Add really basic testing for memory API

weaviate does not work; the cluster URL seems malformed

llama_stack/providers/tests/inference/test_inference.py

@@ -5,21 +5,15 @@
 # the root directory of this source tree.
 
 import itertools
-import json
-import os
-from datetime import datetime
 
 import pytest
 import pytest_asyncio
-import yaml
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
 
 from llama_stack.distribution.datatypes import *  # noqa: F403
-from llama_stack.distribution.configure import parse_and_maybe_upgrade_config
-from llama_stack.distribution.request_headers import set_request_provider_data
-from llama_stack.distribution.resolver import resolve_impls_with_routing
+from llama_stack.providers.tests.resolver import resolve_impls_for_test
 
 
 def group_chunks(response):
@@ -39,68 +33,6 @@ def get_expected_stop_reason(model: str):
     return StopReason.end_of_message if "Llama3.1" in model else StopReason.end_of_turn
 
 
-async def stack_impls(model):
-    if "PROVIDER_CONFIG" not in os.environ:
-        raise ValueError(
-            "You must set PROVIDER_CONFIG to a YAML file containing provider config"
-        )
-
-    with open(os.environ["PROVIDER_CONFIG"], "r") as f:
-        config_dict = yaml.safe_load(f)
-
-    if "providers" not in config_dict:
-        raise ValueError("Config file should contain a `providers` key")
-
-    providers_by_id = {x["provider_id"]: x for x in config_dict["providers"]}
-    if len(providers_by_id) == 0:
-        raise ValueError("No providers found in config file")
-
-    if "PROVIDER_ID" in os.environ:
-        provider_id = os.environ["PROVIDER_ID"]
-        if provider_id not in providers_by_id:
-            raise ValueError(f"Provider ID {provider_id} not found in config file")
-        provider = providers_by_id[provider_id]
-    else:
-        provider = list(providers_by_id.values())[0]
-        provider_id = provider["provider_id"]
-        print(f"No provider ID specified, picking first `{provider_id}`")
-
-    run_config = dict(
-        built_at=datetime.now(),
-        image_name="test-fixture",
-        apis=[
-            Api.inference,
-            Api.models,
-        ],
-        providers=dict(
-            inference=[
-                Provider(**provider),
-            ]
-        ),
-        models=[
-            ModelDef(
-                identifier=model,
-                llama_model=model,
-                provider_id=provider["provider_id"],
-            )
-        ],
-        shields=[],
-        memory_banks=[],
-    )
-    run_config = parse_and_maybe_upgrade_config(run_config)
-    impls = await resolve_impls_with_routing(run_config)
-
-    # may need something cleaner here
-    if "provider_data" in config_dict:
-        provider_data = config_dict["provider_data"].get(provider_id, {})
-        if provider_data:
-            set_request_provider_data(
-                {"X-LlamaStack-ProviderData": json.dumps(provider_data)}
-            )
-
-    return impls
-
-
 # This is going to create multiple Stack impls without tearing down the previous one
 # Fix that!
 @pytest_asyncio.fixture(
@@ -113,7 +45,17 @@ async def stack_impls(model):
 )
 async def inference_settings(request):
     model = request.param["model"]
-    impls = await stack_impls(model)
+    impls = await resolve_impls_for_test(
+        Api.inference,
+        models=[
+            ModelDef(
+                identifier=model,
+                llama_model=model,
+                provider_id="",
+            )
+        ],
+    )
+
     return {
         "impl": impls[Api.inference],
         "common_params": {
@@ -266,12 +208,11 @@ async def test_chat_completion_with_tool_calling_streaming(
     assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
     assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
 
-    end = grouped[ChatCompletionResponseEventType.complete][0]
-
     # This is not supported in most providers :/ they don't return eom_id / eot_id
     # expected_stop_reason = get_expected_stop_reason(
     #     inference_settings["common_params"]["model"]
     # )
+    # end = grouped[ChatCompletionResponseEventType.complete][0]
     # assert end.event.stop_reason == expected_stop_reason
 
     model = inference_settings["common_params"]["model"]