Building GenAI Applications with Vectara and Unstructured

Vectara's serverless Retrieval Augmented Generation (RAG) platform offers a user-friendly API for developing scalable, enterprise-ready RAG pipelines and chat applications, supporting the ingestion of data from various enterprise sources into a Vectara corpus via the Standard Indexing API or FILE_UPLOAD API. The platform recently integrated with the Unstructured Python library, enabling advanced preprocessing of diverse file types to transform complex natural language data into text. This blog post demonstrates using Vectara's capabilities with reports from the Consumer Financial Protection Bureau (CFPB) by leveraging the Unstructured Ingest CLI to ingest data and creating a question-answering demo with create-ui. The process involves obtaining OAuth 2 credentials from Vectara, installing necessary software, and executing commands to ingest data. Once data is ingested, queries can be made using Vectara’s Query API, exemplified through a question-answering application that provides generative summaries based on ingested documents. Vectara facilitates the development of trusted and scalable GenAI applications by simplifying data ingestion, allowing developers to focus on application building.