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.
A 100-line minimalist LLM framework for Agents, Task Decomposition, RAG, etc. It models the LLM workflow as a Graph + Shared Store with nodes handling simple tasks, connected through actions for agents, and orchestrated by flows for task decomposition.
Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.
A guide on implementing prompt engineering patterns to make RAG implementations more effective and efficient, covering patterns like Direct Retrieval, Chain of Thought, Context Enrichment, Instruction-Tuning, and more.
The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.
This article explores the use of Google's NotebookLM (NLM) as a tool for research, particularly in analyzing the impact of the Aswan High Dam on schistosomiasis in Egypt. The author details how NLM can be used to create a research assistant-like experience, allowing users to 'have a conversation' with uploaded content to gain insights and answers from the material.
A simple project demonstrating Retrieval Augmented Generation (RAG) using SQLite, sqlite-vec, and OpenAI. It embeds text files, stores them in a SQLite database, and retrieves relevant documents using vector search. The project features lightweight single-file SQLite databases, vector search capabilities, and OpenAI integration for embeddings and chat responses.
The article explores the concept of Retrieval-Augmented Generation (RAG) using SQLite, specifically with the sqlite-vec extension and the OpenAI API. It outlines a simplified approach to RAG, moving away from complex frameworks and cloud vector databases, using SQLite's virtual tables for vector search and semantic understanding.
Learn how to use Okta FGA to secure your LangChain RAG agent in Python.
This article provides a step-by-step guide to creating an AI-powered English tutor using Retrieval-Augmented Generation (RAG). It integrates a vector database (ChromaDB) for storing and retrieving relevant English language learning materials and Groq API for generating structured and engaging lessons. The tutorial covers installing necessary libraries, setting up the environment, defining a vector database class, implementing AI lesson generation, and combining vector retrieval with AI generation.
