LlamaIndex Introduction

LlamaIndex operates through a multi-stage process. First, it uses data connectors to ingest information from diverse sources. The ingested data is then indexed and stored in optimized formats for LLM consumption, such as vector stores or graph databases. When a query is made, LlamaIndex employs sophisticated retrieval methods to fetch the most relevant information. This retrieved context is then used to augment prompts sent to the LLM, enabling more accurate and contextually relevant responses. LlamaIndex offers various components like query engines, chat engines, and data agents to facilitate different interaction patterns. It also provides tools for structured data extraction, evaluation, and workflow orchestration, allowing developers to create complex, production-ready LLM applications.