Augmenting Applications with Vector Search and RAG

AI is undoubtedly a formidable capability that poses to bring any enterprise application to the next level. Offering significant benefits for both the consumer and the developer alike, technologies like generative AI (GenAI) and retrieval-augmented generation (RAG) can dramatically revolutionize how applications perform and deal with challenges.

Matthew Groves, DevRel engineer at Couchbase, and Nithish Raghunandanan, senior developer advocate at Couchbase, joined DBTA’s webinar Coding With AI: Vector Search and RAG—Building with AI, and AI for building, to offer tips and tricks for using AI with vector search and vector embedding to make more meaningful, intuitive, and tailored search experiences, as well as solve real-world problems and simplify complex coding tasks.

Groves explained that the AI landscape for developers is huge and only continues to grow. While there is an explosion of tools, models, and frameworks available, Groves clarified that a developer doesn’t have to be experienced with every framework to be able to build something unique and powerful.

While a novice of everything is a master of none, Groves emphasized that there are common development challenges that any dev embarking on an AI journey must face, including:

  • Delivering relevant results by meaning, not just matching letters
  • Being able to generate content as summaries, images, audio, video, and code
  • Building your own data into an LLM chatbot
  • Expensive and time-consuming LLM training that requires expertise and may not generate any valuable results

Fortunately, AI can also help remediate these challenges through two techniques: vector search and RAG.

Groves widened their scope to drill down to the basics, posing the question, What is a vector?

A vector is a set of numbers that correspond to data for which other nearby numbers can be located. These numbers identify the relationships between language and groups of words, and with these relationships identified, AI can detect which vectors are closest in meaning—aka nearest neighbor, or knn.

Raghunandanan then led webinar viewers through a basic vector search to exemplify the ways that search amplified with vectors can demonstrate tangible value.

Groves followed up the demo by introducing LangChain, an open source framework that can chain together interactions between AI components. “Essentially, LangChain is the orchestrator in the middle of all of these AI components,” Groves explained.

LangChain allows developers to mix and match AI elements, combine LLM models, and add memory to LLM models, ultimately expanding the way enterprises craft AI and implement it for richer data interactions.

Transitioning to RAG, Groves summed up the strategy as a way of supplying an existing LLM with relevant context—as opposed to building an LLM with enterprise data from scratch.

“All that RAG is, essentially, is you're both [the LLM and the enterprise and its data] doing what you’re good at,” said Groves. “You and your domain gathering the best, most up-to-date information, then packing that up and sending it off with a question to an LLM to actually do the work of language parsing and generation.”

For a detailed discussion of using AI to augment application development, featuring detailed examples, demos, and more, you can view an archived version of the webinar here.