#39 Alex Garcia on Local-First Search, How to Push Search To End-Devices | Search

How AI Is Built

Alex Garcia is a developer focused on making vector search accessible and practical. As he puts it: "I'm a SQLite guy. I use SQLite for a lot of projects... I want an easier vector search thing that I don't have to install 10,000 dependencies to use.”

Core Mantra: "Simple, Local, Scalable"

Why SQLite Vec?

"I didn't go along thinking, 'Oh, I want to build vector search, let me find a database for it.' It was much more like: I use SQLite for a lot of projects, I want something lightweight that works in my current workflow."

SQLiteVec uses row-oriented storage with some key design choices:

• Vectors are stored in large chunks (megabytes) as blobs
• Data is split across 4KB SQLite pages, which affects analytical performance
• Currently uses brute force linear search without ANN indexing
• Supports binary quantization for 32x size reduction
• Handles tens to hundreds of thousands of vectors efficiently

Practical limits:

• 500ms search time for 500K vectors (768 dimensions)
• Best performance under 100ms for user experience
• Binary quantization enables scaling to ~1M vectors
• Metadata filtering and partitioning coming soon

Key advantages:

• Fast writes for transactional workloads
• Simple single-file database
• Easy integration with existing SQLite applications
• Leverages SQLite's mature storage engine

Garcia's preferred tools for local AI:

• Sentence Transformers models converted to GGUF format
• Llama.cpp for inference
• Small models (30MB) for basic embeddings
• Larger models like Arctic Embed (hundreds of MB) for recent topics
• SQLite L-Embed extension for text embeddings
• Transformers.js for browser-based implementations

1. Choose Your Storage

"There's two ways of storing vectors within SQLiteVec. One way is a manual way where you just store a JSON array... [second is] using a virtual table."

• Traditional row storage: Simple, flexible, good for small vectors
• Virtual table storage: Optimized chunks, better for large datasets
• Performance sweet spot: Up to 500K vectors with 500ms search time

2. Optimize Performance

"With binary quantization it's 1/32 of the space... and holds up at 95 percent quality"

• Binary quantization reduces storage 32x with 95% quality
• Default page size is 4KB - plan your vector storage accordingly
• Metadata filtering dramatically improves search speed

3. Integration Patterns

"It's a single file, right? So you can like copy and paste it if you want to make a backup."

• Two storage approaches: manual columns or virtual tables
• Easy backups: single file database
• Cross-platform: desktop, mobile, IoT, browser (via WASM)

4. Real-World Tips

"I typically choose the really small model... it's 30 megabytes. It quantizes very easily... I like it because it's very small, quick and easy."

• Start with smaller, efficient models (30MB range)
• Use binary quantization before trying complex solutions
• Plan for partitioning when scaling beyond 100K vectors

Alex Garcia

• LinkedIn
• X (Twitter)
• GitHub
• sqlite-vec
• sqllite-vss
• Website

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)