docs: Update vecs references from create_collection to get_or_create_collection (#17707)

.gitignore

@@ -4,7 +4,7 @@ node_modules
 out
 .docz
 tmp
-.swp
+*.swp
 
 coverage
 allure-results

apps/docs/pages/guides/ai/examples/image-search-openai-clip.mdx

@@ -83,8 +83,8 @@ def seed():
     # create vector store client
     vx = vecs.create_client(DB_CONNECTION)
 
-    # create a collection of vectors with 512 dimensions
-    images = vx.create_collection(name="image_vectors", dimension=512)
+    # create a collection of vectors with 3 dimensions
+    images = vx.get_or_create_collection(name="image_vectors", dimension=512)
 
     # Load CLIP model
     model = SentenceTransformer('clip-ViT-B-32')

apps/docs/pages/guides/ai/google-colab.mdx

@@ -61,7 +61,7 @@ Now we're going to create a new collection and insert some documents.
 Create a new code block below the install block (`ctrl+m b`). Add the following code to the code block and execute it (`ctrl+enter`):
 
 ```py
-collection = vx.create_collection(name="colab_collection", dimension=3)
+collection = vx.get_or_create_collection(name="colab_collection", dimension=3)
 
 collection.upsert(
     vectors=[

apps/docs/pages/guides/ai/structured-unstructured.mdx

@@ -63,7 +63,7 @@ Client libraries like python's [vecs](https://github.com/supabase/vecs) use this
 #!/usr/bin/env python3
 import vecs
 
-docs = vx.create_collection(name="docs", dimension=1536)
+docs = vx.get_or_create_collection(name="docs", dimension=1536)
 
 docs.upsert(vectors=[
   ('79409372-7556-4ccc-ab8f-5786a6cfa4f7', [100, 200, 300], { url: '/hello-world' })
@@ -71,7 +71,7 @@ docs.upsert(vectors=[
 
 ```
 
-automatically creates the unstructured SQL table during the call to `create_collection`.
+automatically creates the unstructured SQL table during the call to `get_or_create_collection`.
 
 Note that when working with client libraries that emit SQL DDL, like `create table ...`, you should add that SQL to your migrations when moving to production to maintain a single source of truth for your database's schema.
 

apps/docs/pages/guides/ai/vecs-python-client.mdx

@@ -37,7 +37,7 @@ import vecs
 vx = vecs.create_client("postgresql://postgres:postgres@localhost:54322/postgres")
 
 # create a collection of vectors with 3 dimensions
-docs = vx.create_collection(name="docs", dimension=3)
+docs = vx.get_or_create_collection(name="docs", dimension=3)
 ```
 
 ### Add embeddings
@@ -48,7 +48,7 @@ Now we can insert some embeddings into our "docs" collection using the `upsert()
 import vecs
 
 # create vector store client
-docs = vecs.get_collection(name="docs")
+docs = vecs.get_or_create_collection(name="docs", dimension=3)
 
 # a collection of vectors with 3 dimensions
 vectors=[
@@ -67,7 +67,7 @@ You can now query the collection to retrieve a relevant match:
 ```py
 import vecs
 
-docs = vecs.get_collection(name="docs")
+docs = vecs.get_or_create_collection(name="docs", dimension=3)
 
 # query the collection filtering metadata for "year" = 2012
 docs.query(

apps/www/_blog/2023-05-31-vecs.mdx

@@ -45,10 +45,10 @@ DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
 vx = vecs.create_client(DB_CONNECTION)
 
 # create a collection of vectors with 3 dimensions
-docs = vx.create_collection(name="docs", dimension=3)
+docs = vx.get_or_create_collection(name="docs", dimension=3)
 ```
 
-The `create_collection` call sets up a table in the Postgres database specified by `DB_CONNECTION` in a schema named `vecs` with the user defined name `docs`.
+The `get_or_create_collection` call sets up a table in the Postgres database specified by `DB_CONNECTION` in a schema named `vecs` with the user defined name `docs`.
 
 Or, more specifically:
 
@@ -166,7 +166,7 @@ One option we're exploring is to optionally assign transformation pipelines to c
 ```python
 # This is mock code only, not currently functional
 
-docs: Collection =vx.create_collection(
+docs: Collection =vx.get_or_create_collection(
     docs='docs',
     dimension=512,
     transform = TextPreprocessor(  # this is new

examples/ai/face_similarity.ipynb

@@ -206,7 +206,7 @@
     "vx = vecs.create_client(DB_CONNECTION)\n",
     "\n",
     "# create a PostgreSQL/pgvector table named \"faces\" to contain the face embeddings\n",
-    "faces = vx.create_collection(name=\"faces\", dimension=128)"
+    "faces = vx.get_or_create_collection(name=\"faces\", dimension=128)"
    ]
   },
   {

examples/ai/semantic_text_deduplication.ipynb

@@ -157,7 +157,7 @@
     "vx = vecs.create_client(DB_CONNECTION)\n",
     "\n",
     "# create a PostgreSQL/pgvector table named \"reviews\" to contain the review embeddings\n",
-    "reviews = vx.create_collection(name=\"reviews\", dimension=384)"
+    "reviews = vx.get_or_create_collection(name=\"reviews\", dimension=384)"
    ]
   },
   {

examples/ai/vector_hello_world.ipynb

@@ -82,9 +82,9 @@
         "id": "zbwuFOgQgog6"
       },
       "source": [
-        "###Create collection\n",
+        "###Get or Create Collection\n",
         "\n",
-        "You can create a collection to store vectors specifying the collections name and the number of dimensions in the vectors you intend to store."
+        "You can get or create a collection to store vectors specifying the collections name and the number of dimensions in the vectors you intend to store."
       ]
     },
     {
@@ -95,29 +95,7 @@
       },
       "outputs": [],
       "source": [
-        "docs = vx.create_collection(name=\"docs\", dimension=3)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "djKCRhbbhAQ8"
-      },
-      "source": [
-        "###Get an existing collection\n",
-        "\n",
-        "To access a previously created collection, use `get_collection` to retrieve it by name"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "71IyRUbDhEGz"
-      },
-      "outputs": [],
-      "source": [
-        "docs = vx.get_collection(name=\"docs\")"
+        "docs = vx.get_or_create_collection(name=\"docs\", dimension=3)"
       ]
     },
     {