In the previous article “The Future of Application Orchestration is Pipeline, LlamaIndex Releases Query Pipeline in Preview to Enhance Application Development Flexibility” we mentioned that LlamaIndex has released a new experimental feature that supports defining a Query Pipeline in a declarative manner to create personalized application workflows, along with a case study for RAG applications. … Read more
Overview This article implements a simple intelligent Agent that first queries data from a database and then processes the data using utility functions. This is a very common scenario that can be extended to multiple practical situations. Similarly, all experiments in this article are conducted on a local machine with 16C32G Linux (CPU). Data Preparation … Read more
Overview This article introduces how to use LlamaIndex’s Agent to call multiple custom Agent tool functions. As with the previous articles in this series, this article does not use the OpenAI API and relies entirely on a local large model to complete the entire functionality. The goal of this article is simple: to save the … 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
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
Overview For Agents, it is common to call multiple external tools to achieve various functions. This article introduces how to use external tools through llamaindex. Of course, these tools are all provided by llamaindex. The framework offers several external tools that can infinitely expand the capabilities of the Agent. These tools can be downloaded from … Read more
Project Introduction The easiest way to start using LlamaIndex is by using <span>create-llama</span>. This CLI tool allows you to quickly start building new LlamaIndex applications and sets everything up for you. Quick Run npx create-llama@latest to get started, or refer to the options below for more choices. After generating the application, run npm run dev … Read more
Click the “Blue Words” to Follow Us LlamaIndex, as a powerful framework, provides a solid foundation for building data pipelines that connect with large language models (LLMs). It implements a modular approach to query execution through structured workflows, simplifying solutions to complex problems. Today, let’s discuss the workflows of LlamaIndex. 1. Basics of LlamaIndex Workflows … Read more
First, we need to clarify that we require two types of models: LLM, which is the large model responsible for generating content. Embedding model, which is responsible for generating embeddings that represent text semantics in vector form. Set Up OpenAI API Key By default, LlamaIndex uses OpenAI’s LLM and embedding models, so we first need … Read more
Building projects is the best way to enhance your AI skills. However, for beginners, “What should I build?” is a common dilemma. This article will share 5 AI projects that can be completed quickly, ranging from basic to advanced, and will gradually explain the steps and Python libraries needed to implement these projects. Image from … Read more