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Agents

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DataFunTalk
DataFunTalk
May 28, 2025 · Artificial Intelligence

Google CEO Sundar Pichai on AI‑Driven Platform Transformation and the Rise of Agents

In a post‑I/O interview, Sundar Pichai explains how Google’s new AI products—from multimodal Gemini to AI agents like Flow—signal a second‑stage platform shift where AI becomes a universal interface, reshaping search, content creation, and the future integration of agents across consumer and enterprise ecosystems.

AIAgentsGoogle
1 likes · 35 min read
Google CEO Sundar Pichai on AI‑Driven Platform Transformation and the Rise of Agents
DataFunTalk
DataFunTalk
May 23, 2025 · Artificial Intelligence

2025 AI Landscape: Inference Models Dominate, Open‑Source Momentum Accelerates

The 2025 Q1 AI report from Artificial Analysis highlights six major trends—including a thousand‑fold drop in inference cost, the rise of MoE models, the growing parity of Chinese open‑source labs, the emergence of autonomous AI agents, native multimodal capabilities, and the trade‑off between performance, cost, and context windows—painting a picture of a rapidly evolving, increasingly competitive AI ecosystem.

AIAgentsInference
0 likes · 11 min read
2025 AI Landscape: Inference Models Dominate, Open‑Source Momentum Accelerates
DataFunSummit
DataFunSummit
Apr 7, 2025 · Artificial Intelligence

Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents

This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.

AI applicationsAgentsFinancial Risk Management
0 likes · 8 min read
Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents
iKang Technology Team
iKang Technology Team
Sep 5, 2024 · Artificial Intelligence

What Is LangChain? Overview, Core Advantages, Components, and Use Cases

LangChain is a modular framework that streamlines integration of large language models by providing unified model interfaces, prompt optimization, memory handling, indexing, chains, and agents, enabling developers to quickly build and deploy sophisticated NLP applications such as text generation, information extraction, and dynamic tool‑driven workflows across various industries.

AI FrameworkAgentsChains
0 likes · 6 min read
What Is LangChain? Overview, Core Advantages, Components, and Use Cases
HelloTech
HelloTech
Apr 10, 2024 · Artificial Intelligence

An Overview of LangChain: Architecture, Core Components, and Code Examples

LangChain is an open‑source framework that provides Python and JavaScript SDKs, templates, and services such as LangServe and LangSmith to compose models, embeddings, prompts, indexes, memory, chains, and agents via a concise expression language, enabling rapid prototyping, debugging, and deployment of LLM‑driven applications.

AI EngineeringAgentsJavaScript
0 likes · 19 min read
An Overview of LangChain: Architecture, Core Components, and Code Examples
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 27, 2023 · Artificial Intelligence

Comprehensive Overview of Large Language Models: Capabilities, Limitations, Deployment, and Future Trends

This article provides a detailed examination of large language models, covering their underlying technologies, capabilities and constraints, model families, training processes, cloud and edge deployment challenges, agent architectures, and emerging trends, offering practical insights for developers, product managers, and researchers.

AgentsArtificial IntelligenceLLM
0 likes · 43 min read
Comprehensive Overview of Large Language Models: Capabilities, Limitations, Deployment, and Future Trends
JD Retail Technology
JD Retail Technology
Nov 14, 2023 · Artificial Intelligence

An Overview of LangChain: Core Concepts, Components, and Practical Applications

This article introduces LangChain—a Python framework for building LLM‑driven applications—explains its core components such as models, indexes, chains, memory, and agents, and provides practical code examples for document summarization, retrieval‑augmented QA, and future development directions.

AgentsLLMLangChain
0 likes · 19 min read
An Overview of LangChain: Core Concepts, Components, and Practical Applications
DataFunTalk
DataFunTalk
Aug 17, 2023 · Artificial Intelligence

Introduction to LangChain: Concepts, Tools, and Example Applications

This article introduces the LangChain framework, explains its core concepts such as models, prompts, agents, memory, indexes, and tools, provides detailed code examples for each component, and demonstrates practical applications ranging from chatbots to image generation, helping readers understand and build powerful LLM-powered solutions.

AgentsLangChainPrompt Engineering
0 likes · 27 min read
Introduction to LangChain: Concepts, Tools, and Example Applications
Architect's Guide
Architect's Guide
Aug 10, 2023 · Artificial Intelligence

Getting Started with LangChain: Building LLM Applications in Python

This tutorial introduces LangChain, an open‑source Python framework that provides unified model access, prompt management, memory, retrieval, and tool integration, enabling developers to quickly prototype AI‑driven applications using large language models and various external data sources.

AgentsLLMLangChain
0 likes · 13 min read
Getting Started with LangChain: Building LLM Applications in Python
Architect
Architect
Jul 31, 2023 · Artificial Intelligence

Getting Started with LangChain: Building LLM‑Powered Applications

This article introduces LangChain, explains why it’s useful for building applications with large language models, walks through installation, API‑key setup, model and embedding selection, prompt engineering, chaining, memory, agents, and vector‑store indexing, and provides runnable Python code examples throughout.

AgentsLLMLangChain
0 likes · 16 min read
Getting Started with LangChain: Building LLM‑Powered Applications
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 10, 2023 · Artificial Intelligence

Enhancing Large Language Models with LangChain: Prompt Engineering, Chains, Agents, and Node.js Implementation

This article explains the limitations of large language models, introduces prompt engineering as a remedy, and provides a comprehensive guide to using the LangChain framework—including models, prompts, chains, agents, vector search, and practical Node.js code examples—to enable LLMs to interact with external tools and data sources.

AI DevelopmentAgentsLLM
0 likes · 35 min read
Enhancing Large Language Models with LangChain: Prompt Engineering, Chains, Agents, and Node.js Implementation
DaTaobao Tech
DaTaobao Tech
Jul 7, 2023 · Artificial Intelligence

Introduction to LangChain: Concepts, Tools, and Applications

The article introduces LangChain, a framework that unifies language models, prompts, memory, retrieval, and tool‑driven agents into composable chains, illustrating its core components, code examples, end‑to‑end applications such as retrieval‑augmented QA and image generation, and outlining future uses in customer service, recommendation, and automated code review.

AIAgentsLLM
0 likes · 25 min read
Introduction to LangChain: Concepts, Tools, and Applications