llamaindex

Files (4)

• SKILL.md
• agents.md · references/
• data_connectors.md · references/
• query_engines.md · references/

SKILL.md (first lines)

# 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 = query_engine.query("What did the author do growing up?")
print(response)
```

## Core concepts

### 1. Data connectors - Load documents

```python
from llama_index.core import SimpleDirectoryReader, Document
from llama_index.readers.web import SimpleWebPageReader
from llama_index.readers.github import GithubRepositoryReader

# Directory of files
documents = SimpleDirectoryReader("./data").load_data()

# Web pages
reader = SimpleWebPageReader()
documents = reader.load_data(["https://example.com"])

# GitHub repository
reader = GithubRepositoryReader(owner="user", repo="repo")
documents = reader.load_data(branch="main")

# Manual document creation
doc = Document(
    text="This is the document content",
    metadata={"source": "manual", "date": "2025-01-01"}
)
```

### 2. Indices - Structure data

```python
from llama_index.core import VectorStoreIndex, ListIndex, TreeIndex

# Vector index (most common - semantic search)
vector_index = VectorStoreIndex.from_documents(documents)

# List index (sequential scan)
list_index = ListIndex.from_documents(documents)

# Tree index (hierarchical summary)
tree_index = TreeIndex.from_documents(documents)

# Save index
index.storage_context.persist(persist_dir="./storage")

# Load index
from llama_index.core import load_index_from_storage, StorageContext
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context)
```

### 3. Query engines - Ask questions

```python
# Basic query
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic?")
print(response)

# Streaming response
query_engine = index.as_query_engine(streaming=True)
response = query_engine.query("Explain quantum computing")

From dhananjay09892/KD-prompts, MIT.