RAGoon is now available for batched embeddings generation

It integrates various APIs, enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses. RAGoon's core functionality revolves around the concept of few-shot learning, where language models are provided with a small set of high-quality examples to enhance their understanding and generate more accurate outputs. By curating and retrieving relevant data from the web or index, RAGoon equips language models with the necessary context and knowledge to tackle complex queries and generate insightful responses. At this stage, 5 major classes are available via RAGoon to facilitate:

the production of chain embeddings for several models to simplify a continuous deployment process;
production of LLM requests for web querying and content retrieval via the Google API;
recursive chunking via tokens;
data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;
the creation of binary indexes for search with scalar (int8) rescoring.

Contributions are open and the project is available now on PyPI, GitHub and Hugging Face as a demo Space.

RAGoon is now available for batched embeddings generation

RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.