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...

26 Commits

Author SHA1 Message Date
Andrew Kane
9b89bed701 Version bump to 0.7.2 [skip ci] 2024-06-11 17:26:51 -07:00
Andrew Kane
ad7cad5ecd Improved HnswSearchLayer code 2024-06-11 16:29:14 -07:00
Andrew Kane
2a8b9d689e Moved check 2024-06-11 15:45:03 -07:00
Andrew Kane
18cd8a60c3 Updated comment [skip ci] 2024-06-10 22:02:40 -07:00
Andrew Kane
8c91a9f56a Fixed initialization fork for IVFFlat indexes on unlogged tables - #591 2024-06-10 21:55:17 -07:00
Andrew Kane
9249e7e2de Updated changelog [skip ci] 2024-06-10 21:33:49 -07:00
Andrew Kane
9e91af5989 Added checks for invalid indexes - #591 2024-06-10 21:20:54 -07:00
Narek Galstyan
9dcf1bdc80 Fix init_fork WAL-logging on unlogged indexes (#591)
Currently pgvector does not create any WAL records for unlogged tables

Postgres assumes INIT_FORK of unlogged tables is persistent and uses it
to reset the table index to its default empty state after a server
crash.

This patch makes INIT_FORK of unlogged table WAL-tracked, which ensures
an unlogged table is usable after a crash-restart
2024-06-10 21:16:32 -07:00
Andrew Kane
0eceaa3966 Version bump to 0.7.1 [skip ci] 2024-06-03 13:48:51 -07:00
Andrew Kane
49c1f13095 Improved performance of on-disk HNSW index builds - #570 2024-05-29 12:03:58 -07:00
Andrew Kane
ff9b22977e Updated FAQ [skip ci] 2024-05-20 16:48:38 -04:00
Andrew Kane
0468cbf6e6 Added --pull to Docker build instructions [skip ci] 2024-05-20 11:42:11 -04:00
Andrew Kane
258eaf58fd Added halfvec and sparsevec opclasses to readme - closes #540 [skip ci] 2024-05-08 10:40:55 -07:00
Andrew Kane
fa8d2df1cc Added note about ascending order to troubleshooting docs - #548 [skip ci] 2024-05-08 08:36:24 -07:00
Andrew Kane
69f49290fb Fixed compilation warning with Clang < 14 - closes #546 2024-05-07 20:53:41 -07:00
Andrew Kane
ad91451266 Updated changelog and comment [skip ci] 2024-05-07 18:03:21 -07:00
Andrew Kane
cafd2f6641 Updated comment [skip ci] 2024-05-07 17:53:35 -07:00
Andrew Kane
7923c44efe Switched to __apple_build_version__ [skip ci] 2024-05-07 17:41:16 -07:00
Andrew Kane
9b269e2612 Added separate define for __get_cpuid 2024-05-07 16:55:21 -07:00
Andrew Kane
9894ca3e4e Fixed error with cross-compiling / universal binaries on Mac - #544 [skip ci] 2024-05-07 16:46:47 -07:00
Andrew Kane
19cbbfdd69 Fixed undefined symbol error with GCC 8 - fixes #538 2024-05-02 07:50:06 -07:00
Andrew Kane
24c8a2ff40 Fixed flaky tests [skip ci] 2024-04-29 13:54:30 -07:00
Andrew Kane
6df583a6f6 Fixed regression test for vector type 2024-04-29 13:48:04 -07:00
Andrew Kane
999a2e53dd Updated readme [skip ci] 2024-04-29 10:41:40 -07:00
Andrew Kane
3849f0fd3d Version bump to 0.7.0 [skip ci] 2024-04-29 09:26:06 -07:00
Andrew Kane
df178472d1 Updated readme for 0.7.0 [skip ci] 2024-04-29 09:15:24 -07:00
25 changed files with 828 additions and 114 deletions

View File

@@ -1,4 +1,15 @@
## 0.7.0 (unreleased)
## 0.7.2 (2024-06-11)
- Fixed initialization fork for indexes on unlogged tables
## 0.7.1 (2024-06-03)
- Improved performance of on-disk HNSW index builds
- Fixed `undefined symbol` error with GCC 8
- Fixed compilation error with universal binaries on Mac
- Fixed compilation warning with Clang < 14
## 0.7.0 (2024-04-29)
- Added `halfvec` type
- Added `sparsevec` type

View File

@@ -2,7 +2,7 @@
"name": "vector",
"abstract": "Open-source vector similarity search for Postgres",
"description": "Supports L2 distance, inner product, and cosine distance",
"version": "0.6.2",
"version": "0.7.2",
"maintainer": [
"Andrew Kane <andrew@ankane.org>"
],
@@ -20,7 +20,7 @@
"vector": {
"file": "sql/vector.sql",
"docfile": "README.md",
"version": "0.6.2",
"version": "0.7.2",
"abstract": "Open-source vector similarity search for Postgres"
}
},

View File

@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.6.2
EXTVERSION = 0.7.2
MODULE_big = vector
DATA = $(wildcard sql/*--*.sql)

View File

@@ -1,10 +1,10 @@
EXTENSION = vector
EXTVERSION = 0.6.2
EXTVERSION = 0.7.2
OBJS = src\bitutils.obj src\bitvec.obj src\halfutils.obj src\halfvec.obj src\hnsw.obj src\hnswbuild.obj src\hnswinsert.obj src\hnswscan.obj src\hnswutils.obj src\hnswvacuum.obj src\ivfbuild.obj src\ivfflat.obj src\ivfinsert.obj src\ivfkmeans.obj src\ivfscan.obj src\ivfutils.obj src\ivfvacuum.obj src\sparsevec.obj src\vector.obj
HEADERS = src\halfvec.h src\sparsevec.h src\vector.h
REGRESS = bit btree cast copy halfvec hnsw_bit hnsw_halfvec hnsw_sparsevec hnsw_vector ivfflat_bit ivfflat_halfvec ivfflat_vector sparsevec vector
REGRESS = bit btree cast copy halfvec hnsw_bit hnsw_halfvec hnsw_sparsevec hnsw_vector ivfflat_bit ivfflat_halfvec ivfflat_vector sparsevec vector_type
REGRESS_OPTS = --inputdir=test --load-extension=$(EXTENSION)
# For /arch flags

147
README.md
View File

@@ -5,7 +5,8 @@ Open-source vector similarity search for Postgres
Store your vectors with the rest of your data. Supports:
- exact and approximate nearest neighbor search
- L2 distance, inner product, and cosine distance
- single-precision, half-precision, binary, and sparse vectors
- L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance
- any [language](#languages) with a Postgres client
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
@@ -20,7 +21,7 @@ Compile and install the extension (supports Postgres 12+)
```sh
cd /tmp
git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git
git clone --branch v0.7.2 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
@@ -45,7 +46,7 @@ Then use `nmake` to build:
```cmd
set "PGROOT=C:\Program Files\PostgreSQL\16"
cd %TEMP%
git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git
git clone --branch v0.7.2 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install
@@ -81,7 +82,7 @@ Get the nearest neighbors by L2 distance
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
```
Also supports inner product (`<#>`), cosine distance (`<=>`), and L1 distance (`<+>`, unreleased)
Also supports inner product (`<#>`), cosine distance (`<=>`), and L1 distance (`<+>`, added in 0.7.0)
Note: `<#>` returns the negative inner product since Postgres only supports `ASC` order index scans on operators
@@ -143,7 +144,7 @@ Supported distance functions are:
- `<->` - L2 distance
- `<#>` - (negative) inner product
- `<=>` - cosine distance
- `<+>` - L1 distance (unreleased)
- `<+>` - L1 distance (added in 0.7.0)
Get the nearest neighbors to a row
@@ -216,6 +217,8 @@ L2 distance
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);
```
Note: Use `halfvec_l2_ops` for `halfvec` and `sparsevec_l2_ops` for `sparsevec` (and similar with the other distance functions)
Inner product
```sql
@@ -228,19 +231,19 @@ Cosine distance
CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
```
L1 distance - unreleased
L1 distance - added in 0.7.0
```sql
CREATE INDEX ON items USING hnsw (embedding vector_l1_ops);
```
Hamming distance - unreleased
Hamming distance - added in 0.7.0
```sql
CREATE INDEX ON items USING hnsw (embedding bit_hamming_ops);
```
Jaccard distance - unreleased
Jaccard distance - added in 0.7.0
```sql
CREATE INDEX ON items USING hnsw (embedding bit_jaccard_ops);
@@ -249,9 +252,9 @@ CREATE INDEX ON items USING hnsw (embedding bit_jaccard_ops);
Supported types are:
- `vector` - up to 2,000 dimensions
- `halfvec` - up to 4,000 dimensions (unreleased)
- `bit` - up to 64,000 dimensions (unreleased)
- `sparsevec` - up to 1,000 non-zero elements (unreleased)
- `halfvec` - up to 4,000 dimensions (added in 0.7.0)
- `bit` - up to 64,000 dimensions (added in 0.7.0)
- `sparsevec` - up to 1,000 non-zero elements (added in 0.7.0)
### Index Options
@@ -344,6 +347,8 @@ L2 distance
CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);
```
Note: Use `halfvec_l2_ops` for `halfvec` (and similar with the other distance functions)
Inner product
```sql
@@ -356,7 +361,7 @@ Cosine distance
CREATE INDEX ON items USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
```
Hamming distance - unreleased
Hamming distance - added in 0.7.0
```sql
CREATE INDEX ON items USING ivfflat (embedding bit_hamming_ops) WITH (lists = 100);
@@ -365,8 +370,8 @@ CREATE INDEX ON items USING ivfflat (embedding bit_hamming_ops) WITH (lists = 10
Supported types are:
- `vector` - up to 2,000 dimensions
- `halfvec` - up to 4,000 dimensions (unreleased)
- `bit` - up to 64,000 dimensions (unreleased)
- `halfvec` - up to 4,000 dimensions (added in 0.7.0)
- `bit` - up to 64,000 dimensions (added in 0.7.0)
### Query Options
@@ -440,9 +445,9 @@ Use [partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html
CREATE TABLE items (embedding vector(3), category_id int) PARTITION BY LIST(category_id);
```
## Half Vectors
## Half-Precision Vectors
*Unreleased*
*Added in 0.7.0*
Use the `halfvec` type to store half-precision vectors
@@ -450,11 +455,11 @@ Use the `halfvec` type to store half-precision vectors
CREATE TABLE items (id bigserial PRIMARY KEY, embedding halfvec(3));
```
## Half Indexing
## Half-Precision Indexing
*Unreleased*
*Added in 0.7.0*
Index vectors at half precision for smaller indexes and faster build times
Index vectors at half precision for smaller indexes
```sql
CREATE INDEX ON items USING hnsw ((embedding::halfvec(3)) halfvec_l2_ops);
@@ -475,23 +480,23 @@ CREATE TABLE items (id bigserial PRIMARY KEY, embedding bit(3));
INSERT INTO items (embedding) VALUES ('000'), ('111');
```
Get the nearest neighbors by Hamming distance
```sql
SELECT * FROM items ORDER BY bit_count(embedding # '101') LIMIT 5;
```
Or (unreleased)
Get the nearest neighbors by Hamming distance (added in 0.7.0)
```sql
SELECT * FROM items ORDER BY embedding <~> '101' LIMIT 5;
```
Or (before 0.7.0)
```sql
SELECT * FROM items ORDER BY bit_count(embedding # '101') LIMIT 5;
```
Also supports Jaccard distance (`<%>`)
## Binary Quantization
*Unreleased*
*Added in 0.7.0*
Use expression indexing for binary quantization
@@ -515,7 +520,7 @@ SELECT * FROM (
## Sparse Vectors
*Unreleased*
*Added in 0.7.0*
Use the `sparsevec` type to store sparse vectors
@@ -550,7 +555,7 @@ You can use [Reciprocal Rank Fusion](https://github.com/pgvector/pgvector-python
## Indexing Subvectors
*Unreleased*
*Added in 0.7.0*
Use expression indexing to index subvectors
@@ -726,7 +731,7 @@ Yes, pgvector uses the write-ahead log (WAL), which allows for replication and p
#### What if I want to index vectors with more than 2,000 dimensions?
Youll need to use [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) at the moment.
You can use [half-precision indexing](#half-precision-indexing) to index up to 4,000 dimensions or [binary quantization](#binary-quantization) to index up to 64,000 dimensions. Another option is [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction).
#### Can I store vectors with different dimensions in the same column?
@@ -789,7 +794,7 @@ SELECT pg_size_pretty(pg_relation_size('index_name'));
#### Why isnt a query using an index?
The query needs to have an `ORDER BY` and `LIMIT`, and the `ORDER BY` must be the result of a distance operator, not an expression.
The query needs to have an `ORDER BY` and `LIMIT`, and the `ORDER BY` must be the result of a distance operator (not an expression) in ascending order.
```sql
-- index
@@ -864,23 +869,23 @@ Operator | Description | Added
\+ | element-wise addition |
\- | element-wise subtraction |
\* | element-wise multiplication | 0.5.0
\|\| | concatenate | unreleased
\|\| | concatenate | 0.7.0
<-> | Euclidean distance |
<#> | negative inner product |
<=> | cosine distance |
<+> | taxicab distance | unreleased
<+> | taxicab distance | 0.7.0
### Vector Functions
Function | Description | Added
--- | --- | ---
binary_quantize(vector) → bit | binary quantize | unreleased
binary_quantize(vector) → bit | binary quantize | 0.7.0
cosine_distance(vector, vector) → double precision | cosine distance |
inner_product(vector, vector) → double precision | inner product |
l1_distance(vector, vector) → double precision | taxicab distance | 0.5.0
l2_distance(vector, vector) → double precision | Euclidean distance |
l2_normalize(vector) → vector | Normalize with Euclidean norm | unreleased
subvector(vector, integer, integer) → vector | subvector | unreleased
l2_normalize(vector) → vector | Normalize with Euclidean norm | 0.7.0
subvector(vector, integer, integer) → vector | subvector | 0.7.0
vector_dims(vector) → integer | number of dimensions |
vector_norm(vector) → double precision | Euclidean norm |
@@ -899,35 +904,35 @@ Each half vector takes `2 * dimensions + 8` bytes of storage. Each element is a
Operator | Description | Added
--- | --- | ---
\+ | element-wise addition | unreleased
\- | element-wise subtraction | unreleased
\* | element-wise multiplication | unreleased
\|\| | concatenate | unreleased
<-> | Euclidean distance | unreleased
<#> | negative inner product | unreleased
<=> | cosine distance | unreleased
<+> | taxicab distance | unreleased
\+ | element-wise addition | 0.7.0
\- | element-wise subtraction | 0.7.0
\* | element-wise multiplication | 0.7.0
\|\| | concatenate | 0.7.0
<-> | Euclidean distance | 0.7.0
<#> | negative inner product | 0.7.0
<=> | cosine distance | 0.7.0
<+> | taxicab distance | 0.7.0
### Halfvec Functions
Function | Description | Added
--- | --- | ---
binary_quantize(halfvec) → bit | binary quantize | unreleased
cosine_distance(halfvec, halfvec) → double precision | cosine distance | unreleased
inner_product(halfvec, halfvec) → double precision | inner product | unreleased
l1_distance(halfvec, halfvec) → double precision | taxicab distance | unreleased
l2_distance(halfvec, halfvec) → double precision | Euclidean distance | unreleased
l2_norm(halfvec) → double precision | Euclidean norm | unreleased
l2_normalize(halfvec) → halfvec | Normalize with Euclidean norm | unreleased
subvector(halfvec, integer, integer) → halfvec | subvector | unreleased
vector_dims(halfvec) → integer | number of dimensions | unreleased
binary_quantize(halfvec) → bit | binary quantize | 0.7.0
cosine_distance(halfvec, halfvec) → double precision | cosine distance | 0.7.0
inner_product(halfvec, halfvec) → double precision | inner product | 0.7.0
l1_distance(halfvec, halfvec) → double precision | taxicab distance | 0.7.0
l2_distance(halfvec, halfvec) → double precision | Euclidean distance | 0.7.0
l2_norm(halfvec) → double precision | Euclidean norm | 0.7.0
l2_normalize(halfvec) → halfvec | Normalize with Euclidean norm | 0.7.0
subvector(halfvec, integer, integer) → halfvec | subvector | 0.7.0
vector_dims(halfvec) → integer | number of dimensions | 0.7.0
### Halfvec Aggregate Functions
Function | Description | Added
--- | --- | ---
avg(halfvec) → halfvec | average | unreleased
sum(halfvec) → halfvec | sum | unreleased
avg(halfvec) → halfvec | average | 0.7.0
sum(halfvec) → halfvec | sum | 0.7.0
### Bit Type
@@ -937,15 +942,15 @@ Each bit vector takes `dimensions / 8 + 8` bytes of storage. See the [Postgres d
Operator | Description | Added
--- | --- | ---
<~> | Hamming distance | unreleased
<%> | Jaccard distance | unreleased
<~> | Hamming distance | 0.7.0
<%> | Jaccard distance | 0.7.0
### Bit Functions
Function | Description | Added
--- | --- | ---
hamming_distance(bit, bit) → double precision | Hamming distance | unreleased
jaccard_distance(bit, bit) → double precision | Jaccard distance | unreleased
hamming_distance(bit, bit) → double precision | Hamming distance | 0.7.0
jaccard_distance(bit, bit) → double precision | Jaccard distance | 0.7.0
### Sparsevec Type
@@ -955,21 +960,21 @@ Each sparse vector takes `8 * non-zero elements + 16` bytes of storage. Each ele
Operator | Description | Added
--- | --- | ---
<-> | Euclidean distance | unreleased
<#> | negative inner product | unreleased
<=> | cosine distance | unreleased
<+> | taxicab distance | unreleased
<-> | Euclidean distance | 0.7.0
<#> | negative inner product | 0.7.0
<=> | cosine distance | 0.7.0
<+> | taxicab distance | 0.7.0
### Sparsevec Functions
Function | Description | Added
--- | --- | ---
cosine_distance(sparsevec, sparsevec) → double precision | cosine distance | unreleased
inner_product(sparsevec, sparsevec) → double precision | inner product | unreleased
l1_distance(sparsevec, sparsevec) → double precision | taxicab distance | unreleased
l2_distance(sparsevec, sparsevec) → double precision | Euclidean distance | unreleased
l2_norm(sparsevec) → double precision | Euclidean norm | unreleased
l2_normalize(sparsevec) → sparsevec | Normalize with Euclidean norm | unreleased
cosine_distance(sparsevec, sparsevec) → double precision | cosine distance | 0.7.0
inner_product(sparsevec, sparsevec) → double precision | inner product | 0.7.0
l1_distance(sparsevec, sparsevec) → double precision | taxicab distance | 0.7.0
l2_distance(sparsevec, sparsevec) → double precision | Euclidean distance | 0.7.0
l2_norm(sparsevec) → double precision | Euclidean norm | 0.7.0
l2_normalize(sparsevec) → sparsevec | Normalize with Euclidean norm | 0.7.0
## Installation Notes - Linux and Mac
@@ -1046,9 +1051,9 @@ This adds pgvector to the [Postgres image](https://hub.docker.com/_/postgres) (r
You can also build the image manually:
```sh
git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git
git clone --branch v0.7.2 https://github.com/pgvector/pgvector.git
cd pgvector
docker build --build-arg PG_MAJOR=16 -t myuser/pgvector .
docker build --pull --build-arg PG_MAJOR=16 -t myuser/pgvector .
```
### Homebrew

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@@ -0,0 +1,2 @@
-- complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "ALTER EXTENSION vector UPDATE TO '0.7.1'" to load this file. \quit

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@@ -0,0 +1,2 @@
-- complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "ALTER EXTENSION vector UPDATE TO '0.7.2'" to load this file. \quit

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@@ -11,7 +11,7 @@
#ifdef BIT_DISPATCH
#include <immintrin.h>
#if defined(HAVE__GET_CPUID)
#if defined(USE__GET_CPUID)
#include <cpuid.h>
#else
#include <intrin.h>
@@ -173,7 +173,7 @@ SupportsAvx512Popcount()
{
unsigned int exx[4] = {0, 0, 0, 0};
#if defined(HAVE__GET_CPUID)
#if defined(USE__GET_CPUID)
__get_cpuid(1, &exx[0], &exx[1], &exx[2], &exx[3]);
#else
__cpuid(exx, 1);
@@ -187,7 +187,7 @@ SupportsAvx512Popcount()
if ((_xgetbv(0) & 0xe6) != 0xe6)
return false;
#if defined(HAVE__GET_CPUID)
#if defined(USE__GET_CPUID)
__get_cpuid_count(7, 0, &exx[0], &exx[1], &exx[2], &exx[3]);
#else
__cpuidex(exx, 7, 0);

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@@ -6,7 +6,7 @@
#ifdef HALFVEC_DISPATCH
#include <immintrin.h>
#if defined(HAVE__GET_CPUID)
#if defined(USE__GET_CPUID)
#include <cpuid.h>
#else
#include <intrin.h>
@@ -254,7 +254,7 @@ SupportsCpuFeature(unsigned int feature)
{
unsigned int exx[4] = {0, 0, 0, 0};
#if defined(HAVE__GET_CPUID)
#if defined(USE__GET_CPUID)
__get_cpuid(1, &exx[0], &exx[1], &exx[2], &exx[3]);
#else
__cpuid(exx, 1);

View File

@@ -9,7 +9,7 @@
/* TODO Move to better place */
#ifndef DISABLE_DISPATCH
/* Only enable for more recent compilers to keep build process simple */
#if defined(__x86_64__) && defined(__GNUC__) && __GNUC__ >= 8
#if defined(__x86_64__) && defined(__GNUC__) && __GNUC__ >= 9
#define USE_DISPATCH
#elif defined(__x86_64__) && defined(__clang_major__) && __clang_major__ >= 7
#define USE_DISPATCH
@@ -19,9 +19,17 @@
#endif
/* target_clones requires glibc */
#if defined(USE_DISPATCH) && defined(__gnu_linux__)
#if defined(USE_DISPATCH) && defined(__gnu_linux__) && defined(__has_attribute)
/* Use separate line for portability */
#if __has_attribute(target_clones)
#define USE_TARGET_CLONES
#endif
#endif
/* Apple clang check needed for universal binaries on Mac */
#if defined(USE_DISPATCH) && (defined(HAVE__GET_CPUID) || defined(__apple_build_version__))
#define USE__GET_CPUID
#endif
#if defined(USE_DISPATCH)
#define HALFVEC_DISPATCH

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@@ -393,7 +393,7 @@ void HnswInitNeighbors(char *base, HnswElement element, int m, HnswAllocator *
bool HnswInsertTupleOnDisk(Relation index, Datum value, Datum *values, bool *isnull, ItemPointer heap_tid, bool building);
void HnswUpdateNeighborsOnDisk(Relation index, FmgrInfo *procinfo, Oid collation, HnswElement e, int m, bool checkExisting, bool building);
void HnswLoadElementFromTuple(HnswElement element, HnswElementTuple etup, bool loadHeaptids, bool loadVec);
void HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index, FmgrInfo *procinfo, Oid collation, bool loadVec);
void HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index, FmgrInfo *procinfo, Oid collation, bool loadVec, float *maxDistance);
void HnswSetElementTuple(char *base, HnswElementTuple etup, HnswElement element);
void HnswUpdateConnection(char *base, HnswElement element, HnswCandidate * hc, int lm, int lc, int *updateIdx, Relation index, FmgrInfo *procinfo, Oid collation);
void HnswLoadNeighbors(HnswElement element, Relation index, int m);

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@@ -1121,8 +1121,8 @@ BuildIndex(Relation heap, Relation index, IndexInfo *indexInfo,
BuildGraph(buildstate, forkNum);
if (RelationNeedsWAL(index))
log_newpage_range(index, forkNum, 0, RelationGetNumberOfBlocks(index), true);
if (RelationNeedsWAL(index) || forkNum == INIT_FORKNUM)
log_newpage_range(index, forkNum, 0, RelationGetNumberOfBlocksInFork(index, forkNum), true);
FreeBuildState(buildstate);
}

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@@ -300,6 +300,9 @@ HnswGetMetaPageInfo(Relation index, int *m, HnswElement * entryPoint)
page = BufferGetPage(buf);
metap = HnswPageGetMeta(page);
if (unlikely(metap->magicNumber != HNSW_MAGIC_NUMBER))
elog(ERROR, "hnsw index is not valid");
if (m != NULL)
*m = metap->m;
@@ -545,7 +548,7 @@ HnswLoadElementFromTuple(HnswElement element, HnswElementTuple etup, bool loadHe
* Load an element and optionally get its distance from q
*/
void
HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index, FmgrInfo *procinfo, Oid collation, bool loadVec)
HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index, FmgrInfo *procinfo, Oid collation, bool loadVec, float *maxDistance)
{
Buffer buf;
Page page;
@@ -560,9 +563,6 @@ HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index,
Assert(HnswIsElementTuple(etup));
/* Load element */
HnswLoadElementFromTuple(element, etup, true, loadVec);
/* Calculate distance */
if (distance != NULL)
{
@@ -572,6 +572,10 @@ HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index,
*distance = (float) DatumGetFloat8(FunctionCall2Coll(procinfo, collation, *q, PointerGetDatum(&etup->data)));
}
/* Load element */
if (distance == NULL || maxDistance == NULL || *distance < *maxDistance)
HnswLoadElementFromTuple(element, etup, true, loadVec);
UnlockReleaseBuffer(buf);
}
@@ -599,7 +603,7 @@ HnswEntryCandidate(char *base, HnswElement entryPoint, Datum q, Relation index,
if (index == NULL)
hc->distance = GetCandidateDistance(base, hc, q, procinfo, collation);
else
HnswLoadElement(entryPoint, &hc->distance, &q, index, procinfo, collation, loadVec);
HnswLoadElement(entryPoint, &hc->distance, &q, index, procinfo, collation, loadVec, NULL);
return hc;
}
@@ -795,25 +799,27 @@ HnswSearchLayer(char *base, Datum q, List *ep, int ef, int lc, Relation index, F
{
float eDistance;
HnswElement eElement = HnswPtrAccess(base, e->element);
bool alwaysAdd = wlen < ef;
f = ((HnswPairingHeapNode *) pairingheap_first(W))->inner;
if (index == NULL)
eDistance = GetCandidateDistance(base, e, q, procinfo, collation);
else
HnswLoadElement(eElement, &eDistance, &q, index, procinfo, collation, inserting);
HnswLoadElement(eElement, &eDistance, &q, index, procinfo, collation, inserting, alwaysAdd ? NULL : &f->distance);
Assert(!eElement->deleted);
/* Make robust to issues */
if (eElement->level < lc)
continue;
if (eDistance < f->distance || wlen < ef)
if (eDistance < f->distance || alwaysAdd)
{
/* Copy e */
HnswCandidate *ec = palloc(sizeof(HnswCandidate));
HnswCandidate *ec;
Assert(!eElement->deleted);
/* Make robust to issues */
if (eElement->level < lc)
continue;
/* Copy e */
ec = palloc(sizeof(HnswCandidate));
HnswPtrStore(base, ec->element, eElement);
ec->distance = eDistance;
@@ -1102,7 +1108,7 @@ HnswUpdateConnection(char *base, HnswElement element, HnswCandidate * hc, int lm
HnswElement hc3Element = HnswPtrAccess(base, hc3->element);
if (HnswPtrIsNull(base, hc3Element->value))
HnswLoadElement(hc3Element, &hc3->distance, &q, index, procinfo, collation, true);
HnswLoadElement(hc3Element, &hc3->distance, &q, index, procinfo, collation, true, NULL);
else
hc3->distance = GetCandidateDistance(base, hc3, q, procinfo, collation);

View File

@@ -256,7 +256,7 @@ RepairGraphEntryPoint(HnswVacuumState * vacuumstate)
LockPage(index, HNSW_UPDATE_LOCK, ShareLock);
/* Load element */
HnswLoadElement(highestPoint, NULL, NULL, index, vacuumstate->procinfo, vacuumstate->collation, true);
HnswLoadElement(highestPoint, NULL, NULL, index, vacuumstate->procinfo, vacuumstate->collation, true, NULL);
/* Repair if needed */
if (NeedsUpdated(vacuumstate, highestPoint))
@@ -294,7 +294,7 @@ RepairGraphEntryPoint(HnswVacuumState * vacuumstate)
* is outdated, this can remove connections at higher levels in
* the graph until they are repaired, but this should be fine.
*/
HnswLoadElement(entryPoint, NULL, NULL, index, vacuumstate->procinfo, vacuumstate->collation, true);
HnswLoadElement(entryPoint, NULL, NULL, index, vacuumstate->procinfo, vacuumstate->collation, true, NULL);
if (NeedsUpdated(vacuumstate, entryPoint))
{

View File

@@ -1006,6 +1006,10 @@ BuildIndex(Relation heap, Relation index, IndexInfo *indexInfo,
CreateListPages(index, buildstate->centers, buildstate->dimensions, buildstate->lists, forkNum, &buildstate->listInfo);
CreateEntryPages(buildstate, forkNum);
/* Write WAL for initialization fork since GenericXLog functions do not */
if (forkNum == INIT_FORKNUM)
log_newpage_range(index, forkNum, 0, RelationGetNumberOfBlocksInFork(index, forkNum), true);
FreeBuildState(buildstate);
}

View File

@@ -94,6 +94,9 @@ InsertTuple(Relation index, Datum *values, bool *isnull, ItemPointer heap_tid, R
value = IvfflatNormValue(typeInfo, collation, value);
}
/* Ensure index is valid */
IvfflatGetMetaPageInfo(index, NULL, NULL);
/* Find the insert page - sets the page and list info */
FindInsertPage(index, values, &insertPage, &listInfo);
Assert(BlockNumberIsValid(insertPage));

View File

@@ -170,7 +170,11 @@ IvfflatGetMetaPageInfo(Relation index, int *lists, int *dimensions)
page = BufferGetPage(buf);
metap = IvfflatPageGetMeta(page);
*lists = metap->lists;
if (unlikely(metap->magicNumber != IVFFLAT_MAGIC_NUMBER))
elog(ERROR, "ivfflat index is not valid");
if (lists != NULL)
*lists = metap->lists;
if (dimensions != NULL)
*dimensions = metap->dimensions;

View File

@@ -1,3 +0,0 @@
-- complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "CREATE EXTENSION vector" to load this file. \quit
Use "CREATE EXTENSION vector" to load this file.

View File

@@ -0,0 +1,672 @@
SELECT '[1,2,3]'::vector;
vector
---------
[1,2,3]
(1 row)
SELECT '[-1,-2,-3]'::vector;
vector
------------
[-1,-2,-3]
(1 row)
SELECT '[1.,2.,3.]'::vector;
vector
---------
[1,2,3]
(1 row)
SELECT ' [ 1, 2 , 3 ] '::vector;
vector
---------
[1,2,3]
(1 row)
SELECT '[1.23456]'::vector;
vector
-----------
[1.23456]
(1 row)
SELECT '[hello,1]'::vector;
ERROR: invalid input syntax for type vector: "[hello,1]"
LINE 1: SELECT '[hello,1]'::vector;
^
SELECT '[NaN,1]'::vector;
ERROR: NaN not allowed in vector
LINE 1: SELECT '[NaN,1]'::vector;
^
SELECT '[Infinity,1]'::vector;
ERROR: infinite value not allowed in vector
LINE 1: SELECT '[Infinity,1]'::vector;
^
SELECT '[-Infinity,1]'::vector;
ERROR: infinite value not allowed in vector
LINE 1: SELECT '[-Infinity,1]'::vector;
^
SELECT '[1.5e38,-1.5e38]'::vector;
vector
--------------------
[1.5e+38,-1.5e+38]
(1 row)
SELECT '[1.5e+38,-1.5e+38]'::vector;
vector
--------------------
[1.5e+38,-1.5e+38]
(1 row)
SELECT '[1.5e-38,-1.5e-38]'::vector;
vector
--------------------
[1.5e-38,-1.5e-38]
(1 row)
SELECT '[4e38,1]'::vector;
ERROR: "4e38" is out of range for type vector
LINE 1: SELECT '[4e38,1]'::vector;
^
SELECT '[-4e38,1]'::vector;
ERROR: "-4e38" is out of range for type vector
LINE 1: SELECT '[-4e38,1]'::vector;
^
SELECT '[1e-46,1]'::vector;
vector
--------
[0,1]
(1 row)
SELECT '[-1e-46,1]'::vector;
vector
--------
[-0,1]
(1 row)
SELECT '[1,2,3'::vector;
ERROR: invalid input syntax for type vector: "[1,2,3"
LINE 1: SELECT '[1,2,3'::vector;
^
SELECT '[1,2,3]9'::vector;
ERROR: invalid input syntax for type vector: "[1,2,3]9"
LINE 1: SELECT '[1,2,3]9'::vector;
^
DETAIL: Junk after closing right brace.
SELECT '1,2,3'::vector;
ERROR: invalid input syntax for type vector: "1,2,3"
LINE 1: SELECT '1,2,3'::vector;
^
DETAIL: Vector contents must start with "[".
SELECT ''::vector;
ERROR: invalid input syntax for type vector: ""
LINE 1: SELECT ''::vector;
^
DETAIL: Vector contents must start with "[".
SELECT '['::vector;
ERROR: invalid input syntax for type vector: "["
LINE 1: SELECT '['::vector;
^
SELECT '[ '::vector;
ERROR: invalid input syntax for type vector: "[ "
LINE 1: SELECT '[ '::vector;
^
SELECT '[,'::vector;
ERROR: invalid input syntax for type vector: "[,"
LINE 1: SELECT '[,'::vector;
^
SELECT '[]'::vector;
ERROR: vector must have at least 1 dimension
LINE 1: SELECT '[]'::vector;
^
SELECT '[ ]'::vector;
ERROR: vector must have at least 1 dimension
LINE 1: SELECT '[ ]'::vector;
^
SELECT '[,]'::vector;
ERROR: invalid input syntax for type vector: "[,]"
LINE 1: SELECT '[,]'::vector;
^
SELECT '[1,]'::vector;
ERROR: invalid input syntax for type vector: "[1,]"
LINE 1: SELECT '[1,]'::vector;
^
SELECT '[1a]'::vector;
ERROR: invalid input syntax for type vector: "[1a]"
LINE 1: SELECT '[1a]'::vector;
^
SELECT '[1,,3]'::vector;
ERROR: invalid input syntax for type vector: "[1,,3]"
LINE 1: SELECT '[1,,3]'::vector;
^
SELECT '[1, ,3]'::vector;
ERROR: invalid input syntax for type vector: "[1, ,3]"
LINE 1: SELECT '[1, ,3]'::vector;
^
SELECT '[1,2,3]'::vector(3);
vector
---------
[1,2,3]
(1 row)
SELECT '[1,2,3]'::vector(2);
ERROR: expected 2 dimensions, not 3
SELECT '[1,2,3]'::vector(3, 2);
ERROR: invalid type modifier
LINE 1: SELECT '[1,2,3]'::vector(3, 2);
^
SELECT '[1,2,3]'::vector('a');
ERROR: invalid input syntax for type integer: "a"
LINE 1: SELECT '[1,2,3]'::vector('a');
^
SELECT '[1,2,3]'::vector(0);
ERROR: dimensions for type vector must be at least 1
LINE 1: SELECT '[1,2,3]'::vector(0);
^
SELECT '[1,2,3]'::vector(16001);
ERROR: dimensions for type vector cannot exceed 16000
LINE 1: SELECT '[1,2,3]'::vector(16001);
^
SELECT unnest('{"[1,2,3]", "[4,5,6]"}'::vector[]);
unnest
---------
[1,2,3]
[4,5,6]
(2 rows)
SELECT '{"[1,2,3]"}'::vector(2)[];
ERROR: expected 2 dimensions, not 3
SELECT '[1,2,3]'::vector + '[4,5,6]';
?column?
----------
[5,7,9]
(1 row)
SELECT '[3e38]'::vector + '[3e38]';
ERROR: value out of range: overflow
SELECT '[1,2]'::vector + '[3]';
ERROR: different vector dimensions 2 and 1
SELECT '[1,2,3]'::vector - '[4,5,6]';
?column?
------------
[-3,-3,-3]
(1 row)
SELECT '[-3e38]'::vector - '[3e38]';
ERROR: value out of range: overflow
SELECT '[1,2]'::vector - '[3]';
ERROR: different vector dimensions 2 and 1
SELECT '[1,2,3]'::vector * '[4,5,6]';
?column?
-----------
[4,10,18]
(1 row)
SELECT '[1e37]'::vector * '[1e37]';
ERROR: value out of range: overflow
SELECT '[1e-37]'::vector * '[1e-37]';
ERROR: value out of range: underflow
SELECT '[1,2]'::vector * '[3]';
ERROR: different vector dimensions 2 and 1
SELECT '[1,2,3]'::vector || '[4,5]';
?column?
-------------
[1,2,3,4,5]
(1 row)
SELECT array_fill(0, ARRAY[16000])::vector || '[1]';
ERROR: vector cannot have more than 16000 dimensions
SELECT '[1,2,3]'::vector < '[1,2,3]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector < '[1,2]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector <= '[1,2,3]';
?column?
----------
t
(1 row)
SELECT '[1,2,3]'::vector <= '[1,2]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector = '[1,2,3]';
?column?
----------
t
(1 row)
SELECT '[1,2,3]'::vector = '[1,2]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector != '[1,2,3]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector != '[1,2]';
?column?
----------
t
(1 row)
SELECT '[1,2,3]'::vector >= '[1,2,3]';
?column?
----------
t
(1 row)
SELECT '[1,2,3]'::vector >= '[1,2]';
?column?
----------
t
(1 row)
SELECT '[1,2,3]'::vector > '[1,2,3]';
?column?
----------
f
(1 row)
SELECT '[1,2,3]'::vector > '[1,2]';
?column?
----------
t
(1 row)
SELECT vector_cmp('[1,2,3]', '[1,2,3]');
vector_cmp
------------
0
(1 row)
SELECT vector_cmp('[1,2,3]', '[0,0,0]');
vector_cmp
------------
1
(1 row)
SELECT vector_cmp('[0,0,0]', '[1,2,3]');
vector_cmp
------------
-1
(1 row)
SELECT vector_cmp('[1,2]', '[1,2,3]');
vector_cmp
------------
-1
(1 row)
SELECT vector_cmp('[1,2,3]', '[1,2]');
vector_cmp
------------
1
(1 row)
SELECT vector_cmp('[1,2]', '[2,3,4]');
vector_cmp
------------
-1
(1 row)
SELECT vector_cmp('[2,3]', '[1,2,3]');
vector_cmp
------------
1
(1 row)
SELECT vector_dims('[1,2,3]'::vector);
vector_dims
-------------
3
(1 row)
SELECT round(vector_norm('[1,1]')::numeric, 5);
round
---------
1.41421
(1 row)
SELECT vector_norm('[3,4]');
vector_norm
-------------
5
(1 row)
SELECT vector_norm('[0,1]');
vector_norm
-------------
1
(1 row)
SELECT vector_norm('[3e37,4e37]')::real;
vector_norm
-------------
5e+37
(1 row)
SELECT vector_norm('[0,0]');
vector_norm
-------------
0
(1 row)
SELECT vector_norm('[2]');
vector_norm
-------------
2
(1 row)
SELECT l2_distance('[0,0]'::vector, '[3,4]');
l2_distance
-------------
5
(1 row)
SELECT l2_distance('[0,0]'::vector, '[0,1]');
l2_distance
-------------
1
(1 row)
SELECT l2_distance('[1,2]'::vector, '[3]');
ERROR: different vector dimensions 2 and 1
SELECT l2_distance('[3e38]'::vector, '[-3e38]');
l2_distance
-------------
Infinity
(1 row)
SELECT l2_distance('[1,1,1,1,1,1,1,1,1]'::vector, '[1,1,1,1,1,1,1,4,5]');
l2_distance
-------------
5
(1 row)
SELECT '[0,0]'::vector <-> '[3,4]';
?column?
----------
5
(1 row)
SELECT inner_product('[1,2]'::vector, '[3,4]');
inner_product
---------------
11
(1 row)
SELECT inner_product('[1,2]'::vector, '[3]');
ERROR: different vector dimensions 2 and 1
SELECT inner_product('[3e38]'::vector, '[3e38]');
inner_product
---------------
Infinity
(1 row)
SELECT inner_product('[1,1,1,1,1,1,1,1,1]'::vector, '[1,2,3,4,5,6,7,8,9]');
inner_product
---------------
45
(1 row)
SELECT '[1,2]'::vector <#> '[3,4]';
?column?
----------
-11
(1 row)
SELECT cosine_distance('[1,2]'::vector, '[2,4]');
cosine_distance
-----------------
0
(1 row)
SELECT cosine_distance('[1,2]'::vector, '[0,0]');
cosine_distance
-----------------
NaN
(1 row)
SELECT cosine_distance('[1,1]'::vector, '[1,1]');
cosine_distance
-----------------
0
(1 row)
SELECT cosine_distance('[1,0]'::vector, '[0,2]');
cosine_distance
-----------------
1
(1 row)
SELECT cosine_distance('[1,1]'::vector, '[-1,-1]');
cosine_distance
-----------------
2
(1 row)
SELECT cosine_distance('[1,2]'::vector, '[3]');
ERROR: different vector dimensions 2 and 1
SELECT cosine_distance('[1,1]'::vector, '[1.1,1.1]');
cosine_distance
-----------------
0
(1 row)
SELECT cosine_distance('[1,1]'::vector, '[-1.1,-1.1]');
cosine_distance
-----------------
2
(1 row)
SELECT cosine_distance('[3e38]'::vector, '[3e38]');
cosine_distance
-----------------
NaN
(1 row)
SELECT cosine_distance('[1,2,3,4,5,6,7,8,9]'::vector, '[1,2,3,4,5,6,7,8,9]');
cosine_distance
-----------------
0
(1 row)
SELECT cosine_distance('[1,2,3,4,5,6,7,8,9]'::vector, '[-1,-2,-3,-4,-5,-6,-7,-8,-9]');
cosine_distance
-----------------
2
(1 row)
SELECT '[1,2]'::vector <=> '[2,4]';
?column?
----------
0
(1 row)
SELECT l1_distance('[0,0]'::vector, '[3,4]');
l1_distance
-------------
7
(1 row)
SELECT l1_distance('[0,0]'::vector, '[0,1]');
l1_distance
-------------
1
(1 row)
SELECT l1_distance('[1,2]'::vector, '[3]');
ERROR: different vector dimensions 2 and 1
SELECT l1_distance('[3e38]'::vector, '[-3e38]');
l1_distance
-------------
Infinity
(1 row)
SELECT l1_distance('[1,2,3,4,5,6,7,8,9]'::vector, '[1,2,3,4,5,6,7,8,9]');
l1_distance
-------------
0
(1 row)
SELECT l1_distance('[1,2,3,4,5,6,7,8,9]'::vector, '[0,3,2,5,4,7,6,9,8]');
l1_distance
-------------
9
(1 row)
SELECT '[0,0]'::vector <+> '[3,4]';
?column?
----------
7
(1 row)
SELECT l2_normalize('[3,4]'::vector);
l2_normalize
--------------
[0.6,0.8]
(1 row)
SELECT l2_normalize('[3,0]'::vector);
l2_normalize
--------------
[1,0]
(1 row)
SELECT l2_normalize('[0,0.1]'::vector);
l2_normalize
--------------
[0,1]
(1 row)
SELECT l2_normalize('[0,0]'::vector);
l2_normalize
--------------
[0,0]
(1 row)
SELECT l2_normalize('[3e38]'::vector);
l2_normalize
--------------
[1]
(1 row)
SELECT binary_quantize('[1,0,-1]'::vector);
binary_quantize
-----------------
100
(1 row)
SELECT binary_quantize('[0,0.1,-0.2,-0.3,0.4,0.5,0.6,-0.7,0.8,-0.9,1]'::vector);
binary_quantize
-----------------
01001110101
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, 1, 3);
subvector
-----------
[1,2,3]
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, 3, 2);
subvector
-----------
[3,4]
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, -1, 3);
subvector
-----------
[1]
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, 3, 9);
subvector
-----------
[3,4,5]
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, 1, 0);
ERROR: vector must have at least 1 dimension
SELECT subvector('[1,2,3,4,5]'::vector, 3, -1);
ERROR: vector must have at least 1 dimension
SELECT subvector('[1,2,3,4,5]'::vector, -1, 2);
ERROR: vector must have at least 1 dimension
SELECT subvector('[1,2,3,4,5]'::vector, 2147483647, 10);
ERROR: vector must have at least 1 dimension
SELECT subvector('[1,2,3,4,5]'::vector, 3, 2147483647);
subvector
-----------
[3,4,5]
(1 row)
SELECT subvector('[1,2,3,4,5]'::vector, -2147483644, 2147483647);
subvector
-----------
[1,2]
(1 row)
SELECT avg(v) FROM unnest(ARRAY['[1,2,3]'::vector, '[3,5,7]']) v;
avg
-----------
[2,3.5,5]
(1 row)
SELECT avg(v) FROM unnest(ARRAY['[1,2,3]'::vector, '[3,5,7]', NULL]) v;
avg
-----------
[2,3.5,5]
(1 row)
SELECT avg(v) FROM unnest(ARRAY[]::vector[]) v;
avg
-----
(1 row)
SELECT avg(v) FROM unnest(ARRAY['[1,2]'::vector, '[3]']) v;
ERROR: expected 2 dimensions, not 1
SELECT avg(v) FROM unnest(ARRAY['[3e38]'::vector, '[3e38]']) v;
avg
---------
[3e+38]
(1 row)
SELECT vector_avg(array_agg(n)) FROM generate_series(1, 16002) n;
ERROR: vector cannot have more than 16000 dimensions
SELECT sum(v) FROM unnest(ARRAY['[1,2,3]'::vector, '[3,5,7]']) v;
sum
----------
[4,7,10]
(1 row)
SELECT sum(v) FROM unnest(ARRAY['[1,2,3]'::vector, '[3,5,7]', NULL]) v;
sum
----------
[4,7,10]
(1 row)
SELECT sum(v) FROM unnest(ARRAY[]::vector[]) v;
sum
-----
(1 row)
SELECT sum(v) FROM unnest(ARRAY['[1,2]'::vector, '[3]']) v;
ERROR: different vector dimensions 2 and 1
SELECT sum(v) FROM unnest(ARRAY['[3e38]'::vector, '[3e38]']) v;
ERROR: value out of range: overflow

View File

@@ -91,7 +91,7 @@ for my $i (0 .. $#operators)
));
# Test approximate results
my $min = $operator eq "<#>" ? 0.98 : 0.99;
my $min = $operator eq "<#>" ? 0.97 : 0.99;
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");

View File

@@ -100,7 +100,7 @@ for my $i (0 .. $#operators)
}
# Test approximate results
my $min = $operator eq "<#>" ? 0.98 : 0.99;
my $min = $operator eq "<#>" ? 0.97 : 0.99;
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");

View File

@@ -91,7 +91,7 @@ for my $i (0 .. $#operators)
));
# Test approximate results
my $min = $operator eq "<#>" ? 0.98 : 0.99;
my $min = $operator eq "<#>" ? 0.97 : 0.99;
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");

View File

@@ -100,7 +100,7 @@ for my $i (0 .. $#operators)
}
# Test approximate results
my $min = $operator eq "<#>" ? 0.98 : 0.99;
my $min = $operator eq "<#>" ? 0.97 : 0.99;
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");

View File

@@ -1,4 +1,4 @@
comment = 'vector data type and ivfflat and hnsw access methods'
default_version = '0.6.2'
default_version = '0.7.2'
module_pathname = '$libdir/vector'
relocatable = true