How many data sources does LlamaIndex support?

Through LlamaHub, LlamaIndex supports over 300 data connectors, including PDF, GitHub, SQL, Slack, and various web scrapers.

Can I use LlamaIndex with different vector databases?

Yes, this skill provides implementation patterns for popular vector stores including Chroma, Pinecone, and FAISS.

Does this skill support multi-modal RAG?

Yes, it includes patterns for multi-modal indexing and querying, enabling Claude to process and reason across both text and images.

What is the primary use of the LlamaIndex skill?

The LlamaIndex skill is primarily used to build Retrieval-Augmented Generation (RAG) applications, allowing LLMs to access and query private or domain-specific data.

When should I use LlamaIndex over LangChain?

LlamaIndex is specialized for data-centric tasks and search-heavy RAG, whereas LangChain is a more general-purpose framework for complex agentic workflows.

LlamaIndex RAG Framework

Name: LlamaIndex RAG Framework
Author: davila7

bydavila7

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16,312

Data Science & ML

Simplifies the creation of data-centric LLM applications by providing patterns for RAG, document ingestion, and vector indexing.

About

This LlamaIndex skill equips Claude with the ability to architect sophisticated Retrieval-Augmented Generation (RAG) applications. It provides comprehensive implementation patterns for document ingestion from over 300 sources, efficient vector indexing, and advanced query engine configurations. Whether you are building internal knowledge bases, document Q&A systems, or multi-modal AI pipelines, this skill provides the necessary guidance and code structures to bridge the gap between private enterprise data and large language models.

Key Features

Structured data extraction using Pydantic output parsing
300+ pre-built data connectors for seamless document ingestion
Autonomous RAG agents with advanced tool-calling capabilities
16,312 GitHub stars
Efficient vector store indexing and semantic search retrieval
Advanced RAG patterns including multi-modal support and chat engines

Use Cases

Creating automated knowledge retrieval agents for customer support
Building private document Q&A systems over enterprise datasets
Integrating diverse data sources like GitHub, PDFs, and SQL into LLM workflows

About

Key Features

Structured data extraction using Pydantic output parsing
300+ pre-built data connectors for seamless document ingestion
Autonomous RAG agents with advanced tool-calling capabilities
16,312 GitHub stars
Efficient vector store indexing and semantic search retrieval
Advanced RAG patterns including multi-modal support and chat engines

Use Cases

Creating automated knowledge retrieval agents for customer support
Building private document Q&A systems over enterprise datasets
Integrating diverse data sources like GitHub, PDFs, and SQL into LLM workflows