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## Project Overview
Qdrant is well-known for its vector search capabilities, but my task was to go one step further—introducing cross-encoders for re-ranking. Traditionally, the FastEmbed library would generate embeddings, but cross-encoders don’t do that. Instead, they provide a list of scores based on how well a query matches a list of documents. This kind of re-ranking is critical when you want to refine search results and bring the most relevant answers to the top.
Qdrant is well known for its vector search capabilities, but my task was to go one step further — introducing cross-encoders for re-ranking. Traditionally, the FastEmbed library would generate embeddings, but cross-encoders don’t do that. Instead, they provide a list of scores based on how well a query matches a list of documents. This kind of re-ranking is critical when you want to refine search results and bring the most relevant answers to the top.
The project revolved around creating a new input-output scheme: text data to scores. For this, I designed a family of classes to support ONNX models. Some of the key models I worked with included Xenova/ms-marco-MiniLM-L-6-v2, Xenova/ms-marco-MiniLM-L-12-v2, and BAAI/bge-reranker, all designed for re-ranking tasks.