How to build Enterprise Search using RAG

The article outlines the process of building an enterprise search application and a Q&A bot using a Retrieval-Augmented-Generation (RAG) pipeline, leveraging Unified's data integrations and OpenAI embedding models. RAG is employed to efficiently index large volumes of information, facilitating quick searches, which is beneficial for applications like enterprise search and Q&A bots that query a company's knowledge base. The tutorial includes accessing internal company data from platforms like Google Drive and Notion, embedding content using OpenAI models, and storing it in a vector database for retrieval. It emphasizes the importance of chunking documents effectively to maintain context and relevance during embedding, using libraries like LangChain, Haystack, and Hugging Face for streamlined processing. The final steps involve indexing the content with metadata for easy retrieval and implementing a system to answer user queries by performing similarity searches in the vector database and using the top matching documents as context for generating answers through Unified's GenAI endpoint. This approach provides a scalable solution for adding AI-powered intelligence to applications across various industries, supported by Unified's extensive integrations.