LocalAI is a free and open-source AI stack that allows you to run language models, autonomous agents, and document intelligence locally on your hardware. It's an OpenAI API-compatible alternative focused on privacy, ease of use, and extensibility.
The article explores whether combining a command-line agent (like Claude Code or Gemini CLI) with Unix-like file system tools and SemTools is sufficient for complex tasks, particularly document search. It details a benchmark testing the limits of coding agents with and without SemTools, focusing on search, cross-referencing, and temporal analysis. The conclusion is that CLI access is powerful and SemTools enhances agent capabilities for document search and RAG.
Semantic search and document parsing tools for the command line. A collection of high-performance CLI tools for document processing and semantic search, built with Rust for speed and reliability.
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.
Foundational concepts, practical implementation of semantic search, and the workflow of RAG, highlighting its advantages and versatile applications.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
An article discussing the use of embeddings in natural language processing, focusing on comparing open source and closed source embedding models for semantic search, including techniques like clustering and re-ranking.
The author explores semantic search using embeddings on U.S. Presidents, comparing four models: BGE, ST, Ada, and Large. The findings show that while embeddings capture interesting data, their limitations and inability to understand subtext and perform certain semantic tasks highlight their shallowness compared to full language models.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Andrew Ng has launched a new short course on embedding models, covering their history, architecture, and capabilities. The course, taught by Vectara's Ofer Mendelevitch, explores word, sentence, and cross-encoder models, BERT training, and building dual encoder models for semantic search.
txtai is an open-source embeddings database for various applications such as semantic search, LLM orchestration, language model workflows, and more. It allows users to perform vector search with SQL, create embeddings for text, audio, images, and video, and run pipelines powered by language models for question-answering, transcription, translation, and more.
