https://github.com/drlordbasil/experimental_python_agents

A collection of Python functions that leverage Large Language Models (LLMs) to replace traditional programming functions and libraries. The goal is to create a library where every operation is handled by LLM responses rather than hardcoded functions.
https://github.com/drlordbasil/experimental_python_agents

ai functional-programming functions ollama openai

Last synced: 6 days ago
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A collection of Python functions that leverage Large Language Models (LLMs) to replace traditional programming functions and libraries. The goal is to create a library where every operation is handled by LLM responses rather than hardcoded functions.

• Host: GitHub
• URL: https://github.com/drlordbasil/experimental_python_agents
• Owner: Drlordbasil
• Created: 2024-11-09T13:25:13.000Z (8 days ago)
• Default Branch: AI-Created-Programs
• Last Pushed: 2024-11-09T16:13:47.000Z (8 days ago)
• Last Synced: 2024-11-09T17:22:15.852Z (8 days ago)
• Topics: ai, functional-programming, functions, ollama, openai
• Language: Python
• Homepage:
• Size: 159 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# LLM-Powered Function Library: Reimagining Software Functions as Language Models

## Project Structure

```

experimental_python_agents/

├── README.md                 # Project overview and documentation

├── config.py                 # Configuration for LLM models and logging

├── main.py                  # Main showcase of all functions

├── requirements.txt         # Project dependencies

├── basic_functions/        # Core LLM Function Agents

│   ├── __init__.py

│   ├── math_function.py    # Mathematical operations

│   ├── string_function.py  # String manipulations

│   ├── embedding_function.py # Text embeddings

│   ├── vision_function.py  # Image analysis

│   ├── web_function.py     # Web component generation

│   └── debug_function.py   # Error analysis

└── applications/           # Practical Implementations

    └── code_reviewer/     # Code review system

        ├── reviewer.py

        ├── test_reviewer.py

        └── reports/

```

## Core Concept

This project reimagines traditional programming functions as LLM-powered agents. Instead of writing static functions, we use LLMs to create dynamic, context-aware operations that adapt to input and provide intelligent outputs.

## Current Functions

### Basic Functions

1. **Math Function** (`math_function.py`)

   - Natural language math processing

   - Direct numerical output

   - Example: `math_function("What is 15% of 200?")`

2. **String Function** (`string_function.py`)

   - Text transformations (reverse, capitalize)

   - Word counting

   - Punctuation handling

   - Example: `string_function("reverse", "Hello World")`

3. **Vision Function** (`vision_function.py`)

   - Image analysis and captioning

   - Uses llama3.2-vision:11b model

   - Example: `vision_function("analyze", "image.png")`

4. **Web Function** (`web_function.py`)

   - HTML/CSS generation

   - Component creation

   - Example: `web_function("component", "Create a login form")`

5. **Embedding Function** (`embedding_function.py`)

   - Text vectorization using nomic-embed-text

   - Example: `embedding_function("Convert this text to vectors")`

6. **Debug Function** (`debug_function.py`)

   - Error analysis and solutions

   - Root cause identification

   - Example: `debug_function(error_info, context)`

### Applications

#### Code Review System

- Located in `applications/code_reviewer/`

- Analyzes code quality

- Suggests optimizations

- Generates documentation

- Creates markdown reports

## Requirements

- Python 3.x

- Ollama running locally

- OpenAI library for API structure

- Required Ollama models:

  - smollm2:1.7b (text generation)

  - llama3.2-vision:11b (vision processing)

  - nomic-embed-text (embeddings)

## Quick Start

```bash

# Install dependencies

pip install -r requirements.txt

# Pull required Ollama models

ollama pull smollm2:1.7b

ollama pull llama3.2-vision:11b

ollama pull nomic-embed-text

```

## Usage Example

```python

from basic_functions import math_function, string_function, vision_function

# Math operations

result = math_function("2 + 2=")

print(f"Math result: {result}")

# String operations

text = string_function("reverse", "Hello World")

print(f"Reversed text: {text}")

# Image analysis

description = vision_function("caption", "image.png")

print(f"Image caption: {description}")

```

## Future Development

1. Enhanced error handling

2. Additional function agents

3. More practical applications

4. Performance optimizations

5. Extended documentation