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AI Step-by-Step
AI Step-by-Step
May 15, 2026 · Artificial Intelligence

AI‑First Architecture Constraints: Tool Limits, Refactor Triggers, and Context

The article examines six practical challenges of AI‑First development—oversized tool libraries, when to trigger refactoring, propagating newly extracted methods, duplicate code from parallel sub‑agents, context aging, and the lack of a unified framework—while presenting concrete solutions such as three‑layer loading, sub‑agent isolation, semantic search, consolidation agents, persistent context files, and adaptive compression strategies.

AI agentsContext managementparallel agents
0 likes · 24 min read
AI‑First Architecture Constraints: Tool Limits, Refactor Triggers, and Context
AI Engineer Programming
AI Engineer Programming
May 14, 2026 · Artificial Intelligence

RAG Retrieval: Comparing Bi-encoder and Cross-encoder Architectures

The article reviews the three‑step RAG pipeline, explains why retrieval quality hinges on fast, accurate semantic matching, contrasts Bi-encoder’s offline vector indexing and speed with Cross-encoder’s token‑level interaction and higher precision, and discusses hybrid solutions such as ColBERT and LLM rerankers with practical engineering guidelines.

Bi-encoderColBERTCross-Encoder
0 likes · 10 min read
RAG Retrieval: Comparing Bi-encoder and Cross-encoder Architectures
DeepHub IMBA
DeepHub IMBA
May 1, 2026 · Artificial Intelligence

How to Build Intelligent Contextual Memory for AI Agents

The article examines why naïvely feeding all dialogue history to large language models is costly and unreliable, and it walks through rolling context windows, inverted‑index pruning, semantic vector search, and GraphRAG as complementary techniques for creating efficient, reasoning‑capable AI agent memory.

AIAgent MemoryContext Window
0 likes · 11 min read
How to Build Intelligent Contextual Memory for AI Agents
AI Architect Hub
AI Architect Hub
Apr 30, 2026 · Artificial Intelligence

How AI Understands Your Queries: Core Techniques of Semantic Vector Search

The article explains why traditional keyword search often fails when user questions differ from knowledge‑base wording, introduces semantic search that matches queries and documents via vector similarity, details query understanding and rewriting techniques, lists common pitfalls, provides a full Python implementation, and shares best‑practice recommendations.

AIPythonRAG
0 likes · 16 min read
How AI Understands Your Queries: Core Techniques of Semantic Vector Search
AI Architecture Path
AI Architecture Path
Apr 23, 2026 · Artificial Intelligence

MemPalace: Offline, Local‑First AI Memory System Built on a Memory‑Palace Architecture

MemPalace is an open‑source, local‑first AI memory library that stores raw conversation and project content without summarisation, uses a hierarchical "memory palace" structure for fast semantic retrieval, provides plug‑in retrieval back‑ends, knowledge‑graph support, and achieves the highest publicly reported offline benchmark scores.

AI memoryBenchmarkKnowledge Graph
0 likes · 17 min read
MemPalace: Offline, Local‑First AI Memory System Built on a Memory‑Palace Architecture
AI Engineering
AI Engineering
Apr 11, 2026 · Artificial Intelligence

GBrain: Open-Source AI Memory Engine that Gives OpenClaw and Hermes Long-Term Recall

GBrain, an open‑source AI memory hub created by YC partner Garry Tan, combines Postgres tsvector keyword search with pgvector semantic search via RRF, manages thousands of Markdown notes, and runs an automated nightly agent that refines and links memories, offering a practical long‑term recall layer for agents like OpenClaw and Hermes.

AI memoryGBrainHermes
0 likes · 4 min read
GBrain: Open-Source AI Memory Engine that Gives OpenClaw and Hermes Long-Term Recall
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 5, 2026 · Artificial Intelligence

LLM‑Powered Knowledge Management: Insights from Karpathy, Lex Fridman, and kepano

The article analyzes three leading AI experts' approaches to personal knowledge management—Karpathy’s five‑module LLM pipeline, Lex Fridman’s interactive voice‑driven consumption, and kepano’s cautionary separation of AI‑generated content—while detailing the author’s own downstream content‑production workflow that turns raw material into articles, videos, and social posts.

AI agentsContent ProductionLLM
0 likes · 13 min read
LLM‑Powered Knowledge Management: Insights from Karpathy, Lex Fridman, and kepano
AI Tech Publishing
AI Tech Publishing
Mar 28, 2026 · Artificial Intelligence

Designing Agent Memory Systems: Four Types, Three Strategies, and Full Python Implementation

This article breaks down agentic memory into four distinct types—In‑context, External, Episodic, and Semantic/Parametric—explains three forgetting strategies (time decay, importance scoring, periodic consolidation), shows how memory flows through an agent loop, and provides complete Python code using OpenAI embeddings and ChromaDB for a production‑ready memory layer.

Agent MemoryChromaDBLLM
0 likes · 22 min read
Designing Agent Memory Systems: Four Types, Three Strategies, and Full Python Implementation
Architect's Guide
Architect's Guide
Mar 21, 2026 · Artificial Intelligence

Turn PDFs, Word Docs, and Images into Instant Answers with WeKnora’s LLM‑Powered Search

WeKnora is a Tencent‑open‑source LLM‑based document understanding and semantic search framework that extracts structured content from PDFs, Word files and images, offers agent‑driven reasoning, multi‑modal retrieval, and a modular architecture, with step‑by‑step Docker deployment and a web UI for instant querying.

AILLMRAG
0 likes · 7 min read
Turn PDFs, Word Docs, and Images into Instant Answers with WeKnora’s LLM‑Powered Search
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Mar 11, 2026 · Backend Development

How to Achieve One‑Line Semantic Search for Nearby Clean Coffee Shops with Elasticsearch

This article walks through building a practical Elasticsearch demo that lets users type a single query like “nearby clean coffee shop” and get results by combining dense‑vector semantic search, geo filtering, BM25, and a hybrid RRF‑style ranking, with both LLM‑based structuring and a fallback hash‑based embedding.

BM25FlaskHybrid Search
0 likes · 10 min read
How to Achieve One‑Line Semantic Search for Nearby Clean Coffee Shops with Elasticsearch
AI Engineering
AI Engineering
Feb 27, 2026 · Artificial Intelligence

jina-grep: Adding Semantic Search Capabilities to Grep on Apple Silicon

Jina-grep is an open-source CLI that adds fast, MLX-powered semantic search to grep on Apple Silicon, offering three modes, sub-millisecond latency, high token throughput, and easy installation, making local code and log searching more accurate than keyword matching.

Apple SiliconCLIMLX
0 likes · 4 min read
jina-grep: Adding Semantic Search Capabilities to Grep on Apple Silicon
AI Frontier Lectures
AI Frontier Lectures
Feb 3, 2026 · Artificial Intelligence

Inside Moltbook: How AI Agents Are Building Their Own Social Network

Moltbook, the AI‑only community formerly known as Motlbot, now hosts over 140,000 agents, 12,000 sub‑communities and tens of thousands of posts, while enforcing API‑key authentication, rate‑limit controls, heartbeat scheduling and semantic search, sparking debates about emergent AI behavior and safety.

AIMoltbookSocial network
0 likes · 8 min read
Inside Moltbook: How AI Agents Are Building Their Own Social Network
Shuge Unlimited
Shuge Unlimited
Feb 2, 2026 · Artificial Intelligence

Moltbook Architecture Deep Dive: Design Philosophy Behind the First OpenClaw AI Agent Social Network

Moltbook is a pioneering AI‑only social platform that lets agents post, comment and like, built with a Next.js front‑end, RESTful API back‑end, semantic vector search, heartbeat checks, dual human/agent modes and strict rate‑limiting to encourage quality over quantity while addressing scalability and security challenges.

HeartbeatMoltbookNext.js
0 likes · 18 min read
Moltbook Architecture Deep Dive: Design Philosophy Behind the First OpenClaw AI Agent Social Network
AI Tech Publishing
AI Tech Publishing
Feb 1, 2026 · Artificial Intelligence

How Clawdbot Implements a Persistent, Search‑Driven Memory System

Clawdbot, an open‑source AI assistant, uses local Markdown files and a SQLite‑based vector index to provide a transparent, searchable, and long‑term memory that separates temporary context from durable storage, enabling autonomous task handling across sessions.

AI AssistantClawdbotSQLite
0 likes · 10 min read
How Clawdbot Implements a Persistent, Search‑Driven Memory System
Architecture Digest
Architecture Digest
Jan 22, 2026 · Artificial Intelligence

Unlock AI-Powered Document Search with WeKnora: A Hands‑On Guide

WeKnora is an open‑source LLM‑driven framework that transforms complex, multi‑format documents into searchable semantic knowledge, offering features such as Agent mode, hybrid retrieval, secure private deployment, and an easy‑to‑use web UI, with step‑by‑step installation instructions and demo screenshots.

AILLMWeKnora
0 likes · 7 min read
Unlock AI-Powered Document Search with WeKnora: A Hands‑On Guide
AI Algorithm Path
AI Algorithm Path
Jan 11, 2026 · Artificial Intelligence

How Vector Embeddings Enable AI to Understand Anything

This article explains the principle of vector embeddings, shows how they turn words, images, audio and other data into dense numeric vectors, compares them with one‑hot encoding, describes static and contextual models, training methods, similarity metrics, and a wide range of real‑world AI applications.

AI fundamentalsRAGembedding models
0 likes · 15 min read
How Vector Embeddings Enable AI to Understand Anything
Youzan Coder
Youzan Coder
Jan 6, 2026 · Artificial Intelligence

How to Build Efficient Code Search with Vector Embeddings and AST Indexing

This article explains the motivations, techniques, and practical implementations of code indexing—covering semantic vector‑based RAG pipelines and AST‑based structural analysis—to improve code navigation, AI‑assisted queries, security scanning, and development efficiency.

AI DevelopmentASTRAG
0 likes · 17 min read
How to Build Efficient Code Search with Vector Embeddings and AST Indexing
AI Tech Publishing
AI Tech Publishing
Nov 23, 2025 · Artificial Intelligence

How Agents Leverage File Systems for Context Engineering

The article examines why file system access is crucial for autonomous agents, outlining common context‑engineering failures such as missing, excessive, or irrelevant information, and demonstrates how using file‑system tools like ls, grep, and write‑file can reduce token waste, enable dynamic storage, improve targeted search, and support continual learning.

Autonomous AgentsContext EngineeringLLM
0 likes · 11 min read
How Agents Leverage File Systems for Context Engineering
Tech Freedom Circle
Tech Freedom Circle
Nov 5, 2025 · Artificial Intelligence

Elasticsearch: BM25, TF‑IDF, Dense Vectors, kNN, L2 & Cosine Distances, RRF

This article provides a comprehensive technical guide to Elasticsearch’s core retrieval models—BM25 and TF‑IDF—while detailing modern vector‑based search using dense_vector, kNN, L2 and cosine distances, and demonstrates how to combine keyword and semantic results through hybrid search and Reciprocal Rank Fusion (RRF) with practical configuration examples.

BM25ElasticsearchRRF
0 likes · 42 min read
Elasticsearch: BM25, TF‑IDF, Dense Vectors, kNN, L2 & Cosine Distances, RRF
360 Smart Cloud
360 Smart Cloud
Oct 31, 2025 · Artificial Intelligence

APICLOUD Enterprise Knowledge Base: Architecture, AI Search & Optimization

This article presents a comprehensive solution for constructing an enterprise‑level knowledge base using APICLOUD share‑link data, covering data characteristics, system architecture, core algorithms such as streaming token chunking and semantic vector retrieval, performance optimizations, and real‑world integration scenarios.

APICLOUDEnterprise AIKnowledge Base
0 likes · 16 min read
APICLOUD Enterprise Knowledge Base: Architecture, AI Search & Optimization
BirdNest Tech Talk
BirdNest Tech Talk
Oct 21, 2025 · Artificial Intelligence

How Vector Stores Enable Lightning‑Fast Semantic Search in LangChain

This article explains what vector stores are, outlines their core workflow of adding, querying, and searching embeddings, compares popular back‑ends like FAISS, Chroma, and Pinecone, and walks through a complete Chinese‑language example using LangChain’s FAISS integration with detailed code and result analysis.

AIFAISSLangChain
0 likes · 10 min read
How Vector Stores Enable Lightning‑Fast Semantic Search in LangChain
BirdNest Tech Talk
BirdNest Tech Talk
Oct 20, 2025 · Artificial Intelligence

How Embedding Models Power Semantic Search: A Hands‑On LangChain Guide

This article explains what embeddings are, how LangChain’s Embeddings interface abstracts various providers, compares common models, and walks through a complete Python example that uses a Chinese‑optimized HuggingFace model to generate document and query vectors, compute cosine similarity, and identify the most relevant text.

LangChainNLPPython
0 likes · 9 min read
How Embedding Models Power Semantic Search: A Hands‑On LangChain Guide
IT Services Circle
IT Services Circle
Sep 29, 2025 · Artificial Intelligence

How Memvid Stores AI Knowledge in MP4 Videos with 10× Less Space

Memvid replaces traditional vector databases by encoding text chunks as QR codes inside MP4 video frames, achieving up to ten‑fold storage reduction, millisecond‑level semantic search, zero‑infrastructure deployment, and a built‑in conversational interface, while providing a fast‑install Python SDK and CLI.

AIMemoryMemvid
0 likes · 9 min read
How Memvid Stores AI Knowledge in MP4 Videos with 10× Less Space
Data Thinking Notes
Data Thinking Notes
Aug 31, 2025 · Artificial Intelligence

Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future

This article explains how embedding technology converts unstructured data into vector representations, powers precise retrieval in Retrieval‑Augmented Generation (RAG), outlines the evolution of embedding models, discusses current challenges such as long‑text handling and domain adaptation, and highlights emerging solutions.

AIEmbeddingRAG
0 likes · 12 min read
Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future
Ops Development & AI Practice
Ops Development & AI Practice
Aug 25, 2025 · Industry Insights

How AI-Powered Codebase Indexing Transforms Software Development

This article explains how AI-driven codebase indexing converts massive, undocumented repositories into searchable semantic knowledge bases, detailing the workflow from parsing and embedding to storage and retrieval, and highlighting practical benefits such as faster navigation, code reuse, smarter AI assistants, and historical issue tracing.

AI embeddingscode indexingdeveloper productivity
0 likes · 7 min read
How AI-Powered Codebase Indexing Transforms Software Development
AI Large Model Application Practice
AI Large Model Application Practice
Aug 11, 2025 · Artificial Intelligence

How to Build an LLM-Powered Smart Resume Screening System

This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.

AI RecruitmentLLMRAG
0 likes · 18 min read
How to Build an LLM-Powered Smart Resume Screening System
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Aug 8, 2025 · Industry Insights

How CodeRAG Reinvents Large‑Scale Code Repository Knowledge Extraction and Hierarchical Retrieval

CodeRAG leverages AST‑centric parsing and a hierarchical knowledge graph to overcome text‑only retrieval limits in large code repositories, offering multi‑language analysis, incremental parsing, hybrid indexing, and intelligent context selection for tasks such as code completion, Q&A, documentation generation, and impact analysis.

ASTCodeRAGLarge-Scale Repos
0 likes · 15 min read
How CodeRAG Reinvents Large‑Scale Code Repository Knowledge Extraction and Hierarchical Retrieval
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Aug 5, 2025 · Artificial Intelligence

Enterprise Semantic Search: Key Q&A on Scoring, Recall, LSH, Chunking, and Embedding Dimensions

This article answers practical questions about enterprise semantic search, explaining how Reciprocal Rank Fusion normalizes mixed scoring, how to control vector result size, the trade‑offs of LSH parameters, word‑ and sentence‑based chunking strategies with version‑specific defaults, and flexible embedding dimensionality.

ElasticsearchLSHRRF
0 likes · 8 min read
Enterprise Semantic Search: Key Q&A on Scoring, Recall, LSH, Chunking, and Embedding Dimensions
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Aug 4, 2025 · Artificial Intelligence

Building Enterprise‑Grade Semantic Search with Ollama—No External APIs Required

This article walks through the complete design and implementation of a locally deployed, enterprise‑level semantic search system using Ollama for embedding generation and Easysearch for vector retrieval, covering problem analysis, architecture decisions, pipeline configuration, bulk indexing, and hybrid query execution.

EasysearchOllamalocal deployment
0 likes · 12 min read
Building Enterprise‑Grade Semantic Search with Ollama—No External APIs Required
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Jul 30, 2025 · Backend Development

From Keyword Matching to Semantic Understanding: Building an Intelligent E‑Commerce Search Engine

The article analyzes the semantic gap in e‑commerce search, compares traditional keyword matching with vector‑based retrieval, and provides a step‑by‑step implementation using Elasticsearch/Easysearch pipelines, embedding models, and a hybrid search strategy to improve user intent understanding.

EasysearchElasticsearchHybrid Search
0 likes · 11 min read
From Keyword Matching to Semantic Understanding: Building an Intelligent E‑Commerce Search Engine
DataFunSummit
DataFunSummit
Jul 15, 2025 · Artificial Intelligence

Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications

This article explains why traditional keyword search falls short, introduces Elasticsearch's vector search and hybrid retrieval capabilities, and shows how combining it with large language models enables Retrieval‑Augmented Generation (RAG) for more accurate, context‑aware AI-driven search across text and multimedia data.

AIElasticsearchRAG
0 likes · 5 min read
Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 12, 2025 · Artificial Intelligence

Why GraphRAG Is the Future of Retrieval‑Augmented Generation

This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.

AIGraphRAGKnowledge Graph
0 likes · 23 min read
Why GraphRAG Is the Future of Retrieval‑Augmented Generation
AI Algorithm Path
AI Algorithm Path
Jun 15, 2025 · Artificial Intelligence

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

This article explains why generic text‑embedding models often fail in specialized retrieval tasks, then demonstrates how to fine‑tune such models using contrastive learning, curated job‑listing data, and the Sentence‑Transformers library, achieving near‑perfect accuracy on a job‑matching benchmark.

Fine-tuningSentence-Transformerscontrastive learning
0 likes · 11 min read
Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough
dbaplus Community
dbaplus Community
May 3, 2025 · Backend Development

Boost Elasticsearch with Vector Embeddings: Python & Logstash Step‑by‑Step Guide

This article explains how vector embeddings enhance Elasticsearch for semantic search and recommendation, walks through the complete workflow of generating, storing, and querying embeddings, and provides detailed Python and Logstash implementations with code samples, pros and cons, and guidance on choosing the right approach.

ElasticsearchLogstashPython
0 likes · 11 min read
Boost Elasticsearch with Vector Embeddings: Python & Logstash Step‑by‑Step Guide
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Apr 8, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

This article explains Retrieval‑Augmented Generation (RAG), its three‑step workflow of retrieval, augmentation, and generation, its key advantages such as improved accuracy and explainability, and compares RAG with traditional pre‑trained models, fine‑tuned models, hybrid models, knowledge‑distillation methods, and RLHF, while also covering vector, full‑text, and hybrid retrieval modes and the role of rerank models.

AIKnowledge RetrievalRAG
0 likes · 18 min read
What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?
JavaEdge
JavaEdge
Mar 30, 2025 · Artificial Intelligence

How GenAI Can Transform E‑Commerce Product Review Analysis

This article examines the critical role of product reviews for buyers and sellers, outlines the limitations of traditional review processing, and proposes a GenAI‑powered solution—including platform and model choices, batch inference, and semantic search—to efficiently analyze large‑scale e‑commerce feedback.

Batch ProcessingGenAINLP
0 likes · 12 min read
How GenAI Can Transform E‑Commerce Product Review Analysis
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
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 28, 2025 · Artificial Intelligence

Boost Elasticsearch Semantic Search with Alibaba Cloud AI: Step‑by‑Step Guide

This tutorial walks through configuring Alibaba Cloud AI services, creating sparse embedding and rerank endpoints, setting up Elasticsearch mappings, indexing Agatha Christie data, and combining semantic search, reranking, and completion APIs to achieve more relevant search results and a RAG‑style answer generation pipeline.

AI integrationAlibaba Cloud AICompletion
0 likes · 19 min read
Boost Elasticsearch Semantic Search with Alibaba Cloud AI: Step‑by‑Step Guide
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 DataConfigurationCursor
0 likes · 6 min read
Configuring and Using DeepSeek Search Engine in Cursor for Efficient Data Retrieval
21CTO
21CTO
Nov 19, 2024 · Databases

Why Vector Databases Like Milvus Outperform Elasticsearch in Hybrid Search

This article explains how combining dense vector‑based semantic search with traditional keyword matching using a unified vector database such as Milvus delivers superior performance, scalability, and simplicity compared to maintaining separate Elasticsearch and vector‑search stacks.

ElasticsearchHybrid SearchMilvus
0 likes · 9 min read
Why Vector Databases Like Milvus Outperform Elasticsearch in Hybrid Search
Alibaba Cloud Native
Alibaba Cloud Native
Oct 26, 2024 · Artificial Intelligence

Build a Real‑Time Semantic Search with EventBridge, DashVector, and FunctionCompute

This tutorial walks through constructing a zero‑to‑one RAG pipeline that ingests OSS text files via EventBridge, transforms them into embeddings with DashScope, stores vectors in DashVector, and performs semantic search using FunctionCompute and a Qwen‑Turbo LLM, complete with code samples and configuration steps.

DashVectorEmbeddingEventBridge
0 likes · 10 min read
Build a Real‑Time Semantic Search with EventBridge, DashVector, and FunctionCompute
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.

AIKnowledge RetrievalLLM
0 likes · 13 min read
Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 18, 2024 · Artificial Intelligence

Integrate Alibaba Cloud AI Search with Elasticsearch: A Step‑by‑Step Guide

This tutorial walks you through configuring Elasticsearch’s Open Inference API to connect with Alibaba Cloud AI Search, covering setup of text generation, rerank, sparse and dense vector services, and demonstrates end‑to‑end requests with code examples for building RAG and semantic search applications.

Alibaba Cloud AI SearchElasticsearchInference API
0 likes · 11 min read
Integrate Alibaba Cloud AI Search with Elasticsearch: A Step‑by‑Step Guide
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 RetrievalEmbedding
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 ManagementEnterprise AI
0 likes · 15 min read
Knowledge Graph and RAG Applications in 360 Document Cloud: Challenges and Solutions
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 23, 2024 · Artificial Intelligence

How Elasticsearch Evolved into a Hybrid AI-Powered Search Engine

This article traces Elasticsearch's transformation from a pure text search engine to a versatile hybrid platform that integrates structured, geospatial, aggregation, and vector search capabilities, highlighting its AI-driven innovations, performance optimizations, and growing adoption across enterprises and academia.

AI searchElasticsearchHybrid Search
0 likes · 13 min read
How Elasticsearch Evolved into a Hybrid AI-Powered Search Engine
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 ExtractionNLP
0 likes · 20 min read
Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI
Data Thinking Notes
Data Thinking Notes
Jun 20, 2024 · Artificial Intelligence

Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA

This article explores how large language models can transform data workflows by using embeddings for semantic search, building private domain knowledge bases, generating SQL code from natural language with visualized results, and enhancing exploratory data analysis, outlining practical steps and benefits for enterprises.

EDAEmbeddingKnowledge Base
0 likes · 7 min read
Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA
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 QianfanEmbedding
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.

AIPineconePython
0 likes · 9 min read
Pinecone Vector Database and Embedding Model Summary from DeepLearning.AI’s AI Course
Cloud Native Technology Community
Cloud Native Technology Community
Feb 8, 2024 · Artificial Intelligence

How Retrieval‑Augmented Generation Boosts LLM Accuracy and Trust

Retrieval‑augmented generation (RAG) enhances large language models by fetching up‑to‑date, authoritative information from external sources, addressing hallucinations, outdated knowledge, and lack of citations, while offering cost‑effective implementation, improved relevance, user trust, and greater developer control through vector databases, semantic search, and prompt engineering.

AIPrompt engineeringRAG
0 likes · 10 min read
How Retrieval‑Augmented Generation Boosts LLM Accuracy and Trust
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.

Enterprise AILLM fine-tuningknowledge management
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
Data Thinking Notes
Data Thinking Notes
Nov 12, 2023 · Artificial Intelligence

Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams

This article explores how large language models can boost data workflows by using embeddings for semantic retrieval, building domain‑specific knowledge bases for private Q&A, generating SQL code from natural language, and automating exploratory data analysis, offering practical steps and visual examples.

EmbeddingKnowledge BaseLLM
0 likes · 7 min read
Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams
phodal
phodal
Sep 17, 2023 · Artificial Intelligence

How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG

This article explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.

AI AssistantKotlinRetrieval Augmented Generation
0 likes · 11 min read
How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG
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.

AIapproximate nearest neighborlarge language model
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.

Deep LearningErnieINT8 Quantization
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 QuantizationSmoothQuant
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.

LLMembeddingsinformation retrieval
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.

CypherGraphQLKnowledge Graph
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.

Industry analysisKnowledge Graphartificial intelligence
0 likes · 12 min read
Intelligent Industry Analysis Tool Based on Knowledge Graphs and Industry Atoms
DataFunTalk
DataFunTalk
Jun 3, 2022 · Artificial Intelligence

Construction and Application of Meituan's Life‑Service Knowledge Graph

This article explains Meituan's 'Meituan Brain' initiative, detailing the construction of life‑service knowledge graphs—including tag and dish graphs—through data mining, semantic extraction, synonym discovery, graph labeling, and applications such as open QA, search ranking, and recommendation using AI and GNN techniques.

AIGraph Neural NetworkKnowledge Graph
0 likes · 13 min read
Construction and Application of Meituan's Life‑Service Knowledge Graph
DataFunSummit
DataFunSummit
May 28, 2022 · Artificial Intelligence

Construction and Application of a Power Industry Knowledge Graph

This article outlines the development of a power‑sector knowledge graph by China Electric Power Research Institute, covering the AI institute overview, the background of power knowledge engineering, methods for representation and ontology construction, practical applications in dispatch, inspection and customer service, and future challenges.

Knowledge Graphartificial intelligencedomain ontology
0 likes · 30 min read
Construction and Application of a Power Industry Knowledge Graph
DataFunSummit
DataFunSummit
Mar 16, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

This article presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage recall, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network (SearchGCN), a transformer‑based synonym generation system, and a joint index‑training approach that integrates product quantization to improve recall accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model
0 likes · 17 min read
Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training
DataFunTalk
DataFunTalk
Mar 9, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training

The talk presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage retrieval, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network for low‑frequency items, automatic synonym generation via transformer models, and a joint training approach that integrates product quantization directly into the model to improve accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model
0 likes · 16 min read
Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training
Xianyu Technology
Xianyu Technology
Jan 29, 2022 · Artificial Intelligence

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Xianyu search recall stage upgraded from simple text matching to semantic vector retrieval using DSSM with Electra‑Small, query graph attention, and behavior‑based I2I, adding structured attributes and OCR tags, improving AUC to 0.824 and HitRate@10 to 90.1%, boosting purchase metrics by up to 4%.

Deep LearningVector RetrievalXianyu
0 likes · 17 min read
Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search
Code DAO
Code DAO
Dec 14, 2021 · Artificial Intelligence

Semantic Search on Wikipedia with Weaviate, GraphQL, Sentence‑BERT, and BERT Q&A

This article walks through building a large‑scale semantic search system on the English Wikipedia using the Weaviate vector database, GraphQL queries, and pre‑trained Sentence‑BERT and BERT Q&A models, covering dataset preparation, schema design, import pipelines, query examples, and production deployment strategies.

GraphQLSentence-BERTWeaviate
0 likes · 8 min read
Semantic Search on Wikipedia with Weaviate, GraphQL, Sentence‑BERT, and BERT Q&A
DataFunTalk
DataFunTalk
Nov 14, 2021 · Artificial Intelligence

Knowledge Graph Construction and Entity Linking: Techniques, Applications, and Recent Advances

This article provides a comprehensive overview of knowledge graphs and entity linking, covering their definitions, practical uses in question answering, search and recommendation, the standard pipeline of mention detection, candidate generation and scoring, challenges such as scalability and multilinguality, and recent research advances including dual‑encoder, RELIC, deep retrieval, and multilingual BERT‑based models, followed by a discussion of modern knowledge‑graph construction methods.

AIKnowledge Graphentity linking
0 likes · 21 min read
Knowledge Graph Construction and Entity Linking: Techniques, Applications, and Recent Advances
Baidu Geek Talk
Baidu Geek Talk
Sep 13, 2021 · Artificial Intelligence

Upgrading WanFang Academic Paper Retrieval System with PaddleNLP

WanFang upgraded its academic paper retrieval system by adopting PaddleNLP’s Chinese pre‑trained Sentence‑BERT models, using weakly supervised SimCSE data and Milvus vector indexing, compressing the transformer for TensorRT‑accelerated inference, achieving 70% better matching quality and 2600 QPS latency‑optimized performance.

Model DeploymentPaddleNLPSentence-BERT
0 likes · 8 min read
Upgrading WanFang Academic Paper Retrieval System with PaddleNLP
DataFunTalk
DataFunTalk
Aug 2, 2021 · Databases

From Text Search to Vector Search: Generalizing Unstructured Data Retrieval

The article explains why traditional text‑based search engines like ElasticSearch struggle with modern multimodal data, introduces vector databases that store implicit semantic embeddings, and proposes a generalized search architecture that decouples data‑to‑vector mapping from the engine while leveraging clustering or graph indexes for similarity search.

AIEmbeddinginformation retrieval
0 likes · 12 min read
From Text Search to Vector Search: Generalizing Unstructured Data Retrieval
DataFunSummit
DataFunSummit
Jul 25, 2021 · Artificial Intelligence

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

This article details Zhihu Search's engineering solutions for long‑tail query challenges, covering historical development, term weighting, synonym expansion, query rewriting with reinforcement learning, and semantic recall using BERT‑based models, while also outlining future research directions such as GAN‑based rewriting and lightweight pre‑training.

BERTEmbedding RetrievalQuery Rewriting
0 likes · 14 min read
Advances in Query Understanding and Semantic Retrieval at Zhihu Search
58 Tech
58 Tech
Mar 29, 2021 · Artificial Intelligence

Deep Semantic Model Exploration and Application in 58 Search

This article presents a comprehensive overview of 58 Search's multi‑stage retrieval system, compares term‑match and semantic matching, details the design, training, and optimization of interactive, dual‑tower, and semi‑interactive BERT‑based semantic models, and discusses their practical deployment in ranking and recall stages.

AIBERTdual-tower
0 likes · 18 min read
Deep Semantic Model Exploration and Application in 58 Search
DataFunTalk
DataFunTalk
Dec 22, 2020 · Artificial Intelligence

Construction and Application of Financial Knowledge Graphs

This article explains how financial institutions can leverage large amounts of structured and unstructured data to build and apply financial knowledge graphs, covering AI key technologies, schema design, data extraction, graph construction, storage solutions, and real-world use cases such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.

Financial AIKnowledge Graphentity extraction
0 likes · 14 min read
Construction and Application of Financial Knowledge Graphs
DataFunSummit
DataFunSummit
Dec 9, 2020 · Artificial Intelligence

Construction and Application of Financial Knowledge Graphs: AI Key Technologies, Building Practices, and Real‑World Use Cases

This article explains how financial institutions can leverage massive structured and unstructured data by building a financial knowledge graph, detailing AI core technologies, schema design, extraction methods, storage solutions, and a range of practical applications such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.

Information ExtractionKnowledge Graphartificial intelligence
0 likes · 16 min read
Construction and Application of Financial Knowledge Graphs: AI Key Technologies, Building Practices, and Real‑World Use Cases
DataFunSummit
DataFunSummit
Nov 29, 2020 · Artificial Intelligence

Vertical Domain Knowledge Graph Construction with OpenIE Techniques

This article explores the challenges of enterprise knowledge management and presents a comprehensive OpenIE-based approach for building vertical domain knowledge graphs, covering data extraction, SPO triple generation, case studies, and applications such as chatbots, semantic search, and intelligent QA.

Enterprise AIKnowledge GraphOpenIE
0 likes · 18 min read
Vertical Domain Knowledge Graph Construction with OpenIE Techniques
DataFunTalk
DataFunTalk
Nov 14, 2020 · Artificial Intelligence

Xiaomi Knowledge Graph: Architecture, Key Technologies, and Business Applications

The article presents an in‑depth overview of Xiaomi's knowledge graph, describing its evolution, core technologies such as entity linking, knowledge fusion and concept mining, and illustrating how it powers diverse AI‑driven services like smart Q&A, virtual assistants, e‑commerce and financial applications.

AI applicationsKnowledge GraphXiaomi
0 likes · 22 min read
Xiaomi Knowledge Graph: Architecture, Key Technologies, and Business Applications
DataFunTalk
DataFunTalk
Sep 21, 2020 · Artificial Intelligence

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

This article explains how keyword matching in search advertising works, outlines the challenges of semantic gaps, matching‑mode determination and scalability, and describes data‑driven synonym transformation techniques—including rule‑based, sequence‑to‑sequence, metric‑space and graph‑based models—to improve recall, efficiency, and robustness.

Ad Techkeyword matchingmachine learning
0 likes · 18 min read
Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising
iQIYI Technical Product Team
iQIYI Technical Product Team
Jun 19, 2020 · Artificial Intelligence

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

iQIYI Douya’s emoji search evolved from ElasticSearch to a pure Lucene implementation and added semantic vector retrieval, enabling fast, scalable, and more accurate text‑based search of AI‑generated images for small‑to‑medium businesses by combining custom tokenization, dense embeddings, and hybrid ranking.

ElasticsearchSearch ArchitectureVector Retrieval
0 likes · 14 min read
Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval
DataFunTalk
DataFunTalk
Jun 3, 2020 · Artificial Intelligence

Semantic Retrieval and Product Ranking in JD E‑commerce Search

This article presents JD's e‑commerce search system, detailing the semantic vector retrieval and product ranking pipelines, the two‑tower deep learning architecture, attention‑based personalization, negative sampling strategies, training optimizations, and real‑world performance gains achieved in production.

Deep LearningVector Retrievale‑commerce
0 likes · 11 min read
Semantic Retrieval and Product Ranking in JD E‑commerce Search
DataFunTalk
DataFunTalk
Feb 14, 2020 · Artificial Intelligence

OpenKG COVID‑19 Knowledge Graphs: Datasets, Schemas, and Applications

The OpenKG initiative, together with dozens of university and industry partners, has released a series of open‑source COVID‑19 knowledge graphs—including encyclopedia, research, clinical, hero, hotspot‑event, and upcoming prevention and resource graphs—detailing their data sources, scale, schema designs, and potential AI‑driven applications such as semantic search and intelligent question answering.

AICOVID-19Dataset
0 likes · 11 min read
OpenKG COVID‑19 Knowledge Graphs: Datasets, Schemas, and Applications
Architecture Digest
Architecture Digest
May 13, 2019 · Artificial Intelligence

Enterprise Knowledge Graphs: Development Trends, Use Cases, Database Selection, and Implementation Practices

This article outlines the evolution of knowledge graphs, describes typical enterprise application scenarios, compares graph database options such as Neo4j, Cayley and Dgraph, and presents a six‑step methodology for building, storing, and applying knowledge graphs in large‑scale business environments.

Data IntegrationEnterprise AIKnowledge Graph
0 likes · 13 min read
Enterprise Knowledge Graphs: Development Trends, Use Cases, Database Selection, and Implementation Practices
DataFunTalk
DataFunTalk
Dec 27, 2018 · Artificial Intelligence

Construction and Application of a Tourism Knowledge Graph

This article explains what a tourism knowledge graph is, discusses its architecture, construction methods, practical applications such as QA and recommendation, and explores future directions integrating knowledge graphs with deep learning and multi‑domain fusion.

AIKnowledge GraphNLP
0 likes · 10 min read
Construction and Application of a Tourism Knowledge Graph
Meituan Technology Team
Meituan Technology Team
Nov 22, 2018 · Artificial Intelligence

Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications

Meituan Brain builds a massive multi‑modal knowledge graph of billions of entities and triples across food, entertainment, and travel, using advanced extraction, validation, fusion, and reasoning techniques to empower search, recommendation, merchant tools, and fraud detection while addressing scalability and schema‑evolution challenges.

AIKnowledge GraphMeituan
0 likes · 28 min read
Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications
Youku Technology
Youku Technology
Nov 2, 2018 · Artificial Intelligence

How AI Powers Next‑Gen Multimedia Content Retrieval: From OCR to Knowledge Graphs

This article examines the evolution of search, defines multimedia content retrieval, explores user scenarios such as voice, image, and video input, and details key AI techniques—including OCR, face recognition, and content knowledge graphs—that enable semantic understanding and ranking of video content.

Knowledge GraphOCRface recognition
0 likes · 12 min read
How AI Powers Next‑Gen Multimedia Content Retrieval: From OCR to Knowledge Graphs