Add ANN search support for vec0 virtual table (#273)

Add approximate nearest neighbor infrastructure to vec0: shared distance
dispatch (vec0_distance_full), flat index type with parser, NEON-optimized
cosine/Hamming for float32/int8, amalgamation script, and benchmark suite
(benchmarks-ann/) with ground-truth generation and profiling tools. Remove
unused vec_npy_each/vec_static_blobs code, fix missing stdint.h include.

benchmarks-ann/README.md

@@ -0,0 +1,81 @@
+# KNN Benchmarks for sqlite-vec
+
+Benchmarking infrastructure for vec0 KNN configurations. Includes brute-force
+baselines (float, int8, bit); index-specific branches add their own types
+via the `INDEX_REGISTRY` in `bench.py`.
+
+## Prerequisites
+
+- Built `dist/vec0` extension (run `make` from repo root)
+- Python 3.10+
+- `uv` (for seed data prep): `pip install uv`
+
+## Quick start
+
+```bash
+# 1. Download dataset and build seed DB (~3 GB download, ~5 min)
+make seed
+
+# 2. Run a quick smoke test (5k vectors, ~1 min)
+make bench-smoke
+
+# 3. Run full benchmark at 10k
+make bench-10k
+```
+
+## Usage
+
+### Direct invocation
+
+```bash
+python bench.py --subset-size 10000 \
+  "brute-float:type=baseline,variant=float" \
+  "brute-int8:type=baseline,variant=int8" \
+  "brute-bit:type=baseline,variant=bit"
+```
+
+### Config format
+
+`name:type=<index_type>,key=val,key=val`
+
+| Index type | Keys | Branch |
+|-----------|------|--------|
+| `baseline` | `variant` (float/int8/bit), `oversample` | this branch |
+
+Index branches register additional types in `INDEX_REGISTRY`. See the
+docstring in `bench.py` for the extension API.
+
+### Make targets
+
+| Target | Description |
+|--------|-------------|
+| `make seed` | Download COHERE 1M dataset |
+| `make ground-truth` | Pre-compute ground truth for 10k/50k/100k |
+| `make bench-smoke` | Quick 5k baseline test |
+| `make bench-10k` | All configs at 10k vectors |
+| `make bench-50k` | All configs at 50k vectors |
+| `make bench-100k` | All configs at 100k vectors |
+| `make bench-all` | 10k + 50k + 100k |
+
+## Adding an index type
+
+In your index branch, add an entry to `INDEX_REGISTRY` in `bench.py` and
+append your configs to `ALL_CONFIGS` in the `Makefile`. See the existing
+`baseline` entry and the comments in both files for the pattern.
+
+## Results
+
+Results are stored in `runs/<dir>/results.db` using the schema in `schema.sql`.
+
+```bash
+sqlite3 runs/10k/results.db "
+  SELECT config_name, recall, mean_ms, qps
+  FROM bench_results
+  ORDER BY recall DESC
+"
+```
+
+## Dataset
+
+[Zilliz COHERE Medium 1M](https://zilliz.com/learn/datasets-for-vector-database-benchmarks):
+768 dimensions, cosine distance, 1M train vectors + 10k query vectors with precomputed neighbors.