Build a Multilingual PDF Search App with LangChain, Cohere and Bubble

Exciting news! We're launching a comprehensive course that provides a step-by-step walkthrough of Bubble, LangChain, Flowise, and LangFlow. Join us to learn how to build multiple consumer-ready AI apps! Limited spots for early bird pricing https://builtbyou.com/ In this video, we will go over how to build a semantic search application using LangChain and Cohere embeddings API. With LangChain, you can easily integrate the powerful Cohere multilingual embeddings API that supports search in over 100+ languages, and save vector embeddings in the Qdrant vector database. This no-code application is built on Bubble, which allows you to quickly create and deploy your application without needing to write any code. With this application, users can upload PDF documents and semantically search them in their preferred language. Have an AI app idea, and want us to build it? Reachout to [email protected]. We help individuals, businesses, and organizations with no-code, as well as, full-stack code AI solutions. Github Repo Link: https://github.com/menloparklab/langchain-cohere-qdrant-retrieval Link to the bubble app built with workflows: https://bubble.io/page?type=page&name=index&id=multilingual-docsqna&tab=tabs-1 Link to the discord server for any help: https://discord.gg/yGZBqwXEn7