Agentic RAG

Generative AI Based on RAG: Building Custom Retrieval-Augmented Generation Pipelines Using LlamaIndex, Deep Lake, and Pinecone

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Generative AI Based on RAG: Building Custom Retrieval-Augmented Generation Pipelines Using LlamaIndex, Deep Lake, and Pinecone

Book Introduction Minimize AI hallucinations and build accurate custom generative AI pipelines that leverage embedded vector databases and integrated human feedback for retrieval-augmented generation (RAG). Purchasing the physical or Kindle version of this book includes a free PDF eBook. Main Features Implement traceable outputs for RAG, linking each response to its source document, and build … Read more

Strategies to Enhance RAG System Performance

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Strategies to Enhance RAG System Performance

The RAG (Retrieval-Augmented Generation) model, commonly referred to as the RAG system, is widely used in large model applications. The principle of the model is quite simple: it retrieves information from a dataset based on user needs and then uses a large model for reasoning and generation. The advantage of RAG lies in its ability … Read more

Unlocking Intelligence in Your RAG Applications: The Secrets of Agentic Hybrid Search

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Unlocking Intelligence in Your RAG Applications: The Secrets of Agentic Hybrid Search

If you are building a retrieval-augmented generation (RAG) application, you know how powerful they can be when they work well. But semantic embedding models are not magic. Most RAG implementations rely solely on semantic similarity as the unique retrieval mechanism, throwing every document into a vector database and applying the same retrieval logic to each … Read more

Overview of Agentic Retrieval-Augmented Generation

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Overview of Agentic Retrieval-Augmented Generation

Large language models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their responsiveness to dynamic real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) serves as a solution by integrating real-time data retrieval to enhance … Read more

MedGPT: Demonstrating Excellent Medical Performance Based on RAG Evaluation Framework

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MedGPT: Demonstrating Excellent Medical Performance Based on RAG Evaluation Framework

The Retrieval-Augmented Generation (RAG) technology is revolutionizing the AI application field by integrating external knowledge bases with internal knowledge of LLM (Large Language Model), enhancing the accuracy and reliability of AI systems. The knowledge “recall ability” of the multimodal knowledge extractor directly determines whether the large model can obtain accurate professional knowledge when answering reasoning … Read more

Comprehensive Overview of Agentic RAG

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Comprehensive Overview of Agentic RAG

https://arxiv.org/pdf/2501.09136 Overview of Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) represents a significant advancement in the field of artificial intelligence by combining the generative capabilities of Large Language Models (LLMs) with real-time data retrieval. While LLMs excel in natural language processing, their reliance on static pre-trained data often results in outdated or incomplete responses. RAG achieves … Read more

How to Handle Table Data in RAG Knowledge Base Documents?

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How to Handle Table Data in RAG Knowledge Base Documents?

In developing the RAG system, the data formats in the knowledge base can be diverse, and most of them are unstructured data content. For example, PDF documents in the knowledge base are likely to contain table data, and our approach to handling this needs special attention to ensure that the table information can be correctly … Read more

Reject Module in Large Model RAG

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Reject Module in Large Model RAG

To effectively implement <span>RAG</span>, there are indeed many aspects that need refinement, and today we will learn about the Reject Module. Official Explanation In the RAG (Retrieval-Augmented Generation) model, the Reject Module is an important component designed to enhance the robustness of the generation model when facing irrelevant queries or information. Plain Explanation A simple … Read more

RAG 2.0 Performance Improvement: Strategies and Practices for Optimizing Indexing and Recall Mechanisms

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RAG 2.0 Performance Improvement: Strategies and Practices for Optimizing Indexing and Recall Mechanisms

Introduction This sharing is titled “RAG 2.0 Engine Design Challenges and Implementation”. Main content includes the following parts: 1. Pain points and solutions of RAG 1.0 2. How to effectively Chunking 3. How to accurately recall 4. Advanced RAG and preprocessing 5. How RAG will develop in the future 6. Q&A Guest Speaker｜Zhang Yingfeng Founder … Read more