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16
README.md
16
README.md
@@ -5,7 +5,7 @@ Open-source vector similarity search for Postgres
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Store your vectors with the rest of your data. Supports:
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Store your vectors with the rest of your data. Supports:
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- exact and approximate nearest neighbor search
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- exact and approximate nearest neighbor search
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- L2 distance, inner product, cosine distance, and more
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- L2 distance, inner product, and cosine distance
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- any [language](#languages) with a Postgres client
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- any [language](#languages) with a Postgres client
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Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres
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Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres
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@@ -221,19 +221,7 @@ Cosine distance
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CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
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CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
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```
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```
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Hamming distance - added in 0.7.0
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Vectors with up to 2,000 dimensions can be indexed.
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```sql
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CREATE INDEX ON items USING hnsw (embedding bit_hamming_ops);
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```
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Jaccard distance - added in 0.7.0
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```sql
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CREATE INDEX ON items USING hnsw (embedding bit_jaccard_ops);
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```
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Vectors with up to 2,000 dimensions can be indexed, or bit vectors with up to 64,000 dimensions.
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### Index Options
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### Index Options
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