Agentic RAG

Comparison of Technical Architecture Between AutoGen and phiData Frameworks

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Comparison of Technical Architecture Between AutoGen and phiData Frameworks

In the current development frameworks for distributed AI systems, AutoGen led by Microsoft and phiData represent two different technical paths. Architectural Design Philosophy AutoGen adopts an event-driven distributed architecture, achieving decoupling and collaboration of complex tasks through dynamic Agent orchestration. Its core is a decentralized event bus that supports asynchronous message passing and state synchronization … Read more

Enhancing RAG Effectiveness with LangChain and LangGraph

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Introduction on how to use LangGraph to improve RAG. Long press to follow “Python Learning Base”, join the reader group, and share more wonderful content. 1. Introduction LangGraph is the latest member of the LangChain, LangServe, and LangSmith series, aimed at building generative AI applications using LLMs. Remember, all these are independent packages and must … Read more

Enhancing RAG Performance with LangChain and LangGraph

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Enhancing RAG Performance with LangChain and LangGraph

▼Recently, there have been many live broadcasts,make an appointment to ensure you gain something —1— The Two Core Concepts of LangChain 1、Process-Oriented Architecture Design of Chains：Programs written based on large models that follow predefined steps and rules, which cannot be flexibly adjusted, used for executing tasks like: automatic SQL writing or multi-turn dialogue, etc. 2、Goal-Oriented … Read more

Unlocking CrewAI 6: Enhancing AI Intelligence with 5 Memory Modes

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Unlocking CrewAI 6: Enhancing AI Intelligence with 5 Memory Modes

Types of Memory in CrewAI In CrewAI, the memory system enhances the capabilities of AI agents by enabling them to remember, reason, and learn from past interactions. Previous Chapter: The system consists of several key components: • Short-Term Memory: This component temporarily stores recent interactions and outcomes, allowing the agent to recall relevant information during … Read more

Building Agentic RAG with CrewAI and Langchain

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Building Agentic RAG with CrewAI and Langchain

In the rapidly evolving field of AI, the ability to provide accurate, context-aware responses to user queries is a game changer. Retrieval-Augmented Generation (RAG) is a powerful paradigm that combines the retrieval of relevant information from external sources with the generative capabilities of large language models (LLMs). However, as queries become increasingly complex and diverse, … Read more

Principles and Practical Applications of RAG Retrieval-Augmented Generation

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Principles and Practical Applications of RAG Retrieval-Augmented Generation

RAG 75% for retrieval, 25% for generation. All aimed at enhancing the ability to retrieve relevant contextual information. 1. RAG Working Principle and Technical Architecture RAG (Retrieval-Augmented Generation) is a technical architecture that combines information retrieval and generation models. Its basic working principle is to enhance the output of the generation model through the retrieval … Read more

RAG-Check: A Novel AI Framework for Multimodal Retrieval-Augmented Generation

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RAG-Check: A Novel AI Framework for Multimodal Retrieval-Augmented Generation

Large Language Models (LLMs) have made significant progress in the field of generative artificial intelligence, but they face the “hallucination” problem, which is the tendency to generate inaccurate or irrelevant information. This issue is particularly severe in high-risk applications such as medical assessments and insurance claims processing. To address this challenge, researchers from the University … Read more

Introduction to RAG Technology: A New Journey in Large Model Application Development

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Introduction to RAG Technology: A New Journey in Large Model Application Development

What is RAG Technology RAG, or Retrieval-Augmented Generation technology, is an AI architecture that combines retrieval and generation. It enhances the output quality of language models by retrieving external knowledge. This is akin to equipping large language models with an intelligent knowledge base, enabling them to retrieve and reference relevant information in real-time while generating … Read more

Roaming RAG Technology: Features and Advantages

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Roaming RAG Technology: Features and Advantages

Roaming RAG, as an innovative RAG technology, can be elaborated on in detail regarding its characteristics and advantages from the following aspects: Working Principle and Process The core of Roaming RAG lies in utilizing the hierarchical structure of documents to enhance the information retrieval capabilities of large language models (LLMs). The specific process includes: Document … Read more

RAG Architecture Explained: 7 Patterns from Basic to Advanced

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RAG Architecture Explained: 7 Patterns from Basic to Advanced

The RAG technology introduces external knowledge retrieval in the AI generation process, evolving from basic document queries to intelligent architectures with multi-modal and Multi-Agent collaborations, allowing AI to provide more accurate and comprehensive answers. Core Components: Embedding Model: Converts text into vector representations. Generation Model: Responsible for the final content generation. Re-ranking Model: Optimizes the … Read more