When should I use LlamaIndex instead of LangChain?

LlamaIndex is preferred when your primary focus is on data retrieval, indexing, and building RAG pipelines, whereas LangChain is often better for complex general-purpose agent workflows.

Which vector databases are supported by this skill?

The skill provides implementation guidance for popular vector stores including Chroma, Pinecone, FAISS, and many others via LlamaIndex's storage context.

Does this skill support multi-modal data types?

Yes, it includes patterns for multi-modal RAG, allowing Claude to help you build systems that query both text and visual data like diagrams or photos.

How do I evaluate the quality of my RAG pipeline?

This skill includes implementation patterns for Relevancy and Faithfulness evaluators to ensure your AI responses are accurate and grounded in the source context.

What is the LlamaIndex Claude Code skill?

It is a specialized capability that provides Claude with best practices, code snippets, and implementation patterns for using the LlamaIndex framework to build data-intensive LLM applications.

LlamaIndex RAG Framework

Name: LlamaIndex RAG Framework
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Connects LLMs to private data sources through advanced document ingestion, vector indexing, and retrieval-augmented generation (RAG) pipelines.

About

This skill equips Claude with the comprehensive LlamaIndex framework, enabling the development of sophisticated RAG applications and data-centric LLM agents. It facilitates seamless data ingestion from over 300 connectors, provides robust vector storage and indexing strategies, and supports advanced query engines for structured knowledge retrieval. Whether building chatbots on enterprise documentation or complex multi-modal research agents, this skill provides the implementation patterns and best practices needed to transform raw organizational data into actionable AI context.

Key Features

384 GitHub stars
Support for multi-modal RAG including images and unstructured PDFs
High-performance vector indices and semantic search retrieval
Automated evaluation tools for response relevancy and faithfulness
Unified data ingestion with 300+ connectors via LlamaHub
Built-in agentic frameworks for multi-tool orchestration

Use Cases

Developing AI research agents that synthesize information from diverse web and local sources
Implementing structured data extraction and automated summarization from unstructured text
Building enterprise-grade document Q&A systems over private knowledge bases

About

Key Features

384 GitHub stars
Support for multi-modal RAG including images and unstructured PDFs
High-performance vector indices and semantic search retrieval
Automated evaluation tools for response relevancy and faithfulness
Unified data ingestion with 300+ connectors via LlamaHub
Built-in agentic frameworks for multi-tool orchestration

Use Cases

Developing AI research agents that synthesize information from diverse web and local sources
Implementing structured data extraction and automated summarization from unstructured text
Building enterprise-grade document Q&A systems over private knowledge bases