This article examines why basic text extraction from PDFs often falls short when building Retrieval Augmented Generation (RAG) pipelines. It highlights how losing visual layout information results in lost semantic context, affecting model accuracy and retrieval performance. The author introduces the concept of two critical layers within a document: the physical layer involving raw character data and coordinates, and the logical layer that constructs meaning through structural elements like headings, tables, and multi-column layouts.
- Why standard text extraction limits RAG performance
- Understanding physical versus logical PDF layers
- The role of layout awareness in preserving semantic context
- Why standard text extraction limits RAG performance
- Understanding physical versus logical PDF layers
- The role of layout awareness in preserving semantic context
A post-retrieval temporal layer designed to improve RAG systems by addressing time-blindness in vector searches. This library implements validity filtering, document kind classification, and exponential decay scoring to ensure retrieved information is fresh and accurate. It functions downstream of existing vector search systems without requiring re-indexing or new infrastructure.
* **Naive RAG:** Uses simple vector similarity for direct, fact-based queries.
* **Multimodal RAG:** Retrieves information across various formats, including text, images, and audio.
* **HyDE (Hypothetical Document Embeddings):** Generates a "fake" answer first to improve the retrieval of real documents.
* **Corrective RAG:** Verifies retrieved data against trusted sources to ensure accuracy.
* **Graph RAG:** Utilizes knowledge graphs to capture complex relationships between entities.
* **Hybrid RAG:** Combines vector-based retrieval with graph-based methods for richer context.
* **Adaptive RAG:** Dynamically switches between simple retrieval and complex reasoning based on the query.
* **Agentic RAG:** Employs AI agents to manage complex workflows involving multiple tools and sources.
* **Multimodal RAG:** Retrieves information across various formats, including text, images, and audio.
* **HyDE (Hypothetical Document Embeddings):** Generates a "fake" answer first to improve the retrieval of real documents.
* **Corrective RAG:** Verifies retrieved data against trusted sources to ensure accuracy.
* **Graph RAG:** Utilizes knowledge graphs to capture complex relationships between entities.
* **Hybrid RAG:** Combines vector-based retrieval with graph-based methods for richer context.
* **Adaptive RAG:** Dynamically switches between simple retrieval and complex reasoning based on the query.
* **Agentic RAG:** Employs AI agents to manage complex workflows involving multiple tools and sources.
This article discusses how AI tools can be used to enhance the reading experience by providing instant access to information and background details, similar to using a dictionary or Wikipedia, but with the ability to ask more complex questions. The author shares personal examples of using AI while reading 'The Dark Forest' and other books to clarify plot points and gain a better understanding of the material.
This article explores the architecture enabling AI chatbots to perform web searches, covering retrieval-augmented generation (RAG), vector databases, and the challenges of integrating search with LLMs.
This paper addresses the misalignment between traditional IR evaluation metrics and the requirements of modern Retrieval-Augmented Generation (RAG) systems. It proposes a novel annotation schema and the UDCG metric to better evaluate retrieval quality for LLM consumers.
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.
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.
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.
