Open Source

Good for: Enhanced context-aware interactions

The letters in Llama contain “LLM”, and the word stands for “Large Language Models AI”. Llama was created by Meta AI as a family of LLMs, but not originally created as open source. It was trained on data to generate natural sounding outputs for chatbots and other interactive AI applications.

Llama 2 is Meta’s second LLM model creation. This model is open source and similar to Chat GPT 4, but smaller in scale. Llama 2 uses a data network to guess subsequent text, and makes responses seem very human-like based on the parameters it’s given.

Then you have the Llama Index: a library used to facilitate ETL/ELT (extract, transform, load– moving data from one place to another) processes in preparation for running and doing LLM activity. It is licensed under an open-source license.

Finally, there is the Llama Hub which is a community platform for implementations of ETL processes for Llama index. It is licensed under an open-source license.

Varied licenses

Good for: Community knowledge-sharing

Hugging Face is a newer player, partnered with Meta to make the model available to their customers. Hugging Face is aiming to be the GitHub of AI models as it is a community where people like researchers, data scientists, and machine learning engineers can share and engage.

For consumers and implementers of AI models, Hugging Face is a platform that will increasingly become important as they utilize training data sets, share and collaborate on models, and deploy public-facing AI models. Those 3 components - datasets, models, and PaaS/SaaS services form the core of Hugging Face.

Open source

Good for: Generative data summarization (chatbots)

LangChain is the biggest python library for extracted applied LLM usage. Essentially, you can create applications that adapt to data changes and utilize the latest advancements in natural language processing. Upstream, LangChain uses models (like those from Open AI + others) to work correctly. It combines those with various other capabilities to produce a full-featured experience — such as combining a large language model with a math tool + wikipedia lookup — in order to produce meaningful products.

In general, Langchain efficiently configures prompts for chatbots and anything that requires a generative data summarization, making them interactive and compatible.

Open source

Good for: Large information systems

Similar to LangChain, Haystack is a framework for building applications built on LLM’s and AI. While it has a different license, it is also a python library for applied LLM products. It has slightly different patterns for implementing products and use cases.

Haystack uses pipelines to retrieve, read, and generate information. To make life — and application building — easier, the company has ready-made pipelines available for download.

Open source

Good for: A more personalized user experience

Kernel (yes, like popcorn) is the middle of an operating system. Microsoft's stab at a Applied LLM framework builds on this concept, and aims to provide the ability to create applied LLM capabilities outside of Python, and with their own spin. Instead of only using Python, Semantic Kernel also allows people to utilize C#.. Like LangChain, it builds "chains" that can be glued together to make a full LLM-powered experience. Semantic Kernel uses machine learning to help improve online searches, suggesting content you might like, and making AI chatbots and assistants more human-like in their responses.