Tag

#retrieval

4 articles

Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation

Build a multimodal Retrieval-Augmented Generation (RAG) pipeline using NVIDIA NeMo Retriever, LanceDB, and hosted NIMs for processing PDFs and generating contextually relevant answers.

Aug 718

RAG-Anything Tutorial: Build a Multimodal Retrieval Pipeline for Text, Tables, Equations, and Images in Colab

This article explains the RAG-Anything framework, a multimodal extension of Retrieval-Augmented Generation that retrieves and integrates information across text, tables, equations, and images.

Jul 255

Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker

Learn to build a high-precision retrieve-and-rerank pipeline using the zeroentropy/zerank-2-reranker model, combining fast retrieval with advanced reranking for improved search quality.

May 2669

Tencent Open-Sources TencentDB Agent Memory: A 4-Tier Local Memory Pipeline for AI Agents

This article explains Tencent's open-sourced TencentDB Agent Memory, a 4-tier local memory system for AI agents that combines symbolic and vector memory representations to improve efficiency and accuracy.

May 2397