What is the hottest topic in the era of large models? In addition to ChatGPT, tools like LangChain and LlamaIndex, designed for building large model applications, have also been gaining significant attention. To help everyone get started easily, we launched the 【Decoding LangChain】 tutorial series, and now we present the 【Unveiling LlamaIndex】 series, which you … Read more
Source: DeepHub IMBA This article is about 2500 words long and is recommended to be read in 5 minutes. This article will introduce the integration of LLMLingua with the proprietary LlamaIndex for efficient inference. The emergence of large language models (llm) has spurred innovation across multiple fields. However, with strategies driven by chain of thought … Read more
Explanation Querying is the most important part of LLM applications. In LlamaIndex, once you have completed: data loading, building the index, and storing the index, you can proceed to the most crucial part of LLM applications: querying. A simple query is just a prompt call to the large language model: it can be a question … Read more
Click 01 Magician Society Follow the official account, and never get lost in AI learning LlamaIndex is a simple and flexible data framework for connecting custom data sources with large language models. RAG is the process of optimizing LLM outputs by referencing knowledge bases outside of their training data sources before generating responses. RAG extends … Read more
The popular AI orchestration frameworkLlamaIndex has introduced the Agent Document Workflow (ADW), a new architecture that the company claims surpasses the Retrieval-Augmented Generation (RAG) process and enhances the efficiency of agents. As orchestration frameworks continue to improve, this approach provides organizations with options to enhance agent decision-making capabilities. LlamaIndex states that ADW can assist agents … Read more
In previous articles, we explored document question answering, dialogue systems, RAG pipelines, and system monitoring. Today, we will delve into how to build AI agents using LlamaIndex, particularly focusing on the ReAct (Reasoning and Acting) model, which enables AI to reason and perform specific actions. 1. Introduction to ReAct Model ReAct is an AI model … Read more
When building AI applications, understanding the system’s operational status, performance bottlenecks, and potential issues is crucial. This article will introduce how to add observability to LlamaIndex applications using Phoenix, helping you better monitor and optimize your system. What Is Observability? Observability refers to the ability to understand the internal state of a system from its … Read more
Data is key. Before we can operate on our data with LLM, we first need to process and load the data. This is similar to ETL (Extract, Transform, Load) in machine learning engineering. In LlamaIndex, the data ingestion pipeline typically consists of three main stages: Loading Data Transforming Data Indexing and Storing Data We will … Read more
Click the blue text above to follow us. 1. Introduction 1.1 Background When using OpenAI’s GPT series models, we might find that for some simple questions, such as factual questions in Chinese, the AI tends to fabricate answers. And when asked about recent news events, the AI will directly state that it does not know … Read more
In the previous article, we learned how to use LlamaIndex to build a basic document Q&A system. Today, we will take it a step further and explore how to build a more intelligent dialogue system. The Chat Engine of LlamaIndex offers various dialogue modes that enable a more natural and coherent conversation experience. 1. Introduction … Read more