docs fixes

docs/api.md

@@ -184,14 +184,14 @@ Adapters are an optional feature to transform data before adding to or querying
 
 For a complete list of available adapters, see [built-in adapters](concepts_adapters.md#built-in-adapters).
 
-As an example, we'll create a collection with an adapter that chunks text into paragraphs and converts each chunk into an embedding vector using the `all-Mini-LM6-v2` model.
+As an example, we'll create a collection with an adapter that chunks text into paragraphs and converts each chunk into an embedding vector using the `all-MiniLM-L6-v2` model.
 
 First, install `vecs` with optional dependencies for text embeddings:
 ```sh
 pip install "vecs[text_embedding]"
 ```
 
-Then create a collection with an adapter to chunk text into paragraphs and embed each paragraph using the `all-Mini-LM6-v2` 384 dimensional text embedding model.
+Then create a collection with an adapter to chunk text into paragraphs and embed each paragraph using the `all-MiniLM-L6-v2` 384 dimensional text embedding model.
 
 ```python
 import vecs
@@ -206,7 +206,7 @@ docs = vx.get_or_create_collection(
     adapter=Adapter(
         [
             ParagraphChunker(skip_during_query=True),
-            TextEmbedding(model='all-Mini-LM6-v2'),
+            TextEmbedding(model='all-MiniLM-L6-v2'),
         ]
     )
 )

docs/concepts_adapters.md

@@ -5,14 +5,14 @@ Adapters are an optional feature to transform data before adding to or querying
 Additionally, adapter transformations are applied lazily and can internally batch operations which can make them more memory and CPU efficient compared to manually executing transforms.
 
 ## Example:
-As an example, we'll create a collection with an adapter that chunks text into paragraphs and converts each chunk into an embedding vector using the `all-Mini-LM6-v2` model.
+As an example, we'll create a collection with an adapter that chunks text into paragraphs and converts each chunk into an embedding vector using the `all-MiniLM-L6-v2` model.
 
 First, install `vecs` with optional dependencies for text embeddings:
 ```sh
 pip install "vecs[text_embedding]"
 ```
 
-Then create a collection with an adapter to chunk text into paragraphs and embed each paragraph using the `all-Mini-LM6-v2` 384 dimensional text embedding model.
+Then create a collection with an adapter to chunk text into paragraphs and embed each paragraph using the `all-MiniLM-L6-v2` 384 dimensional text embedding model.
 
 ```python
 import vecs
@@ -27,7 +27,7 @@ docs = vx.get_or_create_collection(
     adapter=Adapter(
         [
             ParagraphChunker(skip_during_query=True),
-            TextEmbedding(model='all-Mini-LM6-v2'),
+            TextEmbedding(model='all-MiniLM-L6-v2'),
         ]
     )
 )
@@ -111,7 +111,7 @@ vx.get_or_create_collection(
     name="docs",
     adapter=Adapter(
         [
-            TextEmbedding(model='all-Mini-LM6-v2')
+            TextEmbedding(model='all-MiniLM-L6-v2')
         ]
     )
 )

docs/support_changelog.md

@@ -12,9 +12,10 @@
 - Feature: Uses (indexed) containment operator `@>` for metadata equality filters where possible
 - Docs: Added docstrings to all methods, functions and modules
 
-## master
+## 0.3.0
 
 - Feature: Collections can have `adapters` allowing upserting/querying by native media t types
 - Breaking Change: Renamed argument `Collection.upsert(vectors, ...)` to `Collection.upsert(records, ...)` in support of adapters
 - Breaking Change: Renamed argument `Collection.query(query_vector, ...)` to `Collection.query(data, ...)` in support of adapters
-- Added
+
+## master