This is a "Question and Answer" application built using IBM watsonx.ai flows engine. The project leverages a vector database to enhance the Large Language Model's (LLM) context awareness with a set of documents, specifically watsonxdocs.
The application is designed to provide accurate and contextually relevant answers to user queries by utilizing advanced AI technologies. The integration with IBM watsonx.ai flows engine facilitates the seamless processing of natural language questions and delivers precise responses by referencing the most pertinent information from the document set.
- Context-Aware Responses: Utilizes a vector database to maintain contextual awareness of
watsonxdocs, allowing for more accurate and relevant answers. - IBM watsonx.ai Flows Engine: Employs IBM's advanced AI platform to manage the flow of information and processing of user queries.
- Scalable Architecture: Designed to handle a large number of queries efficiently, making it suitable for deployment in various environments.
- IBM watsonx.ai Account: Ensure you have an active account to access the watsonx.ai flows engine.
- Vector Database: Set up a compatible vector database for storing and retrieving document embeddings.
- Document Set: The application uses
watsonxdocsas the source of information for answering questions.
- Clone the Repository
git clone https://github.com/yourusername/rag-question-answer.git cd rag-question-answer