LlamaIndex Cheat Sheet
Ingest, index, and query your own data for LLM apps using LlamaIndex's data connectors, indices, retrievers, and query engines.
2 PagesIntermediateFeb 10, 2026
Load Documents and Build an Index
Read a directory of files and build a vector index in a few lines.
python
from llama_index.core import SimpleDirectoryReader, VectorStoreIndexdocuments = SimpleDirectoryReader("./docs").load_data()index = VectorStoreIndex.from_documents(documents)index.storage_context.persist(persist_dir="./storage")
Query the Index
Turn an index into a query engine and ask natural-language questions over your data.
python
query_engine = index.as_query_engine(similarity_top_k=5, response_mode="compact")response = query_engine.query("What were Q3 revenue drivers?")print(response)for node in response.source_nodes: print(node.score, node.node.metadata.get("file_name"))
Custom Node Parsing (Chunking)
Control how documents are split into nodes before embedding.
python
from llama_index.core.node_parser import SentenceSplittersplitter = SentenceSplitter(chunk_size=512, chunk_overlap=64)nodes = splitter.get_nodes_from_documents(documents)index = VectorStoreIndex(nodes)
Reload a Persisted Index
Restore a previously built index from disk without re-embedding everything.
python
from llama_index.core import StorageContext, load_index_from_storagestorage_context = StorageContext.from_defaults(persist_dir="./storage")index = load_index_from_storage(storage_context)query_engine = index.as_query_engine()
Index Types
Different index structures for different retrieval patterns.
- VectorStoreIndex- similarity search over embeddings, the most common choice
- SummaryIndex- linear scan, good for small corpora needing full-context summarization
- TreeIndex- hierarchical summarization tree for large documents
- KeywordTableIndex- keyword-based lookup, useful as a retrieval fallback
- KnowledgeGraphIndex- extracts and queries entity-relation triples
- as_chat_engine()- wraps a query engine with conversational memory
Pro Tip
Persist your storage_context after every index build — re-embedding a large corpus on every process restart is the single most common source of wasted API spend in LlamaIndex apps.
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