Alternatives LLM Framework

LlamaIndex

Alternative

Data framework for LLM applications

Difficulty

Intermediate

Strengths

Excellent for RAG applications
Strong indexing capabilities
Good documentation
Focus on data ingestion

Considerations

Less agent support
Smaller ecosystem
Limited chain types

Best Use Cases

Document Q&A systems
Knowledge base applications
RAG implementations

LlamaIndex

LlamaIndex is a data framework for LLM applications. It provides tools to ingest, structure, and access private or domain-specific data for use with large language models. Originally known as GPT Index, it has evolved into a comprehensive framework focused on making it easy to connect LLMs with your data.

Why Choose LlamaIndex Over LangChain?

While LangChain is our core stack recommendation, LlamaIndex might be the better choice if:

RAG-focused applications: Your primary use case is Retrieval-Augmented Generation
Data ingestion priority: You need robust data loading and indexing capabilities
Simpler architecture: You prefer a more focused framework over a general-purpose one
Document-heavy workflows: You’re working primarily with documents and knowledge bases

Key Features

Data Connectors: 100+ integrations for loading data from various sources
Data Indexes: Efficient storage and retrieval structures for your data
Query Engines: Natural language querying over your indexed data
Chat Engines: Conversational interfaces over your data
Observability: Built-in logging and debugging tools

Installation

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pip install llama-index

For specific integrations:

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pip install llama-index-readers-file
pip install llama-index-vector-stores-chroma
pip install llama-index-embeddings-openai

Quick Start

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.settings import Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
Settings.embed_model = OpenAIEmbedding()

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

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query the index
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic of these documents?")
print(response)

Building a Document Q&A System

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.settings import Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure global settings
Settings.llm = OpenAI(model="gpt-4", temperature=0)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

# Load and parse documents
documents = SimpleDirectoryReader("./docs").load_data()

# Configure text splitter
text_splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=20)
Settings.text_splitter = text_splitter

# Create index
index = VectorStoreIndex.from_documents(documents)

# Create query engine with custom settings
query_engine = index.as_query_engine(
    similarity_top_k=3,
    response_mode="compact"
)

# Query the system
while True:
    question = input("Ask a question (or 'quit' to exit): ")
    if question.lower() == 'quit':
        break
    
    response = query_engine.query(question)
    print(f"\nAnswer: {response}")
    print(f"Sources: {[node.metadata.get('file_name', 'Unknown') for node in response.source_nodes]}")

Advanced RAG with Custom Retrievers

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from llama_index.core import VectorStoreIndex, get_response_synthesizer
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.postprocessor import SimilarityPostprocessor

# Create index (assuming documents are already loaded)
index = VectorStoreIndex.from_documents(documents)

# Configure retriever
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=10,
)

# Configure response synthesizer
response_synthesizer = get_response_synthesizer(
    response_mode="tree_summarize",
)

# Add post-processing
postprocessor = SimilarityPostprocessor(similarity_cutoff=0.7)

# Assemble query engine
query_engine = RetrieverQueryEngine(
    retriever=retriever,
    response_synthesizer=response_synthesizer,
    node_postprocessors=[postprocessor],
)

# Query with enhanced retrieval
response = query_engine.query("Explain the key concepts in detail")

Chat Engine for Conversational RAG

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from llama_index.core.memory import ChatMemoryBuffer

# Create chat engine from index
chat_engine = index.as_chat_engine(
    chat_mode="condense_question",
    memory=ChatMemoryBuffer.from_defaults(token_limit=3900),
    verbose=True
)

# Have a conversation
print("Chat with your documents (type 'quit' to exit)")
while True:
    user_input = input("You: ")
    if user_input.lower() == 'quit':
        break
    
    response = chat_engine.chat(user_input)
    print(f"Assistant: {response}")

LlamaIndex vs LangChain Comparison

Feature	LlamaIndex	LangChain
Primary Focus	Data indexing & RAG	General LLM applications
Learning Curve	Moderate	Moderate to High
RAG Capabilities	Excellent	Good
Agent Support	Limited	Extensive
Data Connectors	100+ specialized	Many general-purpose
Query Engines	Highly optimized	Flexible chains
Documentation	Excellent	Good
Ecosystem	Growing	Large

Data Connectors

LlamaIndex supports numerous data sources:

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from llama_index.readers.file import PDFReader
from llama_index.readers.web import SimpleWebPageReader
from llama_index.readers.database import DatabaseReader

# PDF documents
pdf_reader = PDFReader()
pdf_docs = pdf_reader.load_data("document.pdf")

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

# Database
db_reader = DatabaseReader(
    sql_database=sql_database,
    query="SELECT * FROM articles"
)
db_docs = db_reader.load_data()

# Combine all documents
all_docs = pdf_docs + web_docs + db_docs
index = VectorStoreIndex.from_documents(all_docs)

Custom Data Structures

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from llama_index.core import TreeIndex, KeywordTableIndex
from llama_index.core.composability import ComposableGraph

# Create different index types
vector_index = VectorStoreIndex.from_documents(documents)
tree_index = TreeIndex.from_documents(documents)
keyword_index = KeywordTableIndex.from_documents(documents)

# Create a composable graph
graph = ComposableGraph.from_indices(
    TreeIndex,
    [vector_index, tree_index, keyword_index],
    index_summaries=["Vector index", "Tree index", "Keyword index"]
)

# Query the composed graph
query_engine = graph.as_query_engine()
response = query_engine.query("Complex query requiring multiple approaches")

Integration with Vector Databases

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from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
import chromadb

# Initialize Chroma client
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")

# Create vector store
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create index with persistent storage
index = VectorStoreIndex.from_documents(
    documents, 
    storage_context=storage_context
)

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

# Later, load the index
from llama_index.core import load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir="./storage")
loaded_index = load_index_from_storage(storage_context)

When to Choose LlamaIndex

Choose LlamaIndex if you:

Are building RAG-focused applications
Need robust data ingestion and indexing
Want optimized document Q&A systems
Prefer specialized tools over general frameworks
Need excellent vector database integrations
Want simpler architecture for data-centric apps

Choose LangChain if you:

Need complex agent workflows
Want extensive chain compositions
Are building diverse LLM applications
Need broader ecosystem support
Want more flexibility in application architecture

Migration Between Frameworks

You can often use both frameworks together:

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# Use LlamaIndex for data indexing
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)

# Use LangChain for agent workflows
from langchain.agents import initialize_agent
from langchain.tools import Tool

def query_documents(query: str) -> str:
    query_engine = index.as_query_engine()
    return str(query_engine.query(query))

tools = [
    Tool(
        name="Document Search",
        func=query_documents,
        description="Search through indexed documents"
    )
]

agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

Resources

Comparison with Core Stack

View all alternatives →

#python #llm #rag #indexing #ai