llamaindex

Index and query data for LLM-powered apps using a unified framework.

1.0k|117|Updated Feb 26, 2026

Search Tags:#llm #rag #multimodal #agents #vector-store #llamaindex #data-connectors

Ready to run

npx skills add https://github.com/OpenLAIR/dr-claw --skill llamaindex-openlair

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llamaindex
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/agents/llamaindex
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill llamaindex-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Index and query data for LLM-powered apps using a unified framework that unifies ingestion, indexing, and retrieval across diverse sources.

Core Features & Use Cases

Ingestion, indexing, and querying of data for LLMs
Supports 300+ data connectors via LlamaHub
Build RAG pipelines, knowledge bases, and enterprise chatbots
Multi-modal data access and agent-enabled workflows

Quick Start

Set up a private data Q&A pipeline using LlamaIndex to ingest, index, and query documents with an LLM.

Frequently Asked Questions about llamaindex

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a RAG pipeline with LlamaIndex for a private knowledge base?▼

To build a RAG pipeline with LlamaIndex, you use its unified framework to ingest, index, and query private documents. It supports 300+ data connectors to easily integrate diverse sources for enterprise chatbots and research workflows.

Can I ingest multimodal data into LLM-powered apps using a unified framework?▼

Yes, you can ingest multimodal data into LLM-powered apps using this unified framework. It provides multi-modal data access and supports tool-enabled agent workflows to process diverse inputs across connected sources.

Does LlamaIndex work with specific data connectors and vector stores?▼

Yes, LlamaIndex works with 300+ data connectors via LlamaHub and integrates with various vector stores. This allows you to unify ingestion, indexing, and retrieval across diverse cross-source document formats.

What is the best way to set up a private data Q&A pipeline for an enterprise chatbot?▼

The best way to set up a private data Q&A pipeline is using LlamaIndex to ingest, index, and query documents with an LLM. This approach directly supports building enterprise chatbots and assistants.

Can I use tool-enabled agents for cross-source document ingestion and research workflows?▼

Yes, you can use tool-enabled agents for cross-source document ingestion to support research workflows. The framework enables multi-modal data access and unifies retrieval across 300+ data connectors.