GITBOOK-119: No subject

Daniel Illenberger · gitbook-bot · commit 3b1bcd767b1f · 2024-01-09T20:33:58.000Z
diff --git a/pgml-cms/docs/introduction/apis/README.md b/pgml-cms/docs/introduction/apis/README.md
@@ -2,15 +2,15 @@
 
 ## Introduction
 
-PostgresML adds extensions to the PostgreSQL database, as well as providing separate Client SDKs in JavaScript and Python that leverage the database to implement common ML & AI use cases.&#x20;
+PostgresML adds extensions to the PostgreSQL database, as well as providing separate Client SDKs in JavaScript and Python that leverage the database to implement common ML & AI use cases.
 
-The extensions provide all of the ML & AI functionality via SQL APIs, like training and inference. They are designed to be used directly for all ML practitioners who implement dozens of different use cases on their own machine learning models.&#x20;
+The extensions provide all of the ML & AI functionality via SQL APIs, like training and inference. They are designed to be used directly for all ML practitioners who implement dozens of different use cases on their own machine learning models.
 
 We also provide Client SDKs that implement the best practices on top of the SQL APIs, to ease adoption and implement common application use cases in applications, like chatbots or search engines.
 
 ## SQL Extensions
 
-Postgres is designed to be _**extensible**_. This has created a rich open-source ecosystem of additional functionality built around the core project. Some [extensions](https://www.postgresql.org/docs/current/contrib.html) are include in the base Postgres distribution, but others are also available via the [PostgreSQL Extension Network](https://pgxn.org/). \
+Postgres is designed to be _**extensible**_. This has created a rich open-source ecosystem of additional functionality built around the core project. Some [extensions](https://www.postgresql.org/docs/current/contrib.html) are include in the base Postgres distribution, but others are also available via the [PostgreSQL Extension Network](https://pgxn.org/).\
 \
 There are 2 foundational extensions included in a PostgresML deployment that provide functionality inside the database through SQL APIs.
 
@@ -27,8 +27,3 @@ These SDKs delegate all work to the extensions running in the database, which mi
 
 Learn more about developing with the [client-sdks](client-sdks/ "mention")
 
-
-
-
-
-##
diff --git a/pgml-cms/docs/introduction/getting-started/import-your-data/README.md b/pgml-cms/docs/introduction/getting-started/import-your-data/README.md
@@ -2,7 +2,7 @@
 
 Machine learning always depends on input data, whether it's generating text with pretrained LLMs, training a retention model on customer data, or predicting session abandonment in real time. Just like any PostgreSQL database, PostgresML can be configured as the authoritative application data store, a streaming replica from some other primary, or use foreign data wrappers to query another data host on demand. Depending on how frequently your data changes and where your authoritative data resides, different methodologies imply different tradeoffs.
 
-PostgresML can easily ingest data from your existing data stores.&#x20;
+PostgresML can easily ingest data from your existing data stores.
 
 ## Static data
 
@@ -20,4 +20,3 @@ Importing data from online databases can be done with foreign data wrappers. Hos
 [foreign-data-wrapper.md](foreign-data-wrapper.md)
 {% endcontent-ref %}
 
-####
diff --git a/pgml-cms/docs/introduction/getting-started/import-your-data/csv.md b/pgml-cms/docs/introduction/getting-started/import-your-data/csv.md
@@ -20,13 +20,13 @@ If you're using a Postgres database already, you can export any table as CSV wit
 psql -c "\copy your_table TO '~/Desktop/your_table.csv' CSV HEADER"
 ```
 
-If you're using another  data store, it should almost always provide a CSV export functionality, since CSV is the most commonly used data format in machine learning.
+If you're using another data store, it should almost always provide a CSV export functionality, since CSV is the most commonly used data format in machine learning.
 
 ### Create table in Postgres
 
 Creating a table in Postgres with the correct schema is as easy as:
 
-```
+```sql
 CREATE TABLE your_table (
   name TEXT,
   age INTEGER,
@@ -48,6 +48,6 @@ We took our export command and changed `TO` to `FROM`, and that's it. Make sure
 
 If your data changed, repeat this process again. To avoid duplicate entries in your table, you can truncate (or delete) all rows beforehand:
 
-```
+```sql
 TRUNCATE your_table;
 ```
diff --git a/pgml-cms/docs/introduction/getting-started/import-your-data/foreign-data-wrapper.md b/pgml-cms/docs/introduction/getting-started/import-your-data/foreign-data-wrapper.md
@@ -16,7 +16,7 @@ Once you have them, we can setup our live foreign data wrapper connection. All f
 
 To connect to your database from PostgresML, first create a corresponding `SERVER`:
 
-```
+```sql
 CREATE SERVER live_db
 FOREIGN DATA WRAPPER postgres_fdw
 OPTIONS (
@@ -30,7 +30,7 @@ Replace `Host`, `Port` and `Database name` with details you've collected in the
 
 Once you have a `SERVER`, let's authenticate to your database:
 
-```
+```sql
 CREATE USER MAPPING
 FOR CURRENT_USER
 SERVER live_db
@@ -42,7 +42,7 @@ OPTIONS (
 
 Replace `Postgres user` and `Postgres password` with details collected in the previous step. If everything went well, we'll be able to validate that everything is working with just one query:
 
-```
+```sql
 SELECT * FROM dblink(
   'live_db',
   'SELECT 1 AS one'
@@ -55,7 +55,7 @@ You can now execute any query you want on your live database from inside your Po
 
 Instead of creating temporary tables for each query, you can import your entire schema into PostgresML using foreign data wrappers:
 
-```
+```sql
 CREATE SCHEMA live_db_tables;
 
 IMPORT FOREIGN SCHEMA public
@@ -65,7 +65,7 @@ INTO live_db_tables;
 
 All your tables from your `public` schema are now available in the `live_db_tables` schema. You can read and write to those tables as if they were hosted in PostgresML. For example, if you have a table called `users`, you could access it with:
 
-```
+```sql
 SELECT * FROM live_db_tables.users LIMIT 1;
 ```
 
@@ -75,7 +75,7 @@ That's it, your PostgresML database is directly connected to your production dat
 
 To speed up access to your data, you can cache it in PostgresML by copying it from a foreign table into a regular table. Taking the example of the `users` table:
 
-```
+```sql
 CREATE TABLE public.users (LIKE live_db_tables.users);
 INSERT INTO public.users SELECT * FROM live_db_tables.users;
 ```
