chore: remove dependency on llama_models completely (#1344)

docs/source/providers/index.md

@@ -36,7 +36,7 @@ Evaluates the outputs of the system.
 Collects telemetry data from the system.
 
 ## Tool Runtime
-Is associated with the ToolGroup resouces. 
+Is associated with the ToolGroup resouces.
 
 ## Vector IO
 

docs/source/providers/vector_io/chromadb.md

@@ -1,10 +1,10 @@
 ---
 orphan: true
 ---
-# Chroma 
+# Chroma
 
-[Chroma](https://www.trychroma.com/) is an inline and remote vector 
-database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. 
+[Chroma](https://www.trychroma.com/) is an inline and remote vector
+database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
 That means you're not limited to storing vectors in memory or in a separate service.
 
 ## Features

docs/source/providers/vector_io/faiss.md

@@ -3,7 +3,7 @@ orphan: true
 ---
 # Faiss
 
-[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It 
+[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 
@@ -29,5 +29,5 @@ You can install Faiss using pip:
 pip install faiss-cpu
 ```
 ## Documentation
-See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for 
+See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for
 more details about Faiss in general.

docs/source/providers/vector_io/pgvector.md

@@ -3,7 +3,7 @@ orphan: true
 ---
 # Postgres PGVector
 
-[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It 
+[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 

docs/source/providers/vector_io/qdrant.md

@@ -3,7 +3,7 @@ orphan: true
 ---
 # Qdrant
 
-[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It 
+[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 

docs/source/providers/vector_io/sqlite-vec.md

@@ -3,8 +3,8 @@ orphan: true
 ---
 # SQLite-Vec
 
-[SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It 
-allows you to store and query vectors directly within an SQLite database. 
+[SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It
+allows you to store and query vectors directly within an SQLite database.
 That means you're not limited to storing vectors in memory or in a separate service.
 
 ## Features

docs/source/providers/vector_io/weaviate.md

@@ -1,10 +1,10 @@
 ---
 orphan: true
 ---
-# Weaviate 
+# Weaviate
 
-[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack. 
-It allows you to store and query vectors directly within a Weaviate database. 
+[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
+It allows you to store and query vectors directly within a Weaviate database.
 That means you're not limited to storing vectors in memory or in a separate service.
 
 ## Features
@@ -27,7 +27,7 @@ To use Weaviate in your Llama Stack project, follow these steps:
 
 ## Installation
 
-To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart). 
+To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart).
 
 ## Documentation
 See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.