Turbocharge RAG with LangChain and Vespa Streaming Mode for Sharded Data

This blog post provides a comprehensive guide on integrating LangChain with Vespa streaming mode to create cost-efficient RAG (Retrieval-Augmented Generation) applications over sharded data. It explains how Vespa’s streaming search solution allows for efficient data grouping by integrating a sharding key into the Vespa document ID, enabling low-latency searches without using memory, which significantly reduces deployment costs. The article details a step-by-step process of deploying a Vespa application using PyVespa, processing PDFs with LangChain, and developing a custom LangChain retriever that utilizes Vespa's capabilities to extract meaningful context from PDF documents. Additionally, it demonstrates the deployment to Vespa Cloud and the querying of data using a custom retriever, emphasizing the benefits of Vespa's streaming mode, such as eliminating precision compromises and achieving higher write throughput without the need for index builds.