Across the past two days, we encountered several challenges while working on various features for your project:

1. Chatbot Query Processing

While developing a robust document-based chatbot, challenges arose in implementing features like cosine similarity for embeddings and query-type classification. Ensuring accurate, efficient query responses from diverse document formats like PDFs and images was a major hurdle. Particularly, integrating OCR for extracting text from images and PDFs with embedded images added complexity. Preprocessing techniques such as denoising and thresholding were fine-tuned for OCR accuracy, while text chunking and embedding generation required optimization for performance and relevance.

2. API Integration for Call Reminders

When designing the call reminder feature, there were decisions around selecting an API that supported reminders before and after deadlines. Implementing efficient queries in MongoDB to fetch tasks with near-deadline or missed status posed challenges in ensuring scalability. Proper synchronization of task statuses in MongoDB, avoiding redundant reminders, and handling missed deadlines seamlessly were key concerns that required careful planning.

3. MongoDB Usage Optimization

Using MongoDB efficiently as the backbone posed its own set of issues. Structuring collections to handle evolving schemas, managing atomic updates for task statuses, and leveraging aggregation for analytics required a balanced approach to maintain performance and flexibility. Implementing real-time data updates without inconsistencies was another challenge, especially with concurrent calls and reminders.

4. Technical Implementation and Debug

During API testing and chatbot enhancements, debugging unforeseen issues like mismatched data formats and embedding inconsistencies consumed considerable effort. Additionally, selecting the right tools (e.g., pytesseract for OCR or HuggingFace for embeddings) and integrating them.