LLM-Solutions-Playbook

Unlock the potential of AI-driven solutions and delve into the world of Large Language Models. Explore cutting-edge concepts, real-world applications, and best practices to build powerful systems with these state-of-the-art models.

This repository showcases the transformative capabilities of Large Language Models and LangChain. Dive into a curated collection of Jupyter notebooks that demonstrate:

• the practical applications of these technologies, highlighting their time-saving potential and the power of AI-driven solutions.
• cutting-edge concepts and best practices to build powerful systems with these state-of-the-art models.

Unleash your creativity and explore the included resources to kickstart your journey and delve deeper into these groundbreaking technologies.

Requirements

Python 3.11 Dependencies (listed in requirements.txt)

Installation

Clone this repository to your local machine. Install the required dependencies by running pip install -r requirements.txt.

Usage

The notebooks directory is organized into sub-directories, each dedicated to a specific topic. You can explore these sub-directories in any order based on your interests. Each sub-directory contains a collection of Jupyter notebooks that delve into the concepts related to its respective topic. The notebooks are sequentially ordered within each sub-directory, so it is recommended to follow the suggested order. The notebooks are designed to be self-contained, allowing you to run them independently without dependencies on other notebooks.

Get OpenAI API Key

To use the OpenAI API, you will need to create an account and obtain an API key. You can create an account here. Once you have created an account, you can find your API key on the API Keys page.

Use OpenAI API Key

Follow the instructions below to use your OpenAI API Key securely within code:

1. Create a file named .env in the root directory of this repository.
2. Define the environment variable OPENAI_API_KEY in the .env file and set it equal to your OpenAI API key. For example, if your API key is abc123, then the contents of the .env file should be OPENAI_API_KEY="abc123".
3. Use the following code to load your API key from the .env file:

import os
from dotenv import load_dotenv
import openai

load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

Get Activeloop API Key

• Signup/Login to Activeloop.
• Create API token from the home page.
• Copy the API token and paste it in the .env file like this ACTIVELOOP_TOKEN="<token>".
• Use the similar code as shown above to load your API token from the .env file.

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request. See the CONTRIBUTING.md file for more information.

License

This project is licensed under the MIT License.

Disclaimer

Please note that this software is provided as-is, and the author and contributors shall not be held responsible for any consequences or damages arising from its use. Refer to the DISCLAIMER.md file for more information.

Additional Resources

• The Novice's LLM Training Guide
• Mastering LLM Techniques: Inference Optimization
• LLM Patterns
• Deep Dive into evals
• Design Patterns in Machine Learning Code and Systems
• More Design Patterns For Machine Learning Systems
• Building LLM applications for production
• Red Teaming LLM Applications