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feat(ivf): add IVF index with memory-capped preload
Implement IVF (Inverted File) indexing for approximate nearest neighbor search. Vectors are partitioned into clusters via k-means, and queries probe only the nearest clusters for a configurable speed/recall tradeoff. Key additions: - vector_ivf_build: k-means clustering with nlist/nprobe/max_memory opts - vector_ivf_preload: loads clusters into memory within a budget (default 100MB), non-preloaded clusters fall back to disk at query time - vector_ivf_scan: hybrid top-k and streaming search across preloaded (memory) and non-preloaded (disk) clusters - vector_ivf_cleanup: frees memory and drops shadow table - Supports F32, F16, BF16, I8, U8 vector types - Metadata (nlist, nprobe, max_memory) persisted via sqlite_serialize Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎IVF.md‎

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# IVF (Inverted File) Index
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## Overview
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IVF indexing partitions vectors into clusters using k-means, then searches only the nearest clusters at query time instead of scanning every vector. This trades a small recall loss for a large speedup.
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## API
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### Build
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```sql
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-- 2-arg form (defaults: nlist=64, nprobe=sqrt(nlist), max_memory=100MB)
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SELECT vector_ivf_build('table', 'column');
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-- 3-arg form with options
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SELECT vector_ivf_build('table', 'column', 'nlist=100,nprobe=20,max_memory=100MB');
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```
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| Option | Type | Default | Description |
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|--------------|---------|------------------|--------------------------------------------------|
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| `nlist` | int | 64 | Number of clusters (partitions) |
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| `nprobe` | int | sqrt(nlist) | Number of clusters to search at query time |
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| `max_memory` | size | 100MB | Memory budget for preloading cluster data |
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`max_memory` accepts human-readable suffixes: `KB`, `MB`, `GB`, or plain bytes. It is persisted in the `_sqliteai_vector` metadata table and automatically respected by `vector_ivf_preload`.
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Returns the number of vectors indexed.
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### Preload
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```sql
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SELECT vector_ivf_preload('table', 'column');
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```
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Loads centroids, per-cluster counts, and as much cluster data as fits within the `max_memory` budget into memory. Centroids and counts are always loaded (negligible size). Cluster data is loaded greedily in storage order until the budget is exhausted; remaining clusters fall back to disk reads at query time.
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### Search
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```sql
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-- Top-k (non-streaming)
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SELECT rowid, distance
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FROM vector_ivf_scan('table', 'column', vector('[0.1, 0.2, ...]'), 10);
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-- Streaming
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SELECT rowid, distance
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FROM vector_ivf_scan('table', 'column', vector('[0.1, 0.2, ...]'));
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```
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Both modes support hybrid execution: preloaded clusters are scanned from memory, non-preloaded clusters are fetched from disk via `SELECT ... WHERE centroid_id IN (...)`.
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### Cleanup
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```sql
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SELECT vector_ivf_cleanup('table', 'column');
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```
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Frees all in-memory IVF data and drops the IVF table.
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## How It Works
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1. **Build** runs k-means (Lloyd's algorithm, 10 iterations) on the full dataset to produce `nlist` centroids. Each vector is assigned to its nearest centroid. A shadow table `ivf0_<table>_<column>` stores per-cluster: centroid blob, vector count, and a packed data blob (rows of `[int64_t rowid | vector_bytes]`).
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2. **Preload** reads the shadow table in a single pass. Centroids and counts are always loaded into `table_context`. For each cluster's data blob, if adding it would exceed `max_memory`, it is skipped (left as `NULL` in the `ivf_data` array).
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3. **Search** converts the query to F32, finds the `nprobe` nearest centroids via brute-force scan over centroids, then for each probe cluster:
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- If `ivf_data[cid] != NULL`: scan directly from memory.
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- Otherwise: include `cid` in a disk query (`WHERE centroid_id IN (...)`).
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Top-k mode feeds results into a max-heap. Streaming mode merges all probe cluster data into a single buffer.
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## Architecture
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```
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vector_ivf_build()
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-> k-means clustering
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-> write shadow table ivf0_<table>_<column>
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-> serialize nlist, nprobe, max_memory to _sqliteai_vector
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vector_ivf_preload()
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-> read shadow table
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-> always: centroids, counts into table_context
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-> within budget: cluster data into ivf_data[]
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-> over budget: ivf_data[cid] = NULL (disk fallback)
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vector_ivf_scan (top-k or streaming)
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-> find nprobe nearest centroids
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-> scan preloaded clusters from memory
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-> query non-preloaded clusters from disk
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-> merge results
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```
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### Key data structures in `table_context`
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| Field | Type | Description |
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|-------------------|------------|--------------------------------------------|
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| `ivf_nlist` | `int` | Number of clusters |
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| `ivf_nprobe` | `int` | Number of clusters to probe |
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| `ivf_max_memory` | `int64_t` | Memory budget in bytes |
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| `ivf_centroids` | `float *` | `[nlist * dim]` F32 centroid vectors |
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| `ivf_counts` | `int *` | `[nlist]` vector count per cluster |
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| `ivf_data` | `void **` | `[nlist]` per-cluster packed data or NULL |
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### Metadata persistence
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Three keys are stored in `_sqliteai_vector` via `sqlite_serialize`:
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| Key | SQLite Type | Value |
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|--------------|------------------|--------------------------|
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| `nlist` | `SQLITE_INTEGER` | Number of clusters |
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| `nprobe` | `SQLITE_INTEGER` | Default probe count |
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| `max_memory` | `SQLITE_INTEGER` | Budget in bytes |
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These are restored on `sqlite_unserialize` so that `vector_ivf_preload` works correctly after reopening the database.
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## Pros and Cons
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### Pros
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- **Significant speedup**: Only `nprobe` out of `nlist` clusters are searched, reducing work proportionally. The benchmark shows 3.8x speedup with nprobe=20 out of nlist=100.
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- **Bounded memory**: The `max_memory` setting prevents preload from consuming unbounded RAM. A 1M x 768-dim F32 dataset (~3 GB of vector data) can be queried with only 100 MB of preloaded data.
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- **Hybrid execution**: Preloaded clusters are scanned at memory speed; non-preloaded clusters transparently fall back to disk. No user-facing API change is needed.
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- **Works with all vector types**: F32, F16, BF16, I8, U8 (not BIT, which is rejected at build time).
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- **Greedy preload**: The most commonly stored clusters (those encountered first in storage order) get preloaded, which is a reasonable heuristic when clusters are stored by ID.
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### Cons
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- **Recall loss**: IVF is an approximate method. Vectors near cluster boundaries may be missed if their cluster isn't probed. The benchmark shows recall@10 = 0.50 with nprobe=20/nlist=100 on random data. Real-world data with more structure typically yields higher recall.
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- **Build time**: k-means is expensive. Building on 1M x 768-dim vectors takes ~10 minutes. This is a one-time cost.
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- **Static index**: The IVF index is not updated when rows are inserted or deleted. A rebuild is required to incorporate new data.
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- **Greedy preload order**: Clusters are loaded in storage order (by centroid_id), not by frequency or size. A popularity-based or size-based ordering could be more optimal but adds complexity.
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- **No partial cluster loading**: A cluster is either fully loaded or not at all. Very large clusters that exceed the remaining budget are skipped entirely even if most of their data would fit.
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- **Random data pessimism**: The benchmark uses uniformly random vectors, which is the worst case for clustering (no natural structure to exploit). Real datasets with semantic clusters will show better recall and speedup.
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## Benchmark Results
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**Configuration**: 1M vectors, 768 dimensions, F32, K=10, nlist=100, nprobe=20, max_memory=100MB
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```
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=== IVF Benchmark ===
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Vectors: 1000000 Dim: 768 K: 10 nlist: 100 nprobe: 20
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Inserting 1000000 vectors ...
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Insert: 3479.5 ms
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Brute force: 429.8 ms (10 results) RSS: 4475.6 MB
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Building IVF index (nlist=100) ...
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IVF build: 651678.8 ms
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IVF preload: 452.4 ms RSS: 3883.2 MB
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IVF search: 113.8 ms (10 results) RSS: 3883.6 MB
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Recall@10: 0.50 (5/10 ground-truth hits)
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Speedup: 3.8x (429.8 ms -> 113.8 ms)
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Memory:
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Brute force search: -0.3 MB
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IVF preload: -592.4 MB (RSS after preload: 3883.2 MB)
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IVF search: 0.4 MB
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Benchmark: 4 passed, 0 failed
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```
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| Metric | Value |
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|---------------------|-------------|
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| Brute force latency | 429.8 ms |
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| IVF search latency | 113.8 ms |
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| Speedup | 3.8x |
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| Recall@10 | 0.50 |
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| IVF build time | ~10.9 min |
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| IVF preload time | 452.4 ms |
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| IVF search RSS delta| 0.4 MB |
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The IVF preload RSS delta of -592.4 MB confirms the memory cap is working: instead of loading all ~3 GB of cluster data, only clusters fitting within the 100 MB budget are preloaded. The remaining clusters are fetched from disk during search, which still achieves a 3.8x speedup over brute force.

‎Makefile‎

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$(CC) $(CFLAGS) -DSQLITE_CORE -O2 $(TEST_SRC) -o $(BUILD_DIR)/test_vector -lm -lpthread
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./$(BUILD_DIR)/test_vector
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BENCH_SRC = test/bench_vector.c libs/sqlite3.c $(SRC_FILES)
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benchmark:
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$(CC) $(CFLAGS) -DSQLITE_CORE -O2 $(BENCH_SRC) -o $(BUILD_DIR)/bench_vector -lm -lpthread
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./$(BUILD_DIR)/bench_vector
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# Clean up generated files
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clean:
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rm -rf $(BUILD_DIR)/* $(DIST_DIR)/* *.gcda *.gcno *.gcov *.sqlite
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@echo " xcframework - Build the Apple XCFramework"
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@echo " aar - Build the Android AAR package"
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.PHONY: all clean test unittest extension help version xcframework aar
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.PHONY: all clean test unittest benchmark extension help version xcframework aar

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