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Alex Spinov
Alex Spinov

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LlamaIndex Has a Free Data Framework for Building RAG Applications

#ai #llamaindex #rag #python

LangChain gives you building blocks. LlamaIndex gives you the data layer. If your AI app needs to ingest, index, and query documents — PDFs, databases, APIs, Slack, Notion — LlamaIndex is purpose-built for that job.

What LlamaIndex Gives You for Free

  • 160+ data connectors — load from PDF, Notion, Slack, GitHub, databases, APIs
  • Advanced indexing — vector, keyword, knowledge graph, tree, and hybrid indexes
  • Query engines — natural language queries over your data
  • Agents — AI agents that can search, filter, and combine data sources
  • Evaluation — measure RAG quality with built-in metrics
  • LlamaCloud — managed service for production RAG (free tier)

Quick Start 

pip install llama-index
export OPENAI_API_KEY=sk-...
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RAG in 5 Lines 

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load all documents from a directory
documents = SimpleDirectoryReader('data/').load_data()

# Create vector index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is the refund policy?")
print(response)
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That's a complete RAG pipeline. Drop PDFs in data/, ask questions in natural language.

Data Connectors (160+ Sources) 

from llama_index.readers.notion import NotionPageReader
from llama_index.readers.slack import SlackReader
from llama_index.readers.github import GithubRepositoryReader

# Notion
notion_docs = NotionPageReader(integration_token=token).load_data(
    page_ids=["page_id_1", "page_id_2"]
)

# Slack
slack_docs = SlackReader(slack_token=token).load_data(
    channel_ids=["C01234567"]
)

# GitHub
github_docs = GithubRepositoryReader(
    owner="facebook", repo="react"
).load_data()

# Combine everything into one index
all_docs = notion_docs + slack_docs + github_docs
index = VectorStoreIndex.from_documents(all_docs)
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Advanced: Sub-Question Query Engine 

from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool, ToolMetadata

# Create separate indexes for different data sources
financial_tool = QueryEngineTool(
    query_engine=financial_index.as_query_engine(),
    metadata=ToolMetadata(name="financials", description="Company financial data")
)

hr_tool = QueryEngineTool(
    query_engine=hr_index.as_query_engine(),
    metadata=ToolMetadata(name="hr", description="HR policies and procedures")
)

# Engine that breaks complex questions into sub-questions
engine = SubQuestionQueryEngine.from_defaults(
    query_engine_tools=[financial_tool, hr_tool]
)

# This query gets broken into sub-questions automatically:
response = engine.query(
    "Compare our Q4 revenue with headcount growth from HR reports"
)
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Evaluation (Measure RAG Quality) 

from llama_index.core.evaluation import FaithfulnessEvaluator, RelevancyEvaluator

faithfulness = FaithfulnessEvaluator()
relevancy = RelevancyEvaluator()

query = "What is the vacation policy?"
response = query_engine.query(query)

# Is the response faithful to the source documents?
faith_result = faithfulness.evaluate_response(response=response)
print(f"Faithful: {faith_result.passing}")  # True/False

# Is the response relevant to the query?
rel_result = relevancy.evaluate_response(query=query, response=response)
print(f"Relevant: {rel_result.passing}")
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LlamaIndex vs LangChain

Aspect LlamaIndex LangChain
Focus Data indexing & retrieval General LLM orchestration
RAG Purpose-built, advanced Basic implementation
Data connectors 160+ built-in Community-maintained
Index types Vector, keyword, graph, tree Vector only
Evaluation Built-in metrics Separate (LangSmith)
Best for RAG-heavy applications Agent-heavy applications

The Verdict

If your AI app is primarily about querying your own data, LlamaIndex is the specialized tool for the job. Better indexing, more data connectors, built-in evaluation, and a cleaner API for RAG-specific workflows.

Need help building AI-powered data pipelines or web scrapers? I build custom solutions. Reach out: spinov001@gmail.com

Check out my awesome-web-scraping collection — 400+ tools for extracting web data.

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