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.
A curated cheatsheet of useful Emacs keybindings, intended as a companion to 'Mastering Emacs' and focusing on core functionality rather than specialized configurations.
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.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
This paper introduces a multi-agent NLP framework to address prompt injection vulnerabilities in generative AI systems. The framework utilizes specialized agents for generating responses, sanitizing outputs, and enforcing policy compliance, evaluated using novel metrics like Injection Success Rate (ISR), Policy Override Frequency (POF), Prompt Sanitization Rate (PSR), and Compliance Consistency Score (CCS). The system employs OVON for inter-agent communication.
A flexible Python library and CLI tool for interacting with Model Context Protocol (MCP) servers using OpenAI, Anthropic, and Ollama models.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
