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
Originally from Akash Mathur’s Blog Abstract: In the field of information retrieval, Retrieval-Augmented Generation (RAG) models signify a paradigm shift, empowering large language models (LLMs) to generate responses that are rich in context and accurate. However, unlocking the full potential of RAG often transcends the limitations of its default query-retrieve-generate framework. This article delves into … 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
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
In the previous article, we primarily used SimpleDirectoryReader to process documents. Today, we will explore how to use LlamaParse to handle more complex PDF documents, achieving more accurate document parsing and information extraction. 1. Introduction to LlamaParse LlamaParse is a tool specifically designed for handling complex documents, with the following features: Accurate layout recognition Table … Read more
Preface The internet is a vast sea of information. Humans are intelligent beings that crave information. However, our attention, computational power, and storage capacity are limited. We cannot process all the information on the internet simultaneously, so we invented a “salvaging” tool for information: search engines. Search engines represented by Google remain the most effective … Read more
In today’s rapidly advancing AI technology, AI Agents have become a hot area of research and application. Both enterprises and individual developers hope to leverage AI Agents to enhance work efficiency, optimize business processes, or create entirely new user experiences. However, building an AI Agent from scratch is not an easy task. Fortunately, there are … Read more
Source: Authorized reproduction from Machine Learning Algorithms and Python Practice Author: Lao Zhang is Busy Discovered an amazing Python library that makes creating large model applications incredibly simple. 8 lines of code is enough (with 2 optional lines). import gradio as gr import ai_gradio gr.load( name='qwen:qwen1.5-14b-chat', src=ai_gradio.registry, title='AI Chat', description='Chat with an AI model' ).launch() … Read more
Getting Started with Mistral: An Introduction The open-source Mixtral 8x7B model launched by Mistral adopts a “Mixture of Experts” (MoE) architecture. Unlike traditional Transformers, the MoE model incorporates multiple expert feedforward networks (this model has 8), and during inference, a gating network is responsible for selecting two experts to work. This setup allows MoE to … Read more
Machine Heart Reports Editor: Demon King This extension utilizes the pre-trained model CodeBERT, which can handle programming languages and natural language, to quickly generate Python docstrings. Visual Studio Code (VS Code) is a free code editor developed by Microsoft that supports operating systems such as Windows, Linux, and macOS. It supports testing and has built-in … Read more