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Semantic Search

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Swan Home Tech Team
Swan Home Tech Team
Apr 21, 2025 · Artificial Intelligence

How Front-End Teams Leverage AI: FastGPT Platform, Intelligent Search, and Video Synthesis

This article examines how a front‑end team uses AI innovations—FastGPT visual platform, AI‑powered semantic search, and AI video synthesis—to rebuild business workflows, cut costs, and boost efficiency, highlighting architecture, technical highlights, and practical use cases.

AISemantic Searchfrontend development
0 likes · 7 min read
How Front-End Teams Leverage AI: FastGPT Platform, Intelligent Search, and Video Synthesis
Sohu Tech Products
Sohu Tech Products
Mar 19, 2025 · Databases

Redis Vector Search Technology for AI Applications: Implementation and Best Practices

The article explains how Redis vector search, powered by RedisSearch’s FLAT and HNSW algorithms and supporting various data types and precisions, enables fast AI-driven similarity queries for text, image, and audio, and provides implementation guidance, optimization tips, and a real‑world customer‑service use case.

AI applicationsDatabase OptimizationHNSW
0 likes · 17 min read
Redis Vector Search Technology for AI Applications: Implementation and Best Practices
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 13, 2025 · Big Data

Configuring and Using DeepSeek Search Engine in Cursor for Efficient Data Retrieval

This article introduces DeepSeek, a high‑efficiency search engine optimized for large‑scale data, explains how to configure it within the Cursor database tool using code snippets, and demonstrates its applications such as semantic search, content recommendation, intelligent data analysis, and document similarity matching.

Big DataCursorData Retrieval
0 likes · 6 min read
Configuring and Using DeepSeek Search Engine in Cursor for Efficient Data Retrieval
DaTaobao Tech
DaTaobao Tech
Oct 23, 2024 · Artificial Intelligence

Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges

Retrieval-Augmented Generation (RAG) combines a retriever that fetches relevant external documents and a generator that uses them, improving LLM accuracy, relevance, privacy, and up-to-date information, but faces challenges such as retrieval latency, computational cost, chunking strategies, embedding selection, and system integration complexity.

AILLMRAG
0 likes · 13 min read
Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges
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 6, 2024 · Artificial Intelligence

Knowledge Graph and RAG Applications in 360 Document Cloud: Challenges and Solutions

This article presents a comprehensive overview of 360's document cloud knowledge management and Q&A scenarios, discussing business pain points, large‑model challenges, the advantages of the intelligent document solution, and how knowledge graphs enhance retrieval‑augmented generation and document standardization for AI‑driven enterprise applications.

AIDocument ManagementRAG
0 likes · 15 min read
Knowledge Graph and RAG Applications in 360 Document Cloud: Challenges and Solutions
AntTech
AntTech
Aug 13, 2024 · Artificial Intelligence

Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI

From August 11‑16, 2024 the ACL conference in Bangkok featured 14 Ant Group papers covering large‑scale information extraction, decomposed LLMs for semantic search, multimodal hallucination detection, long‑context attention mechanisms, concept‑reasoning datasets, knowledge‑graph alignment, and more, highlighting the group's breadth in natural language processing and AI research.

ACL2024Information ExtractionLarge Language Models
0 likes · 20 min read
Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 9, 2024 · Artificial Intelligence

Exploring Sentence and Paragraph Search with Milvus Vector Search vs Elasticsearch

This article examines how vector search using Milvus handles sentence and paragraph queries compared to traditional Elasticsearch, demonstrating the advantages of embedding‑based semantic matching for document search scenarios through practical experiments and visual results.

ElasticsearchMilvusSemantic Search
0 likes · 11 min read
Exploring Sentence and Paragraph Search with Milvus Vector Search vs Elasticsearch
Sohu Tech Products
Sohu Tech Products
Mar 27, 2024 · Artificial Intelligence

Building a RAG Application with Baidu Vector Database and Qianfan Embedding

This tutorial walks through building a Retrieval‑Augmented Generation application by setting up Baidu’s Vector Database and Qianfan embedding service, configuring credentials, creating a document database and vector table, loading and chunking PDFs, generating embeddings, storing them, and performing scalar, vector and hybrid similarity searches, ready for integration with Wenxin LLM for answer generation.

AI applicationsBaidu QianfanHNSW
0 likes · 11 min read
Building a RAG Application with Baidu Vector Database and Qianfan Embedding
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Mar 22, 2024 · Artificial Intelligence

Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration

This article explains how traditional keyword search with Elasticsearch often yields inaccurate or incomplete results for document retrieval, introduces vectorization and semantic search using NLP embeddings, and demonstrates a practical workflow that combines these techniques with the Milvus vector database to achieve more accurate and efficient document search.

AIElasticsearchMilvus
0 likes · 13 min read
Improving Document Search with Vector Search: From Elasticsearch Limitations to Milvus Integration
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 25, 2024 · Artificial Intelligence

Pinecone Vector Database and Embedding Model Summary from DeepLearning.AI’s AI Course

This article reviews the author’s hands‑on experience with Pinecone’s serverless vector database, various embedding and generation models such as all‑MiniLM‑L6‑v2, text‑embedding‑ada‑002, clip‑ViT‑B‑32, and GPT‑3.5‑turbo‑instruct, and demonstrates how they are applied to semantic search, RAG, recommendation, hybrid, and facial similarity tasks using Python code examples.

AIEmbedding ModelsPinecone
0 likes · 9 min read
Pinecone Vector Database and Embedding Model Summary from DeepLearning.AI’s AI Course
DataFunTalk
DataFunTalk
Dec 29, 2023 · Artificial Intelligence

Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models

This article explores the emerging enterprise knowledge assistant paradigm in the era of large models, detailing traditional knowledge management challenges, solution architecture using vector databases and LLMs, core technologies such as ETL pipelines, reranking, secure fine‑tuning, and future prospects for intelligent enterprise applications.

LLM fine-tuningSemantic Searchenterprise AI
0 likes · 11 min read
Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models
DataFunTalk
DataFunTalk
Nov 17, 2023 · Databases

Cost as the Primary Driver of Vector Database Industry Development

Vector databases gain traction because they dramatically reduce storage, learning, scaling, and large‑model limitations costs by enabling semantic similarity search, RAG‑based prompt optimization, efficient high‑dimensional indexing, and cloud‑native architectures, making them essential for modern AI applications despite the promotional context.

AIBig DataRAG
0 likes · 11 min read
Cost as the Primary Driver of Vector Database Industry Development
DataFunSummit
DataFunSummit
Aug 3, 2023 · Artificial Intelligence

Integrating Vector Databases with Large Language Models for Enterprise AI Applications

The article explains how combining vector databases with large language models can help governments and enterprises leverage massive private data for AI, covering semantic search, approximate nearest neighbor techniques, alignment challenges across modalities, and future directions for fine‑grained data integration.

AISemantic Searchapproximate nearest neighbor
0 likes · 7 min read
Integrating Vector Databases with Large Language Models for Enterprise AI Applications
Architecture & Thinking
Architecture & Thinking
Jun 30, 2023 · Artificial Intelligence

How INT8 Quantization Supercharges Baidu's Search Models: Techniques and Insights

This article explores the rapid evolution of Baidu's semantic search models, the large GPU consumption they entail, and how extensive INT8 quantization, sensitivity analysis, calibration data augmentation, hyper‑parameter auto‑tuning, and advanced methods like Quantization‑Aware Training and SmoothQuant dramatically improve inference performance while preserving business metrics.

ERNIEINT8 quantizationSemantic Search
0 likes · 17 min read
How INT8 Quantization Supercharges Baidu's Search Models: Techniques and Insights
Baidu Geek Talk
Baidu Geek Talk
Jun 26, 2023 · Artificial Intelligence

INT8 Quantization for Baidu Search Semantic Models (ERNIE)

Baidu applied large‑scale INT8 quantization to its ERNIE search semantic models, achieving over 25% inference speedup with less than 1% degradation in relevance metrics by selectively quantizing less‑sensitive fully‑connected layers, using automated calibration, hyper‑parameter tuning, and techniques such as QAT and SmoothQuant, while paving the way for even lower‑bit quantization and token pruning.

ERNIEINT8 quantizationQuantization Aware Training
0 likes · 15 min read
INT8 Quantization for Baidu Search Semantic Models (ERNIE)
Architect
Architect
May 29, 2023 · Artificial Intelligence

Understanding Embeddings and Vector Databases for LLM Applications

This article explains what embeddings and vector databases are, how they are generated with models like OpenAI's Ada, why they enable semantic search and help overcome large language model token limits, and demonstrates a practical workflow for retrieving relevant document chunks using cosine similarity.

EmbeddingsLLMSemantic Search
0 likes · 7 min read
Understanding Embeddings and Vector Databases for LLM Applications
DataFunTalk
DataFunTalk
Jul 13, 2022 · Databases

Technical Analysis and Case Studies of Knowledge Graphs by Neo4j

This presentation explains where knowledge resides in data architectures, demonstrates knowledge‑graph‑driven skill discovery, metadata management, and semantic search, and concludes with a comparison of GraphQL and Cypher for graph queries, illustrated with real‑world Neo4j case studies.

CypherGraphQLNeo4j
0 likes · 11 min read
Technical Analysis and Case Studies of Knowledge Graphs by Neo4j
DataFunSummit
DataFunSummit
Jul 10, 2022 · Artificial Intelligence

Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms

This article introduces VentureSights, an AI‑driven intelligent industry analysis platform built on knowledge‑graph technology and the concept of industry atoms, detailing its core modules, workflow, industry‑atom representation, extraction algorithms, and overall system architecture for generating comprehensive industry reports and insights.

Artificial IntelligenceBig DataIndustry Analysis
0 likes · 12 min read
Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms
DataFunTalk
DataFunTalk
Jun 20, 2022 · Artificial Intelligence

SMedBERT: Knowledge‑Enhanced Pre‑trained Language Model for Medical Text Mining and Its Business Applications

The article introduces Dingxiangyuan's medical knowledge‑graph ecosystem, describes the construction of a four‑layer taxonomy, presents the ACL‑published SMedBERT model that injects structured medical semantics into a pre‑trained language model, and discusses its deployment in search, query expansion, and semantic matching while outlining future challenges.

Healthcare AISMedBERTSemantic Search
0 likes · 13 min read
SMedBERT: Knowledge‑Enhanced Pre‑trained Language Model for Medical Text Mining and Its Business Applications