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High-Level Concepts#

This is a quick guide to the high-level concepts you'll encounter frequently when building LLM applications.

Large Language Models (LLMs)#

LLMs are the fundamental innovation that launched LlamaIndex. They are an artificial intelligence (AI) computer system that can understand, generate, and manipulate natural language, including answering questions based on their training data or data provided to them at query time. You can learn more about using LLMs.

Retrieval Augmented Generation (RAG)#

Retrieval-Augmented Generation (RAG) is a core technique for building data-backed LLM applications with LlamaIndex. It allows LLMs to answer questions about your private data by providing it to the LLM at query time, rather than training the LLM on your data. To avoid sending all of your data to the LLM every time, RAG indexes your data and selectively sends only the relevant parts along with your query. You can learn more about RAG.

Agents#

An agent is a piece of software that semi-autonomously performs tasks by combining LLMs with other tools. You can learn more about agents.

Use cases#

There are endless use cases for data-backed LLM applications but they can be roughly grouped into four categories:

Structured Data Extraction Pydantic extractors allow you to specify a precise data structure to extract from your data and use LLMs to fill in the missing pieces in a type-safe way. This is useful for extracting structured data from unstructured sources like PDFs, websites, and more, and is key to automating workflows.

Query Engines: A query engine is an end-to-end flow that allows you to ask questions over your data. It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.

Chat Engines: A chat engine is an end-to-end flow for having a conversation with your data (multiple back-and-forth instead of a single question-and-answer).

Agents: An agent is an automated decision-maker powered by an LLM that interacts with the world via a set of tools. Agents can take an arbitrary number of steps to complete a given task, dynamically deciding on the best course of action rather than following pre-determined steps. This gives it additional flexibility to tackle more complex tasks.

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