To index data with LlamaIndex, you start by preparing your data and using the library’s tools to structure it for efficient querying. First, install LlamaIndex (via pip install llama-index) and import necessary modules like SimpleDirectoryReader to load data and VectorStoreIndex to create the index. Load your documents—text files, PDFs, or data from APIs/databases—using built-in readers. For example, SimpleDirectoryReader("data") reads all files in a “data” folder. The data is split into “nodes” (text chunks with metadata), which you can customize by adjusting chunk size or overlap to balance context retention and processing efficiency.
Next, create the index by passing nodes to a storage system. The most common approach is a vector index, which converts text into numerical representations (embeddings) for semantic search. Use VectorStoreIndex(nodes) to build it. This step typically involves embedding models (e.g., OpenAI’s text-embedding-ada-002) and vector databases like FAISS or Pinecone. For simpler use cases, a list index (ListIndex) stores raw text for keyword-based lookup. You can also combine index types—for example, using a vector index for semantic queries and a list index for exact matches—via ComposableGraph for hybrid search.
Finally, customize the pipeline to suit your needs. For structured data, define metadata (e.g., dates or categories) and use MetadataExtractor to enhance nodes. Adjust settings like chunk size (e.g., 512 tokens) or embedding dimensions to optimize performance. Save and reload indexes using index.storage_context.persist("storage") to avoid reprocessing data. For advanced workflows, integrate LlamaIndex with tools like LangChain or use its QueryEngine to handle complex queries. For example, indexing a research paper repository might involve extracting sections as nodes, adding metadata like authors, and enabling both keyword and semantic searches for users.