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llamaindex

Build RAG pipelines with document ingestion, vector indexing, and query engines.

1|Updated Mar 5, 2026
Search Tags:#rag#retrieval#chatbot#document ingestion#vector index#llamaindex#query engine
One-click install
npx skills add https://github.com/Clay-HHK/claude-skills --skill llamaindex-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llamaindex
Source: https://github.com/Clay-HHK/claude-skills/tree/main/llamaindex
Command: npx skills add https://github.com/Clay-HHK/claude-skills --skill llamaindex-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Connecting LLMs to private data for question-answering requires building ingestion, indexing, retrieval, and generation pipelines from scratch, which is complex and error-prone. ## Core Features & Use Cases - Document Ingestion: Load data from 300+ connectors including files, web pages, GitHub, databases, and APIs via LlamaHub. - Indexing & Retrieval: Create vector, list, and tree indices with support for Chroma, Pinecone, and FAISS vector stores. - Query Engines & Agents: Build query engines with streaming, metadata filtering, chat memory, and agents that combine RAG with custom tools. - Use Case: Build a chatbot that answers questions over your company's internal documentation by loading a directory of PDFs, indexing them, and querying with a conversational chat engine. ## Quick Start Use the llamaindex skill to build a RAG pipeline that indexes the documents in my data folder and answers 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 with LlamaIndex?▼

Load documents with SimpleDirectoryReader, create a VectorStoreIndex from them, then call index.as_query_engine() to query. The whole pipeline takes about five lines of Python code.

What is the difference between LlamaIndex and LangChain?▼

LlamaIndex specializes in RAG and document Q&A with 300+ data connectors and a simpler API for retrieval. LangChain is more general-purpose and better suited for complex agents and multi-step workflows.

Which vector stores does LlamaIndex support?▼

LlamaIndex integrates with Chroma for local storage, Pinecone for cloud, and FAISS for fast in-memory search, among others. Configure them through a StorageContext passed to VectorStoreIndex.

How do I add conversation memory to a LlamaIndex chatbot?▼

Use index.as_chat_engine() with chat_mode set to condense_plus_context. The chat engine maintains history across turns, and you can call reset() to start a new conversation.

When should I not use LlamaIndex?▼

Avoid LlamaIndex when building complex general-purpose agents where LangChain fits better, or when you only need vector storage, where Chroma alone suffices. It is optimized for data-centric RAG workloads.

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