Neura AI v0.2.0 - Modularization of the API Endpoint, Bug fixes, and Azure Blob Migration

In this latest update, we've tackled a plethora of critical issues and implemented significant changes to elevate the overall user experience.

Modularization of Handle LLM Interaction Module

This marks a significant update to our system, focusing on the modularization of the handle LLM interaction module, which was previously a complex and monolithic component. This change enables us to maintain and update the code more efficiently, reducing the likelihood of errors and improving overall system reliability but also brings numerous benefits, including improved scalability, stability, and efficiency.

New Modules

• build_image_response: This module is responsible for building the image response based on the input text and triggers.

• content_fetcher: This module fetches the content from the database or external sources based on the input text and triggers.

• handle_text: This module handles text-only inputs, sending them to query_supabase for text-only.

• process_image: This module processes the image input, sending it to analyze_image and then generate_image.

• logging_setup: This module sets up the logging configuration for the system, ensuring that errors and exceptions are properly logged and tracked.

• process_solo_url: This module fetched url-only inputs, sending them to computer vision and then to diffusion model to generate something similar and bring a description.

Key Changes and Fixes

• Refactoring the handle_llm_interaction function into smaller, more modular functions, improving code readability and maintainability.

• Introduction of a new check_for_trigger_words function to determine whether the input text contains trigger words, enabling the system to differentiate between inputs that require image generation and those that don't.

• Implemented a new process_image_interaction function to handle image inputs, sending them to analyze_image and then generate_image.

• We've updated the logging setup to avoid duplication in multiple modules and ensure that errors and exceptions are properly logged and tracked.

Technical Highlights

• Modular Codebase: Our modularized codebase enables us to maintain and update the code more efficiently, reducing the likelihood of errors and improving overall system reliability.

• Improved Code Readability: Our refactored code is more readable and maintainable, making it easier for developers to understand and update the codebase.

• Enhanced System Reliability: Our updated logging setup ensures that errors and exceptions are properly logged and tracked, enabling us to identify and fix issues more quickly.

API Improvement

LLM API Endpoint Update: Our updated interact-with-LLM API endpoint handles Azure URLs gracefully, ensuring seamless integration with our new image storage solution.

• We've refactored the interact_with_llm endpoint to ensure it always handles requests to the handle_llm_interaction module, which then sends requests to other modules as needed.

• We've fixed the bug where users could share just a URL asking for analysis of the image before prompting.

IMGBB to Azure Blob Migration

We've successfully migrated our image upload storage from ImgBB to Azure Blob, providing a more reliable and scalable solution. This change enables us to handle a higher volume of image requests and reduces the likelihood of downtime.

Bugs Fix

Key Fixes and Enhancements

• Bug Fix: URL + Text Handling: We've resolved the issue of URLs accompanied by text not being handled correctly by implementing a conditional check for triggers in the input text.

• Bug Fix: URL-only Input: We've fixed the issue of URLs without accompanying text not being processed correctly by sending them to analyze_image and then generate_image.

• Bug Fix: LLM URL Handling: We've fixed the critical bug where the LLM was handling some URLs directly, sending them to the diffusion model without proper analysis.

• Enhanced: Text-only Input with Triggers: We've resolved the issue of text-only inputs with triggers not being handled correctly by sending them to generate_image.

• Enhanced: Text-only Input without Triggers: We've fixed the issue of text-only inputs without triggers not being processed correctly by sending them to query_supabase for text-only processing using GPT-3.5-Turbo-16K-0613.

• Enhanced: Interact-with-LLM API Endpoint Update: We've updated the interact-with-LLM API endpoint to handle Azure URLs gracefully, ensuring seamless integration with our new image storage solution.

Technical Highlights

• Conditional Trigger Checks: We've implemented conditional checks for triggers in input text, enabling the system to differentiate between inputs that require image generation and those that don't.

• Streamlined Image Processing: Our updated image processing pipeline ensures that inputs are routed to the correct processing path, reducing latency and improving overall system efficiency.

• LLM API Endpoint Update: Our updated interact-with-LLM API endpoint handles Azure URLs gracefully, ensuring seamless integration with our new image storage solution.

This update marks a significant milestone in our journey to create a more scalable, efficient, and reliable system. We're excited to continue improving and refining our system to provide the best possible experience for our users.