Tag: RAG

2025-04-13

Vector Databases in RAG Applications: Bridging Human Language and Machine Understanding

This article explores the crucial role of vector databases in Retrieval Augmented Generation (RAG) applications, analyzing how they bridge the gap between human language and machine understanding through semantic vector representations, providing a comprehensive guide from basic concepts to practical implementation.

Vector Databases RAG Semantic Search Embedding Models pgvector Approximate Nearest Neighbor Search Information Retrieval

Application Development

2025-04-13

Building Intelligent Knowledge Applications with LangChain and RAG

This article provides a detailed guide on building intelligent knowledge applications by combining LangChain framework with Retrieval Augmented Generation (RAG) technology, addressing limitations of large language models in specialized knowledge, real-time information, and hallucinations through key components like document loading, text chunking, vector storage, and intelligent retrieval.

LangChain RAG Knowledge Applications Document Retrieval Vector Databases Embedding Models Application Architecture

AI Technology

2025-04-13

Hierarchical Memory in RAG Systems: Enhancing LLaMA 3 with Long-term Knowledge Retention

This article introduces a framework for implementing hierarchical memory systems with LLaMA 3, enhancing RAG applications with long-term knowledge retention capabilities by mimicking human memory organization through tiered structures, creating AI systems that effectively maintain, organize, and leverage knowledge.

LLaMA 3 RAG Knowledge Retrieval Hierarchical Memory Long-term Memory Cognitive Science Enterprise Knowledge Management

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