Merge branch 'main' into use-openai-for-cerebras

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -11,6 +11,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 | agents | `inline::meta-reference` |
 | datasetio | `inline::localfs`, `remote::nvidia` |
 | eval | `remote::nvidia` |
+| files | `inline::localfs` |
 | inference | `remote::nvidia` |
 | post_training | `remote::nvidia` |
 | safety | `remote::nvidia` |

docs/source/getting_started/detailed_tutorial.md

@@ -460,10 +460,12 @@ client = LlamaStackClient(base_url="http://localhost:8321")
 embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
 embedding_model = embed_lm.identifier
 vector_db_id = f"v{uuid.uuid4().hex}"
-client.vector_dbs.register(
+# The VectorDB API is deprecated; the server now returns its own authoritative ID.
+# We capture the correct ID from the response's .identifier attribute.
+vector_db_id = client.vector_dbs.register(
     vector_db_id=vector_db_id,
     embedding_model=embedding_model,
-)
+).identifier
 
 # Create Documents
 urls = [

docs/source/providers/vector_io/remote_milvus.md

@@ -23,7 +23,13 @@ To use Milvus in your Llama Stack project, follow these steps:
 
 ## Installation
 
-You can install Milvus using pymilvus:
+If you want to use inline Milvus, you can install:
+
+```bash
+pip install pymilvus[milvus-lite]
+```
+
+If you want to use remote Milvus, you can install:
 
 ```bash
 pip install pymilvus