LlamaIndex

Definition

LlamaIndex (formerly GPT Index) is a data framework that bridges large language models and your own data sources. Its primary purpose is ingesting, indexing, and querying documents, databases, and APIs so that LLMs can answer questions grounded in private or domain-specific information. It provides a high degree of control over every stage of retrieval-augmented generation: data loading, node parsing (chunking), embedding selection, index construction, retrieval strategy, reranking, and response synthesis.

Where LangChain emphasizes composable orchestration and agent loops, LlamaIndex is optimized for the data layer: you can swap chunking strategies, retrieval algorithms, and synthesis approaches without rebuilding the pipeline. It ships with query engines, chat engines, and sub-question decomposition out of the box. Multiple index types (vector, summary, knowledge graph, keyword) can be combined in a single query for hybrid retrieval.

LlamaIndex also supports agents: query engines can be registered as tools, and agent reasoning loops (ReAct, OpenAI function calling) can select which engine to query. An evaluation suite (faithfulness, relevance, context precision) helps diagnose RAG quality and guides chunking or retrieval tuning for production.

How it works

Ingestion pipeline

Query pipeline

Key abstractions

Nodes are the unit of retrieval — chunks of a document with metadata. Index stores nodes and supports vector, keyword, or graph-based lookup. Query engine wraps index + retriever + synthesizer into a single callable. Chat engine maintains conversation history. Sub-question engine decomposes complex queries into simpler ones distributed across multiple indices.

When to use / When NOT to use

Scenario	Use LlamaIndex	Do NOT use LlamaIndex
RAG over large document corpora with chunking control	Yes — fine-grained node parsers and multiple index types
Connecting LLMs to internal databases and APIs	Yes — data connectors for SQL, Notion, Slack, S3, etc.
Evaluating retrieval faithfulness and relevance	Yes — built-in evaluation modules
Multi-step agent workflows calling many external APIs		Prefer LangChain for richer agent tooling
Simple single-turn completions without retrieval		Overhead is unnecessary; call the LLM API directly
Production pipeline needing LangSmith tracing		Integrate with LangChain or use a dedicated tracing tool

Comparisons

Feature	LlamaIndex	LangChain
Primary focus	Data indexing and retrieval (RAG)	Orchestration, chains, agents
Chunking control	Fine-grained node parsers	High-level text splitters
Index types	Vector, keyword, graph, summary, hybrid	Primarily vector via retrievers
Evaluation	Built-in (faithfulness, relevance)	Via LangSmith
Agent support	Query engines as tools, ReAct	First-class LCEL agent
Best for	Deep RAG over large corpora	Multi-step agent orchestration

Pros and cons

Pros	Cons
Fine-grained control over every RAG stage	Steeper learning curve than simple LLM wrappers
Multiple index types including knowledge graphs	Fewer non-RAG integrations compared to LangChain
Built-in evaluation suite for production RAG	Some abstractions add verbosity
Composable pipelines that swap components easily	Documentation can lag behind rapid releases

Code examples

# Simple RAG pipeline with LlamaIndex
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

# Configure LLM and embedding model
Settings.llm = OpenAI(model="gpt-4o-mini")

# 1. Load documents from a directory
documents = SimpleDirectoryReader("./data").load_data()

# 2. Build a vector index (embeds and stores nodes automatically)
index = VectorStoreIndex.from_documents(documents)

# 3. Create a query engine with top-k retrieval
query_engine = index.as_query_engine(similarity_top_k=3)

# 4. Query
response = query_engine.query("What are the main topics covered?")
print(response)

Tips for effective use

Choose chunk size based on your documents: 256–512 tokens works well for factual Q&A; 1024+ for summarization tasks.
Use a reranker (e.g. SentenceTransformerRerank) to improve retrieval precision without changing the index.
Combine a vector index for semantic search with a keyword index for exact-match retrieval using a QueryFusionRetriever.
Run the built-in evaluation suite periodically during development to catch regressions in retrieval quality.
Use IngestionPipeline with a RedisDocumentStore for incremental ingestion so documents are not re-embedded on re-run.

Practical resources

LlamaIndex documentation — Full guides, API reference, and tutorials
LlamaIndex — RAG guide — Ingestion, indexing, and query pipelines
LlamaIndex — Agents — Building agents with query engines as tools
LlamaIndex — Evaluation — Faithfulness, relevance, and context precision metrics
LlamaHub — Community data connectors, tools, and integrations