Индексирование данных — это процесс организации данных или knowledge base, чтобы relevant information можно было эффективно искать, retrieve и использовать. В generative AI и retrieval-augmented generation indexing может включать metadata, chunking, embeddings или vector search structures, которые помогают моделям получать appropriate context.
In the context of generative AI, data indexing is the process of structuring and organizing a knowledge base to optimize search and retrieval. This involves creating an index, often called a corpus, which allows for efficient searching of the data. The process is separate from corpus creation, and the indexed data can be used to enrich the context of large language models (LLMs), reducing hallucinations and improving the accuracy of responses. For example, in a website context, data indexing might involve adding metadata like datePublished and dateModified to improve search functionality. Different methods exist for indexing data, including using vector search for similarity search in applications like retrieving relevant information for LLMs at query time. For more information, see RAG Engine overview.