What it's used for

LlamaIndex is the go-to framework for building RAG (Retrieval-Augmented Generation) applications that connect LLMs to your private data. It handles the entire pipeline from data ingestion to query-time retrieval and response generation.

Key use cases include:

Building question-answering systems over private documents, wikis, and knowledge bases
Ingesting data from 160+ sources — PDFs, databases, APIs, Notion, Slack, Google Drive, and more
Advanced retrieval strategies: hybrid search, re-ranking, recursive retrieval, and knowledge graphs
Building data agents that reason over structured and unstructured data
Document summarization and report generation from large document collections

LlamaIndex is used by teams building enterprise search, document Q&A, and knowledge management products. Its LlamaHub provides pre-built data connectors that dramatically reduce integration effort, and it supports all major vector stores and LLM providers.

The framework includes LlamaIndex Python, LlamaIndex.TS for TypeScript, and LlamaCloud for managed parsing and retrieval.

Getting started

Install LlamaIndex:
```
pip install llama-index
```
Set your LLM provider key:
```
export OPENAI_API_KEY='sk-...'
```

Build your first RAG pipeline:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query('What is this document about?')

For production, connect a persistent vector store like Pinecone or Chroma instead of the in-memory default.

Pricing: LlamaIndex is free and open source (MIT). LlamaCloud offers managed parsing starting at $0.003/page. You pay only for underlying LLM and vector DB costs.

Case studies

Real LlamaIndex projects

Submitted by verified specialists

73% hallucination reduction Manufacturing

Hybrid Retrieval Cutting Hallucinations 73%

Fortune 500 manufacturing company

› Challenge

An early RAG system built with naive vector search was producing hallucinated answers 28% of the time — unacceptable for compliance-critical product documentation queries.

› Solution

Redesigned the retrieval layer using LlamaIndex's hybrid retriever combining dense vectors and BM25 keyword search. Added a re-ranking step with sentence-window retrieval to improve context quality before generation.

› Results

Hallucination rate dropped from 28% to 7.6% — a 73% reduction. Answer grounding scores (measured with RAGAS) improved from 0.61 to 0.94. System now serves 50k employees globally.

Tools

LlamaIndex Pinecone OpenAI RAGAS

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96.3% answer accuracy Government / Regulatory

40 Years of Regulatory Docs — 96.3% Accuracy

Federal government agency

› Challenge

5,000 policy analysts were manually searching through 40 years of regulatory guidance documents, a process taking 3–5 hours per research task with inconsistent results.

› Solution

Built a LlamaIndex document hierarchy with recursive summarization indexing — chapters, sections, and paragraphs all separately indexed with cross-references. Used metadata filtering to scope searches by regulation type, year, and jurisdiction.

› Results

Research time reduced 68%. A domain-expert evaluation panel validated answer accuracy at 96.3%. The system processes 12,000 queries per month with zero PII leakage.

Tools

LlamaIndex Weaviate RAGAS Haystack

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<80ms at 50k concurrent users Legal Tech

15-Source Document Pipeline for Real-Time Sync

Legal intelligence SaaS startup

› Challenge

A legal tech company needed to ingest 15 different document types (PDFs, Word, HTML, email threads, court transcripts) with automatic chunking, metadata extraction, and real-time updates as source documents changed.

› Solution

Built a LlamaIndex ingestion pipeline using custom node parsers for each document type. Implemented incremental indexing with change detection so only modified documents re-index, cutting update latency from 4 hours to under 5 minutes.

› Results

Supports 50k concurrent users at <80ms search latency. 99.98% uptime over 12 months. Processing cost reduced 60% vs the previous batch-ingestion approach.

Tools

LlamaIndex Pinecone Langfuse

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