This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
This article introduces the pyramid search approach using Agentic Knowledge Distillation to address the limitations of traditional RAG strategies in document ingestion.
The pyramid structure allows for multi-level retrieval, including atomic insights, concepts, abstracts, and recollections. This structure mimics a knowledge graph but uses natural language, making it more efficient for LLMs to interact with.
**Knowledge Distillation Process**:
- **Conversion to Markdown**: Documents are converted to Markdown for better token efficiency and processing.
- **Atomic Insights Extraction**: Each page is processed using a two-page sliding window to generate a list of insights in simple sentences.
- **Concept Distillation**: Higher-level concepts are identified from the insights to reduce noise and preserve essential information.
- **Abstract Creation**: An LLM writes a comprehensive abstract for each document, capturing dense information efficiently.
- **Recollections/Memories**: Critical information useful across all tasks is stored at the top of the pyramid.
The pyramid structure allows for multi-level retrieval, including atomic insights, concepts, abstracts, and recollections. This structure mimics a knowledge graph but uses natural language, making it more efficient for LLMs to interact with.
**Knowledge Distillation Process**:
- **Conversion to Markdown**: Documents are converted to Markdown for better token efficiency and processing.
- **Atomic Insights Extraction**: Each page is processed using a two-page sliding window to generate a list of insights in simple sentences.
- **Concept Distillation**: Higher-level concepts are identified from the insights to reduce noise and preserve essential information.
- **Abstract Creation**: An LLM writes a comprehensive abstract for each document, capturing dense information efficiently.
- **Recollections/Memories**: Critical information useful across all tasks is stored at the top of the pyramid.
Re-ranking is integral to retrieval pipelines, but implementation methods vary. We introduce rerankers, a Python library offering a unified interface for common re-ranking approaches.
This article explores the limitations of position-based chunking in Retrieval Augmented Generation (RAG) systems and proposes semantic chunking as a better alternative for improved performance.
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.
This article introduces PersonaRAG, a new AI method that enhances Retrieval-Augmented Generation (RAG) systems by incorporating user-centric agents for personalized information retrieval. It addresses the limitations of traditional RAG systems by dynamically adapting to user profiles and information needs, improving accuracy and relevance of responses.
Perplexity AI is a revolutionary search engine powered by AI, providing accurate and insightful answers to your questions. Our AI-powered chat assistant helps you explore information comprehensively.
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
This blog post demonstrates how to create a reusable retrieval evaluation dataset using an LLM to judge query-document pairs. It discusses the process, including building a small labeled dataset, aligning LLM judgments with human preferences, and using the LLM to judge a large set of queries and documents.
