
pip install wanyun_JitSdk
~~~

2. Obtain Authorization Credentials from the Authorization Provider
~~~
API URL: http://demoain/api/orgId/appName
accessKey: xxxx
accessSecret: xxxxxxxxxxxxxxxx
~~~

4. Import and Use in Your Project (refer to the demo file)
~~~ python
authApi = JitApi("http://demoain/api/orgId/appName")
authApi.setAccessKey("xxxx")
authApi.setAccessSecret("xxxxxxxxxxxxxxxx")
authApi.setApi("services.MySvc.func1")

req = JitApiRequest()
req.setMethod("POST")
req.setParams({})
resp = req.execute(authApi)
print(resp.data)
~~~
xf-core 0.0.1
test
Requires Python: >=3.13

# xfao-core

[![](https://img.shields.io/pypi/v/xfao-core.svg?label=PyPI&style=flat-square)](https://pypi.org/pypi/xfao-core/)
[![](https://img.shields.io/pypi/pyversions/xfao-core.svg?label=Python&color=yellow&style=flat-square)](https://pypi.org/pypi/xfao-core/)
[![Downloads](https://static.pepy.tech/personalized-badge/xfao-core?period=total&units=international_system&left_color=black&right_color=brightgreen&left_text=Downloads&style=flat-square)]
types-xlrd 2.0.0.20251020
Typing stubs for xlrd
Requires Python: >=3.9

## Typing stubs for xlrd

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`xlrd`](https://github.com/python-excel/xlrd) package. It can be used by type checkers
to check code that uses `xlrd`. This version of
`types-xlrd` aims to provide accurate annotations for
`xlrd==2.0.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/xlrd`](https://github.com/python/typeshed/tree/main/stubs/xlrd)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.406

It was generated from typeshed commit
[`11e7d904b9745bf33fe5b9b64bcd274ff788b189`](https://github.com/python/typeshed/commit/11e7d904b9745bf33fe5b9b64bcd274ff788b189).
retrieve-sentence-embeddings 0.1.0a0
A simple function to retrieve sentence embeddings from an OpenAI Embeddings-compatible API.
Requires Python: >=2

# `retrieve-sentence-embeddings`

A simple function to retrieve sentence embeddings from
an [OpenAI Embeddings](https://platform.openai.com/docs/guides/embeddings)-compatible API. That includes:

- [OpenAI's embedding models](https://platform.openai.com/docs/api-reference/embeddings) (`base_url='https://api.openai.com/v1`)
- [Google Gemini](https://ai.google.dev/gemini-api/docs/openai#embeddings) (`base_url='https://generativelanguage.googleapis.com/v1beta/openai'`)
- [ollama](https://github.com/ollama/ollama/blob/main/docs/openai.md#v1embeddings) (`base_url='http://localhost:11434/v1'`).

with compatible models.

## Features

- **Compatibility:** Unlike [openai](https://github.com/openai/openai-python), works on Python 2+ with zero non-Python
  dependencies.

## Installation

```bash
pip install retrieve-sentence-embeddings
```

## Usage

```python
# coding=utf-8
from retrieve_sentence_embeddings import retrieve_sentence_embeddings

api_key = u'OPENAI_API_KEY'
base_url = u'https://api.openai.com/v1'
model = u'text-embedding-ada-002'
sentences = [
    u'Hello, world!',
    u'This is a test sentence.',
    u'Embeddings are useful!'
]

sentence_embeddings = retrieve_sentence_embeddings(api_key, base_url, model, sentences)
```

`sentence_embeddings` is a `List[List[float]]`, e.g.,

```
[
    [0.123, -0.982, ...],
    [0.222, 0.088, ...],
    ...
]
```

## Error Handling

If a network error or malformed response is encountered, the exception is propagated.

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## License

This project is licensed under the [MIT License](LICENSE).