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Updated readme [skip ci]

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Andrew Kane
2023-08-28 15:25:11 -07:00
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README.md
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@@ -166,7 +166,7 @@ Supported index types are:
## IVFFlat
An IVFFlat index divides vectors into lists, and then searches a subset of those lists. It has faster build times and uses less memory than HNSW, but has lower query performance.
An IVFFlat index divides vectors into lists, and then searches a subset of those lists. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff).
Three keys to achieving good recall are:
@@ -217,7 +217,7 @@ COMMIT;
## HNSW
An HNSW index creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance. There’s no training step like IVFFlat, so the index can be created without any data in the table.
An HNSW index creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance (in terms of speed-recall tradeoff). There’s no training step like IVFFlat, so the index can be created without any data in the table.
Add an index for each distance function you want to use.