Overview ReAct is an agent-based chat mode built on top of a data query engine. For each chat interaction, the agent enters a ReAct loop: First, decide whether to use the query engine tool and propose appropriate input (Optional) Use the query engine tool and observe its output Decide whether to repeat or give a … Read more
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
Prompt is the fundamental input that grants LLM expressive capabilities. LlamaIndex uses prompts to build indexes, execute inserts, retrieve during queries, and synthesize final answers. LlamaIndex provides a set of out-of-the-box default prompt templates: https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py Additionally, here are some prompts specifically written for chat models like gpt-3.5-turbo: https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/chat_prompts.py Custom Prompts Users can also provide their … Read more
One of the primary steps to consider when building any LLM application based on data is choosing the right LLM. LLMs are a core component of LlamaIndex. They can be used as standalone modules or inserted into other core LlamaIndex modules (indexers, retrievers, query engines). They are generally used during the response synthesis step after … 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
The following article is sourced from WeChat public account: PaperAgent Author: PaperAgent Link: https://mp.weixin.qq.com/s/n-TuvbrBsiEHP64BRMdXHQ This article is for academic sharing only. If there is any infringement, please contact the backend for deletion. Introduction In 2025, “Agentic systems” will become a technical focus, and Agents will enter a new stage. This article explores the technology stack … Read more
Hello everyone! This is a channel focused on AI agents~ Have you ever wondered how those AI “agents” that can autonomously perform tasks and call tools operate? In 2024, we witnessed the transformation of AI technology from simple chatbots to more complex agents. However, as we delve into these agents, we find that their underlying … Read more
Selected from the HuggingFace blog Translated by: Zhao Yang This article will introduce the building blocks of MoE, training methods, and the trade-offs to consider when using them for inference. Mixture of Experts (MoE) is a commonly used technique in LLMs aimed at improving efficiency and accuracy. The way this method works is by breaking … Read more
Recommended Reading Multi-Agent Framework Comparison —- Magentic-One, AutoGen, LangGraph, CrewAI, Swarm Open Source Search Engine MiniPerplx: An AI Search Engine Built with Agent Brain Pydantic Agents: A Recommendation System for Context Processing Based on Prompt Injection Top 5 Frameworks for Building Multi-Agents and Their Usage How to Build a General LLM Agent Overview of Foreign … Read more