semvec/pypi-packages huggingface dataset:
infix-postfix 1.2.0
Converts Infix to Postfix and vice versa...
License: MIT
Keywords: python,arithmetic,tools,arith-tools,arithmetic operations,Math,python3,pip,pip3,fibonacci,armstrong,boiled-egg,terrace,unique,amicable,infix,postfix,infix2postfix,infixtopostfix,infix-postfix,infix_postfix

<div align="center">
<p>
<image src="./res/banner.jpg"/><br>
An application to convert infix to postfix and vice versa...</p>
</div>

# Key Features
- Runs standalone
- Converts Infix expression to postfix expression.
- Converts postfix expression to infix expression.


# Basic Requirements (Pre-installed)
- Python 3.x
- Windows OS or MacOS or Other Linux Distribution.


# Installation
- Open terminal/cmd in windows/macos respectively.
- Run the command `pip install infix-postfix`.


# Usage
- To convert infix to postfix, run `infix-postfix --infix "${infix_expression}"`
- To convert postfix to infix, run `infix-postfix --postfix "${postfix_expression}"`
> NOTE : Double quotes ("") is must for writing the expression to get correct output..

```
How to write expressions? 
- INFIX expression
    - Correct Methods
        - "( A+B)* C/D"
        - "((A+B)*C/D+E^F)/G"
        - "20.1 + (5 ^ 2 / 2)"
        - "20.1 + ( 5 ^ 2 / 2)"
        - etc...

- POSTFIX ecpression
    - Correct Methods
        - "A B + C * D /"
        - "A B C D + E F + * G / + * H *"
        - "20.1 5 +"
    - Wrong Methods
        - "AB+C*D/"
        - "ABCD+EF+*G/+*H*"
        - "20.1 5+"
        - "20 5+"
    - In One line, You have to give spaces after every value
```


- To run this application in your own application or python script, run
 
```python
from infix_postfix.infix_postfix import infix,postfix
$(variable) = $(infix/postfix){expression}
print($(variable))
```

Example :- 



<image src="./res/example.jpg" />

# Contribution
Anyone is free to contribute to this open source project. 🎉🎉
auto-labeling-pipeline 0.1.23
Auto labeling pipeline for doccano
License: MIT

# Auto labeling pipeline

Auto labeling pipeline helps doccano to annotate a document automatically. This package is intended to be used from the inside of doccano. You shouldn't use this package directly.

## Installation

To install auto-labeling-pipeline, simply run:

```bash
pip install auto-labeling-pipeline
```

## How to contribute

You can contribute this project by adding new templates as follows:

1. Add a new request model class to [models.py](https://github.com/doccano/auto-labeling-pipeline/blob/master/auto_labeling_pipeline/models.py).
2. Add a new template(Jinja2 format) to [templates/](https://github.com/doccano/auto-labeling-pipeline/tree/master/auto_labeling_pipeline/templates) directory.
3. Add a new template class to [mappings.py](https://github.com/doccano/auto-labeling-pipeline/blob/master/auto_labeling_pipeline/mappings.py).
4. Add a new option to [menu.py](https://github.com/doccano/auto-labeling-pipeline/blob/master/auto_labeling_pipeline/menu.py).
5. Testing.

## License

[MIT](https://github.com/doccano/auto-labeling-pipeline/blob/master/LICENSE)
chatting-with-pdfs 0.0.12
Load a PDF file and ask questions via llama_index and GPT.
License: MIT
Requires Python: >=3.10

# About Python Chat PDF (GPT Index) Project

Load your PDFs data folder and ask questions via llama_index and GPT.

---

## What is LlamaIndex
LlamaIndex (GPT Index) is a <b>data framework</b> for your LLM application.

### Context
- LLMs are a phenomenonal piece of technology for knowledge generation and reasoning. They 
are pre-trained on large amounts of publicly available data.
- The best approach to augment LLMs with our own private data, we need a comprehensive toolkit 
to help perform this data augmentation for LLMs.

---

### Proposed Solution
That’s where LlamaIndex comes in. LlamaIndex is a “data framework” to help you build LLM apps. 
It provides the following tools:

- Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)

- Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.

- Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get 
back retrieved context and knowledge-augmented output.

- Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, 
ChatGPT, anything else).

LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows 
beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. Our lower-level 
APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, 
query engines, reranking modules), to fit their needs.

---

### What does load_index_from_storage do and how does it work? 
load_index_from_storage is a function that loads an index from a 
StorageContext object. It takes in a StorageContext object and an optional 
index_id as parameters. If the index_id is not specified, it assumes there is 
only one index in the index store and loads it. It then passes the index_ids 
and any additional keyword arguments to the load_indices_from_storage 
function. This function then retrieves the index structs from the index store 
and creates a list of BaseGPTIndex objects. If the index_ids are specified, it
will only load the indices with the specified ids. Finally, the function 
returns the list of BaseGPTIndex objects.

---

## Getting Started

## Instructions

- Install the requirements

```bash
pip install -r requirements.txt
```

- Get a GPT API key from [OpenAI](https://platform.openai.com/account/api-keys) if you don't have one already.

- Run the script.

```bash
python3 chat_with_pdfs.py <"data_folder_path"> <"open_api_key">
```

- Ask any questions about the content of the PDF.

### How to use our package:

```bash
from chat_pdf.chat_with_pdfs import ask_a_question
import sys

folder_name = sys.argv[1]
api_key = sys.argv[2]
print(ask_a_question(folder_name, api_key))
```