This article details the process of building a fast vector search system for a large legal dataset (Australian High Court decisions). It covers choosing embedding providers, performance benchmarks, using USearch and Isaacus embeddings, and the importance of API terms of service. It focuses on achieving speed and scalability while maintaining reasonable accuracy.
An extremely lightweight universal grammar implementation with provable recursion, based on Chomsky's Minimalist Grammar theory, fitting in under 50kB with zero runtime dependencies. It includes a probabilistic language model extension and formal verification.
This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The article details training a FASTopic model and labeling its results using GPT-4.0 mini, emphasizing reproducibility and control over the labeling process.
Multi-class zero-shot embedding classification and error checking. This project improves zero-shot image/text classification using a novel dimensionality reduction technique and pairwise comparison, resulting in increased agreement between text and image classifications.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
This article details the often overlooked cost of storing embeddings for RAG systems, and how quantization techniques (int8 and binary) can significantly reduce storage requirements and improve retrieval speed without substantial accuracy loss.
Ryan speaks with Edo Liberty, Founder and CEO of Pinecone, about building vector databases, the power of embeddings, the evolution of RAG, and fine-tuning AI models.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This study demonstrates that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.
