Building an LLM open source search engine in 100 lines using LangChain and Ray

This blog series introduces LangChain and Ray Serve, two powerful tools for building a search engine using LLM embeddings and a vector database. LangChain provides an amazing suite of tools for everything around LLMs, including indexing, generating, and summarizing text, while Ray Serve makes it easy to deploy a LangChain service in the cloud. The blog series will show how to build a store, speed up indexing by parallelizing embedding, serve the search results, and enable request batching. It also covers scalability and cost, and will share Part 2 and Part 3 of the series where they will discuss turning this into a chatgpt-like answering system and talk about scalability and cost respectively. The code for this is available in a Github repo, and there are resources available to learn more about Ray, including a hosted service for ML Training and Serving.