To use LlamaIndex for document summarization, you start by loading your documents and creating a structured index that enables efficient querying. LlamaIndex provides tools to parse and organize text into “nodes” (chunks of text with metadata) and build indexes optimized for retrieval. For example, you can use the SimpleDirectoryReader to load documents from a folder, split them into manageable chunks using a NodeParser, and create a VectorStoreIndex to enable semantic search. The index allows the Large Language Model (LLM) to quickly locate relevant sections of the text when generating summaries. This setup is critical because summarization often requires the model to process large documents without exceeding token limits or losing context.
Next, you query the index using a summarization-specific prompt. LlamaIndex’s QueryEngine lets you pass natural language requests like “Summarize this document” or “What are the main points?” along with parameters to control output length or focus areas. For instance, after building an index, you could run query_engine.query("Provide a 3-sentence summary of the document") to generate a concise overview. The system retrieves relevant nodes from the index, feeds them to the LLM, and returns a structured summary. You can also customize the prompt (e.g., emphasizing technical details for a developer audience) or adjust parameters like similarity_top_k to determine how many text chunks the model considers when generating the summary.
For more advanced use cases, LlamaIndex offers features like hierarchical indexing and multi-document aggregation. If you’re summarizing a lengthy report, you could first create a high-level summary of each section using a ListIndex, then combine those section summaries into a master summary. Additionally, the ResponseSynthesizer component lets you choose between modes like tree_summarize (which iteratively refines outputs) or compact (which optimizes for token usage). For example, a developer documenting an API might use tree_summarize to ensure all endpoints are covered without redundancy. These tools provide flexibility to balance detail, accuracy, and computational efficiency based on your specific needs.