[client sdk test] add options for inference_model, safety_shield, embedding_model (#843)

# What does this PR do?
Default inference_model for testing: "meta-llama/Llama-3.1-8B-Instruct"
Default vision inference_model for testing:
"meta-llama/Llama-3.2-11B-Vision-Instruct"


## Test Plan
`/opt/miniconda3/envs/stack/bin/pytest -s -v
--inference-model=meta-llama/Llama-3.2-3B-Instruct
tests/client-sdk/agents`


`/opt/miniconda3/envs/stack/bin/pytest -s -v
--embedding-model=all-MiniLM-L6-v2 tests/client-sdk/vector_io`

`/opt/miniconda3/envs/stack/bin/pytest -s -v
--safety-shield=meta-llama/Llama-Guard-3-1B tests/client-sdk/safety`

## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

tests/client-sdk/vector_io/test_vector_io.py

@@ -6,39 +6,13 @@
 
 import random
 
-import pytest
 
-
-@pytest.fixture(scope="function")
-def empty_vector_db_registry(llama_stack_client):
-    vector_dbs = [
-        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
-    ]
-    for vector_db_id in vector_dbs:
-        llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
-
-
-@pytest.fixture(scope="function")
-def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry):
-    vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
-    llama_stack_client.vector_dbs.register(
-        vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
-        embedding_dimension=384,
-        provider_id="faiss",
-    )
-    vector_dbs = [
-        vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
-    ]
-    return vector_dbs
-
-
-def test_vector_db_retrieve(llama_stack_client, empty_vector_db_registry):
+def test_vector_db_retrieve(llama_stack_client, embedding_model):
     # Register a memory bank first
     vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
     llama_stack_client.vector_dbs.register(
         vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
+        embedding_model=embedding_model,
         embedding_dimension=384,
         provider_id="faiss",
     )
@@ -47,23 +21,23 @@ def test_vector_db_retrieve(llama_stack_client, empty_vector_db_registry):
     response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
     assert response is not None
     assert response.identifier == vector_db_id
-    assert response.embedding_model == "all-MiniLM-L6-v2"
+    assert response.embedding_model == embedding_model
     assert response.provider_id == "faiss"
     assert response.provider_resource_id == vector_db_id
 
 
-def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
+def test_vector_db_list(llama_stack_client):
     vector_dbs_after_register = [
         vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]
     assert len(vector_dbs_after_register) == 0
 
 
-def test_vector_db_register(llama_stack_client, empty_vector_db_registry):
+def test_vector_db_register(llama_stack_client, embedding_model):
     vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
     llama_stack_client.vector_dbs.register(
         vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
+        embedding_model=embedding_model,
         embedding_dimension=384,
         provider_id="faiss",
     )
@@ -74,7 +48,7 @@ def test_vector_db_register(llama_stack_client, empty_vector_db_registry):
     assert vector_dbs_after_register == [vector_db_id]
 
 
-def test_vector_db_unregister(llama_stack_client, single_entry_vector_db_registry):
+def test_vector_db_unregister(llama_stack_client):
     vector_dbs = [
         vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
     ]