semvec/pypi-packages huggingface dataset:
sheetjuggler 0.1.1
🤹🏻‍♂️ SheetJuggler helps you to copy sheets on google sheets.
Requires Python: >=3.11

# 🤹🏻‍♂️ SheetJuggler v0.1.1

SheetJuggler helps you to copy sheets on gsheets.

### How to use

Initiate using a credentials file:
```python
from sheetjuggler.sheetjuggler import SheetJuggler

sj = SheetJuggler(service_account_file="credentials.json")
gsheet_from = "MY_SOURCE_GSHEET_ID_OR_URL"
gsheet_to = "MY_DESTINY_GSHEET_ID_OR_URL"

sj.copy_sheet(
    gsheet_from=gsheet_from,
    sheet_range_from="Sheet1",
    gsheet_to=gsheet_to,
    sheet_range_to="Sheet1"
    )
```

You can use your own `credentials` instance like:
```python
credentials = "my_own_credentials"
sj = SheetJuggler(credentials=credentials)
```

You can use the `SERVICE_ACCOUNT_FILE` environement variable and initiate directely:
```python
sj = SheetJuggler()
```

## See Also

- Github: https://github.com/bouli/sheetjuggler
- PyPI: https://pypi.org/project/sheetjuggler/

## License
This package is distributed under the [MIT license](https://opensource.org/license/MIT).
adlibo 1.3.0
DEPRECATED - This package has moved to git.adlibo.com (Swiss sovereign registry)
Requires Python: >=3.8
Keywords: adlibo, deprecated, moved, sovereign

# ⚠️ This package has moved

**adlibo** is now hosted on **git.adlibo.com** - our 100% Swiss sovereign infrastructure.

## Why the move?

- PyPI is hosted in the USA (subject to CLOUD Act)
- git.adlibo.com is hosted by Infomaniak in Geneva, Switzerland
- Full data sovereignty compliance (nLPD, GDPR)

## How to install from Swiss registry

```bash
# Install from Adlibo's sovereign registry
pip install adlibo -i https://git.adlibo.com/api/packages/Adlibo/pypi/simple/

# Or add to pip.conf for permanent configuration
# [global]
# extra-index-url = https://git.adlibo.com/api/packages/Adlibo/pypi/simple/
```

## Documentation

- https://www.adlibo.com/docs/sdks
- https://git.adlibo.com/Adlibo/sdk-python
securelend 0.1.0
SecureLend SDK for Python
Requires Python: >=3.8

# SecureLend SDK for Python [![PyPI version](https://img.shields.io/pypi/v/securelend.svg)](https://pypi.org/project/securelend/)

The official Python SDK for SecureLend - Financial services infrastructure for AI assistants.

## Installation

```bash
pip install securelend
```

## Quick Start

```python
import asyncio
import os
from securelend import SecureLend

async def main():
    # The API server is public and does not require an API key.
    # A key can optionally be provided for usage tracking.
    async with SecureLend(api_key=os.environ.get("SECURELEND_API_KEY")) as securelend:
        response = await securelend.compare_business_loans({
            "loanAmount": 200000,
            "purpose": "equipment",
            "annualRevenue": 1200000,
        })

        print(f"Found {response.summary.total_offers} loan offers.")

        if response.offers:
            top_offer = response.offers[0]
            apr = top_offer.terms.interest_rate.apr * 100
            print(f"Top offer from {top_offer.lender.name} with {apr:.2f}% APR.")

            # Calculate payment details for the top offer
            payment_details = await securelend.calculate_loan_payment({
                "loanAmount": top_offer.terms.amount.amount,
                "interestRate": apr,
                "loanTermInMonths": top_offer.terms.term_months,
            })
            print(f"  - Estimated Monthly Payment: ${payment_details.monthly_payment:,.2f}")


if __name__ == "__main__":
    asyncio.run(main())
```

## Documentation

- [Full Documentation](https://docs.securelend.ai)
- [API Reference](https://docs.securelend.ai/api)
- [Guides](https://docs.securelend.ai/guides)
llama-index-llms-siliconflow 0.4.3
llama-index llms siliconflow integration
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: SiliconFlow

## 1. Product Introduction

SiliconCloud provides cost-effective GenAI services based on an excellent open-source foundation model.
introduction: https://docs.siliconflow.cn/introduction

## 2. Product features

- As a one-stop cloud service platform that integrates top large models, SiliconCloud is committed to providing developers with faster, cheaper, more comprehensive, and smoother model APIs.

  - SiliconCloud has been listed on Qwen2.5-72B, DeepSeek-V2.5, Qwen2, InternLM2.5-20B-Chat, BCE, BGE, SenseVoice-Small, Llama-3.1, FLUX.1, DeepSeek-Coder-V2, SD3 Medium, GLM-4-9B-Chat, A variety of open-source large language models, image generation models, code generation models, vector and reordering models, and multimodal large models, including InstantID.

  - Among them, Qwen 2.5 (7B), Llama 3.1 (8B) and other large model APIs are free to use, so that developers and product managers do not need to worry about the computing power costs caused by the R&D stage and large-scale promotion, and realize "token freedom".

- Provide out-of-the-box large model inference acceleration services to bring a more efficient user experience to your GenAI applications.

## 3. Installation

```shell
pip install llama-index-llms-siliconflow
```

## 4. Usage

### Complete/Chat

```python
import asyncio
import os
from llama_index.core.llms import ChatMessage
from llama_index.llms.siliconflow import SiliconFlow

llm = SiliconFlow(
    api_key=os.getenv("SILICONFLOW_API_KEY"),
)

response = llm.complete("...")
print(response)

response = asyncio.run(llm.acomplete("..."))
print(response)

messages = [ChatMessage(role="user", content="...")]

response = llm.chat(messages)
print(response)

response = asyncio.run(llm.achat(messages))
print(response)
```

### Function Calling

```python
from llama_index.llms.siliconflow import SiliconFlow

llm = SiliconFlow(
    api_key=os.getenv("SILICONFLOW_API_KEY"),
)
tools = [
    {
        "type": "function",
        "function": {
            "name": "add",
            "description": "Compute the sum of two numbers",
            "parameters": {
                "type": "object",
                "properties": {
                    "a": {
                        "type": "int",
                        "description": "A number",
                    },
                    "b": {
                        "type": "int",
                        "description": "A number",
                    },
                },
                "required": ["a", "b"],
            },
        },
    },
    ...,
]
response = llm.complete("...", tools=tools)
print(llm.get_tool_calls_from_response(response))

# output
# [ToolSelection(tool_id='...', tool_name='add', tool_kwargs={'a': x, 'b': x})]
```
llama-index-embeddings-siliconflow 0.3.2
llama-index embeddings siliconflow integration
Requires Python: <4.0,>=3.9

# LlamaIndex Embeddings Integration: SiliconFlow

## 1. Product Introduction

SiliconCloud provides cost-effective GenAI services based on an excellent open-source foundation model.
introduction: https://docs.siliconflow.cn/introduction

## 2. Product features

- As a one-stop cloud service platform that integrates top large models, SiliconCloud is committed to providing developers with faster, cheaper, more comprehensive, and smoother model APIs.

  - SiliconCloud has been listed on Qwen2.5-72B, DeepSeek-V2.5, Qwen2, InternLM2.5-20B-Chat, BCE, BGE, SenseVoice-Small, Llama-3.1, FLUX.1, DeepSeek-Coder-V2, SD3 Medium, GLM-4-9B-Chat, A variety of open-source large language models, image generation models, code generation models, vector and reordering models, and multimodal large models, including InstantID.

  - Among them, Qwen 2.5 (7B), Llama 3.1 (8B) and other large model APIs are free to use, so that developers and product managers do not need to worry about the computing power costs caused by the R&D stage and large-scale promotion, and realize "token freedom".

- Provide out-of-the-box large model inference acceleration services to bring a more efficient user experience to your GenAI applications.

## 3. Installation

```shell
pip install llama-index-embeddings-siliconflow
```

## 4. Usage

```python
import asyncio
import os
from llama_index.embeddings.siliconflow import SiliconFlowEmbedding

embedding = SiliconFlowEmbedding(
    model="BAAI/bge-m3",
    api_key=os.getenv("SILICONFLOW_API_KEY"),
)

response = embedding.get_query_embedding("...")
print(response)

response = asyncio.run(embedding.aget_query_embedding("..."))
print(response)
```
compiled-knowledge 4.2.2
A Python package for compiling and querying discrete probabilistic graphical models.
Requires Python: >=3.12

Compiled Knowledge
==================

Compiled Knowledge is a Python package for compiling and querying discrete probabilistic graphical models.
The aim of the project is:
  - to provide a Python library for compiling and querying
    probabilistic graphical models, specifically discrete factor graphs,
    which includes Bayesian networks
  - to be extremely efficient, flexible, and easy to use
  - to exhibit excellent design, code, and documentation
  - to support researchers and businesses wanting to explore and use 
    probabilistic artificial intelligence.

License
=======

MIT license (see the file `LICENSE.txt`).


More Information
================

Refer to the project online documentation at
[compiled-knowledge.readthedocs.io](https://compiled-knowledge.readthedocs.io/).

The primary repository for the project is 
[github.com/ropeless/compiled_knowledge](https://github.com/ropeless/compiled_knowledge). 

The Python package is available on PyPI, see
[pypi.org/project/compiled-knowledge](https://pypi.org/project/compiled-knowledge/).

For more information email
[info@compiledknowledge.org](mailto:info@compiledknowledge.org).