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Brute-force vec0 vector search for sqlite_plus, with exact filtered ranking.

Changelog #

0.1.4 #

Depends on sqlite_plus ^0.1.11. The library behavior is the 0.1.3 release.

0.1.3 #

Depends on sqlite_plus ^0.1.9. The README opens on the distance. The library behavior is the 0.1.2 release.

0.1.2 #

Depends on sqlite_plus ^0.1.8.

rankVec0 scores a vec0 shadow table one page at a time. Pass the eligible row ids and every one of those rows gets a distance; an empty list reads nothing. embedding MATCH ? AND k = ? is unchanged and still applies k before a later SQL filter. A MATCH scan keeps the embedding bytes of the returned neighbors rather than a second copy of every stored blob.

0.1.1 #

Depends on sqlite_plus ^0.1.7. vec0 is unchanged. The README links the other packages on pub.dev and shows a short example of each.

0.1.0 #

Brute-force vec0 virtual table for one little-endian float32 column.

  • SqlitePlusVec.register accepts a SqliteDatabase, a MutableSqliteDatabase, or the connection's SqlFunctionRegistry.
  • CREATE VIRTUAL TABLE ... USING vec0(embedding float[N] distance_metric=l2|cosine) scans every row for embedding MATCH ? AND k = ?.
  • vecDistance, encodeVec, and decodeVec are the same float32 math. Dimensions are 1 to 4096. k is 1 to 100000.
  • An ORM table uses DbModules.vec0 and @DbVector. nearest returns the k closest rows.
  • The scan is not an approximate index. The file is not a sqlite-vec database.
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Brute-force vec0 vector search for sqlite_plus, with exact filtered ranking.

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Topics

#sqlite #vector #embeddings

License

unknown (license)

Dependencies

sqlite_plus

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