docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)

This PR focuses on improving the developer experience by adding
comprehensive docstrings to the API data models across the Llama Stack.
These docstrings provide detailed explanations for each model and its
fields, making the API easier to understand and use.

**Key changes:**
- **Added Docstrings:** Added reST formatted docstrings to Pydantic
models in the `llama_stack/apis/` directory. This includes models for:
  - Agents (`agents.py`)
  - Benchmarks (`benchmarks.py`)
  - Datasets (`datasets.py`)
  - Inference (`inference.py`)
  - And many other API modules.
- **OpenAPI Spec Update:** Regenerated the OpenAPI specification
(`docs/_static/llama-stack-spec.yaml` and
`docs/_static/llama-stack-spec.html`) to include the new docstrings.
This will be reflected in the API documentation, providing richer
information to users.

**Impact:**
- Developers using the Llama Stack API will have a better understanding
of the data structures.
- The auto-generated API documentation is now more informative.

---------

Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -15,6 +15,13 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 @json_schema_type
 class VectorDB(Resource):
+    """Vector database resource for storing and querying vector embeddings.
+
+    :param type: Type of resource, always 'vector_db' for vector databases
+    :param embedding_model: Name of the embedding model to use for vector generation
+    :param embedding_dimension: Dimension of the embedding vectors
+    """
+
     type: Literal[ResourceType.vector_db] = ResourceType.vector_db
 
     embedding_model: str
@@ -31,6 +38,14 @@ class VectorDB(Resource):
 
 
 class VectorDBInput(BaseModel):
+    """Input parameters for creating or configuring a vector database.
+
+    :param vector_db_id: Unique identifier for the vector database
+    :param embedding_model: Name of the embedding model to use for vector generation
+    :param embedding_dimension: Dimension of the embedding vectors
+    :param provider_vector_db_id: (Optional) Provider-specific identifier for the vector database
+    """
+
     vector_db_id: str
     embedding_model: str
     embedding_dimension: int
@@ -39,6 +54,11 @@ class VectorDBInput(BaseModel):
 
 
 class ListVectorDBsResponse(BaseModel):
+    """Response from listing vector databases.
+
+    :param data: List of vector databases
+    """
+
     data: list[VectorDB]