Table of Content
Pre-generated context: Reconstructing the key project of RAG and building the foundation for AI programming
In-depth analysis of how pre-generated context reconstructs RAG key projects and builds the foundation for AI programming. The article elaborates on the principles of RAG technology, including the difficulties and challenges in the indexing and retrieval stages. Despite many uncertainties, pre-generated context is becoming a new trend to improve RAG effects. Interested in RAG technology? Click...
Alibaba Cloud AI Search Open Platform adds: Service Development Capabilities
Explore the latest developments of Alibaba Cloud AI Search Open Platform, focusing on its newly added service development capabilities, including a variety of atomic services. In-depth analysis of RAG-related services, such as document parsing, image parsing, etc. At the same time, the technical principles and technical architecture of large models are explained to help users solve development...
Another open source project: Using LLM to convert unstructured text into knowledge graph
Explore new areas of open source! This article introduces how to use large language models to convert unstructured text into knowledge graphs, and elaborates on the knowledge graph construction process and visualization methods. It covers practical content such as environment configuration and key library installation. The code can be obtained in the GitHub repository. It helps you easily...
The two core tools of the RAG retrieval system - Embedding model and Rerank model
In-depth analysis of the core of the RAG retrieval system, exploring the mysteries of the Embedding model and the Rerank model. Detailed interpretation of the rag technical principles and architecture, and understanding of how these two models play a key role in RAG. From functional goals, application stages to technical implementation, a comprehensive analysis. Click to read and start your...
Technical principles for implementing RAG based on LangChain
In-depth analysis of the technical principles of implementing RAG based on LangChain! Introduced the shortcomings of large models and the commonly used RAG methods in the industry, and detailed the implementation steps using LangChain as an example. Including key links such as text processing, vectorization, retrieval and matching. Also listed a variety of common RAG technical frameworks. Want...
BM25: Text Relevance Ranking in RAG
In-depth analysis of RAG technology, focusing on BM25 text relevance ranking. As a key algorithm in the RAG retrieval stage, BM25 can accurately match relevant paragraphs from massive documents. Its principle involves factors such as word frequency and inverse document frequency, and its advantages are significant. Want to learn more about the principles of RAG technology? Click to read!
Say goodbye to outdated information and embrace accurate insights! Refly Grayscale version integrates Context7 to revolutionize your AI knowledge interaction experience!
Say goodbye to the trouble of outdated information. The gray version of Refly integrates Context7 to bring you a new AI knowledge interaction experience! In-depth analysis of the big model knowledge base, detailed explanation of the technical principles and architecture of the big model. Accurately solve the pain points such as code hallucinations and outdated APIs, and innovate the way of...
RAG Architecture Overview: Finding the Most Suitable RAG Solution
In-depth interpretation of RAG technical architecture, comprehensive exploration of its various types and optimization strategies. Covering the efficient retrieval and generation of standard RAG, as well as the unique advantages of corrective and speculative types. Whether you are a developer or an AI enthusiast, you can gain valuable knowledge from it and provide a strong reference for...
AI Search and Vector Data-How do models encode information and data into knowledge?
In-depth analysis of AI search and vector data, exploring how models encode information and data into knowledge. Detailed explanation of technologies such as vector embedding, covering applications in multiple fields such as natural language processing and computer vision. Also introduced knowledge graph visualization tools, as well as the application of knowledge graphs in the field of...
Say goodbye to RAG and embrace CAG: the road to innovation of knowledge tasks
Explore new directions in knowledge task processing in the field of artificial intelligence! The once mainstream RAG technology has frequent problems in practical applications, such as retrieval delays and document selection errors. The new CAG paradigm came into being, and its core principle is the clever combination of preloading and caching. CAG has all-round advantages over RAG, with...