Greetings! Welcome back to another week of EchoPilot! As we traverse the intricate landscape of knowledge retrieval and refinement, the team has been diligently working on several fronts to enhance the efficiency and accuracy of our project.

Refactored Reranking for Enhanced Flexibility

In a significant stride towards versatility, our team successfully refactored the Cohere reranking mechanism, introducing toggling and parameter tuning options. These changes were deployed to the development environment, setting the stage for meticulous testing. This adjustment not only allows for better customization but also aligns with our strategic vision for adaptable knowledge retrieval.

Additionally, we optimized the global reranking to seamlessly integrate with the Cohere reranking. This strategic approach ensures minimal document transfer to Cohere, optimizing cost-effectiveness. Rigorous testing of the deployed reranking logic yielded exceeding results, with almost 90% accuracy achieved with GPT-4 and approximately 78% with GPT-3.5. This achievement solidifies our confidence in the effectiveness of our refined reranking strategies.

Automation Advancements and Streamlining Architecture

Our pursuit towards efficiency led us to explore the implementation of an automated ingestion system through GitHub Actions. This innovation aims to simplify the expansion of our knowledge store, streamlining the process and minimizing manual interventions. Simultaneously, we cleaned up our architecture by removing the scripting retriever, reducing unnecessary corpus volume, and creating a more streamlined and efficient model.

That’s a wrap for this week! See you all next time.