agents-llamaindex

1# LlamaIndex - Data Framework for LLM Applications
2 
3The leading framework for connecting LLMs with your data.
4 
5## When to use LlamaIndex
6 
7**Use LlamaIndex when:**
8- Building RAG (retrieval-augmented generation) applications
9- Need document question-answering over private data
10- Ingesting data from multiple sources (300+ connectors)
11- Creating knowledge bases for LLMs
12- Building chatbots with enterprise data
13- Need structured data extraction from documents
14 
15**Metrics**:
16- **45,100+ GitHub stars**
17- **23,000+ repositories** use LlamaIndex
18- **300+ data connectors** (LlamaHub)
19- **1,715+ contributors**
20- **v0.14.7** (stable)
21 
22**Use alternatives instead**:
23- **LangChain**: More general-purpose, better for agents
24- **Haystack**: Production search pipelines
25- **txtai**: Lightweight semantic search
26- **Chroma**: Just need vector storage
27 
28## Quick start
29 
30### Installation
31 
32```bash
33# Starter package (recommended)
34pip install llama-index
35 
36# Or minimal core + specific integrations
37pip install llama-index-core
38pip install llama-index-llms-openai
39pip install llama-index-embeddings-openai
40```
41 
42### 5-line RAG example
43 
44```python
45from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
46 
47# Load documents
48documents = SimpleDirectoryReader("data").load_data()
49 
50# Create index
51index = VectorStoreIndex.from_documents(documents)
52 
53# Query
54query_engine = index.as_query_engine()
55response = query_engine.query("What did the author do growing up?")
56print(response)
57```
58 
59## Core concepts
60…