About

I’m Aakash. I am a Btech student in Data science and AI at IIT Bhilai. I am passionate about open source. I find NLP, graph ML and Databases interesting.

I had created this blog during my GSoC to keep track of my progress. Now I plan to use it to document my work, which I can refer back to.

Open source highlights

• February 2024
  • Came up with an idea of a new entity-based RAG evaluation metric and implemented in RAGAS. This metric is useful in RAG use-cases where entities are of importance and hence measuring their coverage by the context is important.
  • Expanded GPTCache with Marqo integration for vector store and Nomic integration for embeddings.
• January 2024:
  • Created a smart query routing method to route query to most relevant sink in Neum AI.
  • Implemented BLEU and METEOR metrics for machine translation in UpTrain AI.
  • Created Marqo vector store integration for EvaDB.
• December 2023
  • Expanded filtering in Neum AI by adding unified filter.
  • Integrated LanceDB sink and Marqo sink to Neum AI.
• November 2023
  • Integrated marqo tensor search as retrieval mechanism in dspy.
• May 2023
  • Developed approx-KNN based graph construction to pytorch-geometric.
• July 2022
  • Implemented new distance metrics to weaviate.
• May 2022
  • Developed an example application - an attendance system - using a vector DB. It was also used by their GSoC contributor that year.
• March and April 2022
  • Developed a multi-modal search example which was also used in their GSoC project.