LlamaIndex: RAG Over Your Own Data

LlamaIndex

LlamaIndex is a Python and TypeScript framework for building LLM applications on top of your own data — documents, wikis, databases, tickets, and APIs. Its core job is retrieval-augmented generation: ingest data, split it into retrievable pieces, index it with embeddings and metadata, retrieve the most relevant pieces for a question, and have an LLM answer from them with citations. Around that core it adds hundreds of data connectors, vector store integrations, rerankers, evaluation tools, and Workflows for orchestrating multi-step and agentic applications.

LlamaIndex makes a working prototype take minutes. Production quality — accurate retrieval, fresh data, access control, measurable answers — depends on tuning each stage, which the framework exposes as composable parts.

TL;DR

Readers load data into Documents; node parsers split them into Nodes (chunks with metadata).
An IngestionPipeline applies transformations (splitting, metadata extraction, embedding) with caching and deduplication.
A VectorStoreIndex stores embeddings in memory or in an external vector database.
Retrievers fetch candidate nodes; postprocessors filter and rerank them.
Query engines and chat engines combine retrieval with response synthesis and source citations.
Workflows orchestrate multi-step and agentic logic; built-in evaluators measure retrieval and answer quality.

Quick Example

Index a folder of policy documents and answer questions with sources:

Every answer comes with the nodes it was generated from, so the application can show citations and the team can debug bad answers by looking at what was retrieved.

Core Concepts

Documents and Nodes

A Document is loaded content plus metadata (source, title, author, permissions). Node parsers split documents into Nodes — chunks that keep metadata and relationships to neighbors and parents. Splitters include sentence- and token-based splitters, semantic splitting by embedding similarity, Markdown- and code-aware parsers, and hierarchical parsing for parent-child retrieval. See RAG Chunking.

Ingestion Pipelines

An IngestionPipeline runs transformations — cleaning, splitting, metadata extraction (titles, summaries, keywords), and embedding — and writes results to a vector store. With a cache, unchanged inputs are not re-embedded; with a document store, documents are deduplicated and updated by ID when sources change. That is the foundation for keeping indexes fresh. See RAG Ingestion, Freshness, and Access Control.

Indexes and Storage

Indexes can live in memory with local persistence or in external vector stores — PostgreSQL with pgvector, Qdrant, Pinecone, Weaviate, Elasticsearch, OpenSearch, and many more. See Vector Databases.

Retrieval and Postprocessing

A retriever returns candidate nodes; postprocessors refine them: similarity cutoffs, metadata filters, deduplication, and rerankers (cross-encoders or LLM rerankers) that reorder candidates by relevance. Retrieving broadly (for example 20–50 candidates) and reranking to a few is a common pattern. See Reranking and Hybrid Search.

Query Engines, Chat Engines, and Workflows

A query engine answers one question: retrieve, then synthesize a response with a configurable strategy (compact, refine, tree summarize). A chat engine adds conversation memory, condensing follow-up questions. Workflows are event-driven, step-based programs for multi-step retrieval, routing between indexes, tool-using agents, and human review. See Agentic RAG.

Evaluation

LlamaIndex includes evaluators for faithfulness (is the answer supported by retrieved context?), relevancy, and retrieval metrics such as hit rate and mean reciprocal rank, plus utilities to generate question-answer datasets from documents. Integrate them into regression tests so changes to chunking, embeddings, or prompts are measured. See RAG Evaluation.

Production Considerations

Access control: store permissions in node metadata and filter retrieval by the user's entitlements; never rely on the LLM to hide content.
Freshness: schedule ingestion from sources with change detection, and delete nodes when source documents are removed.
Parsing quality: PDFs, tables, and slides often need specialized parsers; poor parsing caps answer quality.
Observability: trace retrieval and LLM calls with the framework's instrumentation and an observability backend.
Cost and latency: cache embeddings, batch ingestion, choose top-k carefully, and stream responses.

Best Practices

Evaluate Retrieval Before Prompting

Most bad answers come from retrieving the wrong chunks. Measure hit rate on a question set before tuning prompts.

Keep Rich Metadata on Nodes

Source, section, date, product, and permissions enable filtering, citations, and debugging.

Use Ingestion Pipelines With a Document Store

They make re-ingestion incremental and deduplicated, so indexes can be refreshed safely and cheaply.

Pin Framework and Integration Versions

LlamaIndex is split into many integration packages that evolve quickly. Pin versions and test upgrades.

Common Mistakes

Default Chunking for Every Corpus

Permissions Enforced in the Prompt

Rebuilding the Index on Every Change

FAQ

What is LlamaIndex used for?

Building LLM applications that answer questions, chat, summarize, or act over private data — retrieval-augmented generation, document Q&A, knowledge assistants, and agents that use indexes as tools.

How is LlamaIndex different from LangChain?

Both build LLM applications. LlamaIndex focuses on data: ingestion, indexing, retrieval, and evaluation. LangChain offers broad components for chains and agents, with LangGraph for orchestration. Many teams use either, and some combine them.

Which vector database should I use with LlamaIndex?

Any of its many integrations. PostgreSQL with pgvector suits teams already on Postgres; dedicated vector databases suit large-scale or specialized needs. See Vector Database Selection.

How do I keep a LlamaIndex index up to date?

Use an ingestion pipeline with a document store and stable document IDs, re-ingest changed sources on a schedule or from change events, and delete nodes for removed documents.

How do I measure answer quality?

Create a dataset of representative questions with expected sources or answers, then track retrieval metrics (hit rate, MRR) and answer metrics (faithfulness, relevancy) with evaluators as you change the pipeline.