Tag

embedding

0 views collected around this technical thread.

Java Architecture Diary
Java Architecture Diary
Jun 9, 2025 · Artificial Intelligence

How Qwen3 Embedding Redefines Multilingual Vector Search Performance

This article examines the Qwen3 Embedding series released by Alibaba's Qwen team, detailing its architecture, multilingual capabilities, benchmark superiority across MTEB and C‑MTEB tests, and provides practical deployment guidance via Ollama and API integration.

AIOllamaQwen3
0 likes · 8 min read
How Qwen3 Embedding Redefines Multilingual Vector Search Performance
Tencent Technical Engineering
Tencent Technical Engineering
Apr 22, 2025 · Artificial Intelligence

Conan-Embedding-V2: A 1.4B LLM‑Based Multilingual Embedding Model Achieving SOTA on MTEB

Conan‑Embedding‑V2, a newly trained 1.4 B‑parameter LLM with a custom tokenizer, 32 k token context, SoftMask, cross‑lingual retrieval data and dynamic hard‑negative mining, delivers state‑of‑the‑art multilingual embeddings that surpass larger models on both English and Chinese MTEB benchmarks while remaining compact and fast.

MTEBRetrievalcross-lingual retrieval
0 likes · 14 min read
Conan-Embedding-V2: A 1.4B LLM‑Based Multilingual Embedding Model Achieving SOTA on MTEB
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Apr 10, 2025 · Artificial Intelligence

Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama

This guide walks through creating a Retrieval‑Augmented Generation (RAG) system using Spring Boot 3.4.2, Milvus vector database, and the bge‑m3 embedding model via Ollama, covering environment setup, dependency configuration, vector store operations, and integration with a large language model to deliver refined, similarity‑based answers.

LLMMilvusRAG
0 likes · 11 min read
Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama
58 Tech
58 Tech
Mar 11, 2025 · Artificial Intelligence

Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques

This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.

AI optimizationReal EstateRecommendation systems
0 likes · 14 min read
Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques
DevOps
DevOps
Mar 9, 2025 · Artificial Intelligence

A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents

This article provides a comprehensive introduction to developing large language model (LLM) applications, covering prompt engineering, zero‑ and few‑shot techniques, function calling, retrieval‑augmented generation (RAG) with embedding and vector databases, code assistants, and the MCP protocol for building AI agents, all aimed at non‑AI specialists.

AI AgentFunction CallingLLM
0 likes · 48 min read
A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents
Cognitive Technology Team
Cognitive Technology Team
Mar 7, 2025 · Artificial Intelligence

From Word Embeddings to Large Language Models: A Comprehensive Overview of AI Model Evolution

This article traces the development of AI models—from early word embeddings like Word2Vec and ELMo, through transformer‑based encoders such as BERT and decoder‑only models like GPT‑1/2/3, to recent multimodal systems and scaling laws—explaining their architectures, training methods, and impact on modern AI applications.

AITransformerembedding
0 likes · 22 min read
From Word Embeddings to Large Language Models: A Comprehensive Overview of AI Model Evolution
Tencent Cloud Developer
Tencent Cloud Developer
Mar 4, 2025 · Artificial Intelligence

A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents

The guide teaches non‑AI developers how to build practical LLM‑powered applications by mastering prompt engineering, function calling, retrieval‑augmented generation, and AI agents, and introduces the Modal Context Protocol for seamless tool integration, offering a clear learning path to leverage large language models without deep theory.

AI AgentFunction CallingLLM
0 likes · 48 min read
A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents
JD Tech Talk
JD Tech Talk
Jan 9, 2025 · Artificial Intelligence

Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java

This article provides a step‑by‑step tutorial for Java engineers on using the LangChain4j framework to implement Retrieval‑Augmented Generation (RAG) with large language models, covering concepts, environment setup, code integration, document splitting, embedding, vector‑store operations, and prompt engineering.

LangChain4jRAGembedding
0 likes · 35 min read
Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java
DataFunSummit
DataFunSummit
Nov 26, 2024 · Information Security

AI‑Driven Security Operations (AISECOPS): Architecture, Practices, and Evaluation

This article explains how large‑model AI can be integrated into security operations (AISECOPS) to simplify application integration, improve fault detection, and automate protection across complex north‑south and east‑west network layers, while addressing challenges such as data quality, cost control, model selection, and safety frameworks.

AISECOPSLarge Modelscost-optimization
0 likes · 22 min read
AI‑Driven Security Operations (AISECOPS): Architecture, Practices, and Evaluation
AntTech
AntTech
Nov 26, 2024 · Databases

From Big Data to Large Models: Modern Data Paradigms and the Evolution of Database Technologies

This article explores how modern data technologies—from relational databases and NoSQL to vector databases and AI‑driven retrieval—address the 4V challenges of volume, velocity, variety, and value, enabling polyglot persistence, semantic embeddings, and retrieval‑augmented generation for next‑generation applications.

AIBig DataNoSQL
0 likes · 29 min read
From Big Data to Large Models: Modern Data Paradigms and the Evolution of Database Technologies
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 20, 2024 · Artificial Intelligence

Resolving 02_DocQA.py Errors and Using LangChain to Call Large Models Locally

This guide explains how to fix the ArkNotFoundError in the 02_DocQA.py script by configuring a Doubao‑embedding endpoint, setting up a Conda environment with the latest LangChain packages, and provides step‑by‑step code examples for invoking both Zhipu glm‑4 and Volcano large language models via LangChain.

Environment SetupLangChainembedding
0 likes · 9 min read
Resolving 02_DocQA.py Errors and Using LangChain to Call Large Models Locally
System Architect Go
System Architect Go
Nov 19, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp

This article explains the concept, architecture, and step‑by‑step implementation of Retrieval Augmented Generation (RAG), covering indexing, retrieval & generation processes, a practical LangChain‑Redis‑llama.cpp example on Kubernetes, code snippets, test results, challenges, and references.

AILLMLangChain
0 likes · 6 min read
Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp
AntData
AntData
Nov 18, 2024 · Databases

Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI

This article surveys the evolution of modern data technologies—from the 4V characteristics of big data and the limitations of traditional relational databases, through the rise of NoSQL and polyglot persistence, to embedding‑driven vector search, hybrid retrieval and RAG, illustrating how each paradigm frees applications from data constraints.

Artificial IntelligenceBig DataData Architecture
0 likes · 30 min read
Modern Data Paradigms: From Relational Databases to Vector Retrieval and AI
DevOps
DevOps
Oct 27, 2024 · Artificial Intelligence

Best Practices for Building Efficient Retrieval‑Augmented Generation (RAG) Systems

This article reviews Wang et al.'s 2024 research on Retrieval‑Augmented Generation, outlining optimal practices such as query classification, chunk sizing, hybrid metadata search, embedding selection, vector databases, query transformation, reranking, document repacking, summarization, fine‑tuning, and multimodal retrieval to guide developers in constructing high‑performance RAG pipelines.

LLMRAGRetrieval
0 likes · 11 min read
Best Practices for Building Efficient Retrieval‑Augmented Generation (RAG) Systems
DataFunSummit
DataFunSummit
Oct 18, 2024 · Artificial Intelligence

Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab

This article details how PingCAP's three‑person AI Lab leveraged Retrieval‑Augmented Generation (RAG) techniques—including basic RAG, fine‑tuned embeddings, re‑ranking, graph RAG, and agent‑based RAG—to create scalable, multilingual document‑question answering services while addressing large‑scale documentation challenges, model limitations, and user feedback loops.

Fine-tuningLLMRAG
0 likes · 14 min read
Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab
JD Tech Talk
JD Tech Talk
Oct 8, 2024 · Artificial Intelligence

Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant

This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding using the candle library, data ingestion, and integration of a Rust‑based inference service based on mistral.rs, while also discussing resource usage and common pitfalls.

AILLMQdrant
0 likes · 16 min read
Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 26, 2024 · Artificial Intelligence

AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation

iQIYI’s AI‑powered search expands beyond title‑only queries by handling fuzzy role, plot, star, award, and semantic searches, using Chain‑of‑Thought‑generated TIPS, Retrieval‑Augmented Generation with sophisticated indexing, chunking, embedding, reranking, and prompt‑engineering to deliver personalized, accurate video recommendations that boost user engagement.

AI SearchChain-of-ThoughtQuery Guidance
0 likes · 15 min read
AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation
ByteDance Data Platform
ByteDance Data Platform
Sep 25, 2024 · Artificial Intelligence

How LLMs Power the “Find Data Assistant” for Smarter Data Retrieval

This article explains how the Volcano Engine DataLeap team leveraged large‑language models to build the “Find Data Assistant”, detailing its design, challenges, embedding‑and‑reranker enhancements, LLM‑driven semantic search, mixing architecture, and practical lessons for improving data asset management and retrieval.

Data Asset ManagementData RetrievalLLM
0 likes · 17 min read
How LLMs Power the “Find Data Assistant” for Smarter Data Retrieval
DataFunSummit
DataFunSummit
Sep 21, 2024 · Artificial Intelligence

DataLeap "Find Data Assistant": Leveraging Large Language Models for Data Asset Retrieval and Management

This article details how the DataLeap team applied large language model technology to build the "Find Data Assistant" platform, addressing the challenges of locating and using massive data assets through a hybrid retrieval architecture, enhanced embedding, reranking, mixed ranking, and answer summarization, while sharing practical lessons and future directions.

Data Asset ManagementData RetrievalHybrid Ranking
0 likes · 17 min read
DataLeap "Find Data Assistant": Leveraging Large Language Models for Data Asset Retrieval and Management
Code Mala Tang
Code Mala Tang
Sep 12, 2024 · Artificial Intelligence

Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js

This article explains the core concepts of Retrieval‑Augmented Generation (RAG), walks through its implementation steps with LangChain.js—including text chunking, embedding, storage, retrieval, and generation—and showcases practical use cases, challenges, and best practices for building reliable AI‑powered applications.

AI applicationsLLMLangChain
0 likes · 16 min read
Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js