benchmark updates

benchmarks/exhaustive-memory/README.md

@@ -1,35 +1,25 @@
-```
-python3 bench/bench.py \
-  -n "sift1m" \
-  -i sift/sift_base.fvecs \
-  -q sift/sift_query.fvecs \
-  --sample 10000 --qsample 100 \
-  -k 10
-```
+# `sqlite-vec` In-memory benchmark comparisions
 
-```
-python3 bench.py \
-  -n "sift1m" \
-  -i ../../sift/sift_base.fvecs \
-  -q ../../sift/sift_query.fvecs \
-  --qsample 100 \
-  -k 20
-```
-```
-python3 bench.py \
-  -n "sift1m" \
-  -i ../../sift/sift_base.fvecs \
-  -q ../../sift/sift_query.fvecs \
-  --qsample 100 \
-  -x faiss,vec-scalar.4096,vec-static,vec-vec0.4096.16,vec-vec0.8192.1024,usearch,duckdb,hnswlib,numpy \
-  -k 20
-```
+This repo contains a benchmarks that compares KNN queries of `sqlite-vec` to other in-process vector search tools using **brute force linear scans only**. These include:
 
 
-
-```
-python bench.py -n gist -i ../../gist/gist_base.fvecs -q ../../gist/gist_query.fvecs --qsample 100 -k 20 --sample 500000 -x faiss,vec-static,vec-scalar.8192,vec-scalar.16384,vec-scalar.32768,vec-vec0.16384.64,vec-vec0.16384.128,vec-vec0.16384.256,vec-vec0.16384.512,vec-vec0.16384.1024,vec-vec0.16384.2048
-```
+- [Faiss IndexFlatL2](https://faiss.ai/)
+- [usearch with `exact=True`](https://github.com/unum-cloud/usearch)
+- [libsql vector search with `vector_distance_cos`](https://turso.tech/vector)
+- [numpy](https://numpy.org/), using [this approach](https://github.com/EthanRosenthal/nn-vs-ann)
+- [duckdb with `list_cosine_similarity`](https://duckdb.org/docs/sql/functions/nested.html#list_cosine_similaritylist1-list2)
+- [`sentence_transformers.util.semantic_search`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.util.semantic_search)
+- [hnswlib BFIndex](https://github.com/nmslib/hnswlib/blob/c1b9b79af3d10c6ee7b5d0afa1ce851ae975254c/TESTING_RECALL.md?plain=1#L8)
 
 
-python bench.py -n gist -i ../../gist/gist_base.fvecs -q ../../gist/gist_query.fvecs --qsample 100 -k 20 --sample 500000 -x faiss,vec-static,sentence-transformers,numpy
+Again **ONLY BRUTE FORCE LINEAR SCANS ARE TESTED**. This benchmark does **not** test approximate nearest neighbors (ANN) implementations. This benchmarks is extremely narrow to just testing KNN searches using brute force.
+
+A few other caveats:
+
+- Only brute-force linear scans, no ANN
+- Only CPU is used. The only tool that does offer GPU is Faiss anyway.
+- Only in-memory datasets are used. Many of these tools do support serializing and reading from disk (including `sqlite-vec`) and possibly `mmap`'ing, but this only tests in-memory datasets. Mostly because of numpy
+- Queries are made one after the other, **not batched.** Some tools offer APIs to query multiple inputs at the same time, but this benchmark runs queries sequentially. This was done to emulate "server request"-style queries, but multiple users would send queries at different times, making batching more difficult. To note, `sqlite-vec` does **not** support batched queries yet.
+
+
+These tests are run in Python. Vectors are provided as an in-memory numpy array, and each test converts that numpy array into whatever makes sense for the given tool. For example, `sqlite-vec` tests will read those vectors into a SQLite table. DuckDB will read them into an Array array then create a DuckDB table from that.