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Skill

llamaindex

From Orchestra-Research

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 LLM applications.

Facts

Repository
Orchestra-Research/AI-Research-SKILLs
Status
Actively maintained
Last commit
Source file
14-agents/llamaindex/SKILL.md

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The instructions Claude Code reads when this skill runs.

# 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

```bash
# Starter package (recommended)
pip install llama-index

# Or minimal core + specific integrations
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-embeddings-openai
```

### 5-line RAG example

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader("data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = que
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