chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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import copy
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import os
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from collections.abc import Awaitable, Callable
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from typing import Any
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import pytest
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from pytest import mark, param
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from samples.concepts.auto_function_calling.chat_completion_with_auto_function_calling import (
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main as chat_completion_with_function_calling,
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)
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from samples.concepts.auto_function_calling.functions_defined_in_json_prompt import (
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main as function_defined_in_json_prompt,
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)
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from samples.concepts.auto_function_calling.functions_defined_in_yaml_prompt import (
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main as function_defined_in_yaml_prompt,
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)
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from samples.concepts.caching.semantic_caching import main as semantic_caching
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from samples.concepts.chat_completion.simple_chatbot import main as simple_chatbot
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from samples.concepts.chat_completion.simple_chatbot_kernel_function import main as simple_chatbot_kernel_function
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from samples.concepts.chat_completion.simple_chatbot_logit_bias import main as simple_chatbot_logit_bias
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from samples.concepts.chat_completion.simple_chatbot_streaming import main as simple_chatbot_streaming
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from samples.concepts.chat_completion.simple_chatbot_with_image import main as simple_chatbot_with_image
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from samples.concepts.embedding.text_embedding_generation import main as text_embedding_generation
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from samples.concepts.filtering.auto_function_invoke_filters import main as auto_function_invoke_filters
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from samples.concepts.filtering.function_invocation_filters import main as function_invocation_filters
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from samples.concepts.filtering.function_invocation_filters_stream import main as function_invocation_filters_stream
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from samples.concepts.filtering.prompt_filters import main as prompt_filters
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from samples.concepts.filtering.retry_with_different_model import main as retry_with_different_model
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from samples.concepts.functions.kernel_arguments import main as kernel_arguments
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from samples.concepts.grounding.grounded import main as grounded
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from samples.concepts.images.image_generation import main as image_generation
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from samples.concepts.local_models.lm_studio_chat_completion import main as lm_studio_chat_completion
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from samples.concepts.local_models.lm_studio_text_embedding import main as lm_studio_text_embedding
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from samples.concepts.local_models.ollama_chat_completion import main as ollama_chat_completion
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from samples.concepts.mcp.agent_with_mcp_agent import main as agent_with_mcp_agent
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from samples.concepts.memory.simple_memory import main as simple_memory
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from samples.concepts.plugins.openai_function_calling_with_custom_plugin import (
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main as openai_function_calling_with_custom_plugin,
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)
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from samples.concepts.plugins.plugins_from_dir import main as plugins_from_dir
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from samples.concepts.prompt_templates.azure_chat_gpt_api_handlebars import main as azure_chat_gpt_api_handlebars
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from samples.concepts.prompt_templates.azure_chat_gpt_api_jinja2 import main as azure_chat_gpt_api_jinja2
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from samples.concepts.prompt_templates.configuring_prompts import main as configuring_prompts
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from samples.concepts.prompt_templates.load_yaml_prompt import main as load_yaml_prompt
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from samples.concepts.prompt_templates.template_language import main as template_language
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from samples.concepts.rag.rag_with_vector_collection import main as rag_with_vector_collection
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from samples.concepts.service_selector.custom_service_selector import main as custom_service_selector
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from samples.concepts.text_completion.text_completion import main as text_completion
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from samples.getting_started_with_agents.chat_completion.step01_chat_completion_agent_simple import (
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main as step1_chat_completion_agent_simple,
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)
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from samples.getting_started_with_agents.chat_completion.step03_chat_completion_agent_with_kernel import (
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main as step2_chat_completion_agent_with_kernel,
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)
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from samples.getting_started_with_agents.chat_completion.step04_chat_completion_agent_plugin_simple import (
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main as step3_chat_completion_agent_plugin_simple,
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)
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from samples.getting_started_with_agents.chat_completion.step05_chat_completion_agent_plugin_with_kernel import (
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main as step4_chat_completion_agent_plugin_with_kernel,
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)
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from samples.getting_started_with_agents.chat_completion.step06_chat_completion_agent_group_chat import (
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main as step5_chat_completion_agent_group_chat,
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)
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from samples.getting_started_with_agents.openai_assistant.step1_assistant import main as step1_openai_assistant
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from tests.utils import retry
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# These environment variable names are used to control which samples are run during integration testing.
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# This has to do with the setup of the tests and the services they depend on.
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COMPLETIONS_CONCEPT_SAMPLE = "COMPLETIONS_CONCEPT_SAMPLE"
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MEMORY_CONCEPT_SAMPLE = "MEMORY_CONCEPT_SAMPLE"
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concepts = [
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param(
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semantic_caching,
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[],
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id="semantic_caching",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_chatbot,
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["Why is the sky blue in one sentence?", "exit"],
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id="simple_chatbot",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_chatbot_streaming,
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["Why is the sky blue in one sentence?", "exit"],
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id="simple_chatbot_streaming",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_chatbot_with_image,
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["exit"],
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id="simple_chatbot_with_image",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_chatbot_logit_bias,
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["Who has the most career points in NBA history?", "exit"],
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id="simple_chatbot_logit_bias",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_chatbot_kernel_function,
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["Why is the sky blue in one sentence?", "exit"],
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id="simple_chatbot_kernel_function",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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chat_completion_with_function_calling,
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["What is 3+3?", "exit"],
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id="chat_completion_with_function_calling",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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auto_function_invoke_filters,
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["What is 3+3?", "exit"],
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id="auto_function_invoke_filters",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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function_invocation_filters,
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["What is 3+3?", "exit"],
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id="function_invocation_filters",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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function_invocation_filters_stream,
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["What is 3+3?", "exit"],
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id="function_invocation_filters_stream",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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prompt_filters,
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["What is the fastest animal?", "exit"],
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id="prompt_filters",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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retry_with_different_model,
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[],
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id="retry_with_different_model",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None,
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reason="Not running completion samples.",
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),
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),
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param(
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kernel_arguments,
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[],
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id="kernel_arguments",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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grounded,
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[],
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id="grounded",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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openai_function_calling_with_custom_plugin,
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[],
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id="openai_function_calling_with_custom_plugin",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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plugins_from_dir,
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[],
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id="plugins_from_dir",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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azure_chat_gpt_api_handlebars,
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["What is 3+3?", "exit"],
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id="azure_chat_gpt_api_handlebars",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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azure_chat_gpt_api_jinja2,
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["What is 3+3?", "exit"],
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id="azure_chat_gpt_api_jinja2",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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agent_with_mcp_agent,
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["what restaurants can I choose from?", "the farm sounds nice, what are the specials there?", "exit"],
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id="agent_with_mcp_agent",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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configuring_prompts,
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["What is my name?", "exit"],
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id="configuring_prompts",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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load_yaml_prompt,
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[],
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id="load_yaml_prompt",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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template_language,
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[],
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id="template_language",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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simple_memory,
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[],
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id="simple_memory",
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marks=pytest.mark.skipif(os.getenv(MEMORY_CONCEPT_SAMPLE, None) is None, reason="Not running memory samples."),
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),
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param(rag_with_vector_collection, [], id="rag_with_vector_collection"),
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param(
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custom_service_selector,
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[],
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id="custom_service_selector",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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function_defined_in_json_prompt,
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["What is 3+3?", "exit"],
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id="function_defined_in_json_prompt",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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function_defined_in_yaml_prompt,
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["What is 3+3?", "exit"],
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id="function_defined_in_yaml_prompt",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step1_chat_completion_agent_simple,
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[],
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id="step1_chat_completion_agent_simple",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step2_chat_completion_agent_with_kernel,
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[],
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id="step2_chat_completion_agent_with_kernel",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step3_chat_completion_agent_plugin_simple,
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[],
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id="step3_chat_completion_agent_plugin_simple",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step4_chat_completion_agent_plugin_with_kernel,
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[],
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id="step4_chat_completion_agent_plugin_with_kernel",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step5_chat_completion_agent_group_chat,
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[],
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id="step5_chat_completion_agent_group_chat",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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step1_openai_assistant,
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[],
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id="step1_openai_assistant",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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ollama_chat_completion,
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["Why is the sky blue?", "exit"],
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id="ollama_chat_completion",
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marks=pytest.mark.skip(reason="Need to set up Ollama locally. Check out the module for more details."),
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),
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param(
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lm_studio_chat_completion,
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["Why is the sky blue?", "exit"],
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id="lm_studio_chat_completion",
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marks=pytest.mark.skip(reason="Need to set up LM Studio locally. Check out the module for more details."),
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),
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param(
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lm_studio_text_embedding,
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[],
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id="lm_studio_text_embedding",
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marks=pytest.mark.skip(reason="Need to set up LM Studio locally. Check out the module for more details."),
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),
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param(
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image_generation,
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[],
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id="image_generation",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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text_completion,
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[],
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id="text_completion",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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param(
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text_embedding_generation,
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[],
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id="text_embedding_generation",
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marks=pytest.mark.skipif(
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os.getenv(COMPLETIONS_CONCEPT_SAMPLE, None) is None, reason="Not running completion samples."
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),
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),
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]
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@mark.parametrize("sample, responses", concepts)
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async def test_concepts(sample: Callable[..., Awaitable[Any]], responses: list[str], monkeypatch):
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saved_responses = copy.deepcopy(responses)
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def reset():
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responses.clear()
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responses.extend(saved_responses)
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monkeypatch.setattr("builtins.input", lambda _: responses.pop(0))
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await retry(sample, retries=3, reset=reset)
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