ava-mosaic-ai 0.1.7

No project description provided

Mosaic-AI

 __   __  _______  _______  _______  ___   _______ 
|  |_|  ||       ||       ||   _   ||   | |       |
|       ||   _   ||  _____||  |_|  ||   | |       |
|       ||  | |  || |_____ |       ||   | |       |
|       ||  |_|  ||_____  ||       ||   | |      _|
| ||_|| ||       | _____| ||   _   ||   | |     |_ 
|_|   |_||_______||_______||__| |__||___| |_______|

About Ava-Mosaic-AI

Mosaic is a lightweight Python library that extends the capabilities of the Instructor library for LLM-based tasks. Born out of a personal project to streamline repetitive processes in GenAI development, Mosaic aims to reduce overhead and simplify common operations in LLM/GenAI projects.

Key Features

• Extends Instructor library functionality
• Simplifies common LLM-based tasks
• Reduces code repetition in GenAI projects
• Lightweight and easy to integrate

Installation

pip install ava-mosaic-ai

Quick Start

import ava_mosaic_ai
from pydantic import BaseModel

# Initialize LLM
llm = ava_mosaic_ai.get_llm("openai")

# Define response model
class ResponseModel(BaseModel):
    response: str

# Use Mosaic's simplified interface
response = llm.create_completion(
    response_model=ResponseModel,
    messages=[{"role": "user", "content": "Tell me a joke about AI"}],
)
print(response)

Documentation

For full documentation, visit our docs site.

Contributing

We welcome contributions! Please see our Contributing Guide for more details.

Roadmap

• Add support for more LLM providers
• Implement advanced prompt engineering tools
• Develop a CLI for quick prototyping

Special Thanks

• A heartfelt shoutout to @daveebbelaar for his implementation of llm_factory, which inspired this project. Check out his work here.
• Immense gratitude to the creators of the Instructor library. Their work has saved countless hours in GenAI project development.

License

Mosaic is released under the MIT License. See the LICENSE file for details.

---

Built with ❤️ by [karan Singh Kochar]