LlamaIndex performs document search by structuring data into indexes optimized for efficient retrieval and combining them with language models. At its core, it transforms documents into searchable formats using embeddings and metadata, enabling queries to find relevant information quickly. The process involves three main stages: data ingestion, index creation, and query execution. Developers can customize each stage to balance speed, accuracy, and resource usage for their specific use case.
First, LlamaIndex processes documents by splitting them into smaller chunks (e.g., paragraphs or sections) and generating vector embeddings for each chunk using models like OpenAI’s text-embedding-ada-002. These embeddings capture semantic meaning, allowing the system to compare text similarity mathematically. For example, a 100-page PDF might be split into 500 text chunks, each converted into a 1536-dimensional vector. These vectors are stored in a vector database such as FAISS or Pinecone, which supports fast similarity searches. Metadata like document titles or timestamps can also be attached to chunks to enable hybrid searches that combine semantic matching with keyword or date filters.
When a query is made, LlamaIndex uses the same embedding model to convert the search input (e.g., “How do neural networks learn?”) into a vector. The system then scans the vector database for chunks whose embeddings are closest to the query’s vector, typically using cosine similarity. For instance, a search for “machine learning techniques” might retrieve chunks discussing decision trees, gradient descent, and backpropagation. Optionally, a language model like GPT-4 can refine results by re-ranking matches or synthesizing answers from multiple chunks. Developers can adjust parameters like chunk size (to balance context vs. granularity) or the number of retrieved results (top-k) to optimize performance. This approach avoids brute-force text comparisons, making searches scalable even for large datasets.