Experiment with ColBERTv2.0 for Embedding Comparison

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Objective

Explore the efficacy of ColBERTv2.0 from Hugging Face against the current embedding methods used in our project. This is an initial experiment to understand how ColBERTv2.0 compares in terms of search accuracy, speed, and storage requirements.

Background

Currently, our project utilizes standard embeddings, which may not fully leverage the token-level representations offered by models like ColBERTv2.0. ColBERT, or Contextualized Late Interaction over BERT, promises enhanced representation by generating multiple vectors per document, representing token-level or segment-level semantic information.

Plan

1. Setup: Install and configure ColBERTv2.0 from Hugging Face.
   • Reference Model: ColBERTv2.0
2. Integration:
   • Update the existing embedding generation pipeline to incorporate ColBERTv2.0 as an alternative.
   • Use pgvector with Django to store multi-vector representations.
3. Comparison:
   • Implement search functionality using both the new ColBERT-based embeddings and the current method.
   • Compare both methods based on retrieval accuracy, processing time, and database storage utilization.
4. Evaluation:
   • Analyze the outcomes of both methods and document observations regarding their effectiveness and practicality for our needs.

Expected Outcome

• Determine if ColBERTv2.0 offers significant improvements in search precision and if it justifies the potential increase in complexity and storage.
• Decide whether to fully integrate ColBERT in the primary project pipeline based on comparative results.

Additional Notes

• Considering the experiment's early-stage status, be prepared to iterate on the methodology as insights are gathered.
• Ensure that the experiments can be replicated and verified by other team members or contributors.