LlamaIndex Cheat Sheet

Updated 2026-04-28

Next Topic: LLM APIs and Integration Cheat Sheet

LlamaIndex is an open-source data orchestration framework for building production-ready LLM applications, specializing in retrieval-augmented generation (RAG) and agentic document workflows. It connects private or domain-specific data to large language models through sophisticated indexing, retrieval, and query mechanisms. Unlike general-purpose orchestration frameworks, LlamaIndex prioritizes data ingestion pipelines, advanced retrieval strategies, and context engineering — making it the go-to choice when your application's success hinges on how well you retrieve and structure information before passing it to an LLM. The framework treats documents as first-class citizens, offering deep control over chunking, embedding, metadata extraction, hierarchical relationships, multi-step retrieval patterns, agentic workflows, and MCP integration — essential for knowledge-intensive production applications.

What This Cheat Sheet Covers

This topic spans 29 focused tables and 195 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Index TypesTable 2: Data Loaders and ConnectorsTable 3: Ingestion PipelineTable 4: Node Parsers and Text SplittersTable 5: LLM IntegrationsTable 6: Embedding ModelsTable 7: Query EnginesTable 8: Query PipelineTable 9: RetrieversTable 10: Response SynthesizersTable 11: Postprocessors and RerankersTable 12: Chat EnginesTable 13: Agents and ToolsTable 14: Workflows and Event-Driven ArchitectureTable 15: Vector Stores IntegrationTable 16: MultiModal RAGTable 17: Storage and PersistenceTable 18: Query TransformationsTable 19: Metadata ExtractionTable 20: Evaluation MetricsTable 21: Advanced Retrieval StrategiesTable 22: Settings and ConfigurationTable 23: Prompt CustomizationTable 24: Observability and TracingTable 25: Structured OutputTable 26: StreamingTable 27: Document ManagementTable 28: MCP IntegrationTable 29: LlamaCloud Services

Table 1: Core Index Types

Index	Example	Description
VectorStoreIndex	`index = VectorStoreIndex.from_documents(docs)`	• Stores vector embeddings of document chunks • retrieves via similarity search (cosine, Euclidean) • most common index for semantic retrieval in RAG.
SummaryIndex	`index = SummaryIndex.from_documents(docs)`	• Stores nodes as a sequential chain with no complex structure • retrieves all nodes or filters by keywords • formerly called ListIndex.
DocumentSummaryIndex	`index = DocumentSummaryIndex.from_documents(docs)`	• Extracts a summary per document and stores it alongside nodes • retrieves by matching query to document summaries first, then fetches relevant nodes.
PropertyGraphIndex	`index = PropertyGraphIndex.from_documents(docs)`	• Creates a knowledge graph with entities and relationships • supports Cypher queries, hybrid search, and graph-based retrieval for complex relational data.

Table 1: Core Index Types

Index	Example	Description
VectorStoreIndex	`index = VectorStoreIndex.from_documents(docs)`	• Stores vector embeddings of document chunks • retrieves via similarity search (cosine, Euclidean) • most common index for semantic retrieval in RAG.
SummaryIndex	`index = SummaryIndex.from_documents(docs)`	• Stores nodes as a sequential chain with no complex structure • retrieves all nodes or filters by keywords • formerly called ListIndex.
DocumentSummaryIndex	`index = DocumentSummaryIndex.from_documents(docs)`	• Extracts a summary per document and stores it alongside nodes • retrieves by matching query to document summaries first, then fetches relevant nodes.
PropertyGraphIndex	`index = PropertyGraphIndex.from_documents(docs)`	• Creates a knowledge graph with entities and relationships • supports Cypher queries, hybrid search, and graph-based retrieval for complex relational data.