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feat(tests): enable MCP tests in server mode (#4146)
We would like to run all OpenAI compatibility tests using only the openai-client library. This is most friendly for contributors since they can run tests without needing to update the client-sdks (which is getting easier but still a long pole.) This is the first step in enabling that -- no using "library client" for any of the Responses tests. This seems like a reasonable trade-off since the usage of an embeddeble library client for Responses (or any OpenAI-compatible) behavior seems to be not very common. To do this, we needed to enable MCP tests (which only worked in library client mode) for server mode.
This commit is contained in:
Ashwin Bharambe
GitHub
parent
9eb81439d2
commit
1e81056a22
29 changed files with 13388 additions and 127 deletions
17
tests/integration/responses/conftest.py
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17
tests/integration/responses/conftest.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import pytest
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from llama_stack.core.library_client import LlamaStackAsLibraryClient
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@pytest.fixture
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def responses_client(compat_client):
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"""Provide a client for responses tests, skipping library client mode."""
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if isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("Responses API tests are not supported in library client mode")
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return compat_client
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@ -0,0 +1,549 @@
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"usage": null,
|
||||
"obfuscation": "grbygHexDT4JwGx"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-ed89b57fec93",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_cbf1785567",
|
||||
"usage": null,
|
||||
"obfuscation": "upSRpiQQKE"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-ed89b57fec93",
|
||||
"choices": [],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_cbf1785567",
|
||||
"usage": {
|
||||
"completion_tokens": 18,
|
||||
"prompt_tokens": 195,
|
||||
"total_tokens": 213,
|
||||
"completion_tokens_details": {
|
||||
"accepted_prediction_tokens": 0,
|
||||
"audio_tokens": 0,
|
||||
"reasoning_tokens": 0,
|
||||
"rejected_prediction_tokens": 0
|
||||
},
|
||||
"prompt_tokens_details": {
|
||||
"audio_tokens": 0,
|
||||
"cached_tokens": 0
|
||||
}
|
||||
},
|
||||
"obfuscation": "psE6Es6zZ2Kz4"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -13,8 +13,8 @@ from .streaming_assertions import StreamingValidator
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_basic(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=False,
|
||||
|
|
@ -31,10 +31,10 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
|||
"Total tokens should equal input + output tokens"
|
||||
)
|
||||
|
||||
retrieved_response = compat_client.responses.retrieve(response_id=response.id)
|
||||
retrieved_response = responses_client.responses.retrieve(response_id=response.id)
|
||||
assert retrieved_response.output_text == response.output_text
|
||||
|
||||
next_response = compat_client.responses.create(
|
||||
next_response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Repeat your previous response in all caps.",
|
||||
previous_response_id=response.id,
|
||||
|
|
@ -44,8 +44,8 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_streaming_basic(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_streaming_basic(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=True,
|
||||
|
|
@ -98,15 +98,15 @@ def test_response_streaming_basic(compat_client, text_model_id, case):
|
|||
validator.assert_response_consistency()
|
||||
|
||||
# Verify stored response matches streamed response
|
||||
retrieved_response = compat_client.responses.retrieve(response_id=response_id)
|
||||
retrieved_response = responses_client.responses.retrieve(response_id=response_id)
|
||||
final_event = events[-1]
|
||||
assert retrieved_response.output_text == final_event.response.output_text
|
||||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_streaming_incremental_content(compat_client, text_model_id, case):
|
||||
def test_response_streaming_incremental_content(responses_client, text_model_id, case):
|
||||
"""Test that streaming actually delivers content incrementally, not just at the end."""
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=True,
|
||||
|
|
@ -170,10 +170,10 @@ def test_response_streaming_incremental_content(compat_client, text_model_id, ca
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_test_cases)
|
||||
def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn(responses_client, text_model_id, case):
|
||||
previous_response_id = None
|
||||
for turn_input, turn_expected in case.turns:
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=turn_input,
|
||||
previous_response_id=previous_response_id,
|
||||
|
|
@ -184,8 +184,8 @@ def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", image_test_cases)
|
||||
def test_response_non_streaming_image(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_image(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=False,
|
||||
|
|
@ -195,10 +195,10 @@ def test_response_non_streaming_image(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_image_test_cases)
|
||||
def test_response_non_streaming_multi_turn_image(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn_image(responses_client, text_model_id, case):
|
||||
previous_response_id = None
|
||||
for turn_input, turn_expected in case.turns:
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=turn_input,
|
||||
previous_response_id=previous_response_id,
|
||||
|
|
|
|||
|
|
@ -131,18 +131,18 @@ class TestConversationResponses:
|
|||
assert len(response.output_text.strip()) > 0
|
||||
|
||||
# this is not ready yet
|
||||
# def test_conversation_compat_client(self, compat_client, text_model_id):
|
||||
# def test_conversation_compat_client(self, responses_client, text_model_id):
|
||||
# """Test conversation parameter works with compatibility client."""
|
||||
# if not hasattr(compat_client, "conversations"):
|
||||
# pytest.skip("compat_client does not support conversations API")
|
||||
# if not hasattr(responses_client, "conversations"):
|
||||
# pytest.skip("responses_client does not support conversations API")
|
||||
#
|
||||
# conversation = compat_client.conversations.create()
|
||||
# response = compat_client.responses.create(
|
||||
# conversation = responses_client.conversations.create()
|
||||
# response = responses_client.responses.create(
|
||||
# model=text_model_id, input="Tell me a joke", conversation=conversation.id
|
||||
# )
|
||||
#
|
||||
# assert response is not None
|
||||
# assert len(response.output_text.strip()) > 0
|
||||
#
|
||||
# conversation_items = compat_client.conversations.items.list(conversation.id)
|
||||
# conversation_items = responses_client.conversations.items.list(conversation.id)
|
||||
# assert len(conversation_items.data) >= 2
|
||||
|
|
|
|||
|
|
@ -9,8 +9,6 @@ import time
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
|
||||
from .helpers import new_vector_store, upload_file
|
||||
|
||||
|
||||
|
|
@ -28,12 +26,9 @@ from .helpers import new_vector_store, upload_file
|
|||
},
|
||||
],
|
||||
)
|
||||
def test_response_text_format(compat_client, text_model_id, text_format):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API text format is not yet supported in library client.")
|
||||
|
||||
def test_response_text_format(responses_client, text_model_id, text_format):
|
||||
stream = False
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What is the capital of France?",
|
||||
stream=stream,
|
||||
|
|
@ -47,13 +42,10 @@ def test_response_text_format(compat_client, text_model_id, text_format):
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_store_with_filtered_files(compat_client, embedding_model_id, embedding_dimension, tmp_path_factory):
|
||||
def vector_store_with_filtered_files(responses_client, embedding_model_id, embedding_dimension, tmp_path_factory):
|
||||
# """Create a vector store with multiple files that have different attributes for filtering tests."""
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("upload_file() is not yet supported in library client somehow?")
|
||||
|
||||
vector_store = new_vector_store(
|
||||
compat_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
|
||||
responses_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
|
||||
)
|
||||
tmp_path = tmp_path_factory.mktemp("filter_test_files")
|
||||
|
||||
|
|
@ -104,11 +96,11 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
file_path.write_text(file_data["content"])
|
||||
|
||||
# Upload file
|
||||
file_response = upload_file(compat_client, file_data["name"], str(file_path))
|
||||
file_response = upload_file(responses_client, file_data["name"], str(file_path))
|
||||
file_ids.append(file_response.id)
|
||||
|
||||
# Attach file to vector store with attributes
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
file_attach_response = responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
attributes=file_data["attributes"],
|
||||
|
|
@ -117,7 +109,7 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
# Wait for attachment
|
||||
while file_attach_response.status == "in_progress":
|
||||
time.sleep(0.1)
|
||||
file_attach_response = compat_client.vector_stores.files.retrieve(
|
||||
file_attach_response = responses_client.vector_stores.files.retrieve(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
|
@ -127,17 +119,17 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
|
||||
# Cleanup: delete vector store and files
|
||||
try:
|
||||
compat_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
responses_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
for file_id in file_ids:
|
||||
try:
|
||||
compat_client.files.delete(file_id=file_id)
|
||||
responses_client.files.delete(file_id=file_id)
|
||||
except Exception:
|
||||
pass # File might already be deleted
|
||||
except Exception:
|
||||
pass # Best effort cleanup
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_region(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_region(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with region equality filter."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -147,7 +139,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the updates from the US region?",
|
||||
tools=tools,
|
||||
|
|
@ -168,7 +160,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
|
|||
assert "asia" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_category(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_category(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with category equality filter."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -178,7 +170,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Show me all marketing reports",
|
||||
tools=tools,
|
||||
|
|
@ -198,7 +190,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
|
|||
assert "revenue figures" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_date_range(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_date_range(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with date range filter using compound AND."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -222,7 +214,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What happened in Q1 2023?",
|
||||
tools=tools,
|
||||
|
|
@ -241,7 +233,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
|
|||
assert "q3" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_compound_and(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_compound_and(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with compound AND filter (region AND category)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -257,7 +249,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the engineering updates from the US?",
|
||||
tools=tools,
|
||||
|
|
@ -277,7 +269,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
|
|||
assert "promotional" not in result.text.lower() and "revenue" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_compound_or(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_compound_or(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with compound OR filter (marketing OR sales)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -293,7 +285,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Show me marketing and sales documents",
|
||||
tools=tools,
|
||||
|
|
@ -320,7 +312,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
|
|||
assert categories_found.issubset({"marketing", "sales"}), f"Found unexpected categories: {categories_found}"
|
||||
|
||||
|
||||
def test_response_file_search_streaming_events(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_streaming_events(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test that file search emits proper streaming events (in_progress, searching, completed)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -329,7 +321,7 @@ def test_response_file_search_streaming_events(compat_client, text_model_id, vec
|
|||
}
|
||||
]
|
||||
|
||||
stream = compat_client.responses.create(
|
||||
stream = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the marketing updates?",
|
||||
tools=tools,
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import logging # allow-direct-logging
|
|||
import os
|
||||
|
||||
import httpx
|
||||
import llama_stack_client
|
||||
import openai
|
||||
import pytest
|
||||
|
||||
|
|
@ -29,8 +30,8 @@ from .streaming_assertions import StreamingValidator
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", web_search_test_cases)
|
||||
def test_response_non_streaming_web_search(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_web_search(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -48,12 +49,9 @@ def test_response_non_streaming_web_search(compat_client, text_model_id, case):
|
|||
|
||||
@pytest.mark.parametrize("case", file_search_test_cases)
|
||||
def test_response_non_streaming_file_search(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
|
||||
):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
if case.file_content:
|
||||
file_name = "test_response_non_streaming_file_search.txt"
|
||||
|
|
@ -65,16 +63,16 @@ def test_response_non_streaming_file_search(
|
|||
else:
|
||||
raise ValueError("No file content or path provided for case")
|
||||
|
||||
file_response = upload_file(compat_client, file_name, file_path)
|
||||
file_response = upload_file(responses_client, file_name, file_path)
|
||||
|
||||
# Attach our file to the vector store
|
||||
compat_client.vector_stores.files.create(
|
||||
responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
||||
# Wait for the file to be attached
|
||||
wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
|
||||
wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
|
||||
|
||||
# Update our tools with the right vector store id
|
||||
tools = case.tools
|
||||
|
|
@ -83,7 +81,7 @@ def test_response_non_streaming_file_search(
|
|||
tool["vector_store_ids"] = [vector_store.id]
|
||||
|
||||
# Create the response request, which should query our vector store
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -105,15 +103,12 @@ def test_response_non_streaming_file_search(
|
|||
|
||||
|
||||
def test_response_non_streaming_file_search_empty_vector_store(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension
|
||||
):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
# Create the response request, which should query our vector store
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="How many experts does the Llama 4 Maverick model have?",
|
||||
tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
|
||||
|
|
@ -133,13 +128,10 @@ def test_response_non_streaming_file_search_empty_vector_store(
|
|||
|
||||
|
||||
def test_response_sequential_file_search(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
|
||||
):
|
||||
"""Test file search with sequential responses using previous_response_id."""
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
# Create a test file with content
|
||||
file_content = "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture."
|
||||
|
|
@ -147,21 +139,21 @@ def test_response_sequential_file_search(
|
|||
file_path = tmp_path / file_name
|
||||
file_path.write_text(file_content)
|
||||
|
||||
file_response = upload_file(compat_client, file_name, file_path)
|
||||
file_response = upload_file(responses_client, file_name, file_path)
|
||||
|
||||
# Attach the file to the vector store
|
||||
compat_client.vector_stores.files.create(
|
||||
responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
||||
# Wait for the file to be attached
|
||||
wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
|
||||
wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
|
||||
|
||||
tools = [{"type": "file_search", "vector_store_ids": [vector_store.id]}]
|
||||
|
||||
# First response request with file search
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="How many experts does the Llama 4 Maverick model have?",
|
||||
tools=tools,
|
||||
|
|
@ -178,7 +170,7 @@ def test_response_sequential_file_search(
|
|||
assert "128" in response.output_text or "experts" in response.output_text.lower()
|
||||
|
||||
# Second response request using previous_response_id
|
||||
response2 = compat_client.responses.create(
|
||||
response2 = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Can you tell me more about the architecture?",
|
||||
tools=tools,
|
||||
|
|
@ -199,14 +191,11 @@ def test_response_sequential_file_search(
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, caplog):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_non_streaming_mcp_tool(responses_client, text_model_id, case, caplog):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -243,15 +232,15 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
|
||||
exc_type = (
|
||||
AuthenticationRequiredError
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient)
|
||||
else (httpx.HTTPStatusError, openai.AuthenticationError)
|
||||
if isinstance(responses_client, LlamaStackAsLibraryClient)
|
||||
else (httpx.HTTPStatusError, openai.AuthenticationError, llama_stack_client.AuthenticationError)
|
||||
)
|
||||
# Suppress expected auth error logs only for the failing auth attempt
|
||||
with caplog.at_level(
|
||||
logging.CRITICAL, logger="llama_stack.providers.inline.agents.meta_reference.responses.streaming"
|
||||
):
|
||||
with pytest.raises(exc_type):
|
||||
compat_client.responses.create(
|
||||
responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -262,7 +251,7 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
if tool["type"] == "mcp":
|
||||
tool["headers"] = {"Authorization": "Bearer test-token"}
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -272,14 +261,11 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_sequential_mcp_tool(responses_client, text_model_id, case):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -311,7 +297,7 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
|||
text_content = message.content[0].text
|
||||
assert "boiling point" in text_content.lower()
|
||||
|
||||
response2 = compat_client.responses.create(
|
||||
response2 = responses_client.responses.create(
|
||||
model=text_model_id, input=case.input, tools=tools, stream=False, previous_response_id=response.id
|
||||
)
|
||||
|
||||
|
|
@ -323,16 +309,13 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
|||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
@pytest.mark.parametrize("approve", [True, False])
|
||||
def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_mcp_tool_approval(responses_client, text_model_id, case, approve):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
for tool in tools:
|
||||
tool["require_approval"] = "always"
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -352,13 +335,13 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
|
|||
approval_request = response.output[1]
|
||||
assert approval_request.type == "mcp_approval_request"
|
||||
assert approval_request.name == "get_boiling_point"
|
||||
assert json.loads(approval_request.arguments) == {
|
||||
"liquid_name": "myawesomeliquid",
|
||||
"celsius": True,
|
||||
}
|
||||
args = json.loads(approval_request.arguments)
|
||||
assert args["liquid_name"] == "myawesomeliquid"
|
||||
# celsius has a default value of True, so it may be omitted or explicitly set
|
||||
assert args.get("celsius", True) is True
|
||||
|
||||
# send approval response
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
previous_response_id=response.id,
|
||||
model=text_model_id,
|
||||
input=[{"type": "mcp_approval_response", "approval_request_id": approval_request.id, "approve": approve}],
|
||||
|
|
@ -398,8 +381,8 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", custom_tool_test_cases)
|
||||
def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_custom_tool(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -412,8 +395,8 @@ def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", custom_tool_test_cases)
|
||||
def test_response_function_call_ordering_1(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_function_call_ordering_1(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -437,13 +420,13 @@ def test_response_function_call_ordering_1(compat_client, text_model_id, case):
|
|||
"call_id": response.output[0].call_id,
|
||||
}
|
||||
)
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id, input=inputs, tools=case.tools, stream=False, previous_response_id=response.id
|
||||
)
|
||||
assert len(response.output) == 1
|
||||
|
||||
|
||||
def test_response_function_call_ordering_2(compat_client, text_model_id):
|
||||
def test_response_function_call_ordering_2(responses_client, text_model_id):
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
|
|
@ -468,7 +451,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
"content": "Is the weather better in San Francisco or Los Angeles?",
|
||||
}
|
||||
]
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=inputs,
|
||||
tools=tools,
|
||||
|
|
@ -489,7 +472,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
"call_id": output.call_id,
|
||||
}
|
||||
)
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=inputs,
|
||||
tools=tools,
|
||||
|
|
@ -500,15 +483,12 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
|
||||
def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
|
||||
"""Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
input=case.input,
|
||||
model=text_model_id,
|
||||
tools=tools,
|
||||
|
|
@ -550,15 +530,12 @@ def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_mo
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_tool_execution_streaming_test_cases)
|
||||
def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
||||
def test_response_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
|
||||
"""Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
stream = compat_client.responses.create(
|
||||
stream = responses_client.responses.create(
|
||||
input=case.input,
|
||||
model=text_model_id,
|
||||
tools=tools,
|
||||
|
|
|
|||
|
|
@ -10,8 +10,6 @@ import pytest
|
|||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.turn_events import StepCompleted, StepProgress, ToolCallIssuedDelta
|
||||
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
|
||||
AUTH_TOKEN = "test-token"
|
||||
|
||||
from tests.common.mcp import MCP_TOOLGROUP_ID, make_mcp_server
|
||||
|
|
@ -24,9 +22,6 @@ def mcp_server():
|
|||
|
||||
|
||||
def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
|
||||
if not isinstance(llama_stack_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("The local MCP server only reliably reachable from library client.")
|
||||
|
||||
test_toolgroup_id = MCP_TOOLGROUP_ID
|
||||
uri = mcp_server["server_url"]
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue