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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
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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 📦 Ships with claude-code-templates
</> SKILL.md
agents-llamaindex.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 LLM applications.
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
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
- **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")
for text in response.response_gen:
print(text, end="", flush=True)
# Custom configuration
query_engine = index.as_query_engine(
similarity_top_k=3, # Return top 3 chunks
response_mode="compact", # Or "tree_summarize", "simple_summarize"
verbose=True
)
```
### 4. Retrievers - Find relevant chunks
```python
# Vector retriever
retriever = index.as_retriever(similarity_top_k=5)
nodes = retriever.retrieve("machine learning")
# With filtering
retriever = index.as_retriever(
similarity_top_k=3,
filters={"metadata.category": "tutorial"}
)
# Custom retriever
from llama_index.core.retrievers import BaseRetriever
class CustomRetriever(BaseRetriever):
def _retrieve(self, query_bundle):
# Your custom retrieval logic
return nodes
```
## Agents with tools
### Basic agent
```python
from llama_index.core.agent import FunctionAgent
from llama_index.llms.openai import OpenAI
# Define tools
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
# Create agent
llm = OpenAI(model="gpt-4o")
agent = FunctionAgent.from_tools(
tools=[multiply, add],
llm=llm,
verbose=True
)
# Use agent
response = agent.chat("What is 25 * 17 + 142?")
print(response)
```
### RAG agent (document search + tools)
```python
from llama_index.core.tools import QueryEngineTool
# Create index as before
index = VectorStoreIndex.from_documents(documents)
# Wrap query engine as tool
query_tool = QueryEngineTool.from_defaults(
query_engine=index.as_query_engine(),
name="python_docs",
description="Useful for answering questions about Python programming"
)
# Agent with document search + calculator
agent = FunctionAgent.from_tools(
tools=[query_tool, multiply, add],
llm=llm
)
# Agent decides when to search docs vs calculate
response = agent.chat("According to the docs, what is Python used for?")
```
## Advanced RAG patterns
### Chat engine (conversational)
```python
from llama_index.core.chat_engine import CondensePlusContextChatEngine
# Chat with memory
chat_engine = index.as_chat_engine(
chat_mode="condense_plus_context", # Or "context", "react"
verbose=True
)
# Multi-turn conversation
response1 = chat_engine.chat("What is Python?")
response2 = chat_engine.chat("Can you give examples?") # Remembers context
response3 = chat_engine.chat("What about web frameworks?")
```
### Metadata filtering
```python
from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter
# Filter by metadata
filters = MetadataFilters(
filters=[
ExactMatchFilter(key="category", value="tutorial"),
ExactMatchFilter(key="difficulty", value="beginner")
]
)
retriever = index.as_retriever(
similarity_top_k=3,
filters=filters
)
query_engine = index.as_query_engine(filters=filters)
```
### Structured output
```python
from pydantic import BaseModel
from llama_index.core.output_parsers import PydanticOutputParser
class Summary(BaseModel):
title: str
main_points: list[str]
conclusion: str
# Get structured response
output_parser = PydanticOutputParser(output_cls=Summary)
query_engine = index.as_query_engine(output_parser=output_parser)
response = query_engine.query("Summarize the document")
summary = response # Pydantic model
print(summary.title, summary.main_points)
```
## Data ingestion patterns
### Multiple file types
```python
# Load all supported formats
documents = SimpleDirectoryReader(
"./data",
recursive=True,
required_exts=[".pdf", ".docx", ".txt", ".md"]
).load_data()
```
### Web scraping
```python
from llama_index.readers.web import BeautifulSoupWebReader
reader = BeautifulSoupWebReader()
documents = reader.load_data(urls=[
"https://docs.python.org/3/tutorial/",
"https://docs.python.org/3/library/"
])
```
### Database
```python
from llama_index.readers.database import DatabaseReader
reader = DatabaseReader(
sql_database_uri="postgresql://user:pass@localhost/db"
)
documents = reader.load_data(query="SELECT * FROM articles")
```
### API endpoints
```python
from llama_index.readers.json import JSONReader
reader = JSONReader()
documents = reader.load_data("https://api.example.com/data.json")
```
## Vector store integrations
### Chroma (local)
```python
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
# Initialize Chroma
db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("my_collection")
# Create vector store
vector_store = ChromaVectorStore(chroma_collection=collection)
# Use in index
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
```
### Pinecone (cloud)
```python
from llama_index.vector_stores.pinecone import PineconeVectorStore
import pinecone
# Initialize Pinecone
pinecone.init(api_key="your-key", environment="us-west1-gcp")
pinecone_index = pinecone.Index("my-index")
# Create vector store
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
```
### FAISS (fast)
```python
from llama_index.vector_stores.faiss import FaissVectorStore
import faiss
# Create FAISS index
d = 1536 # Dimension of embeddings
faiss_index = faiss.IndexFlatL2(d)
vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
```
## Customization
### Custom LLM
```python
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
# Set global LLM
Settings.llm = Anthropic(model="claude-sonnet-4-5-20250929")
# Now all queries use Anthropic
query_engine = index.as_query_engine()
```
### Custom embeddings
```python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
# Use HuggingFace embeddings
Settings.embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-mpnet-base-v2"
)
index = VectorStoreIndex.from_documents(documents)
```
### Custom prompt templates
```python
from llama_index.core import PromptTemplate
qa_prompt = PromptTemplate(
"Context: {context_str}\n"
"Question: {query_str}\n"
"Answer the question based only on the context. "
"If the answer is not in the context, say 'I don't know'.\n"
"Answer: "
)
query_engine = index.as_query_engine(text_qa_template=qa_prompt)
```
## Multi-modal RAG
### Image + text
```python
from llama_index.core import SimpleDirectoryReader
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
# Load images and documents
documents = SimpleDirectoryReader(
"./data",
required_exts=[".jpg", ".png", ".pdf"]
).load_data()
# Multi-modal index
index = VectorStoreIndex.from_documents(documents)
# Query with multi-modal LLM
multi_modal_llm = OpenAIMultiModal(model="gpt-4o")
query_engine = index.as_query_engine(llm=multi_modal_llm)
response = query_engine.query("What is in the diagram on page 3?")
```
## Evaluation
### Response quality
```python
from llama_index.core.evaluation import RelevancyEvaluator, FaithfulnessEvaluator
# Evaluate relevance
relevancy = RelevancyEvaluator()
result = relevancy.evaluate_response(
query="What is Python?",
response=response
)
print(f"Relevancy: {result.passing}")
# Evaluate faithfulness (no hallucination)
faithfulness = FaithfulnessEvaluator()
result = faithfulness.evaluate_response(
query="What is Python?",
response=response
)
print(f"Faithfulness: {result.passing}")
```
## Best practices
1. **Use vector indices for most cases** - Best performance
2. **Save indices to disk** - Avoid re-indexing
3. **Chunk documents properly** - 512-1024 tokens optimal
4. **Add metadata** - Enables filtering and tracking
5. **Use streaming** - Better UX for long responses
6. **Enable verbose during dev** - See retrieval process
7. **Evaluate responses** - Check relevance and faithfulness
8. **Use chat engine for conversations** - Built-in memory
9. **Persist storage** - Don't lose your index
10. **Monitor costs** - Track embedding and LLM usage
## Common patterns
### Document Q&A system
```python
# Complete RAG pipeline
documents = SimpleDirectoryReader("docs").load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(persist_dir="./storage")
# Query
query_engine = index.as_query_engine(
similarity_top_k=3,
response_mode="compact",
verbose=True
)
response = query_engine.query("What is the main topic?")
print(response)
print(f"Sources: {[node.metadata['file_name'] for node in response.source_nodes]}")
```
### Chatbot with memory
```python
# Conversational interface
chat_engine = index.as_chat_engine(
chat_mode="condense_plus_context",
verbose=True
)
# Multi-turn chat
while True:
user_input = input("You: ")
if user_input.lower() == "quit":
break
response = chat_engine.chat(user_input)
print(f"Bot: {response}")
```
## Performance benchmarks
| Operation | Latency | Notes |
|-----------|---------|-------|
| Index 100 docs | ~10-30s | One-time, can persist |
| Query (vector) | ~0.5-2s | Retrieval + LLM |
| Streaming query | ~0.5s first token | Better UX |
| Agent with tools | ~3-8s | Multiple tool calls |
## LlamaIndex vs LangChain
| Feature | LlamaIndex | LangChain |
|---------|------------|-----------|
| **Best for** | RAG, document Q&A | Agents, general LLM apps |
| **Data connectors** | 300+ (LlamaHub) | 100+ |
| **RAG focus** | Core feature | One of many |
| **Learning curve** | Easier for RAG | Steeper |
| **Customization** | High | Very high |
| **Documentation** | Excellent | Good |
**Use LlamaIndex when:**
- Your primary use case is RAG
- Need many data connectors
- Want simpler API for document Q&A
- Building knowledge retrieval system
**Use LangChain when:**
- Building complex agents
- Need more general-purpose tools
- Want more flexibility
- Complex multi-step workflows
## References
- **[Query Engines Guide](references/query_engines.md)** - Query modes, customization, streaming
- **[Agents Guide](references/agents.md)** - Tool creation, RAG agents, multi-step reasoning
- **[Data Connectors Guide](references/data_connectors.md)** - 300+ connectors, custom loaders
## Resources
- **GitHub**: https://github.com/run-llama/llama_index ⭐ 45,100+
- **Docs**: https://developers.llamaindex.ai/python/framework/
- **LlamaHub**: https://llamahub.ai (data connectors)
- **LlamaCloud**: https://cloud.llamaindex.ai (enterprise)
- **Discord**: https://discord.gg/dGcwcsnxhU
- **Version**: 0.14.7+
- **License**: MIT
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