From de699f6300941784c7a362963df6f65cd702ff3e Mon Sep 17 00:00:00 2001 From: SilasMarvin <19626586+SilasMarvin@users.noreply.github.com> Date: Thu, 7 Sep 2023 10:45:39 -0700 Subject: [PATCH 1/5] Added new blog post --- .../announcing-hnsw-support-in-our-sdk.md | 100 ++++++++++++++++++ ...rating-query-embeddings-in-the-database.md | 2 +- pgml-dashboard/src/api/docs.rs | 2 + pgml-dashboard/templates/layout/nav/top.html | 2 +- 4 files changed, 104 insertions(+), 2 deletions(-) create mode 100644 pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md new file mode 100644 index 000000000..f74e2d99b --- /dev/null +++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md @@ -0,0 +1,100 @@ +--- +author: Silas Marvin +description: HNSW indexing is the latest upgrade in vector recall performance. In this post we announce our updated SDK that utilizes HNSW indexing to give world class performance in vector search. +image: https://postgresml.org/dashboard/static/images/blog/elephant_sky.jpg +image_alt: PostgresML is a composition engine that provides advanced AI capabilities. +--- + +# Announcing HNSW Support in Our SDK + +
+ Author +
+

Silas Marvin

+

September 6, 2023

+
+
+ +PostgresML makes it easy to use machine learning with your database and to scale workloads horizontally in our cloud. Our SDK makes it even easier. + +## Introducing HNSW + +Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDk used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches. + +While this is fast, it is not as fast as HNSW. Thanks to the latest update of [pgvector](https://github.com/pgvector/pgvector) our SDK now utilizes HNSW indexing, creating multi-layer graphs instead of lists and removing the required training step IVFFlat imposed. + +The results are not disappointing. + +## Comparing HNSW and IVFFlat + +In one of our previous posts: [Tuning vector recall while generating query embeddings in the database](/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database) we were working on a datasets with over 5 million Amazon Movie Reviews, and after embedding the reviews, performed semantic similarity search to get the closest 5 reviews. + +Here is the sql query we ran: + +!!! generic + +!!! code_block time="152.037 ms" + +```postgresql +WITH request AS ( + SELECT pgml.embed( + 'intfloat/e5-large', + 'query: Best 1980''s scifi movie' + )::vector(1024) AS embedding +) + +SELECT + review_body, + product_title, + star_rating, + total_votes, + 1 - ( + review_embedding_e5_large <=> ( + SELECT embedding FROM request + ) + ) AS cosine_similarity +FROM pgml.amazon_us_reviews +ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request) +LIMIT 5; +``` + +!!! + +!!! results + +| review_body | product_title | star_rating | total_votes | cosine_similarity | +| --------------------------------------------------- | ------------------------------------------------------------- | ----------- | ----------- | ------------------ | +| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.956207707312679 | +| One of the best 80's sci-fi movies, beyond a doubt! | Close Encounters of the Third Kind [Blu-ray] | 5 | 1 | 0.9298004258989776 | +| One of the Better 80's Sci-Fi, | Krull (Special Edition) | 3 | 5 | 0.9126601222760491 | +| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9095577631102708 | +| Three of the best sci-fi movies of the seventies | Sci-Fi: Triple Feature (BD) [Blu-ray] | 5 | 0 | 0.9024044582495285 | + +!!! + +!!! + +This query utilized IVFFlat indexing and queried through over 5 million rows in 152 milliseconds. Pretty fast! + +Let drop our IVFFlat index and create an HNSW index. + +!!! generic + +!!! code_block time="53236.884 ms (00:53.237)" + +```postgresql +DROP INDEX index_amazon_us_reviews_on_review_embedding_e5_large; +CREATE INDEX CONCURRENTLY ON pgml.amazon_us_reviews USING hnsw (review_embedding_e5_large vector_cosine_ops); +``` + +!!! + +!!! results + +|CREATE INDEX| +|------------| + +!!! + +!!! + diff --git a/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md b/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md index f70054f8f..be46ec4bd 100644 --- a/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md +++ b/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md @@ -144,7 +144,7 @@ SELECT ) ) AS cosine_similarity FROM pgml.amazon_us_reviews -ORDER BY cosine_similarity +ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request) LIMIT 5; ``` diff --git a/pgml-dashboard/src/api/docs.rs b/pgml-dashboard/src/api/docs.rs index 6245a0d2f..e256adce4 100644 --- a/pgml-dashboard/src/api/docs.rs +++ b/pgml-dashboard/src/api/docs.rs @@ -80,6 +80,8 @@ async fn blog_handler<'a>(path: PathBuf, cluster: &Cluster) -> Result --> <% } %>
  • - Blog + Blog
    • From 3a303361649acc3f5d8db8faf6c4be84d25e8e48 Mon Sep 17 00:00:00 2001 From: SilasMarvin <19626586+SilasMarvin@users.noreply.github.com> Date: Thu, 21 Sep 2023 11:31:23 -0700 Subject: [PATCH 2/5] Cleaned up post --- .../announcing-hnsw-support-in-our-sdk.md | 84 ++++++++++++++----- 1 file changed, 63 insertions(+), 21 deletions(-) diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md index f74e2d99b..f15c078ba 100644 --- a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md +++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md @@ -1,8 +1,6 @@ --- author: Silas Marvin description: HNSW indexing is the latest upgrade in vector recall performance. In this post we announce our updated SDK that utilizes HNSW indexing to give world class performance in vector search. -image: https://postgresml.org/dashboard/static/images/blog/elephant_sky.jpg -image_alt: PostgresML is a composition engine that provides advanced AI capabilities. --- # Announcing HNSW Support in Our SDK @@ -11,7 +9,7 @@ image_alt: PostgresML is a composition engine that provides advanced AI capabili Author

    Silas Marvin

    -

    September 6, 2023

    +

    September 21, 2023

    @@ -21,19 +19,19 @@ PostgresML makes it easy to use machine learning with your database and to scale Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDk used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches. -While this is fast, it is not as fast as HNSW. Thanks to the latest update of [pgvector](https://github.com/pgvector/pgvector) our SDK now utilizes HNSW indexing, creating multi-layer graphs instead of lists and removing the required training step IVFFlat imposed. +While the IVFFlat indexing method is fast, it is not as fast as HNSW. Thanks to the latest update of [pgvector](https://github.com/pgvector/pgvector) our SDK now utilizes HNSW indexing, creating multi-layer graphs instead of lists and removing the required training step IVFFlat imposed. The results are not disappointing. ## Comparing HNSW and IVFFlat -In one of our previous posts: [Tuning vector recall while generating query embeddings in the database](/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database) we were working on a datasets with over 5 million Amazon Movie Reviews, and after embedding the reviews, performed semantic similarity search to get the closest 5 reviews. +In one of our previous posts: [Tuning vector recall while generating query embeddings in the database](/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database) we were working on a dataset with over 5 million Amazon Movie Reviews, and after embedding the reviews, performed semantic similarity search to get the closest 5 reviews. -Here is the sql query we ran: +Let's run that query again: !!! generic -!!! code_block time="152.037 ms" +!!! code_block time="89.118 ms" ```postgresql WITH request AS ( @@ -44,10 +42,7 @@ WITH request AS ( ) SELECT - review_body, - product_title, - star_rating, - total_votes, + id, 1 - ( review_embedding_e5_large <=> ( SELECT embedding FROM request @@ -62,25 +57,25 @@ LIMIT 5; !!! results -| review_body | product_title | star_rating | total_votes | cosine_similarity | -| --------------------------------------------------- | ------------------------------------------------------------- | ----------- | ----------- | ------------------ | -| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.956207707312679 | -| One of the best 80's sci-fi movies, beyond a doubt! | Close Encounters of the Third Kind [Blu-ray] | 5 | 1 | 0.9298004258989776 | -| One of the Better 80's Sci-Fi, | Krull (Special Edition) | 3 | 5 | 0.9126601222760491 | -| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9095577631102708 | -| Three of the best sci-fi movies of the seventies | Sci-Fi: Triple Feature (BD) [Blu-ray] | 5 | 0 | 0.9024044582495285 | +| review_body | product_title | star_rating | total_votes | cosine_similarity +| ------------------------------------------------- | ------------------------------------------------------------- | ------------- | ----------- | ------------------ | +| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.9495371273162286 | +| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9097434758143605 | +| Three of the best sci-fi movies of the seventies | Sci-Fi: Triple Feature (BD) [Blu-ray] | 5 | 0 | 0.9008723412875651 | +| best sci fi movie ever | The Day the Earth Stood Still (Special Edition) [Blu-ray] | 5 | 2 | 0.8943620968858654 | +| Great Science Fiction movie | Bloodsport / Timecop (Action Double Feature) [Blu-ray] | 5 | 0 | 0.894282454374093 | !!! !!! -This query utilized IVFFlat indexing and queried through over 5 million rows in 152 milliseconds. Pretty fast! +This query utilized IVFFlat indexing and queried through over 5 million rows in 89.118ms. Pretty fast! -Let drop our IVFFlat index and create an HNSW index. +Let's drop our IVFFlat index and create an HNSW index. !!! generic -!!! code_block time="53236.884 ms (00:53.237)" +!!! code_block time="10255099.233 ms (02:50:55.099)" ```postgresql DROP INDEX index_amazon_us_reviews_on_review_embedding_e5_large; @@ -98,3 +93,50 @@ CREATE INDEX CONCURRENTLY ON pgml.amazon_us_reviews USING hnsw (review_embedding !!! +Now let's try the query again utilizing the new HNSW index we created. + +!!! generic + +!!! code_block time="17.465 ms" + +```postgresql +WITH request AS ( + SELECT pgml.embed( + 'intfloat/e5-large', + 'query: Best 1980''s scifi movie' + )::vector(1024) AS embedding +) + +SELECT + id, + 1 - ( + review_embedding_e5_large <=> ( + SELECT embedding FROM request + ) + ) AS cosine_similarity +FROM pgml.amazon_us_reviews +ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request) +LIMIT 5; +``` + +!!! + +!!! results + +| review_body | product_title | star_rating | total_votes | cosine_similarity +| --------------------------------- | ------------------------------------------------------------- | ------------- | ----------- | ------------------ | +| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.9495371273162286 | +| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9097434758143605 | +| One of the Better 80's Sci-Fi | Krull (Special Edition) | 3 | 5 | 0.9093884940741694 | +| Good 1980s movie | Can't Buy Me Love | 4 | 0 | 0.9090294438721961 | +| great 80's movie | How I Got Into College | 5 | 0 | 0.9016508795301296 | + +!!! + +!!! + +Not only are the results better (the `cosine_similarity` is higher overall), but HNSW is over 3x faster, reducing our search and embedding time to 17.465ms. + +This is a massive upgrade to the recall speed utilized by our SDK and greatly improves overall performance. + +For a deeper dive into HNSW checkout [Jonathan Katz's excellent article on HNSW in pgvector](https://jkatz05.com/post/postgres/pgvector-hnsw-performance/). From d76b96f6b8ef995767a816282706fda7113ea63f Mon Sep 17 00:00:00 2001 From: Silas Marvin <19626586+SilasMarvin@users.noreply.github.com> Date: Thu, 21 Sep 2023 11:39:26 -0700 Subject: [PATCH 3/5] Update pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md Co-authored-by: Lev Kokotov --- .../content/blog/announcing-hnsw-support-in-our-sdk.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md index f15c078ba..963ff16cb 100644 --- a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md +++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md @@ -17,7 +17,7 @@ PostgresML makes it easy to use machine learning with your database and to scale ## Introducing HNSW -Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDk used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches. +Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDK used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches. While the IVFFlat indexing method is fast, it is not as fast as HNSW. Thanks to the latest update of [pgvector](https://github.com/pgvector/pgvector) our SDK now utilizes HNSW indexing, creating multi-layer graphs instead of lists and removing the required training step IVFFlat imposed. From 44c26c362fe132913274dbdab0299701ea7115e3 Mon Sep 17 00:00:00 2001 From: SilasMarvin <19626586+SilasMarvin@users.noreply.github.com> Date: Fri, 22 Sep 2023 11:50:45 -0700 Subject: [PATCH 4/5] Updated speed up text --- .../content/blog/announcing-hnsw-support-in-our-sdk.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md index 963ff16cb..417889d7c 100644 --- a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md +++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md @@ -135,7 +135,7 @@ LIMIT 5; !!! -Not only are the results better (the `cosine_similarity` is higher overall), but HNSW is over 3x faster, reducing our search and embedding time to 17.465ms. +Not only are the results better (the `cosine_similarity` is higher overall), but HNSW is over 5x faster, reducing our search and embedding time to 17.465ms. This is a massive upgrade to the recall speed utilized by our SDK and greatly improves overall performance. From 9aa8577fc93687fa734acc41e741882270955aa8 Mon Sep 17 00:00:00 2001 From: SilasMarvin <19626586+SilasMarvin@users.noreply.github.com> Date: Tue, 26 Sep 2023 10:04:44 -0700 Subject: [PATCH 5/5] Added new image and updated links --- .../blog/announcing-hnsw-support-in-our-sdk.md | 5 +++++ .../src/components/navbar/template.html | 2 +- .../src/components/navbar_web_app/template.html | 4 ++-- .../images/blog/announcing_hnsw_support.webp | Bin 0 -> 30682 bytes .../templates/layout/nav/top_web_app.html | 4 ++-- 5 files changed, 10 insertions(+), 5 deletions(-) create mode 100644 pgml-dashboard/static/images/blog/announcing_hnsw_support.webp diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md index 417889d7c..fb64e1fb4 100644 --- a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md +++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md @@ -1,6 +1,8 @@ --- author: Silas Marvin description: HNSW indexing is the latest upgrade in vector recall performance. In this post we announce our updated SDK that utilizes HNSW indexing to give world class performance in vector search. +image: https://postgresml.org/dashboard/static/images/blog/announcing_hnsw_support.webp +image_alt: HNSW provides a significant improvement in recall speed compared to IVFFlat --- # Announcing HNSW Support in Our SDK @@ -15,6 +17,9 @@ description: HNSW indexing is the latest upgrade in vector recall performance. I PostgresML makes it easy to use machine learning with your database and to scale workloads horizontally in our cloud. Our SDK makes it even easier. +data is always the best medicine +

    HNSW (hierarchical navigable small worlds) is an indexing method that greatly improves vector recall

    + ## Introducing HNSW Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDK used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches. diff --git a/pgml-dashboard/src/components/navbar/template.html b/pgml-dashboard/src/components/navbar/template.html index e4d1362d7..876ccc42c 100644 --- a/pgml-dashboard/src/components/navbar/template.html +++ b/pgml-dashboard/src/components/navbar/template.html @@ -23,7 +23,7 @@ --> <% } %>
  • - Blog + Blog
    • diff --git a/pgml-dashboard/src/components/navbar_web_app/template.html b/pgml-dashboard/src/components/navbar_web_app/template.html index c716e6d9e..72f691e58 100644 --- a/pgml-dashboard/src/components/navbar_web_app/template.html +++ b/pgml-dashboard/src/components/navbar_web_app/template.html @@ -54,7 +54,7 @@
  • - Blog + Blog
    • <% if !account_management_nav.links.is_empty() { %> @@ -82,7 +82,7 @@
  • - Blog + Blog
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    <% if !account_management_nav.links.is_empty() { %> @@ -91,7 +91,7 @@
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