Add Retrieval-Augmented Generation (RAG) Implementation

[GreyActOwl1]

Description:

Enhance the AI chatbot by implementing Retrieval-Augmented Generation (RAG). This feature will combine information retrieval from a knowledge base with generative responses to provide more accurate and contextually relevant answers.

Acceptance Criteria:

1. Knowledge Base Integration:

   • Implement a retrieval mechanism that searches a predefined knowledge base for relevant information based on user queries.

2. Response Generation:

   • Augment the chatbot's responses by combining retrieved information with its generative capabilities for contextually accurate answers.

3. Context Handling:

   • Ensure the retrieval process considers conversation history for contextually relevant information.
   • Handle cases where no relevant information is found by defaulting to generative responses.

4. Performance Optimization:

   • Optimize retrieval to minimize latency and ensure fast response times.
   • Implement caching for frequently retrieved data.

5. UI/UX Considerations:

   • Indicate when a response is enhanced with retrieved information.
   • Ensure the feature works seamlessly across all devices and in both light and dark modes.

6. Preview Builds:

   • Ensure that the RAG feature functions correctly in preview builds for testing and demonstration purposes.