semvec/pypi-packages huggingface dataset:
Fourier-Series-Python 0.5.1
A Fourier Series and Signal Generation Libary to generate coefficients to function

# Fourier-Series-Python

This library is able to generate and plot any number of coefficients passed by the user of any function.
In addition, functionality for generating 1-D signals is added. (for machine learning use cases)

Paper can be found [here](https://drive.google.com/file/d/1qCV_bS05ocZtUJVro-OqHE7ys68Yv-Mi/view?usp=sharing)
transformd 0.3.0
Transform dictionaries into whatever your heart desires (as long as it's another dictionary that kind of looks like the original dictionary)
Requires Python: >=3.8

# transformd

Transform a `dictionary` to another `dictionary`, but keep the same shape based on a `spec`.

## What is a spec?

It is a string (or sequence of strings) that specifies which "parts" of the dictionary should be included or excluded in a new `dictionary` that is returned from the `transform` function.

A `spec` uses dot-notation to specify how to traverse into the `dictionary`. It can also use indexes if the value of the `dictionary` is a list.

Note: `Specs` are applied to the original `dictionary` in the order they are defined -- the `dictionary` gets updated after each spec.

## Examples

```python
from transformd import Transformer

# Initial `dictionary` we'll transform based on a spec below
data = {
    "library": {
        "name": "Main St Library",
        "location": {
            "street": "123 Main St",
            "city": "New York City",
            "state": "NY",
        },
        "books": [
            {
                "title": "The Grapes of Wrath",
                "author": {"first_name": "John", "last_name": "Steinbeck"},
            },
            {
                "title": "Slaughterhouse-Five",
                "author": {"first_name": "Kurt", "last_name": "Vonnegut"},
            },
        ],
    }
}

# Only return a nested part of `data`
assert Transformer(data).transform(spec="library.name") == {
    "library": {
        "name": "Main St Library"
    }
}

# Return multiple parts of `data`
assert Transformer(data).transform(spec=("library.name", "library.location.state")) == {
    "library": {
        "name": "Main St Library",
        "location": {
            "state": "NY"
        },
    }
}

# Return different parts of a nested list in `data`
assert Transformer(data).transform(spec=("library.books.0.title", "library.books.1")) == {
    "library": {
        "books": [
            {
                "title": "The Grapes of Wrath",
            },
            {
                "title": "Slaughterhouse-Five",
                "author": {"first_name": "Kurt", "last_name": "Vonnegut"},
            },
        ],
    }
}

# Exclude pieces from `data` by prefixing a spec with a dash
assert Transformer(data).transform(spec=("-library.books", "-library.location")) == {
    "library": {
        "name": "Main St Library"
    }
}
```

## Why?

I needed this functionality for [`Unicorn`](https://www.django-unicorn.com), but could not find a suitable library. After writing the code, I thought maybe it would be useful for someone else. 🤷

## Run tests

- Install [rye](https://rye-up.com)
- `rye sync`
- `rye run t`

### Test Coverage

- `rye run tc`

## Inspiration

- Django Templates for the dot-notation inspiration
- A lot of existing JSON-related tools, but especially [`glom`](https://glom.readthedocs.io/), [`jello`](https://github.com/kellyjonbrazil/jello), [`jq`](https://jqlang.github.io/jq/), and [`gron`](https://github.com/TomNomNom/gron); all of which did not quite do what I wanted, but were useful on the journey
langassist 0.0.9
The framework for language assistants
Requires Python: >=3.10

# Assistants
Developing language assistants with LLMs in a low-code way.

## Quick Install

Install using pip:

```bash
pip install langassist
```
The framework requires a database to register the assistants. The default database used is MongoAtlas. Future versions of the framework will support additional database types. Currently, there is a MongoDB collection named assistants which registers each assistant as shown below:

```
_id : 6556db6cf114addad09420c6
assistant_name: "test_assist"
assistant_id: "asst_52TLl1OdJW6ulmLCEsDlizz8"
file_id: "file-lZUBJ3ZnxtkTfqsuE0WYMXDI"

```
## What are Assistants?
Assistants is a framework for developing language-based assistants using micro agents, akin to the microservices concept. Each micro agent, like a microservice, can be independently developed and deployed. The framework adheres to the Single Responsibility Principle (SRP), allowing these assistants to be combined to address complex business processes through workflows.

## Capabilities of the Framework
In time, the framework will include built-in assistants for immediate deployment within OpenAI environments. It will also support the development of custom assistants.

## Contribution
Contributions are welcome in any form, be it through new features or improved documentation.

## Example Usage
The repository includes various examples demonstrating framework usage.
```commandline

# This is an example that shows how to use and deploy the summarization agent
# Deploy the agent first
# and then run the assistant
import os

from assistants.langassist.summarize_assistant import SummarizeAssistant

from dotenv import load_dotenv

from assistants.langregister.mongo_db_atlas import MongoAtlas

load_dotenv()
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
MONGO_PASSWORD = os.getenv("MONGO_PASSWORD")

mongo_client = MongoAtlas()
mongo_client.uri = "mongodb+srv://rajib76:{MONGO_PASSWORD}@cluster0.cp3rxai.mongodb.net/?retryWrites=true&w=majority".format(
    MONGO_PASSWORD=MONGO_PASSWORD)

assistant_name = "summarize_assistant_03"
file = "gen_ai.pdf"
def deploy_assistant():
    sc = SummarizeAssistant(file=file,assistant_name=assistant_name)
    fileobj, assistantobj = sc.deploy_assistant(mongo_client)
    print(fileobj,assistantobj)

def run_assistant():
    sc = SummarizeAssistant(assistant_name=assistant_name)
    question = "Summarize the content in 300 words. Please ensure all points are covered"
    print(sc.run_assistant(question,client=mongo_client))



if __name__ == "__main__":
    # First deploy
    # deploy_assistant()

    #Then run
    run_assistant()


```