DefangLabs/docs-chatbot

AI Summary: Implement monitoring and alerting for the Scikit RAG + OpenAI chatbot application to track resource usage (CPU, memory) and send notifications when thresholds are exceeded. This will involve choosing a monitoring tool (e.g., Prometheus, Datadog), integrating it with the Flask application, defining alert thresholds, and setting up notification mechanisms (e.g., email, Slack). The documentation should be updated to reflect the new monitoring setup.

AI Summary: The task involves refactoring the existing Flask-based RAG chatbot to utilize cloud-based embedding models (AWS or GCP) instead of locally downloading and using a Sentence Transformer model. This change aims to improve efficiency and reduce the computational burden during deployment by offloading the embedding generation to a cloud provider's service.

AI Summary: The `get_knowledge_base.py` script, responsible for building the knowledge base for the Scikit RAG + OpenAI chatbot, is failing due to a non-zero exit status (1) from the `npm run prebuild` command within the `defang-docs` directory. The issue likely stems from a problem within the `defang-docs` build process itself, triggered by recent updates to the Defang documentation. The task involves debugging the `npm run prebuild` command within the `.tmp/defang-docs` directory to identify and resolve the underlying error causing the build failure, ensuring the knowledge base is correctly updated.

AI Summary: Implement a timeout mechanism in the Discord bot's user interface (UI) to handle situations where the server response is delayed. This involves adding a timer that triggers a user-friendly message indicating a timeout and suggesting a retry if the server doesn't respond within a specified timeframe. The UI should be updated to display this message clearly.