Skill · em Dados, IA e pesquisa

llamaindex

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for…

Procedência

Antes de instalar

4 arquivos · 28,2 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/llamaindex/.

d=".claude/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Global: instala em ~/.claude/skills/llamaindex/.

d="$HOME/.claude/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Codex

Neste projeto: instala em .agents/skills/llamaindex/.

d=".agents/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Global: instala em ~/.agents/skills/llamaindex/.

d="$HOME/.agents/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Antigravity

Neste projeto: instala em .agents/skills/llamaindex/.

d=".agents/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Global: instala em ~/.gemini/antigravity-cli/skills/llamaindex/.

d="$HOME/.gemini/antigravity-cli/skills/llamaindex"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/agents-llamaindex"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/agents.md" "$u/references/agents.md" \
  -o "$d/references/data_connectors.md" "$u/references/data_connectors.md" \
  -o "$d/references/query_engines.md" "$u/references/query_engines.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.llamaindex

Prévia do SKILL.md

---
name: llamaindex
description: Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric …
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Agents, LlamaIndex, RAG, Document Ingestion, Vector Indices, Query Engines, Knowledge Retrieval, Data Framework, Multimodal, Private Data, Connectors]
dependencies: [llama-index, openai, anthropic]
---

# LlamaIndex - Data Framework for LLM Applications

The leading framework for connecting LLMs with your data.

## When to use LlamaIndex

**Use LlamaIndex when:**
- Building RAG (retrieval-augmented generation) applications
- Need document question-answering over private data
- Ingesting data from multiple sources (300+ connectors)
- Creating knowledge bases for LLMs
- Building chatbots with enterprise data
- Need structured data extraction from documents

**Metrics**:
- **45,100+ GitHub stars**
- **23,000+ repositories** use LlamaIndex
- **300+ data connectors** (LlamaHub)
- **1,715+ contributors**
- **v0.14.7** (stable)

**Use alternatives instead**:
- **LangChain**: More general-purpose, better for agents
- **Haystack**: Production search pipelines
- **txtai**: Lightweight semantic search
- **Chroma**: Just need vector storage

## Quick start

### Installation
…

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