Various GenAI frameworks & Tools

Langchain 

Hugging Face 

LlamaIndex 

Haystack 

Llama2 

SingleStore notebook 

Langchain 

Developed by Harrison Chase and debuted in October 2022, LangChain serves as an open-source platform designed for constructing sturdy applications powered by LLMs, such as chatbots like ChatGPT and various tailor-made applications.

Initially, the system starts with a large document containing a vast array of data. This document is then broken down into smaller, more manageable chunks.

These chunks are subsequently embedded into vectors — a process that transforms the data into a format that can be quickly and efficiently retrieved by the system. These vectors are stored in a vector store, essentially a database optimized for handling vectorized data.

When a user inputs a prompt into the system, LangChain queries this vector store to find information that closely matches or is relevant to the user’s request. The system employs large LLMs to understand the context and intent of the user’s prompt, which guides the retrieval of pertinent information from the vector store.

Once the relevant information is identified, the LLM uses it to generate or complete an answer that accurately addresses the query. This final step culminates in the user receiving a tailored response, which is the output of the system’s data processing and language generation capabilities.

Hugging Face 

Hugging Face is a multifaceted platform that plays a crucial role in the landscape of artificial intelligence, particularly in the field of natural language processing (NLP) and generative AI.

The main parts of it are:

Model Hub : 

Hugging Face houses a massive repository of pre-trained models for diverse NLP tasks, including text classification, question answering, translation, and text generation.

These models are trained on large datasets and can be fine-tuned for specific requirements, making them readily usable for various purposes.

This eliminates the need for users to train models from scratch, saving time and resources.

Datasets:

Alongside the model library, Hugging Face provides access to a vast collection of datasets for NLP tasks.

These datasets cover various domains and languages, offering valuable resources for training and fine-tuning models.

Users can also contribute their own datasets, enriching the platform’s data resources and fostering community collaboration.

 Model Training & Fine-tuning Tools:

Hugging Face offers tools and functionalities for training and fine-tuning existing models on specific datasets and tasks.

This allows users to tailor models to their specific needs, improving their performance and accuracy in targeted applications.

The platform provides flexible options for training, including local training on personal machines or cloud-based solutions for larger models.

Application Building:

Hugging Face facilitates the development of AI applications by integrating seamlessly with popular programming libraries like TensorFlow and PyTorch.

This allows developers to build chatbots, content generation tools, and other AI-powered applications utilizing pre-trained models.

Numerous application templates and tutorials are available to guide users and accelerate the development process.

Community & Collaboration:

Hugging Face boasts a vibrant community of developers, researchers, and AI enthusiasts.

The platform fosters collaboration through features like model sharing, code repositories, and discussion forums.

This collaborative environment facilitates knowledge sharing, accelerates innovation, and drives the advancement of NLP and generative AI technologies.

References:

https://levelup.gitconnected.com/gen-ai-frameworks-and-tools-every-ai-ml-engineer-should-know-1f0ce36f1452