llamaindex

Ingest documents from over 300 sources and query indexed data with RAG.

1|Updated Feb 21, 2026

Search Tags:#knowledge retrieval #llm #rag #agents #llamaindex #data framework

Ready to run

npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill llamaindex-tianhao909

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llamaindex
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/14-agents/llamaindex
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill llamaindex-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llama-index, openai, anthropic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of building applications that leverage Large Language Models (LLMs) by providing a robust framework for connecting them to your own data sources.

Core Features & Use Cases

Data Ingestion: Connects to over 300 data sources (files, web pages, databases, APIs) to load documents.
Indexing: Structures your data into various index types (vector, list, tree) for efficient retrieval.
Querying: Enables asking natural language questions over your indexed data, supporting RAG (Retrieval-Augmented Generation).
Agents: Allows building agents that can use tools, including querying your data, to perform complex tasks.
Use Case: Build a chatbot that can answer questions about your company's internal documentation by ingesting all relevant documents and allowing users to query them conversationally.

Quick Start

Use the llamaindex skill to load documents from the 'data' directory and create a query engine to ask questions about them.

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 application to query my private documents?▼

Use this framework to ingest documents from over 300 data sources, structure them into index types like vector or tree, and create a query engine to ask questions over your private data. This enables natural language retrieval for your RAG application.

What is the best way to connect an LLM to external data sources like databases and APIs?▼

Connect an LLM to external data using data connectors that ingest files, web pages, databases, and APIs. This framework structures the ingested data into indexes for efficient knowledge retrieval and question-answering.

Can I use OpenAI and Anthropic models with this data framework?▼

Yes, you can use OpenAI and Anthropic models. The framework lists both as dependencies to build LLM applications and agents that leverage these models for querying your indexed data.

How do I ingest and index company internal documentation for a chatbot?▼

Ingest internal documentation by connecting to your data sources, then index the loaded documents using vector, list, or tree structures. This creates a query engine allowing users to conversationally ask questions about the company docs.

Does this framework support building agents that can use tools to perform complex tasks?▼

Yes, it supports building agents that can use tools, including querying your indexed data, to perform complex tasks. This allows your LLM applications to go beyond simple retrieval and execute multi-step reasoning.

What index types are available for structuring data for efficient retrieval?▼

Available index types for structuring data include vector, list, and tree indexes. These structures organize your ingested documents to enable efficient knowledge retrieval and querying for your LLM applications.