llamaindex

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:

• 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

# 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

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

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

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

# 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

# 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

Details, code examples and parameter tables: references/agents-with-tools.md. Read it when this step applies.

Advanced RAG patterns

Details, code examples and parameter tables: references/advanced-rag-patterns.md. Read it when this step applies.

Data ingestion patterns

Multiple file types

# Load all supported formats
documents = SimpleDirectoryReader(
    "./data",
    recursive=True,
    required_exts=[".pdf", ".docx", ".txt", ".md"]
).load_data()

Web scraping

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

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

from llama_index.readers.json import JSONReader

reader = JSONReader()
documents = reader.load_data("https://api.example.com/data.json")

Vector store integrations

Chroma (local)

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)

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)

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

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

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

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

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

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

# 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

# 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 - Query modes, customization, streaming
• Agents Guide - Tool creation, RAG agents, multi-step reasoning
• Data Connectors Guide - 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

Agent operating procedure

1. Check the environment. Confirm the framework version, model provider, API keys and rate limits.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Test a single call or chain with a known input and inspect raw outputs.
4. Execute the full task using the instructions and references above.
5. Validate the result. Evaluate on a small labeled set; check structured outputs against their schema; log prompts and responses.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Outputs do not match the expected schema	Add validation and retries, tighten the schema, or simplify the prompt.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never send private or sensitive data to external APIs without the user's consent.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• langchain: Framework for building LLM-powered applications with agents, chains, and RAG.
• chroma: Open-source embedding database for AI applications.
• langsmith-observability: LLM observability platform for tracing, evaluation, and monitoring.