Supercharge RAG Performance Using OctoAI and Unstructured Embeddings

Businesses and researchers can derive valuable insights from text through text embeddings, a key technique in Artificial Intelligence that converts text into machine-interpretable formats. This blog post details how OctoAI and Unstructured platforms facilitate this process, with OctoAI offering scalable AI applications and optimized embedding models and Unstructured managing unstructured data to enhance decision-making. The OctoAI GTE-Large embedding model, trained by Alibaba DAMO Academy, is adaptable for various NLP tasks and performs well on benchmarks. It converts English text into numerical vectors while handling up to 512 tokens. Unstructured, on the other hand, processes unstructured data using advanced algorithms and machine learning, enabling businesses to handle data like PDFs and images. The article demonstrates integrating OctoAI and Unstructured in a Retrieval-Augmented Generation (RAG) application, illustrating how to process text, PDFs, and how to use Pinecone for vector search capabilities, thus showcasing the synergy between these tools in advanced NLP tasks.