diff --git a/pgml-cms/docs/SUMMARY.md b/pgml-cms/docs/SUMMARY.md
index 84e656fcb..bfc9ef6a1 100644
--- a/pgml-cms/docs/SUMMARY.md
+++ b/pgml-cms/docs/SUMMARY.md
@@ -36,7 +36,7 @@
     * [pgml.tune()](introduction/apis/sql-extensions/pgml.tune.md)
   * [Client SDKs](introduction/apis/client-sdks/README.md)
     * [Overview](introduction/apis/client-sdks/getting-started.md)
-    * [Collections](../../pgml-docs/docs/guides/sdks/collections.md)
+    * [Collections](introduction/apis/client-sdks/collections.md)
     * [Pipelines](introduction/apis/client-sdks/pipelines.md)
     * [Search](introduction/apis/client-sdks/search.md)
     * [Tutorials](introduction/apis/client-sdks/tutorials/README.md)
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
index e24dabf05..e5c52f793 100644
--- a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
+++ b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md
@@ -26,11 +26,11 @@ pgml.deploy(
 
 There are 3 different deployment strategies available:
 
-| Strategy      | Description                                                                                                           |
-| ------------- | --------------------------------------------------------------------------------------------------------------------- |
-| `most_recent` | The most recently trained model for this project is immediately deployed, regardless of metrics.                      |
-| `best_score`  | The model that achieved the best key metric score is immediately deployed.                                            |
-| `rollback`    | The model that was last deployed for this project is immediately redeployed, overriding the currently deployed model. |
+| Strategy      | Description                                                                                      |
+| ------------- |--------------------------------------------------------------------------------------------------|
+| `most_recent` | The most recently trained model for this project is immediately deployed, regardless of metrics. |
+| `best_score`  | The model that achieved the best key metric score is immediately deployed.                       |
+| `rollback`    | The model that was deployed before to the current one is deployed.                               |
 
 The default deployment behavior allows any algorithm to qualify. It's automatically used during training, but can be manually executed as well:
 
@@ -40,11 +40,12 @@ The default deployment behavior allows any algorithm to qualify. It's automatica
 
 #### SQL
 
-<pre class="language-sql"><code class="lang-sql"><strong>SELECT * FROM pgml.deploy(
-</strong>    'Handwritten Digit Image Classifier',
+```sql
+SELECT * FROM pgml.deploy(
+   'Handwritten Digit Image Classifier',
     strategy => 'best_score'
 );
-</code></pre>
+```
 
 #### Output
 
@@ -121,3 +122,22 @@ SELECT * FROM pgml.deploy(
  Handwritten Digit Image Classifier | rollback | xgboost
 (1 row)
 ```
+
+### Specific Model IDs
+
+In the case you need to deploy an exact model that is not the `most_recent` or `best_score`, you may deploy a model by id. Model id's can be found in the `pgml.models` table. 
+
+#### SQL
+
+```sql
+SELECT * FROM pgml.deploy(12);
+```
+
+#### Output
+
+```sql
+              project               | strategy | algorithm
+------------------------------------+----------+-----------
+ Handwritten Digit Image Classifier | specific | xgboost
+(1 row)
+```
diff --git a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md
index 8d4aeb222..3362c99bd 100644
--- a/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md
+++ b/pgml-cms/docs/introduction/apis/sql-extensions/pgml.train/data-pre-processing.md
@@ -25,11 +25,11 @@ In this example:
 
 There are 3 steps to preprocessing data:
 
-* [Encoding](data-pre-processing.md#ordinal-encoding) categorical values into quantitative values
-* [Imputing](data-pre-processing.md#imputing-missing-values) NULL values to some quantitative value
-* [Scaling](data-pre-processing.md#scaling-values) quantitative values across all variables to similar ranges
+* [Encoding](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#categorical-encodings) categorical values into quantitative values
+* [Imputing](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#imputing-missing-values) NULL values to some quantitative value
+* [Scaling](../../../../../../pgml-dashboard/content/docs/training/preprocessing.md#scaling-values) quantitative values across all variables to similar ranges
 
-These preprocessing steps may be specified on a per-column basis to the [train()](./) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.
+These preprocessing steps may be specified on a per-column basis to the [train()](../../../../../../docs/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.
 
 ```sql
 SELECT pgml.train(
diff --git a/pgml-cms/docs/resources/developer-docs/contributing.md b/pgml-cms/docs/resources/developer-docs/contributing.md
index 38688dc26..3648acbe3 100644
--- a/pgml-cms/docs/resources/developer-docs/contributing.md
+++ b/pgml-cms/docs/resources/developer-docs/contributing.md
@@ -67,7 +67,7 @@ Once there, you can initialize `pgrx` and get going:
 #### Pgrx command line and environments
 
 ```commandline
-cargo install cargo-pgrx --version "0.9.8" --locked && \
+cargo install cargo-pgrx --version "0.11.2" --locked && \
 cargo pgrx init # This will take a few minutes
 ```
 
diff --git a/pgml-cms/docs/resources/developer-docs/installation.md b/pgml-cms/docs/resources/developer-docs/installation.md
index 990cec5a8..119080bf2 100644
--- a/pgml-cms/docs/resources/developer-docs/installation.md
+++ b/pgml-cms/docs/resources/developer-docs/installation.md
@@ -36,7 +36,7 @@ brew bundle
 PostgresML is written in Rust, so you'll need to install the latest compiler from [rust-lang.org](https://rust-lang.org). Additionally, we use the Rust PostgreSQL extension framework `pgrx`, which requires some initialization steps:
 
 ```bash
-cargo install cargo-pgrx --version 0.9.8 && \
+cargo install cargo-pgrx --version 0.11.2 && \
 cargo pgrx init
 ```
 
@@ -63,8 +63,7 @@ To install the necessary Python packages into a virtual environment, use the `vi
 ```bash
 virtualenv pgml-venv && \
 source pgml-venv/bin/activate && \
-pip install -r requirements.txt && \
-pip install -r requirements-xformers.txt --no-dependencies
+pip install -r requirements.txt
 ```
 {% endtab %}
 
@@ -146,7 +145,7 @@ pgml_test=# SELECT pgml.version();
 We like and use pgvector a lot, as documented in our blog posts and examples, to store and search embeddings. You can install pgvector from source pretty easily:
 
 ```bash
-git clone --branch v0.4.4 https://github.com/pgvector/pgvector && \
+git clone --branch v0.5.0 https://github.com/pgvector/pgvector && \
 cd pgvector && \
 echo "trusted = true" >> vector.control && \
 make && \
@@ -288,7 +287,7 @@ We use the `pgrx` Postgres Rust extension framework, which comes with its own in
 
 ```bash
 cd pgml-extension && \
-cargo install cargo-pgrx --version 0.9.8 && \
+cargo install cargo-pgrx --version 0.11.2 && \
 cargo pgrx init
 ```
 
diff --git a/pgml-docs/docs/guides/sdks/collections.md b/pgml-docs/docs/guides/sdks/collections.md
deleted file mode 100644
index 2ebc415d5..000000000
--- a/pgml-docs/docs/guides/sdks/collections.md
+++ /dev/null
@@ -1,349 +0,0 @@
-# Collections
-
-Collections are the organizational building blocks of the SDK. They manage all documents and related chunks, embeddings, tsvectors, and pipelines.
-
-## Creating Collections
-
-By default, collections will read and write to the database specified by `DATABASE_URL` environment variable.
-
-### **Default `DATABASE_URL`**
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = pgml.newCollection("test_collection")
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-```
-{% endtab %}
-{% endtabs %}
-
-### **Custom DATABASE\_URL**
-
-Create a Collection that reads from a different database than that set by the environment variable `DATABASE_URL`.
-
-{% tabs %}
-{% tab title="Javascript" %}
-```javascript
-const collection = pgml.newCollection("test_collection", CUSTOM_DATABASE_URL)
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection", CUSTOM_DATABASE_URL)
-```
-{% endtab %}
-{% endtabs %}
-
-## Upserting Documents
-
-Documents are dictionaries with two required keys: `id` and `text`. All other keys/value pairs are stored as metadata for the document.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const documents = [
-  {
-    id: "Document One",
-    text: "document one contents...",
-    random_key: "this will be metadata for the document",
-  },
-  {
-    id: "Document Two",
-    text: "document two contents...",
-    random_key: "this will be metadata for the document",
-  },
-];
-await collection.upsert_documents(documents);
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-documents = [
-    {
-        "id": "Document 1",
-        "text": "Here are the contents of Document 1",
-        "random_key": "this will be metadata for the document"
-    },
-    {
-        "id": "Document 2",
-        "text": "Here are the contents of Document 2",
-        "random_key": "this will be metadata for the document"
-    }
-]
-collection = Collection("test_collection")
-await collection.upsert_documents(documents)
-```
-{% endtab %}
-{% endtabs %}
-
-Document metadata can be replaced by upserting the document without the `text` key.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const documents = [
-  {
-    id: "Document One",
-    random_key: "this will be NEW metadata for the document",
-  },
-  {
-    id: "Document Two",
-    random_key: "this will be NEW metadata for the document",
-  },
-];
-await collection.upsert_documents(documents);
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-documents = [
-    {
-        "id": "Document 1",
-        "random_key": "this will be NEW metadata for the document"
-    },
-    {
-        "id": "Document 2",
-        "random_key": "this will be NEW metadata for the document"
-    }
-]
-collection = Collection("test_collection")
-await collection.upsert_documents(documents)
-```
-{% endtab %}
-{% endtabs %}
-
-Document metadata can be merged with new metadata by upserting the document without the `text` key and specifying the merge option.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const documents = [
-  {
-    id: "Document One",
-    text: "document one contents...",
-  },
-  {
-    id: "Document Two",
-    text: "document two contents...",
-  },
-];
-await collection.upsert_documents(documents, {
-  metdata: {
-    merge: true
-  }
-});
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-documents = [
-    {
-        "id": "Document 1",
-        "random_key": "this will be NEW merged metadata for the document"
-    },
-    {
-        "id": "Document 2",
-        "random_key": "this will be NEW merged metadata for the document"
-    }
-]
-collection = Collection("test_collection")
-await collection.upsert_documents(documents, {
-    "metadata": {
-        "merge": True
-    }
-})
-```
-{% endtab %}
-{% endtabs %}
-
-## Getting Documents
-
-Documents can be retrieved using the `get_documents` method on the collection object.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = Collection("test_collection")
-const documents = await collection.get_documents({limit: 100 })
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-documents = await collection.get_documents({ "limit": 100 })
-```
-{% endtab %}
-{% endtabs %}
-
-### Paginating Documents
-
-The SDK supports limit-offset pagination and keyset pagination.
-
-#### Limit-Offset Pagination
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = pgml.newCollection("test_collection")
-const documents = await collection.get_documents({ limit: 100, offset: 10 })
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-documents = await collection.get_documents({ "limit": 100, "offset": 10 })
-```
-{% endtab %}
-{% endtabs %}
-
-#### Keyset Pagination
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = Collection("test_collection")
-const documents = await collection.get_documents({ limit: 100, last_row_id: 10 })
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-documents = await collection.get_documents({ "limit": 100, "last_row_id": 10 })
-```
-{% endtab %}
-{% endtabs %}
-
-The `last_row_id` can be taken from the `row_id` field in the returned document's dictionary.
-
-### Filtering Documents
-
-Metadata and full text filtering are supported just like they are in vector recall.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = pgml.newCollection("test_collection")
-const documents = await collection.get_documents({
-  limit: 100,
-  offset: 10,
-  filter: {
-    metadata: {
-      id: {
-        $eq: 1
-      }
-    },
-    full_text_search: {
-      configuration: "english",
-      text: "Some full text query"
-    }
-  }
-})
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-documents = await collection.get_documents({
-    "limit": 100,
-    "offset": 10,
-    "filter": {
-        "metadata": {
-            "id": {
-                "$eq": 1
-            }
-        },
-        "full_text_search": {
-            "configuration": "english",
-            "text": "Some full text query"
-        }
-    }
-})
-```
-{% endtab %}
-{% endtabs %}
-
-### Sorting Documents
-
-Documents can be sorted on any metadata key. Note that this does not currently work well with Keyset based pagination. If paginating and sorting, use Limit-Offset based pagination.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = pgml.newCollection("test_collection")
-const documents = await collection.get_documents({
-  limit: 100,
-  offset: 10,
-  order_by: {
-    id: "desc"
-  }
-})
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-collection = Collection("test_collection")
-documents = await collection.get_documents({
-    "limit": 100,
-    "offset": 10,
-    "order_by": {
-        "id": "desc"
-    }
-})
-```
-{% endtab %}
-{% endtabs %}
-
-### Deleting Documents
-
-Documents can be deleted with the `delete_documents` method on the collection object.
-
-Metadata and full text filtering are supported just like they are in vector recall.
-
-{% tabs %}
-{% tab title="JavaScript" %}
-```javascript
-const collection = pgml.newCollection("test_collection")
-const documents = await collection.delete_documents({
-  metadata: {
-    id: {
-      $eq: 1
-    }
-  },
-  full_text_search: {
-    configuration: "english",
-    text: "Some full text query"
-  }
-})
-```
-{% endtab %}
-
-{% tab title="Python" %}
-```python
-documents = await collection.delete_documents({
-    "metadata": {
-        "id": {
-            "$eq": 1
-        }
-    },
-    "full_text_search": {
-        "configuration": "english",
-        "text": "Some full text query"
-    }
-})
-```
-{% endtab %}
-{% endtabs %}

<!DOCTYPE html PUBLIC '-//W3C//DTD XHTML 1.0 Transitional//EN' 'http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd'>
<html xmlns='http://www.w3.org/1999/xhtml'>
<head>
<title>pFad - Phonifier reborn</title>
<meta http-equiv='Content-Type' content='text/html; charset=utf-8' />
</head>
<body>
<h1>Pfad - The Proxy pFad of &#169; 2024 Garber Painting. All rights reserved.</h1>


<!-- Disclaimer -->
<p>Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.</p>
<br>
<p>Alternative Proxies:</p><p><a href="http://rainy.clevelandohioweatherforecast.com/php-proxy/index.php?q=https://patch-diff.githubusercontent.com/raw/postgresml/postgresml/pull/1275.diff" target="_blank">Alternative Proxy</a></p><p><a href="http://rainy.clevelandohioweatherforecast.com/pFad/index.php?u=https://patch-diff.githubusercontent.com/raw/postgresml/postgresml/pull/1275.diff" target="_blank">pFad Proxy</a></p><p><a href="http://rainy.clevelandohioweatherforecast.com/pFad/v3index.php?u=https://patch-diff.githubusercontent.com/raw/postgresml/postgresml/pull/1275.diff" target="_blank">pFad v3 Proxy</a></p><p><a href="http://rainy.clevelandohioweatherforecast.com/pFad/v4index.php?u=https://patch-diff.githubusercontent.com/raw/postgresml/postgresml/pull/1275.diff" target="_blank">pFad v4 Proxy</a></p></body>
</html>