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LlamaIndex

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

LlamaIndex and Weaviate

Weaviate is a supported vector store in LlamaIndex.

Create a vector store:

vector_store = WeaviateVectorStore(weaviate_client=client, index_name="LlamaIndex")

Our Resources

The resources are broken into two 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

TopicDescriptionResource
Data Loaders in LlamaIndexLearn how to load data into Weaviate using LlamaIndex, and how to connect LlamaIndex to an existing Weaviate cluster.Notebook
Indexes in LlamaIndexLearn about the various indexes you can build in LlamaIndex.Notebook
Recursive Query EngineLearn how to build a recursive query engine.Notebook
Self Correcting Query EngineSetup your vector store and build a self-correcting query engine.Notebook
Simple Query EngineBuild a simple query engineNotebook
SQL Router Query EngineBuild a SQL Query Engine to search through your vector and SQL database.Notebook
Sub-Question Query EngineBuild a query engine that will break down a complex question into multiple parts.Notebook
Advanced RAGThis notebook walks you through an advanced Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex and Weaviate.Notebook
Naive RAGThis notebook walks you through a naive Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex and Weaviate.Notebook

Read and Listen

TopicDescriptionResource
Episode 1: Data LoadingThis episode shows you how to connect your data to LlamaIndex and Weaviate.Video
Episode 2: Indexes in LlamaIndexThe video covers the three LlamaIndex indexes (Vector Store Index, List Index, and Tree Index) and walks through the architecture design. The video ends with a demo using the Vector Store Index and List Index.Video
Episode 3: RAG Techniques in LlamaIndexLearn about four query engines implemented in LlamaIndex.Video
LlamaIndex and Weaviate BlogAn introduction on LlamaIndex and an overview of the integration.Blog