mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 09:53:45 +00:00
Merge branch 'main' into add-mcp-authentication-param
This commit is contained in:
Ashwin Bharambe
GitHub
commit
fa2b361f46
43 changed files with 15292 additions and 138 deletions
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@ -244,8 +244,14 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal
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timeout = 2
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start_time = time.time()
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server_url = f"http://localhost:{port}/sse"
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logger.debug(f"Waiting for MCP server thread to start on port {port}")
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# Determine the appropriate host for the server URL based on test environment
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# - For library client and server mode: use localhost (both on same host)
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# - For docker mode: use host.docker.internal (container needs to reach host)
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import os
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mcp_host = os.environ.get("LLAMA_STACK_TEST_MCP_HOST", "localhost")
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server_url = f"http://{mcp_host}:{port}/sse"
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logger.debug(f"Waiting for MCP server thread to start on port {port} (accessible via {mcp_host})")
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while time.time() - start_time < timeout:
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if server_thread.is_alive():
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17
tests/integration/responses/conftest.py
Normal file
17
tests/integration/responses/conftest.py
Normal file
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@ -0,0 +1,17 @@
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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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{
|
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-experiment_analysis_streaming]",
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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{
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"role": "user",
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"content": "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Return only one tool call per step. Please stream your analysis process."
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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_Q9Gcxub7UbQsxJWVkiy4FETr",
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"type": "function",
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"function": {
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"name": "get_experiment_id",
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"arguments": "{\"experiment_name\":\"chemical_reaction\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"tool_call_id": "call_Q9Gcxub7UbQsxJWVkiy4FETr",
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"content": [
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{
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"type": "text",
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"text": "exp_003"
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}
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]
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}
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],
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"stream": true,
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"stream_options": {
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"include_usage": true
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},
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "get_user_id",
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"description": "\n Get the user ID for a given username. This ID is needed for other operations.\n\n :param username: The username to look up\n :return: The user ID for the username\n ",
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"parameters": {
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"properties": {
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"username": {
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"title": "Username",
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"type": "string"
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}
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},
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"required": [
|
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"username"
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],
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"title": "get_user_idArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
|
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"function": {
|
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"name": "get_user_permissions",
|
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"description": "\n Get the permissions for a user ID. Requires a valid user ID from get_user_id.\n\n :param user_id: The user ID to check permissions for\n :return: The permissions for the user\n ",
|
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"parameters": {
|
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"properties": {
|
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"user_id": {
|
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"title": "User Id",
|
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"type": "string"
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}
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},
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"required": [
|
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"user_id"
|
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],
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"title": "get_user_permissionsArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
|
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"function": {
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"name": "check_file_access",
|
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"description": "\n Check if a user can access a specific file. Requires a valid user ID.\n\n :param user_id: The user ID to check access for\n :param filename: The filename to check access to\n :return: Whether the user can access the file (yes/no)\n ",
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"parameters": {
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"properties": {
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"user_id": {
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"title": "User Id",
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"type": "string"
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},
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"filename": {
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"title": "Filename",
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"type": "string"
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}
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},
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"required": [
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"user_id",
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"filename"
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],
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"title": "check_file_accessArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
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"function": {
|
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"name": "get_experiment_id",
|
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"description": "\n Get the experiment ID for a given experiment name. This ID is needed to get results.\n\n :param experiment_name: The name of the experiment\n :return: The experiment ID\n ",
|
||||
"parameters": {
|
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"properties": {
|
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"experiment_name": {
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"title": "Experiment Name",
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"type": "string"
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}
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},
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"required": [
|
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"experiment_name"
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],
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"title": "get_experiment_idArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
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"function": {
|
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"name": "get_experiment_results",
|
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"description": "\n Get the results for an experiment ID. Requires a valid experiment ID from get_experiment_id.\n\n :param experiment_id: The experiment ID to get results for\n :return: The experiment results\n ",
|
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"parameters": {
|
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"properties": {
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"experiment_id": {
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"title": "Experiment Id",
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"type": "string"
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}
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},
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"required": [
|
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"experiment_id"
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],
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"title": "get_experiment_resultsArguments",
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"type": "object"
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}
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}
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}
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]
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},
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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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},
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"response": {
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"body": [
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"function": {
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"name": "get_experiment_results"
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},
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"type": "function"
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"id": "rec-ed89b57fec93",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": ".",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"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": "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["authorization"] = "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,
|
||||
|
|
|
|||
|
|
@ -8,8 +8,6 @@ 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
|
||||
|
|
@ -22,9 +20,6 @@ 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"]
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from pydantic import ValidationError
|
|||
|
||||
from llama_stack.core.access_control.access_control import AccessDeniedError
|
||||
from llama_stack.core.datatypes import AuthenticationRequiredError
|
||||
from llama_stack.core.server.server import translate_exception
|
||||
from llama_stack.core.server.server import remove_disabled_providers, translate_exception
|
||||
|
||||
|
||||
class TestTranslateException:
|
||||
|
|
@ -194,3 +194,70 @@ class TestTranslateException:
|
|||
assert isinstance(result3, HTTPException)
|
||||
assert result3.status_code == 403
|
||||
assert result3.detail == "Permission denied: Access denied"
|
||||
|
||||
|
||||
class TestRemoveDisabledProviders:
|
||||
"""Test cases for the remove_disabled_providers function."""
|
||||
|
||||
def test_remove_explicitly_disabled_provider(self):
|
||||
"""Test that providers with provider_id='__disabled__' are removed."""
|
||||
config = {
|
||||
"providers": {
|
||||
"inference": [
|
||||
{"provider_id": "openai", "provider_type": "remote::openai", "config": {}},
|
||||
{"provider_id": "__disabled__", "provider_type": "remote::vllm", "config": {}},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["providers"]["inference"]) == 1
|
||||
assert result["providers"]["inference"][0]["provider_id"] == "openai"
|
||||
|
||||
def test_remove_empty_provider_id(self):
|
||||
"""Test that providers with empty provider_id are removed."""
|
||||
config = {
|
||||
"providers": {
|
||||
"inference": [
|
||||
{"provider_id": "openai", "provider_type": "remote::openai", "config": {}},
|
||||
{"provider_id": "", "provider_type": "remote::vllm", "config": {}},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["providers"]["inference"]) == 1
|
||||
assert result["providers"]["inference"][0]["provider_id"] == "openai"
|
||||
|
||||
def test_keep_models_with_none_provider_model_id(self):
|
||||
"""Test that models with None provider_model_id are NOT removed."""
|
||||
config = {
|
||||
"registered_resources": {
|
||||
"models": [
|
||||
{
|
||||
"model_id": "llama-3-2-3b",
|
||||
"provider_id": "vllm-inference",
|
||||
"model_type": "llm",
|
||||
"provider_model_id": None,
|
||||
"metadata": {},
|
||||
},
|
||||
{
|
||||
"model_id": "gpt-4o-mini",
|
||||
"provider_id": "openai",
|
||||
"model_type": "llm",
|
||||
"provider_model_id": None,
|
||||
"metadata": {},
|
||||
},
|
||||
{
|
||||
"model_id": "granite-embedding-125m",
|
||||
"provider_id": "sentence-transformers",
|
||||
"model_type": "embedding",
|
||||
"provider_model_id": "ibm-granite/granite-embedding-125m-english",
|
||||
"metadata": {"embedding_dimension": 768},
|
||||
},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["registered_resources"]["models"]) == 3
|
||||
assert result["registered_resources"]["models"][0]["model_id"] == "llama-3-2-3b"
|
||||
assert result["registered_resources"]["models"][1]["model_id"] == "gpt-4o-mini"
|
||||
assert result["registered_resources"]["models"][2]["model_id"] == "granite-embedding-125m"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue