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LlamaIndex

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Overview

LlamaIndex is a framework for building large language model (LLM) applications.

LlamaIndex and Weaviate

Weaviate is a supported vector store in LlamaIndex.

Resources

The resources are broken into categories:

  1. Hands on Learning: Build your technical understanding with end-to-end tutorials.
  2. Read and Listen: Develop your conceptual understanding of these technologies.

Hands-on Learning

Notebook

Data Loaders in LlamaIndex

Learn how to load data into Weaviate using LlamaIndex, and how to connect LlamaIndex to an existing Weaviate cluster.

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Notebook

Indexes in LlamaIndex

Learn about the various indexes you can build in LlamaIndex.

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Recursive Query Engine

Learn how to build a recursive query engine.

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Notebook

Self Correcting Query Engine

Setup your vector store and build a self-correcting query engine.

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Notebook

Simple Query Engine

Build a simple query engine

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SQL Router Query Engine

Build a SQL Query Engine to search through your vector and SQL database.

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Notebook

Sub-Question Query Engine

Build a query engine that will break down a complex question into multiple parts.

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Notebook

Advanced RAG

This notebook walks you through an advanced Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex and Weaviate.

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Notebook

Naive RAG

This notebook walks you through a naive Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex and Weaviate.

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Notebook

Agent vs No Agent

Learn the difference between naive RAG, and an agent that has RAG tools.

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LlamaIndex and Weaviate Query Agent

Use the Query Agent as a tool with the LlamaIndex AgentWorkflow

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Notebook

Memory with LlamaIndex, Weaviate, and Gemini

This notebook shows you how you can give your AI agent long-term memory with LlamaIndex, Weaviate, and Gemini.

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