Abstract Large language models (LLMs) have demonstrated impressive capabilities. However, this does not mean they are error-free; anyone who has experienced ChatGPT’s “hallucinations” can attest to that. Retrieval Augmented Generation (RAG) is a framework designed to make LLMs more reliable by extracting relevant, up-to-date data directly related to user queries. In this article, I analyze … Read more
The full name of RAG is Retrieval Augmented Generation, which means “retrieval enhanced generation”. LLMs are trained on a vast amount of data, but this training data does not include your data. RAG solves this problem by adding your data to the data that the LLM already has access to. In RAG, your data is … Read more
Implementing RAG Queries in LlamaIndex Agent Overview This article explains how to integrate a RAG query engine into an Agent, enabling the Agent to utilize external knowledge bases for data queries, thus enhancing its capabilities. This approach is useful in many scenarios, for instance: often we need to query or compute a specific metric first, … Read more
LlamaIndex is an LLM (Large Language Model) application development framework that many developers prefer to use for developing RAG (Retrieval-Augmented Generation) applications. During the development of RAG applications, we often need to evaluate relevant data to better adjust and optimize the applications. With the development of RAG technology, more excellent evaluation tools have emerged, which … Read more
▼Recently, there have been many live broadcasts,make an appointment to ensure you gain something. Today:《LlamaIndex Architecture Design and Application Case Practice》 —1— Analysis of the LlamaIndex Development Framework LlamaIndex is a data development framework that provides applications based on LLM to acquire, build, and access private or domain-specific data. It establishes a bridge between natural … Read more
Introduction to Amazon Bedrock Amazon Bedrock is an advanced generative artificial intelligence (AI) platform launched by Amazon Web Services (AWS) aimed at helping businesses easily build, train, and deploy large-scale generative AI models. By integrating various pre-trained language models, Amazon Bedrock provides users with flexible and scalable AI solutions that support natural language processing, text … Read more
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
A very impressive RAG system introduction. For more resources, please visit: I’m currently discussing interesting topics with friends in the “Large Model Pit Avoidance Guide,” would you like to join? [Code Repository] GitHub
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
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