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montanalow merged 12 commits intomasterfrommontana/docs
May 24, 2024
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102 changes: 51 additions & 51 deletionsREADME.md
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Expand Up@@ -41,7 +41,7 @@ You can select the data type for torch tensors in PostgresML by setting the `tor

!!! code\_block time="4584.906 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"model": "tiiuae/falcon-7b-instruct",
Expand DownExpand Up@@ -102,7 +102,7 @@ PostgresML will automatically use GPTQ or GGML when a HuggingFace model has one

!!! code\_block time="281.213 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -136,7 +136,7 @@ SELECT pgml.transform(

!!! code\_block time="252.213 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -167,7 +167,7 @@ SELECT pgml.transform(

!!! code\_block time="279.888 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -204,7 +204,7 @@ We can specify the CPU by passing a `"device": "cpu"` argument to the `task`.

!!! code\_block time="266.997 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -236,7 +236,7 @@ SELECT pgml.transform(

!!! code\_block time="33224.136 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -274,7 +274,7 @@ HuggingFace and these libraries have a lot of great models. Not all of these mod

!!! code\_block time="3411.324 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -306,7 +306,7 @@ SELECT pgml.transform(

!!! code\_block time="4198.817 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -338,7 +338,7 @@ SELECT pgml.transform(

!!! code\_block time="4198.817 ms"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -372,7 +372,7 @@ Many of these models are published with multiple different quantization methods

!!! code\_block time="6498.597"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand DownExpand Up@@ -410,7 +410,7 @@ Shoutout to [Tostino](https://github.com/Tostino/) for the extended example belo

!!! code\_block time="3784.565"

```sql
```postgresql
SELECT pgml.transform(
task => '{
"task": "text-generation",
Expand Down
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Expand Up@@ -142,7 +142,7 @@ Aside from using this function with strings passed from a client, we can use it

!!! generic

!!!code\_block time="54.820 ms"
!!!code_block time="54.820 ms"

```postgresql
SELECT
Expand All@@ -156,7 +156,7 @@ LIMIT 1;

!!! results

```
```postgressql
CREATE INDEX
```

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Expand Up@@ -36,7 +36,7 @@ Our search application will start with a **documents** table. Our documents have

!!! code\_block time="10.493 ms"

```sql
```postgresql
CREATE TABLE documents (
id BIGSERIAL PRIMARY KEY,
title TEXT,
Expand All@@ -54,7 +54,7 @@ We can add new documents to our _text corpus_ with the standard SQL `INSERT` sta

!!! code\_block time="3.417 ms"

```sql
```postgresql
INSERT INTO documents (title, body) VALUES
('This is a title', 'This is the body of the first document.'),
('This is another title', 'This is the body of the second document.'),
Expand All@@ -79,7 +79,7 @@ You can configure the grammatical rules in many advanced ways, but we'll use the

!!! code\_block time="0.651 ms"

```sql
```postgresql
SELECT *
FROM documents
WHERE to_tsvector('english', body) @@ to_tsquery('english', 'second');
Expand DownExpand Up@@ -109,7 +109,7 @@ The first step is to store the `tsvector` in the table, so we don't have to gene

!!! code\_block time="17.883 ms"

```sql
```postgresql
ALTER TABLE documents
ADD COLUMN title_and_body_text tsvector
GENERATED ALWAYS AS (to_tsvector('english', title || ' ' || body )) STORED;
Expand All@@ -125,7 +125,7 @@ One nice aspect of generated columns is that they will backfill the data for exi

!!! code\_block time="5.145 ms"

```sql
```postgresql
CREATE INDEX documents_title_and_body_text_index
ON documents
USING GIN (title_and_body_text);
Expand All@@ -141,7 +141,7 @@ And now, we'll demonstrate a slightly more complex `tsquery`, that requires both

!!! code\_block time="3.673 ms"

```sql
```postgresql
SELECT *
FROM documents
WHERE title_and_body_text @@ to_tsquery('english', 'another & second');
Expand DownExpand Up@@ -171,7 +171,7 @@ With multiple query terms OR `|` together, the `ts_rank` will add the numerators

!!! code\_block time="0.561 ms"

```sql
```postgresql
SELECT ts_rank(title_and_body_text, to_tsquery('english', 'second | title')), *
FROM documents
ORDER BY ts_rank DESC;
Expand DownExpand Up@@ -201,7 +201,7 @@ A quick improvement we could make to our search query would be to differentiate

!!! code\_block time="0.561 ms"

```sql
```postgresql
SELECT
ts_rank(title, to_tsquery('english', 'second | title')) AS title_rank,
ts_rank(body, to_tsquery('english', 'second | title')) AS body_rank,
Expand DownExpand Up@@ -230,7 +230,7 @@ First things first, we need to record some user clicks on our search results. We

!!! code\_block time="0.561 ms"

```sql
```postgresql
CREATE TABLE search_result_clicks (
title_rank REAL,
body_rank REAL,
Expand All@@ -250,7 +250,7 @@ I've made up 4 example searches, across our 3 documents, and recorded the `ts_ra

!!! code\_block time="2.161 ms"

```sql
```postgresql
INSERT INTO search_result_clicks
(title_rank, body_rank, clicked)
VALUES
Expand DownExpand Up@@ -289,7 +289,7 @@ Here goes some machine learning:

!!! code\_block time="6.867 ms"

```sql
```postgresql
SELECT * FROM pgml.train(
project_name => 'Search Ranking',
task => 'regression',
Expand DownExpand Up@@ -336,7 +336,7 @@ Once a model is trained, you can use `pgml.predict` to use it on new inputs. `pg

!!! code\_block time="3.119 ms"

```sql
```postgresql
SELECT
clicked,
pgml.predict('Search Ranking', array[title_rank, body_rank])
Expand DownExpand Up@@ -389,7 +389,7 @@ It's nice to organize the query into logical steps, and we can use **Common Tabl

!!! code\_block time="2.118 ms"

```sql
```postgresql
WITH first_pass_ranked_documents AS (
SELECT
-- Compute the ts_rank for the title and body text of each document
Expand Down
8 changes: 4 additions & 4 deletionspgml-cms/blog/mindsdb-vs-postgresml.md
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Expand Up@@ -94,7 +94,7 @@ For both implementations, we can just pass in our data as part of the query for

!!! code\_block time="4769.337 ms"

```sql
```postgresql
SELECT pgml.transform(
inputs => ARRAY[
'I am so excited to benchmark deep learning models in SQL. I can not wait to see the results!'
Expand DownExpand Up@@ -124,7 +124,7 @@ The first time `transform` is run with a particular model name, it will download

!!! code\_block time="45.094 ms"

```sql
```postgresql
SELECT pgml.transform(
inputs => ARRAY[
'I don''t really know if 5 seconds is fast or slow for deep learning. How much time is spent downloading vs running the model?'
Expand DownExpand Up@@ -154,7 +154,7 @@ SELECT pgml.transform(

!!! code\_block time="165.036 ms"

```sql
```postgresql
SELECT pgml.transform(
inputs => ARRAY[
'Are GPUs really worth it? Sometimes they are more expensive than the rest of the computer combined.'
Expand DownExpand Up@@ -209,7 +209,7 @@ psql postgres://mindsdb:123@127.0.0.1:55432

And turn timing on to see how long it takes to run the same query:

```sql
```postgresql
\timing on
```

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Expand Up@@ -62,7 +62,7 @@ All system statistics are stored together in this one structure.

!!! code\_block

```sql
```postgresql
SELECT * FROM pg_stat_sysinfo
WHERE metric = 'load_average'
AND at BETWEEN '2023-04-07 19:20:09.3'
Expand DownExpand Up@@ -97,7 +97,7 @@ In the case of the load average, we could handle this situation by having a tabl

!!! code\_block

```sql
```postgresql
CREATE TABLE load_average (
at timestamptz NOT NULL DEFAULT now(),
"1m" float4 NOT NULL,
Expand All@@ -112,7 +112,7 @@ This structure is fine for `load_average` but wouldn't work for CPU, disk, RAM o

!!! code\_block

```sql
```postgresql
CREATE TABLE load_average (
at timestamptz NOT NULL DEFAULT now(),
"1m" float4 NOT NULL,
Expand All@@ -132,7 +132,7 @@ This has the disadvantage of baking in a lot of keys and the overall structure o

!!! code\_block

```sql
```postgresql
CREATE TABLE load_average (
at timestamptz NOT NULL DEFAULT now(),
"1m" float4 NOT NULL,
Expand Down
2 changes: 1 addition & 1 deletionpgml-cms/blog/postgres-full-text-search-is-awesome.md
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Expand Up@@ -54,7 +54,7 @@ These queries can execute in milliseconds on large production-sized corpora with

The following full blown example is for demonstration purposes only of a 3rd generation search engine. You can test it for real in the PostgresML Gym to build up a complete understanding.

```sql
```postgresql
WITH query AS (
-- construct a query context with arguments that would typically be
-- passed in from the application layer
Expand Down
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