Vector Search from Scratch in Pure SQLite

**Wield SQL Set Operations for Search Algorithm Implementation.**
**Discard procedural loops; describe search engine mechanics with just a few lines of query.**
**Zero external libraries. Transform your familiar SQLite into an "Advanced Computational Engine."**
This book is a guide to implementing "Related Article Search" by utilizing SQLite not just as a data store, but as a powerful calculation engine-without relying on any external libraries or even the standard FTS5 extension.
In most development scenarios, complex calculations are offloaded to procedural application-side loops. In this book, you will experience a paradigm shift: treating data as sets and deriving similarity through the "non-procedural" thinking of SQL.
### What's Inside?
* **Chapter 1: Building the Inverted Index**: Designing and implementing the "reverse lookup" data structure-the bedrock of efficient search.
* **Chapter 2: Breaking the Precision Barrier**: Introducing TF-IDF theory to statistically quantify word importance.
* **Chapter 3: Batch Statistical Processing with SQL**: Leveraging temporary tables to calculate weights for all articles in a single, elegant query.
* **Chapter 4: Similarity via Cosine Similarity**: Treating articles as vectors and measuring "closeness" through geometric angles.
* **Chapter 5: Performance Engineering**: Overcoming the $O(N^2)$ barrier with pruning strategies and optimizations.
* **Chapter 6: System Integration**: Integrating the engine into a production-ready app using caching and job queues.
### Core Engineering Insights Explored
* **Paradigm Shift to Set Operations**: Move from "How do I loop?" to "How do I relate?". Re-internalize the power of SQL's abstraction through inverted index implementation.
* **The Beauty of Simplicity**: Condensing seemingly complex TF-IDF and Cosine Similarity math into minimal, appropriate data structures and queries.
* **Computational Load Control**: Strategic performance engineering using index-based "pruning" to tackle theoretical $O(N^2)$ complexity.

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