Retrieval-Augmented Generation (RAG) for the Enterprise
Retrieval-augmented generation (RAG) has emerged as a vital technology for organizations embracing generative AI. By connecting large language models (LLMs) to corporate data in a controlled and secure manner, RAG enables AI to be deployed in specific business use cases, such as enhancing customer service through conversational AI.
Proving the value of RAG in the lab is one thing, but scaling it across an entire enterprise introduces numerous challenges. These include integrating with existing data sources, ensuring strict data privacy and security, delivering required performance, and managing this complex large-scale run-time environment. Scalability is also a significant concern, as AI models must handle vast amounts of growing data and increasingly diverse use cases while maintaining high performance and reliability.
Vespa has been wrestling with these challenges since 2011—long before AI hit the mainstream. Originally developed to address Yahoo’s large-scale requirements, where today, Vespa runs 150 applications integral to the company’s operations. These applications deliver personalized content across Yahoo in real-time and manage targeted advertisements within one of the world’s largest ad exchanges. Collectively, these applications serve an impressive user base of nearly one billion individuals, processing 800,000 queries per second.
As your business expands, so do your data and user demands. Other platforms often compromise speed to handle growing workloads.
Vespa’s unique distributed architecture ensures seamless horizontal and vertical scaling, allowing your applications to process massive data efficiently. By automatically adjusting capacity based on demand, Vespa optimizes resource usage and keeps costs in check.
With Vespa, you deliver lightning-fast responses, no matter how much your business grows.
While vector databases are essential for generative AI, they only form part of the puzzle. Achieving relevant, actionable insights requires a powerful hybrid search approach that combines vector search with traditional methods, all powered by machine learning.
Vespa goes beyond just vectors. As a comprehensive search engine and vector database, it integrates multiple retrieval techniques—like vector search, lexical search, and hybrid search—to enhance relevance and precision. Its advanced indexing and ranking systems ensure you retrieve the most accurate data, every time.
With Vespa, you can ensure accuracy with every query, boosting your decision-making with precise, timely information.
Innovate with cutting-edge ML models while maintaining full control. Vespa seamlessly supports a wide range of retrieval, ranking, and inference methods out of the box—elevating your application’s capabilities.
Vespa’s inherently flexible platform lets you express any computation using tensor math across a combination of text, metadata, and vector/tensor fields. This versatility makes it easy to implement and customize various models and methods on the platform.
With Vespa, you stay ahead of the curve, always leveraging the latest in ML technology.
Security is at the core of Vespa’s architecture. From encryption and key management to secure storage and network protections, Vespa shields your data from unauthorized access, misuse, and corruption.
Comply with industry standards like GDPR, HIPAA, and more with ease. Vespa provides the tools and guidance necessary to ensure your RAG systems meet regulatory requirements, safeguarding user privacy and sensitive data at every turn.
With Vespa, you can trust that your data—and your users—are always secure.
Vespa uses visual models such as ColPali to simplify and enhance information retrieval from complex, visually rich documents, including PDFs. ColPali enhances document retrieval by embedding entire rendered documents, including visual elements, into vector representations optimized for Large Language Models (LLMs). By treating documents as visual entities rather than text, ColPali eliminates complex preprocessing, preserves visual context, and streamlines the RAG pipeline.
Because Vespa brings computation to data distributed across many nodes, reducing network bandwidth costs and latency, Vespa is well suited to billion-scale PDF applications.
Vespa delivers all the building blocks of an AI application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).
Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.
Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.
Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.
Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.
Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.
Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.
Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.
Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.
Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.
Discover Vespa’s RAG features for hybrid search, combining text-vector, token-vector, and machine-learned ranking, all designed to scale effortlessly and handle any query volume or data size without compromising on quality.
To help organizations navigate their choice in RAG adoption, BARC has prepared the research note: Why and How Retrieval-Augmented Generation Improves GenAI Outcomes. Download your free copy here.
“RavenPack has trusted Vespa.ai open source for over five years–no other RAG platform performs at the scale we need to support our users. Following rapid business expansion, we transitioned to Vespa Cloud. This simplifies our infrastructure and gives us access to expert guidance from Vespa engineers on billion-scale vector deployment. This move allows us to concentrate on delivering innovative solutions to meet our users’ increasingly sophisticated demands.”
“We chose Vespa because of its richness of features, the amazing team behind it, and their commitment to staying up to date on every innovation in the search and NLP space. We look forward to the exciting features that the Vespa team is building and are excited to finalize our own migration to Vespa Cloud.” Yuhong Sun, CoFounder/CoCEO DanswerAI.
Perplexity.ai leverages Vespa Cloud as its web search backend, utilizing a hybrid approach that combines multi-vector and text search. Vespa supports advanced multi-phase ranking, ensuring more accurate and relevant search results.
