Compare commits

..

1 Commits

Author SHA1 Message Date
Andrew Kane
bb72a6c063 Test BufferAccessStrategy [skip ci] 2023-04-10 22:07:58 -07:00
5 changed files with 10 additions and 33 deletions

View File

@@ -1,7 +1,6 @@
## 0.4.2 (unreleased) ## 0.4.2 (unreleased)
- Added notice when index created with little data - Added notice when index created with little data
- Fixed installation error with Postgres 12.0-12.2
## 0.4.1 (2023-03-21) ## 0.4.1 (2023-03-21)

View File

@@ -2,11 +2,7 @@
Open-source vector similarity search for Postgres Open-source vector similarity search for Postgres
Supports Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance
- exact and approximate nearest neighbor search
- L2 distance, inner product, and cosine distance
- any [language](#languages) with a Postgres client
[![Build Status](https://github.com/pgvector/pgvector/workflows/build/badge.svg?branch=master)](https://github.com/pgvector/pgvector/actions) [![Build Status](https://github.com/pgvector/pgvector/workflows/build/badge.svg?branch=master)](https://github.com/pgvector/pgvector/actions)
@@ -40,7 +36,7 @@ Create a vector column with 3 dimensions
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3)); CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
``` ```
Insert vectors Insert values
```sql ```sql
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]'); INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
@@ -128,7 +124,7 @@ SELECT embedding <-> '[3,1,2]' AS distance FROM items;
For inner product, multiply by -1 (since `<#>` returns the negative inner product) For inner product, multiply by -1 (since `<#>` returns the negative inner product)
```sql ```sql
SELECT (embedding <#> '[3,1,2]') * -1 AS inner_product FROM items; SELECT -1 * (embedding <#> '[3,1,2]') AS inner_product FROM items;
``` ```
For cosine similarity, use 1 - cosine distance For cosine similarity, use 1 - cosine distance
@@ -137,7 +133,7 @@ For cosine similarity, use 1 - cosine distance
SELECT 1 - (embedding <=> '[3,1,2]') AS cosine_similarity FROM items; SELECT 1 - (embedding <=> '[3,1,2]') AS cosine_similarity FROM items;
``` ```
#### Aggregates #### Averaging
Average vectors Average vectors
@@ -160,8 +156,8 @@ You can add an index to use approximate nearest neighbor search, which trades so
Three keys to achieving good recall are: Three keys to achieving good recall are:
1. Create the index *after* the table has some data 1. Create the index *after* the table has some data
2. Choose an appropriate number of lists - a good place to start is `rows / 1000` for up to 1M rows and `sqrt(rows)` for over 1M rows 2. Choose an appropriate number of lists (a good place to start is `rows / 1000` for up to 1M rows and `sqrt(rows)` for over 1M rows)
3. When querying, specify an appropriate number of [probes](#query-options) (higher is better for recall, lower is better for speed) - a good place to start is `lists / 10` for up to 1M rows and `sqrt(lists)` for over 1M rows 3. When querying, specify an appropriate number of [probes](#query-options) (higher is better for recall, lower is better for speed)
Add an index for each distance function you want to use. Add an index for each distance function you want to use.
@@ -279,10 +275,8 @@ Language | Libraries / Examples
--- | --- --- | ---
C++ | [pgvector-cpp](https://github.com/pgvector/pgvector-cpp) C++ | [pgvector-cpp](https://github.com/pgvector/pgvector-cpp)
C# | [pgvector-dotnet](https://github.com/pgvector/pgvector-dotnet) C# | [pgvector-dotnet](https://github.com/pgvector/pgvector-dotnet)
Crystal | [pgvector-crystal](https://github.com/pgvector/pgvector-crystal)
Elixir | [pgvector-elixir](https://github.com/pgvector/pgvector-elixir) Elixir | [pgvector-elixir](https://github.com/pgvector/pgvector-elixir)
Go | [pgvector-go](https://github.com/pgvector/pgvector-go) Go | [pgvector-go](https://github.com/pgvector/pgvector-go)
Haskell | [pgvector-haskell](https://github.com/pgvector/pgvector-haskell)
Java, Scala | [pgvector-java](https://github.com/pgvector/pgvector-java) Java, Scala | [pgvector-java](https://github.com/pgvector/pgvector-java)
Julia | [pgvector-julia](https://github.com/pgvector/pgvector-julia) Julia | [pgvector-julia](https://github.com/pgvector/pgvector-julia)
Lua | [pgvector-lua](https://github.com/pgvector/pgvector-lua) Lua | [pgvector-lua](https://github.com/pgvector/pgvector-lua)
@@ -293,7 +287,6 @@ Python | [pgvector-python](https://github.com/pgvector/pgvector-python)
R | [pgvector-r](https://github.com/pgvector/pgvector-r) R | [pgvector-r](https://github.com/pgvector/pgvector-r)
Ruby | [pgvector-ruby](https://github.com/pgvector/pgvector-ruby), [Neighbor](https://github.com/ankane/neighbor) Ruby | [pgvector-ruby](https://github.com/pgvector/pgvector-ruby), [Neighbor](https://github.com/ankane/neighbor)
Rust | [pgvector-rust](https://github.com/pgvector/pgvector-rust) Rust | [pgvector-rust](https://github.com/pgvector/pgvector-rust)
Swift | [pgvector-swift](https://github.com/pgvector/pgvector-swift)
## Frequently Asked Questions ## Frequently Asked Questions
@@ -312,10 +305,6 @@ Two things you can try are:
1. use dimensionality reduction 1. use dimensionality reduction
2. compile Postgres with a larger block size (`./configure --with-blocksize=32`) and edit the limit in `src/ivfflat.h` 2. compile Postgres with a larger block size (`./configure --with-blocksize=32`) and edit the limit in `src/ivfflat.h`
#### Why am I seeing less results after adding an index?
The index was likely created with too little data for the number of lists. Drop the index until the table has more data.
## Reference ## Reference
### Vector Type ### Vector Type
@@ -452,7 +441,7 @@ To request a new extension on other providers:
- Google Cloud SQL - vote or comment on [this page](https://issuetracker.google.com/issues/265172065) - Google Cloud SQL - vote or comment on [this page](https://issuetracker.google.com/issues/265172065)
- Azure Database - vote or comment on [this page](https://feedback.azure.com/d365community/idea/7b423322-6189-ed11-a81b-000d3ae49307) - Azure Database - vote or comment on [this page](https://feedback.azure.com/d365community/idea/7b423322-6189-ed11-a81b-000d3ae49307)
- DigitalOcean Managed Databases - vote or comment on [this page](https://ideas.digitalocean.com/app-framework-services/p/pgvector-extension-for-postgresql) - DigitalOcean Managed Databases - vote or comment on [this page](https://ideas.digitalocean.com/app-framework-services/p/pgvector-extension-for-postgresql)
- Heroku Postgres - vote or comment on [this page](https://github.com/heroku/roadmap/issues/156) - Render - vote or comment on [this page](https://feedback.render.com/features/p/add-pgvector-extension-to-postgresql)
## Upgrading ## Upgrading

View File

@@ -206,7 +206,6 @@ typedef struct IvfflatScanOpaqueData
/* Lists */ /* Lists */
pairingheap *listQueue; pairingheap *listQueue;
double minDistance;
IvfflatScanList lists[FLEXIBLE_ARRAY_MEMBER]; /* must come last */ IvfflatScanList lists[FLEXIBLE_ARRAY_MEMBER]; /* must come last */
} IvfflatScanOpaqueData; } IvfflatScanOpaqueData;

View File

@@ -60,9 +60,6 @@ GetScanLists(IndexScanDesc scan, Datum value)
/* Use procinfo from the index instead of scan key for performance */ /* Use procinfo from the index instead of scan key for performance */
distance = DatumGetFloat8(FunctionCall2Coll(so->procinfo, so->collation, PointerGetDatum(&list->center), value)); distance = DatumGetFloat8(FunctionCall2Coll(so->procinfo, so->collation, PointerGetDatum(&list->center), value));
if (distance < so->minDistance)
so->minDistance = distance;
if (listCount < so->probes) if (listCount < so->probes)
{ {
scanlist = &so->lists[listCount]; scanlist = &so->lists[listCount];
@@ -127,18 +124,12 @@ GetScanItems(IndexScanDesc scan, Datum value)
* *
* See postgres/src/backend/storage/buffer/README for description * See postgres/src/backend/storage/buffer/README for description
*/ */
BufferAccessStrategy bas = GetAccessStrategy(BAS_BULKREAD); BufferAccessStrategy bas = GetAccessStrategy(BAS_NORMAL);
/* Search closest probes lists */ /* Search closest probes lists */
while (!pairingheap_is_empty(so->listQueue)) while (!pairingheap_is_empty(so->listQueue))
{ {
IvfflatScanList *scanlist = (IvfflatScanList *) pairingheap_remove_first(so->listQueue); searchPage = ((IvfflatScanList *) pairingheap_remove_first(so->listQueue))->startPage;
/* Query-aware dynamic pruning */
if (scanlist->distance > 2 * so->minDistance)
continue;
searchPage = scanlist->startPage;
/* Search all entry pages for list */ /* Search all entry pages for list */
while (BlockNumberIsValid(searchPage)) while (BlockNumberIsValid(searchPage))
@@ -261,7 +252,6 @@ ivfflatrescan(IndexScanDesc scan, ScanKey keys, int nkeys, ScanKey orderbys, int
so->first = true; so->first = true;
pairingheap_reset(so->listQueue); pairingheap_reset(so->listQueue);
so->minDistance = DBL_MAX;
if (keys && scan->numberOfKeys > 0) if (keys && scan->numberOfKeys > 0)
memmove(scan->keyData, keys, scan->numberOfKeys * sizeof(ScanKeyData)); memmove(scan->keyData, keys, scan->numberOfKeys * sizeof(ScanKeyData));

View File

@@ -100,7 +100,7 @@ CheckStateArray(ArrayType *statearray, const char *caller)
return (float8 *) ARR_DATA_PTR(statearray); return (float8 *) ARR_DATA_PTR(statearray);
} }
#if PG_VERSION_NUM < 120003 #if PG_VERSION_NUM < 120000
static pg_noinline void static pg_noinline void
float_overflow_error(void) float_overflow_error(void)
{ {