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more tests
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‎tests/conftest.py‎

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Original file line numberDiff line numberDiff line change
@@ -61,6 +61,11 @@ async def mock_search(self, *args, **kwargs):
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async def mock_retrieve(self, *args, **kwargs):
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retrieval_request = kwargs.get("retrieval_request")
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assert retrieval_request is not None
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assert retrieval_request.target_index_params is not None
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assert len(retrieval_request.target_index_params) == 1
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self.filter = retrieval_request.target_index_params[0].filter_add_on
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return mock_retrieval_response()
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@@ -377,6 +382,24 @@ def mock_blob_container_client(monkeypatch):
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}
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]
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agent_auth_envs = [
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{
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"OPENAI_HOST": "azure",
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"AZURE_OPENAI_SERVICE": "test-openai-service",
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"AZURE_OPENAI_CHATGPT_MODEL": "gpt-4o-mini",
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"AZURE_OPENAI_CHATGPT_DEPLOYMENT": "gpt-4o-mini",
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"AZURE_OPENAI_EMB_DEPLOYMENT": "test-ada",
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"AZURE_OPENAI_SEARCHAGENT_MODEL": "gpt-4o-mini",
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"AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT": "gpt-4o-mini",
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"USE_AGENTIC_RETRIEVAL": "true",
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"AZURE_USE_AUTHENTICATION": "true",
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"AZURE_SERVER_APP_ID": "SERVER_APP",
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"AZURE_SERVER_APP_SECRET": "SECRET",
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"AZURE_CLIENT_APP_ID": "CLIENT_APP",
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"AZURE_TENANT_ID": "TENANT_ID",
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}
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]
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@pytest.fixture(params=envs, ids=["client0", "client1"])
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def mock_env(monkeypatch, request):
@@ -450,6 +473,29 @@ def mock_agent_env(monkeypatch, request):
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mock_default_azure_credential.return_value = MockAzureCredential()
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yield
452475

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@pytest.fixture(params=agent_auth_envs, ids=["agent_auth_client0"])
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def mock_agent_auth_env(monkeypatch, request):
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with mock.patch.dict(os.environ, clear=True):
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monkeypatch.setenv("AZURE_STORAGE_ACCOUNT", "test-storage-account")
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monkeypatch.setenv("AZURE_STORAGE_CONTAINER", "test-storage-container")
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monkeypatch.setenv("AZURE_STORAGE_RESOURCE_GROUP", "test-storage-rg")
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monkeypatch.setenv("AZURE_SUBSCRIPTION_ID", "test-storage-subid")
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monkeypatch.setenv("ENABLE_LANGUAGE_PICKER", "true")
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monkeypatch.setenv("USE_SPEECH_INPUT_BROWSER", "true")
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monkeypatch.setenv("USE_SPEECH_OUTPUT_AZURE", "true")
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monkeypatch.setenv("AZURE_SEARCH_INDEX", "test-search-index")
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monkeypatch.setenv("AZURE_SEARCH_AGENT", "test-search-agent")
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monkeypatch.setenv("AZURE_SEARCH_SERVICE", "test-search-service")
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monkeypatch.setenv("AZURE_SPEECH_SERVICE_ID", "test-id")
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monkeypatch.setenv("AZURE_SPEECH_SERVICE_LOCATION", "eastus")
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monkeypatch.setenv("ALLOWED_ORIGIN", "https://frontend.com")
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for key, value in request.param.items():
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monkeypatch.setenv(key, value)
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with mock.patch("app.AzureDeveloperCliCredential") as mock_default_azure_credential:
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mock_default_azure_credential.return_value = MockAzureCredential()
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yield
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453499
@pytest_asyncio.fixture(scope="function")
454500
async def client(
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monkeypatch,
@@ -506,6 +552,31 @@ async def agent_client(
506552
mock_openai_embedding(test_app.app.config[app.CONFIG_OPENAI_CLIENT])
507553
yield test_app.test_client()
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555+
@pytest_asyncio.fixture(scope="function")
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async def agent_auth_client(
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monkeypatch,
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mock_agent_auth_env,
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mock_openai_chatcompletion,
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mock_openai_embedding,
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mock_acs_search,
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mock_acs_agent,
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mock_blob_container_client,
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mock_azurehttp_calls,
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mock_confidential_client_success,
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mock_validate_token_success,
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mock_list_groups_success,
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):
569+
quart_app = app.create_app()
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571+
async with quart_app.test_app() as test_app:
572+
test_app.app.config.update({"TESTING": True})
573+
mock_openai_chatcompletion(test_app.app.config[app.CONFIG_OPENAI_CLIENT])
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mock_openai_embedding(test_app.app.config[app.CONFIG_OPENAI_CLIENT])
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client = test_app.test_client()
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client.config = quart_app.config
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yield client
579+
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510581
@pytest_asyncio.fixture(scope="function")
511582
async def client_with_expiring_token(

‎tests/snapshots/test_app/test_ask_rtr_text_agent_filter/agent_auth_client0/result.json‎

Lines changed: 104 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,104 @@
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{
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"context": {
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"data_points": {
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"images": null,
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"text": [
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"Benefit_Options-2.pdf: There is a whistleblower policy."
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]
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},
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"followup_questions": null,
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"thoughts": [
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{
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"description": [
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{
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"content": "What is the capital of France?",
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"role": "user"
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}
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],
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"props": {
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"filter": "category ne 'excluded' and (oids/any(g:search.in(g, 'OID_X')) or groups/any(g:search.in(g, 'GROUP_Y, GROUP_Z')))",
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"max_docs_for_reranker": 150,
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"reranker_threshold": 0
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},
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"title": "Use agentic retrieval"
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},
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{
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"description": [
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{
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"captions": [],
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"category": null,
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"content": "There is a whistleblower policy.",
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"groups": null,
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"id": "Benefit_Options-2.pdf",
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"oids": null,
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"reranker_score": null,
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"score": null,
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"search_agent_query": "whistleblower query",
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"sourcefile": null,
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"sourcepage": "Benefit_Options-2.pdf"
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}
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],
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"props": {
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"deployment": "gpt-4o-mini",
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"model": "gpt-4o-mini",
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"query_plan": [
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{
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"elapsed_ms": 200,
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"id": 0,
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"input_tokens": 10,
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"output_tokens": 20,
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"type": "ModelQueryPlanning"
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},
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{
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"count": 10,
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"elapsed_ms": 50,
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"id": 1,
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"query": {
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"search": "whistleblower query"
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},
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"target_index": "index",
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"type": "AzureSearchQuery"
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}
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]
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},
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"title": "Agentic retrieval results (top 3)"
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},
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{
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"description": [
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{
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"content": "You are an intelligent assistant helping Contoso Inc employees with their healthcare plan questions and employee handbook questions.\nUse 'you' to refer to the individual asking the questions even if they ask with 'I'.\nAnswer the following question using only the data provided in the sources below.\nEach source has a name followed by colon and the actual information, always include the source name for each fact you use in the response.\nIf you cannot answer using the sources below, say you don't know. Use below example to answer",
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"role": "system"
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},
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{
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"content": "What is the deductible for the employee plan for a visit to Overlake in Bellevue?\n\nSources:\ninfo1.txt: deductibles depend on whether you are in-network or out-of-network. In-network deductibles are $500 for employee and $1000 for family. Out-of-network deductibles are $1000 for employee and $2000 for family.\ninfo2.pdf: Overlake is in-network for the employee plan.\ninfo3.pdf: Overlake is the name of the area that includes a park and ride near Bellevue.\ninfo4.pdf: In-network institutions include Overlake, Swedish and others in the region.",
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"role": "user"
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},
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{
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"content": "In-network deductibles are $500 for employee and $1000 for family [info1.txt] and Overlake is in-network for the employee plan [info2.pdf][info4.pdf].",
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"role": "assistant"
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},
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{
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"content": "What is the capital of France?\nSources:\n\nBenefit_Options-2.pdf: There is a whistleblower policy.",
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"role": "user"
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}
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],
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"props": {
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"deployment": "gpt-4o-mini",
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"model": "gpt-4o-mini",
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"token_usage": {
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"completion_tokens": 896,
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"prompt_tokens": 23,
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"reasoning_tokens": 0,
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"total_tokens": 919
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}
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},
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"title": "Prompt to generate answer"
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}
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]
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},
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"message": {
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"content": "The capital of France is Paris. [Benefit_Options-2.pdf].",
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"role": "assistant"
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},
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"session_state": null
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}

‎tests/snapshots/test_app/test_chat_text_agent/agent_client0/result.json‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,96 @@
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{
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"context": {
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"data_points": {
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"images": null,
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"text": [
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"Benefit_Options-2.pdf: There is a whistleblower policy."
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]
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},
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"followup_questions": null,
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"thoughts": [
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{
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"description": [
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{
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"content": "What is the capital of France?",
15+
"role": "user"
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}
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],
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"props": {
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"filter": null,
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"max_docs_for_reranker": 150,
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"reranker_threshold": 0
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},
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"title": "Use agentic retrieval"
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},
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{
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"description": [
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{
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"captions": [],
29+
"category": null,
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"content": "There is a whistleblower policy.",
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"groups": null,
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"id": "Benefit_Options-2.pdf",
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"oids": null,
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"reranker_score": null,
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"score": null,
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"search_agent_query": "whistleblower query",
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"sourcefile": null,
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"sourcepage": "Benefit_Options-2.pdf"
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}
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],
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"props": {
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"deployment": "gpt-4o-mini",
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"model": "gpt-4o-mini",
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"query_plan": [
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{
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"elapsed_ms": 200,
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"id": 0,
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"input_tokens": 10,
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"output_tokens": 20,
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"type": "ModelQueryPlanning"
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},
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{
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"count": 10,
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"elapsed_ms": 50,
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"id": 1,
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"query": {
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"search": "whistleblower query"
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},
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"target_index": "index",
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"type": "AzureSearchQuery"
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}
62+
]
63+
},
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"title": "Agentic retrieval results (top 3)"
65+
},
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{
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"description": [
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{
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"content": "Assistant helps the company employees with their healthcare plan questions, and questions about the employee handbook. Be brief in your answers.\nAnswer ONLY with the facts listed in the list of sources below. If there isn't enough information below, say you don't know. Do not generate answers that don't use the sources below. If asking a clarifying question to the user would help, ask the question.\nIf the question is not in English, answer in the language used in the question.\nEach source has a name followed by colon and the actual information, always include the source name for each fact you use in the response. Use square brackets to reference the source, for example [info1.txt]. Don't combine sources, list each source separately, for example [info1.txt][info2.pdf].",
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"role": "system"
71+
},
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{
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"content": "What is the capital of France?\n\nSources:\n\nBenefit_Options-2.pdf: There is a whistleblower policy.",
74+
"role": "user"
75+
}
76+
],
77+
"props": {
78+
"deployment": "gpt-4o-mini",
79+
"model": "gpt-4o-mini",
80+
"token_usage": {
81+
"completion_tokens": 896,
82+
"prompt_tokens": 23,
83+
"reasoning_tokens": 0,
84+
"total_tokens": 919
85+
}
86+
},
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"title": "Prompt to generate answer"
88+
}
89+
]
90+
},
91+
"message": {
92+
"content": "The capital of France is Paris. [Benefit_Options-2.pdf].",
93+
"role": "assistant"
94+
},
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"session_state": null
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}

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