semvec/pypi-packages huggingface dataset:
topsis-3112 0.1.0
TOPSIS implementation for MCDM problems
Requires Python: >=3.8

# TOPSIS-Atishay-102303112

**Project-1 (UCS659)** **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**

`topsis-aj` is a Python library designed to solve **Multiple Criteria Decision Making (MCDM)** problems. It ranks alternatives based on their distance from an ideal solution and a negative-ideal solution.

---

## 👤 Author Information
* **Name:** Atishay Jain
* **Roll No:** 102303112
* **Group:** 3C14

---

## 📦 Installation

Use the package manager `pip` to install the library:

```bash
pip install topsis-aj
```
## Usage

Run the TOPSIS command using the following syntax:
```bash
topsis <inputFileName.csv> <weights> <impacts> <resultFileName.csv>
```
## Example
topsis sample.csv "1,1,1,1" "+,-,+,+"

## Help
To view the usage instructions:
```bash
topsis /h
```
Example Dataset
sample.csv
| Model | Storage space (in GB) | Camera (in MP) | Price (in $) | Looks (out of 5) |
|-------|-----------------------|---------------|--------------|-----------------|
| M1    | 16                    | 12            | 250          | 5               |
| M2    | 16                    | 8             | 200          | 3               |
| M3    | 32                    | 16            | 300          | 4               |
| M4    | 32                    | 8             | 275          | 4               |
| M5    | 16                    | 16            | 225          | 2               |

## Weights Vector
```bash
[0.25, 0.25, 0.25, 0.25]
```
## Impacts Vector
```bash
[+, +, -, +]
```
## Input command
```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv
```
## Output
TOPSIS RESULTS
-----------------------------

 | Index | P-Score  | Rank |
|-------|----------|------|
| 1     | 0.534277 | 3    |
| 2     | 0.308368 | 5    |
| 3     | 0.691632 | 1    |
| 4     | 0.534737 | 2    |
| 5     | 0.401046 | 4    |


## Notes

* The first column and header row are removed before processing.

* All columns from the second to last must contain numeric values only.

* The number of weights, impacts, and criteria columns must be equal.

* Impacts must be either + or -.

## License

This project is licensed under the MIT License.
llm-workers-tools 1.0.0
Command-line tools for LLM Workers - llm-workers-cli and llm-workers-chat
Requires Python: <3.15,>=3.12

# llm-workers-tools

Command-line tools for LLM Workers.

## Overview

`llm-workers-tools` provides command-line entry points for working with LLM scripts:

- **llm-workers-cli**: Batch processing tool for running LLM scripts with command-line or stdin input
- **llm-workers-chat**: Interactive chat interface for conversational LLM interactions

## Installation

```bash
pip install llm-workers-tools
```

This will install both `llm-workers` (core) and `llm-workers-console` (UI) as dependencies.

## Usage

### Batch CLI Tool

Process prompts in batch mode:

```bash
# Single prompt
llm-workers-cli my-script.yaml "What is the capital of France?"

# Multiple prompts
llm-workers-cli my-script.yaml "prompt 1" "prompt 2" "prompt 3"

# Read from stdin
echo "prompt from stdin" | llm-workers-cli my-script.yaml -

# Enable verbose output
llm-workers-cli --verbose my-script.yaml "prompt"

# Enable debug mode
llm-workers-cli --debug my-script.yaml "prompt"
```

### Interactive Chat

Start an interactive chat session:

```bash
# With specific script
llm-workers-chat my-script.yaml

# With default generic assistant
llm-workers-chat

# Resume last session
llm-workers-chat --resume

# With verbose logging
llm-workers-chat --verbose my-script.yaml
```

### Chat Commands

In chat mode, use these commands:

- `/help` - Show available commands
- `/model <name>` - Switch to different model
- `/rewind [N]` - Rewind to earlier message
- `/display [setting] [value]` - Show/modify display settings
- `/cost` - Show token usage and costs
- `/export <name>` - Export chat as markdown
- `/save <name>` - Save session to YAML
- `/clear` - Reset chat session
- `/exit` - Exit (auto-saves to `.last.chat.yaml`)

## Documentation

Full documentation: https://mrbagheera.github.io/llm-workers/

## License

See main repository for license information.
llm-workers-console 1.0.0
Console UI components for LLM Workers - interactive chat interface and rich terminal output
Requires Python: <3.15,>=3.12

# llm-workers-console

Console UI components for LLM Workers - interactive chat interface and rich terminal output.

## Overview

`llm-workers-console` provides the user interface components for LLM Workers:

- **ChatSession**: Interactive chat interface with command system
- **ConsoleController**: Rich terminal output with markdown rendering, syntax highlighting, and streaming
- **ChatCompleter**: Auto-completion for slash commands and file paths

## Installation

```bash
pip install llm-workers-console
```

This package depends on `llm-workers` (core library).

## Features

- **Interactive Chat**: Multi-line input with history, command completion
- **Rich Output**: Markdown rendering, syntax highlighting, streaming responses
- **Chat Commands**: `/help`, `/rewind`, `/model`, `/display`, `/cost`, `/export`, `/save`, `/clear`, `/exit`
- **Session Management**: Save and resume chat sessions
- **Token Usage Display**: Real-time token tracking and cost estimation
- **File Monitoring**: Auto-open changed files during chat

## Usage

### Programmatic Usage

```python
from llm_workers_console import chat_with_llm_script

# Start an interactive chat session
chat_with_llm_script("my-script.yaml")
```

### With Command-Line Tool

For command-line usage, install `llm-workers-tools`:

```bash
pip install llm-workers-tools
llm-workers-chat my-script.yaml
```

## Documentation

Full documentation: https://mrbagheera.github.io/llm-workers/

## License

See main repository for license information.
llm-workers 1.0.0
Core library for experimenting with LLMs - provides configuration, workers, tools, and LLM integrations
Requires Python: <3.15,>=3.12

# llm-workers

Core library for experimenting with Large Language Models (LLMs).

## Overview

`llm-workers` is the core package providing:

- **Worker System**: Central orchestrator for LLM interactions and tool execution
- **Configuration**: YAML-based configuration system for LLM scripts
- **Tools**: Built-in tools for file operations, web scraping, nested LLM calls, and custom tool building
- **Expression System**: Dynamic value evaluation with Starlark-like expressions
- **LLM Integrations**: Support for OpenAI, Anthropic, Google, and other providers via LangChain
- **MCP Server Support**: Integration with Model Context Protocol servers

## Installation

```bash
pip install llm-workers
```

For console/chat interface, also install:

```bash
pip install llm-workers-console llm-workers-tools
```

## Usage

### Programmatic Usage

```python
from llm_workers.user_context import StandardUserContext
from llm_workers.workers_context import StandardWorkersContext
from llm_workers.cli_lib import run_llm_script

# Run a script programmatically
user_context = StandardUserContext()
context = StandardWorkersContext.create("my-script.yaml", user_context=user_context)
```

### Command-Line Usage

For command-line tools, install `llm-workers-tools`:

```bash
llm-workers-cli my-script.yaml "prompt here"
llm-workers-chat my-script.yaml
```

## Documentation

Full documentation: https://mrbagheera.github.io/llm-workers/

## License

See main repository for license information.