Meghana Puvvadi, Director of AI/ML Enterprise, NVIDIA.
In the digital age, the ability to find relevant information quickly and accurately has become increasingly critical. From simple web searches to complex enterprise-knowledge management systems, search technology has evolved dramatically to meet growing demands.
In this article, I will explore the journey from index based basic search engine to retrieval-based generation, examining how modern techniques are revolutionizing information access.
The Foundation: Traditional Search Systems
Traditional search systems were built on relatively simple principles: matching keywords and ranking results based on relevance, user signals, frequency, positioning and more.
While effective for basic queries, these systems faced significant limitations. They struggled with understanding context, handling complex multipart queries, resolving indirect references, performing nuanced reasoning and providing user-specific personalization. These limitations became particularly apparent in enterprise settings, where information retrieval needs to be both precise and comprehensive.
Enterprise Search: Bridging The Gap
Enterprise search introduced new complexities and requirements that consumer search engines weren’t designed to handle. Organizations needed systems that could search across diverse data sources, respect complex access controls, understand domain-specific terminology and maintain context across different document types.
The Paradigm Shift: From Document Retrieval To Answer Generation
The landscape of information access underwent a dramatic transformation in early 2023 with the widespread adoption of large language models (LLMs) and the emergence of retrieval augmented generation (RAG). Traditional search systems, which primarily focused on returning relevant documents, were no longer sufficient. Instead, organizations needed systems that could not only find relevant information but also provide it in a format that LLMs could effectively use to generate accurate, contextual responses.
This shift was driven by several key developments:
• The emergence of powerful embedding models that could capture semantic meaning more effectively than keyword-based approaches
• The development of efficient vector databases that could store and query these embeddings at scale
• The recognition that LLMs, while powerful, needed accurate and relevant context to provide reliable responses
The traditional retrieval problem evolved into an intelligent, contextual answer generation problem, where the goal wasn’t just to find relevant documents, but to identify and extract the most pertinent pieces of information. This new paradigm required rethinking how we chunk, store and retrieve information, and led to the development of advanced ingestion and retrieval techniques.
The Rise Of Modern Retrieval Systems
Modern retrieval systems employ a two-phase approach to efficiently access relevant information. During the ingestion phase, documents are intelligently split into meaningful chunks, which preserve context and document structure. These chunks are then transformed into high-dimensional vector representations (embeddings) using neural models and stored in specialized vector databases.
During retrieval, the system converts the user’s query into an embedding using the same neural model, then searches the vector database for chunks whose embeddings have the highest cosine similarity to the query embedding. This similarity-based approach allows the system to find semantically relevant content even when exact keyword matches aren’t present, making retrieval more robust and context-aware than traditional search methods.
At the heart of these modern systems lies the critical process of document chunking and retrieval from embeddings, which has evolved significantly over time.
The Evolution Of Document Ingestion
The foundation of modern retrieval systems starts with document chunking: breaking down large documents into manageable pieces. Traditional document chunking began with two fundamental approaches:
1. Fixed-Size Chunking: Documents are split into chunks of exactly specified token length (e.g., 256 or 512 tokens), with configurable overlap between consecutive chunks to maintain context. This straightforward approach ensures consistent chunk sizes but may break natural textual units.
2. Semantic Chunking: This is a more sophisticated approach that respects natural language boundaries while maintaining approximate chunk sizes. This method analyzes the semantic coherence between sentences and paragraphs to create more meaningful chunks.
Drawbacks Of Traditional Chunking
Consider an academic research paper split into 512-token chunks. The abstract might be split midway into two chunks, disconnecting the context of its introduction and conclusions. A retrieval model would struggle to identify the abstract as a cohesive unit, potentially missing the paper’s central theme.
In contrast, semantic chunking may keep the abstract intact but might struggle with other sections, such as cross-referencing between the discussion and conclusion. These sections might end up in separate chunks, and the links between them could still be missed.
Late Chunking: A Revolutionary Approach
Legal documents, such as contracts, frequently contain references to clauses defined in other sections. Consider a 50-page employment contract where Section 2 states, “The Employee shall be subject to the non-compete obligations detailed in Schedule A,” while Schedule A, appearing 40 pages later, contains the actual restrictions like “may not work for competing firms within 100 miles.”
If someone searches, “What are the non-compete restrictions?” then traditional chunking that processes sections separately would likely miss this connection. The chunk with Section 2 lacks the actual restrictions, while the Schedule A chunk lacks the context that these are employee obligations.
Traditional chunking methods would likely split these references across chunks, making it difficult for retrieval models to maintain context. Late chunking, by embedding the entire document first, captures these cross-references seamlessly, enabling precise extraction of relevant clauses during a legal search.
Late chunking represents a significant advancement in how we process documents for retrieval. Unlike traditional methods that chunk documents before processing, late chunking:
• First processes the entire document through a long context embedding model
• Creates embeddings that capture the full document context
• Then applies chunking boundaries to create final chunk representations
This approach offers several advantages:
• Preserves long-range dependencies between different parts of the document
• Maintains context across chunk boundaries
• Improves handling of references and contextual elements
Late chunking is particularly effective when combined with reranking strategies, where it has been shown to reduce retrieval failure rates by up to 49%.
Looking Ahead
While we’ve explored the evolution from basic search to late chunking, the story of retrieval systems continues to evolve. In a future article, I hope to examine recent breakthroughs, including contextual chunking, recursive retrieval approaches, multimodal retrieval capabilities and future directions that promise to make information access more intelligent and context-aware across diverse data types.
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