2024年12月3日 · Here's a streamlined process for building a RAG application with Azure Cosmos DB: Data Ingestion: Store your documents, images, and other content types in Azure Cosmos DB. Utilize the database's support for vector search to index and retrieve vectorized content.

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What is a RAG model? Although you may hear the term “RAG model,” RAG is not actually a model but an architectural framework that expands the native capabilities of LLMs by providing them with external knowledge sources to answer questions.

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.

2024年1月2日 · 通过构建块，rag系统可以更快地检索和处理相关数据。 因为文档层次结构有助于LLM选择包含要提取的最相关数据的部分，所以文档层次结构对RAG的效率至关重要。

2 天之前 · With just a few clicks, it delivers a fully-managed RAG pipeline end-to-end: from ingesting your data and automatically chunking and embedding it, to storing vectors in Cloudflare’s Vectorize database, performing semantic retrieval, and generating high-quality responses using Workers AI. AutoRAG continuously monitors your data sources and ...

2024年12月18日 · Azure AI Search is a proven solution for information retrieval in a RAG architecture. It provides indexing and query capabilities, with the infrastructure and security of the Azure cloud. Through code and other components, you can design a comprehensive RAG solution that includes all of the elements for generative AI over your proprietary content.

2024年5月7日 · 一个典型的 RAG 框架可以分为检索器（Retriever）和生成器（Generator）两块，检索过程包括为数据（如 Documents）做切分、嵌入向量（Embedding）、并构建索引（Chunks Vectors），再通过向量检索以召回相关结果，而生成过程则是利用基于检索结果（Context）增强的 Prompt ...

2025年3月12日 · Efficiently retrieving knowledge in RAG systems is key to providing accurate and timely responses. Vector databases and indexing strategies play a crucial role in strengthening RAG systems’ performance. This article continues the Understanding RAG series by conceptualizing vector databases and indexing techniques commonly used in RAG systems.

2024年12月3日 · Retrieval-augmented generation (RAG) is an architecture designed to overcome LLM limitations. RAG uses vector search to retrieve relevant documents based on an input query, providing these documents as context to the LLM for generating more accurate responses.