Welcome to the LLM Playground repository! This collection is dedicated to showcasing the power of Large Language Models (LLMs) through Google Colab notebooks. Explore a range of projects related to LLMs, including but not limited to those utilizing langchain and similar technologies.
LangChain is a tool for developers that enables the creation of chains using large language models (LLMs). These chains can generate text based on provided prompts. LangChain offers versatility in constructing different types of chains and can be integrated with existing LangChain constructs. Each step in the chain requires specific prompts, and input data must be formatted appropriately for optimal outputs. LangChain is also useful for feeding large documents into LLMs.
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Components: LangChain offers modular abstractions for components necessary to work with language models. Whether you're using the entire framework or just these components, they are designed for ease of use.
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Use-Case Specific Chains: Chains represent specific configurations of components tailored to accomplish particular use cases. These chains provide a higher-level interface for developers, making it easy to get started with specific applications. Chains are also customizable to suit your project's unique needs.
For more detailed information, refer to the LangChain documentation.
Explore a diverse range of projects in this repository, from hosting notebooks as a Flask API to intriguing applications of LangChain. You're also encouraged to contribute your own innovative projects related to LLMs.
To get started with the LLM Playground and LangChain, follow these steps:
- Python (3.7 or higher) installed on your system.
- A basic understanding of Python programming and Jupyter Notebooks.
For detailed information and documentation on how to install LangChain, please refer to the LangChain documentation.
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Clone this repository to your local machine:
git clone https://github.com/TanmayDoesAI/LLM-Playground cd LLM-Playground // Navigate to the project directory pip install -r requirements.txt // Install the required dependencies
Explore the projects in this repository by running the included Jupyter notebooks. Here's how:
- Launch Jupyter Notebook:
jupyter notebook
- Open the notebook of your choice from the notebooks directory.
- Follow the instructions within the notebook to run and experiment with the LLM-powered applications.
To help you navigate the projects and LangChain, we've provided a link to example notebooks and tutorials LangChain Tutorials. These resources offer step-by-step guidance and insights into working with language models.
Thank you for considering contributing to the LLM Playground repository! Whether you're fixing a bug, enhancing features, or introducing new projects, your contributions are highly appreciated.
To get started with contributing, please read our Contribution Guidelines for detailed information on how to:
- Submit issues
- Propose new projects
- Make pull requests
- Report bugs
- Suggest enhancements
We welcome contributions from individuals of all expertise levels. Let's build a welcoming community around LLM projects together!
Happy coding!