Semantic Search Is an Architecture Problem

The provided text discusses several key lessons learned from implementing a vector-based search system using embeddings, focusing on areas such as data consistency, failure handling, and relevance ranking. Here are the main takeaways:

1. Explicit Lifecycle State Management:

   • Documents should move through explicit lifecycle states (e.g., PENDING, READY, FAILED) rather than relying on implicit transitions tied to method execution.
   • This ensures that partially processed data does not leak into retrieval processes and produces inconsistent results or runtime failures.

2. Model Intermediate States Explicitly:

   • By modeling intermediate states explicitly, the system can tolerate failure without losing visibility.
   • Recording errors alongside documents allows for easier debugging and understanding of why a process failed.

3. Separate Retrieval from Ranking:

   • While retrieval (converting queries into vectors and returning similar documents) is relatively straightforward, ranking results based on relevance is where systems often fail.
   • The system should explicitly handle the transformation of raw similarity scores into meaningful relevance rankings to ensure useful search results for users.

4. Explicit Scoring Model:

   • Transforming raw cosine distances into normalized scores makes the scoring model explicit and traceable.
   • This ensures that every result can be

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