semvec/pypi-packages huggingface dataset:
flying-delta-tools-openapi 0.1.0
llama-index tools openapi integration
License: MIT
Requires Python: >=3.8.1,<3.12

# OpenAPI Tool

This tool loads an OpenAPI spec and allow the Agent to retrieve endpoints and details about endpoints. The RequestsToolSpec can also be laoded into the agent to allow the agent to hit the nessecary endpoints with a REST request.

## Usage

This tool has more extensive example usage documented in a Jupyter notebook [here](https://github.com/emptycrown/llama-hub/tree/main/llama_hub/tools/notebooks/openapi_and_requests.ipynb)

Here's an example usage of the OpenAPIToolSpec.

```python
from llama_hub.tools.openapi import OpenAPIToolSpec
from llama_index.agent import OpenAIAgent

f = requests.get(
    "https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/openai.com/1.2.0/openapi.yaml"
).text
open_api_spec = yaml.safe_load(f)
# OR
open_spec = OpenAPIToolSpec(
    url="https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/openai.com/1.2.0/openapi.yaml"
)


tool_spec = OpenAPIToolSpec(open_api_spec)

agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

agent.chat("What is the base url for the API")
agent.chat("What parameters does the x endpoint need?")
```

`load_openapi_spec`: Returns the parsed OpenAPI spec that the class was initialized with

This loader is designed to be used as a way to load data as a Tool in a Agent. See [here](https://github.com/emptycrown/llama-hub/tree/main) for examples.
Topsis-Jatin-102116096 1.0
TOPSIS implementation by Jatin Thakur
License: MIT

## Topsis Python Package

Made By Jatin Thakur (Roll No. 102116096)

# Description

The Topsis Python Package is a Python library that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision-making method used to determine the best alternative among a set of alternatives based on their performance on multiple criteria.

# Features

Efficient Topsis Analysis: The package efficiently calculates Topsis scores and ranks for decision-making alternatives given a dataset.

Command-Line Interface: Use the command-line interface to run Topsis analysis easily with specified parameters.

Error Handling: The package includes robust error handling to provide clear feedback on incorrect inputs or missing files.

# Installation

pip install topsis-jatin-102116096

# Usage

Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

# Example Usage

The below example is for the data have 5 columns.

python program.py InputDataFile Weights Impacts ResultFileName

Example : python Topsis 102116096-data.csv "1,1,1,1,1" "+,-,+,+,+" 102116096-result.csv

# Important Points

There should be only numeric columns except the first column i.e. Fund Name.
Input file must contain atleast three columns.
Topsis-Jasmeet-102116124 0.0.0
TOPSIS implementation by Jasmeet Kaur
License: MIT

## Topsis Python Package

Made By Jasmeet Kaur (Roll No. 102116124)

# Description

The Topsis Python Package is a Python library that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision-making method used to determine the best alternative among a set of alternatives based on their performance on multiple criteria.

# Features

Efficient Topsis Analysis: The package efficiently calculates Topsis scores and ranks for decision-making alternatives given a dataset.

Command-Line Interface: Use the command-line interface to run Topsis analysis easily with specified parameters.

Error Handling: The package includes robust error handling to provide clear feedback on incorrect inputs or missing files.

# Installation

pip install topsis-jasmeet-102116124

# Usage

Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

# Example Usage

The below example is for the data have 5 columns.

python program.py InputDataFile Weights Impacts ResultFileName

Example : python Topsis 102116124-data.csv "1,1,1,1,1" "+,-,+,+,+" 102116124-result.csv

# Important Points

There should be only numeric columns except the first column i.e. Fund Name.
Input file must contain atleast three columns.
topsis-yashas-102103726 0.0.1
This is a Python Package implementing TOPSIS used for multi-criteria decision analysis method
License: MIT
Keywords: topsis,mcda,UCS654,TIET

# topsis-yashas-102103726
topsis-yashas-102103726 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
## Installation
Use pip package manager to install topsis-yashas-102013726. 

        pip install topsis-yashas-102103726
## Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and the output csv file name. 

        topsis-yashas-102103726 sample.csv "1,1,1,1" "+,-,+,+ output.csv"

### Notes

* The first column and first row are removed by the library before processing, in attempt to remove indices and headers.
* Make sure the csv does not contain categorical values.

## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).