Develop a Flask web application that integrates a Large Language Model (LLM) to create an intelligent FAQ assistant. This assistant will dynamically answer user queries based on pre-loaded knowledge (e.g., company policies, product documentation, or customer service FAQs). The solution should also include a mechanism for updating the knowledge base and logging user interactions for analysis.
Pre-load the assistant with a structured or unstructured text knowledge base (e.g., JSON, Markdown, or TXT files). Allow for periodic updates to the knowledge base.
Use an LLM (e.g., OpenAI's GPT models) to process and generate responses based on the context provided by the user queries.
Frontend: Create a simple web interface with a query input box and response display area. (Optional) API Endpoint: Expose a /ask POST endpoint to accept queries programmatically.
Admin Features: Provide an admin interface to update the knowledge base and view logs. (Over API or Frontend)
Log all user queries and responses to a database (e.g., MongoDB) for future analysis and improvement.
Handle instances where the LLM cannot provide a clear answer by giving a polite fallback response or suggesting related topics.
Drop an Email on hr@polynomial.ai and prakhar.k+hiring@polynomial.ai with below things
- Github Repository link
- Proper documentation of the solution
- Recorded video session of usage