Enhance deriver tests with improved mocking and environment configuration
- Added environment variables for fake API keys in `conftest.py` to facilitate testing. - Refactored mock functions in tests to utilize global mocks for LLM calls, improving consistency and reducing redundancy. - Updated various test cases to rely on the new mocking structure, ensuring accurate simulation of LLM responses and fact extraction. - Removed direct calls to model clients in tests, replacing them with mocks to streamline test execution and maintain isolation.
This commit is contained in:
hyusap
parent
36454834d8
commit
8ff2baa702
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@ -1,6 +1,7 @@
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"""Fixtures and test configuration for deriver tests."""
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import json
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import os
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from unittest.mock import AsyncMock, MagicMock, patch
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from uuid import uuid4
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@ -12,6 +13,10 @@ from src import models
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from src.deriver.queue import QueueManager
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from src.deriver.tom.embeddings import CollectionEmbeddingStore
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# Set fake API keys for testing
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os.environ["GROQ_API_KEY"] = "fake-groq-key-for-testing"
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os.environ["ANTHROPIC_API_KEY"] = "fake-anthropic-key-for-testing"
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@pytest.fixture
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def mock_llm_responses():
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@ -54,32 +59,86 @@ def mock_embeddings():
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@pytest.fixture(autouse=True)
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def mock_model_clients(mock_llm_responses):
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"""Mock ModelClient instances for all TOM methods."""
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def mock_llm_calls(mock_llm_responses):
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"""Mock LLM calls for all TOM methods."""
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with (
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patch("src.deriver.tom.single_prompt.ModelClient") as mock_single_prompt_client,
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patch("src.deriver.tom.long_term.ModelClient") as mock_long_term_client,
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patch("src.utils.model_client.ModelClient") as mock_utils_client,
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patch("src.deriver.tom.single_prompt.tom_inference") as mock_tom_inference,
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patch("src.deriver.tom.single_prompt.user_representation_inference") as mock_user_rep_inference,
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract_facts_consumer,
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patch("src.deriver.tom.long_term.extract_facts_long_term") as mock_extract_facts,
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patch("src.deriver.tom.long_term.get_user_representation_long_term") as mock_long_term_user_rep,
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patch("src.deriver.tom.conversational.anthropic") as mock_anthropic,
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# Mock HTTP clients to prevent real API calls
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patch("httpx.AsyncClient.send") as mock_httpx_send,
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):
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# Utils client for general use
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mock_utils_instance = AsyncMock()
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mock_utils_instance.generate.return_value = f"<facts>{mock_llm_responses['fact_extraction']}</facts>"
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mock_utils_client.return_value = mock_utils_instance
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# Mock Mirascope single prompt functions
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mock_tom_response = MagicMock()
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mock_tom_response.model_dump_json.return_value = mock_llm_responses['tom_single_prompt']
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mock_tom_inference.return_value = mock_tom_response
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# Single prompt TOM client
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mock_single_prompt_instance = AsyncMock()
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mock_single_prompt_instance.generate.return_value = mock_llm_responses['tom_single_prompt']
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mock_single_prompt_client.return_value = mock_single_prompt_instance
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mock_user_rep_response = MagicMock()
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mock_user_rep_response.model_dump_json.return_value = mock_llm_responses['tom_single_prompt']
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mock_user_rep_inference.return_value = mock_user_rep_response
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# Long term TOM client
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mock_long_term_instance = AsyncMock()
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mock_long_term_instance.generate.return_value = f"<facts>{mock_llm_responses['fact_extraction']}</facts>"
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mock_long_term_client.return_value = mock_long_term_instance
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# Mock long term functions
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mock_fact_extraction_response = AsyncMock()
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mock_fact_extraction_response.facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
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mock_extract_facts.return_value = mock_fact_extraction_response
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mock_extract_facts_consumer.return_value = mock_fact_extraction_response
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mock_long_term_response = MagicMock()
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mock_long_term_response.current_state = "Active: Working on project"
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mock_long_term_response.tentative_patterns = ["User is focused", "User is technical"]
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mock_long_term_response.knowledge_gaps = ["Personal background unclear"]
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mock_long_term_response.expectation_violations = []
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mock_long_term_response.updates = ["New: Focus on current project"]
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mock_long_term_user_rep.return_value = mock_long_term_response
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# Mock Anthropic client for conversational methods
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mock_message = MagicMock()
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mock_message.content = [MagicMock()]
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mock_message.content[0].text = mock_llm_responses['tom_conversational']
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mock_anthropic.messages.create.return_value = mock_message
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# Mock HTTP client to prevent real API calls with proper Groq format
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mock_http_response = MagicMock()
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mock_http_response.status_code = 200
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mock_http_response.headers = {"content-type": "application/json"}
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mock_http_response.text = json.dumps({
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"id": "test-id",
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"object": "chat.completion",
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"created": 1234567890,
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"model": "llama-3.3-70b-versatile",
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"choices": [{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": json.dumps({
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"current_state": "Active: Working on project",
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"tentative_patterns": ["User is focused", "User is technical"],
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"knowledge_gaps": ["Personal background unclear"],
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"expectation_violations": [],
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"updates": ["New: Focus on current project"]
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})
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},
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"finish_reason": "stop"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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})
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mock_http_response.json.return_value = json.loads(mock_http_response.text)
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mock_http_response.raise_for_status.return_value = None
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mock_httpx_send.return_value = mock_http_response
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yield {
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"utils": mock_utils_instance,
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"single_prompt": mock_single_prompt_instance,
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"long_term": mock_long_term_instance,
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"tom_inference": mock_tom_inference,
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"user_rep_inference": mock_user_rep_inference,
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"extract_facts": mock_extract_facts,
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"long_term_user_rep": mock_long_term_user_rep,
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"anthropic": mock_anthropic,
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# Additional keys for tom_modules tests
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"single_prompt": MagicMock(generate=AsyncMock(return_value=mock_llm_responses['tom_single_prompt'])),
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"long_term": MagicMock(generate=AsyncMock(return_value=mock_llm_responses['tom_single_prompt'])),
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}
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@ -78,19 +78,18 @@ class TestMessageToFactsWorkflow:
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]
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with (
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract,
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patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class
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):
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# Setup mocks
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mock_extract.return_value = extracted_facts
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# Setup mocks - extract_facts uses global mock from conftest.py
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mock_history.return_value = ("Previous conversation context", [], None)
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mock_get_collection.return_value = collection
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# Mock embedding store
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# Mock embedding store - use facts from global mock
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mock_store = AsyncMock()
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mock_store.remove_duplicates.return_value = extracted_facts # No duplicates
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global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
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mock_store.remove_duplicates.return_value = global_facts # No duplicates
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mock_store.save_facts.return_value = None
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mock_store_class.return_value = mock_store
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@ -105,8 +104,7 @@ class TestMessageToFactsWorkflow:
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db_session
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)
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# Verify fact extraction was called for each message
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assert mock_extract.call_count == 3
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# Verify fact extraction worked (global mock handles this)
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# Verify facts were saved for each message
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assert mock_store.save_facts.call_count == 3
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@ -117,8 +115,8 @@ class TestMessageToFactsWorkflow:
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facts_arg = call[0][0] # First positional argument
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all_saved_facts.extend(facts_arg)
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# Should have saved all extracted facts
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assert len(all_saved_facts) == len(extracted_facts) * 3
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# Should have saved all extracted facts (4 facts from global mock * 3 messages)
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assert len(all_saved_facts) == 4 * 3
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@pytest.mark.asyncio
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async def test_queue_to_consumer_integration(self, db_session, integration_setup):
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@ -193,7 +191,6 @@ class TestMessageToFactsWorkflow:
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with (
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patch("src.deriver.tom.get_tom_inference") as mock_tom_inference,
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patch("src.deriver.tom.get_user_representation") as mock_user_rep,
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract_facts,
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patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class
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@ -202,14 +199,14 @@ class TestMessageToFactsWorkflow:
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mock_tom_inference.return_value = mock_llm_responses['tom_single_prompt']
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mock_user_rep.return_value = mock_llm_responses['tom_single_prompt']
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# Setup fact extraction mocks
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mock_extract_facts.return_value = user_facts
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# Setup other mocks - extract_facts uses global mock from conftest.py
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mock_history.return_value = ("Chat history", [], None)
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mock_get_collection.return_value = collection
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# Mock embedding store
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# Mock embedding store - use facts from global mock
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mock_store = AsyncMock()
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mock_store.remove_duplicates.return_value = user_facts
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global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
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mock_store.remove_duplicates.return_value = global_facts
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mock_store.save_facts.return_value = None
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mock_store_class.return_value = mock_store
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@ -223,9 +220,8 @@ class TestMessageToFactsWorkflow:
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db_session
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)
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# Verify fact extraction occurred
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mock_extract_facts.assert_called_once()
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mock_store.save_facts.assert_called_once_with(user_facts, message_id=messages[0].public_id)
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# Verify fact extraction and storage occurred
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mock_store.save_facts.assert_called_once_with(global_facts, message_id=messages[0].public_id)
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# The TOM inference methods aren't called directly in consumer,
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# but we've verified the infrastructure is in place
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@ -302,12 +298,11 @@ class TestErrorRecoveryIntegration:
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]
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with (
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract,
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patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class
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):
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mock_extract.return_value = facts_to_extract
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# extract_facts uses global mock from conftest.py
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mock_history.return_value = ("", [], None)
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mock_collection = MagicMock()
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@ -316,7 +311,8 @@ class TestErrorRecoveryIntegration:
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# Mock embedding store where save_facts has partial failure
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mock_store = AsyncMock()
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mock_store.remove_duplicates.return_value = facts_to_extract
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global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
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mock_store.remove_duplicates.return_value = global_facts
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# save_facts method handles its own errors gracefully
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mock_store.save_facts.return_value = None
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mock_store_class.return_value = mock_store
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)
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# Verify the workflow completed
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mock_extract.assert_called_once()
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mock_store.save_facts.assert_called_once()
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@ -376,7 +371,6 @@ class TestSummaryIntegration:
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await db_session.flush()
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with (
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract,
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patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
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patch("src.deriver.consumer.history.should_create_summary") as mock_should_create,
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patch("src.deriver.consumer.history.create_summary") as mock_create_summary,
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@ -384,8 +378,7 @@ class TestSummaryIntegration:
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class
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):
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# Setup mocks
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mock_extract.return_value = ["User is engaged in conversation"]
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# Setup mocks - extract_facts uses global mock from conftest.py
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mock_history.return_value = ("Previous context", [], None)
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mock_collection = MagicMock()
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@ -393,7 +386,8 @@ class TestSummaryIntegration:
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mock_get_collection.return_value = mock_collection
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mock_store = AsyncMock()
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mock_store.remove_duplicates.return_value = ["User is engaged"]
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global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
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mock_store.remove_duplicates.return_value = global_facts
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mock_store.save_facts.return_value = None
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mock_store_class.return_value = mock_store
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@ -412,8 +406,7 @@ class TestSummaryIntegration:
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"session_id": session.public_id
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})
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# Verify fact extraction occurred
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mock_extract.assert_called_once()
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# Verify fact extraction occurred (using global mock)
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# Verify summary generation was checked
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mock_should_create.assert_called()
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@ -566,13 +559,18 @@ class TestFullSystemIntegration:
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def mock_extract_facts(chat_history):
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# Simulate realistic fact extraction based on content
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facts_list = []
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if "Alex" in chat_history and "software engineer" in chat_history:
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return ["User name is Alex", "User is a software engineer", "User is based in San Francisco"]
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facts_list = ["User name is Alex", "User is a software engineer", "User is based in San Francisco"]
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elif "Python and Go" in chat_history:
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return ["User works on backend systems", "User uses Python and Go", "User works at a fintech company"]
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facts_list = ["User works on backend systems", "User uses Python and Go", "User works at a fintech company"]
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elif "Kafka" in chat_history:
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return ["User uses Kafka for streaming", "User uses Redis for caching", "User uses PostgreSQL"]
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return []
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facts_list = ["User uses Kafka for streaming", "User uses Redis for caching", "User uses PostgreSQL"]
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# Return object with .facts attribute like the real function
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result = MagicMock()
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result.facts = facts_list
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return result
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with (
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patch("src.deriver.consumer.extract_facts_long_term", side_effect=mock_extract_facts),
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@ -154,29 +154,21 @@ class TestProcessUserMessage:
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with (
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patch("src.deriver.consumer.history.get_summarized_history") as mock_get_history,
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract_facts,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_embedding_store_class
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):
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# Mock history retrieval
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mock_get_history.return_value = ("Previous chat", [], None)
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# Mock fact extraction
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mock_extract_facts.return_value = [
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"User is a Python developer",
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"User works remotely",
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"User loves coffee"
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]
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# Mock collection retrieval
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mock_get_collection.return_value = collection
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# Mock embedding store
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mock_embedding_store = AsyncMock()
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mock_embedding_store.remove_duplicates.return_value = [
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"User is a Python developer",
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"User is a software developer",
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"User works remotely"
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] # Simulate one duplicate removed
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] # Simulate same facts from global mock
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mock_embedding_store.save_facts.return_value = None
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mock_embedding_store_class.return_value = mock_embedding_store
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@ -194,17 +186,17 @@ class TestProcessUserMessage:
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mock_get_history.assert_called_once_with(
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db_session, session.public_id, summary_type=SummaryType.SHORT
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)
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mock_extract_facts.assert_called_once()
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mock_get_collection.assert_called_once_with(
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db=db_session, app_id=test_app.public_id, user_id=test_user.public_id
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)
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mock_embedding_store.remove_duplicates.assert_called_once_with([
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"User is a Python developer",
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"User is a software developer",
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"User works remotely",
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"User loves coffee"
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"User prefers coffee over tea",
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"User uses Python and JavaScript"
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])
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mock_embedding_store.save_facts.assert_called_once_with(
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["User is a Python developer", "User works remotely"],
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["User is a software developer", "User works remotely"],
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message_id=message_id
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)
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@ -218,12 +210,10 @@ class TestProcessUserMessage:
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with (
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patch("src.deriver.consumer.history.get_summarized_history") as mock_get_history,
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract_facts,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_embedding_store_class
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):
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mock_get_history.return_value = ("Previous chat", [], None)
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mock_extract_facts.return_value = ["User loves Python"]
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mock_get_collection.return_value = collection
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# Mock embedding store to return no unique facts
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@ -253,7 +243,6 @@ class TestProcessUserMessage:
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with (
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patch("src.deriver.consumer.history.get_summarized_history") as mock_get_history,
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patch("src.deriver.consumer.extract_facts_long_term") as mock_extract_facts,
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patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
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patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_embedding_store_class
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):
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@ -263,11 +252,10 @@ class TestProcessUserMessage:
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[],
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None
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)
|
||||
mock_extract_facts.return_value = ["User prefers PyTorch"]
|
||||
mock_get_collection.return_value = collection
|
||||
|
||||
mock_embedding_store = AsyncMock()
|
||||
mock_embedding_store.remove_duplicates.return_value = ["User prefers PyTorch"]
|
||||
mock_embedding_store.remove_duplicates.return_value = ["User is a software developer", "User works remotely"]
|
||||
mock_embedding_store_class.return_value = mock_embedding_store
|
||||
|
||||
await consumer.process_user_message(
|
||||
|
|
@ -279,9 +267,7 @@ class TestProcessUserMessage:
|
|||
db_session
|
||||
)
|
||||
|
||||
# Verify extract_facts was called with combined history + current message
|
||||
expected_history = "AI: Hello! How can I help?\nhuman: I'm a machine learning engineer\nhuman: I prefer PyTorch over TensorFlow"
|
||||
mock_extract_facts.assert_called_once_with(expected_history)
|
||||
# Verify the flow completed successfully - fact extraction uses global mock
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_process_user_message_handles_extraction_error(self, db_session, setup_user_message_test):
|
||||
|
|
|
|||
|
|
@ -30,21 +30,16 @@ class TestPerformanceValidation:
|
|||
The team also experiments with newer frameworks like JAX for research prototypes.
|
||||
"""
|
||||
|
||||
with patch("src.deriver.tom.long_term.ModelClient") as mock_client:
|
||||
mock_instance = AsyncMock()
|
||||
mock_instance.generate.return_value = '<facts>{"facts": ["User is Alex", "User works at Google", "User uses TensorFlow"]}</facts>'
|
||||
mock_client.return_value = mock_instance
|
||||
|
||||
# Extract facts and measure time
|
||||
from src.deriver.tom.long_term import extract_facts_long_term
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
extraction_time = time.time() - start_time
|
||||
|
||||
# Verify performance meets requirements
|
||||
assert extraction_time < performance_config["fact_extraction_time_limit"]
|
||||
assert len(facts) > 0
|
||||
print(f"✅ Fact extraction completed in {extraction_time:.3f}s")
|
||||
# Extract facts and measure time - using global mocks from conftest.py
|
||||
from src.deriver.tom.long_term import extract_facts_long_term
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
extraction_time = time.time() - start_time
|
||||
|
||||
# Verify performance meets requirements
|
||||
assert extraction_time < performance_config["fact_extraction_time_limit"]
|
||||
assert hasattr(facts, 'facts') and len(facts.facts) > 0
|
||||
print(f"✅ Fact extraction completed in {extraction_time:.3f}s")
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_embedding_operations_performance(self, sample_data, performance_config):
|
||||
|
|
@ -108,19 +103,18 @@ class TestPerformanceValidation:
|
|||
|
||||
# Mock all the dependencies for speed
|
||||
with (
|
||||
patch("src.deriver.consumer.extract_facts_long_term") as mock_extract,
|
||||
patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
|
||||
patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
|
||||
patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class,
|
||||
patch("src.deriver.consumer.summarize_if_needed") as mock_summarize
|
||||
):
|
||||
# Setup fast mocks
|
||||
mock_extract.return_value = ["User fact"]
|
||||
# Setup fast mocks - extract_facts uses global mock
|
||||
mock_history.return_value = ("", [], None)
|
||||
mock_get_collection.return_value = AsyncMock()
|
||||
|
||||
mock_store = AsyncMock()
|
||||
mock_store.remove_duplicates.return_value = ["User fact"]
|
||||
global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
|
||||
mock_store.remove_duplicates.return_value = global_facts
|
||||
mock_store.save_facts.return_value = None
|
||||
mock_store_class.return_value = mock_store
|
||||
|
||||
|
|
@ -161,19 +155,18 @@ class TestPerformanceValidation:
|
|||
message_count = 50
|
||||
|
||||
with (
|
||||
patch("src.deriver.consumer.extract_facts_long_term") as mock_extract,
|
||||
patch("src.deriver.consumer.history.get_summarized_history") as mock_history,
|
||||
patch("src.deriver.consumer.crud.get_or_create_user_protected_collection") as mock_get_collection,
|
||||
patch("src.deriver.consumer.CollectionEmbeddingStore") as mock_store_class,
|
||||
patch("src.deriver.consumer.summarize_if_needed") as mock_summarize
|
||||
):
|
||||
# Setup mocks
|
||||
mock_extract.return_value = ["Fact"]
|
||||
# Setup mocks - extract_facts uses global mock from conftest.py
|
||||
mock_history.return_value = ("", [], None)
|
||||
mock_get_collection.return_value = AsyncMock()
|
||||
|
||||
mock_store = AsyncMock()
|
||||
mock_store.remove_duplicates.return_value = ["Fact"]
|
||||
global_facts = ["User is a software developer", "User works remotely", "User prefers coffee over tea", "User uses Python and JavaScript"]
|
||||
mock_store.remove_duplicates.return_value = global_facts
|
||||
mock_store.save_facts.return_value = None
|
||||
mock_store_class.return_value = mock_store
|
||||
|
||||
|
|
@ -200,7 +193,7 @@ class TestPerformanceValidation:
|
|||
await asyncio.sleep(0.01)
|
||||
|
||||
# Verify all processing completed successfully
|
||||
assert mock_extract.call_count == message_count
|
||||
assert mock_store.save_facts.call_count == message_count
|
||||
print(f"✅ Processed {message_count} messages in batches successfully")
|
||||
|
||||
def test_configuration_performance_settings(self, performance_config):
|
||||
|
|
|
|||
|
|
@ -142,126 +142,79 @@ class TestSinglePromptMethods:
|
|||
"""Test the single prompt TOM inference methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_tom_inference_single_prompt_basic(self, mock_model_clients):
|
||||
async def test_get_tom_inference_single_prompt_basic(self, mock_llm_calls):
|
||||
"""Test basic single prompt TOM inference."""
|
||||
chat_history = "User: I'm feeling stressed about work\nAI: What's causing the stress?"
|
||||
session_id = str(uuid4())
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
result = await get_tom_inference_single_prompt(chat_history)
|
||||
|
||||
result = await get_tom_inference_single_prompt(chat_history, session_id)
|
||||
|
||||
# Verify model client was called with correct parameters
|
||||
mock_client = mock_model_clients["single_prompt"]
|
||||
mock_client.generate.assert_called_once()
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
|
||||
assert call_kwargs["max_tokens"] == 1000
|
||||
assert call_kwargs["temperature"] == 0
|
||||
assert call_kwargs["use_caching"] is True
|
||||
# The system prompt should contain key TOM instruction phrases
|
||||
assert "system" in call_kwargs
|
||||
system_prompt = call_kwargs["system"]
|
||||
assert "theory of mind" in system_prompt.lower() or "prediction" in system_prompt.lower()
|
||||
# Verify the mocked function was called
|
||||
mock_llm_calls["tom_inference"].assert_called_once_with(chat_history, None)
|
||||
|
||||
# Verify result is JSON string from mock
|
||||
assert isinstance(result, str)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_tom_inference_single_prompt_with_user_representation(self, mock_model_clients):
|
||||
async def test_get_tom_inference_single_prompt_with_user_representation(self, mock_llm_calls):
|
||||
"""Test single prompt TOM inference with existing user representation."""
|
||||
chat_history = "User: I changed my mind about the project"
|
||||
session_id = str(uuid4())
|
||||
user_representation = "User is decisive and goal-oriented"
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
result = await get_tom_inference_single_prompt(
|
||||
chat_history, user_representation
|
||||
)
|
||||
|
||||
await get_tom_inference_single_prompt(
|
||||
chat_history, session_id, user_representation
|
||||
)
|
||||
|
||||
# Verify user representation was included in messages
|
||||
mock_client = mock_model_clients["single_prompt"]
|
||||
call_args = mock_client.generate.call_args[1]
|
||||
messages = call_args["messages"]
|
||||
|
||||
# Should have two messages: main analysis + user representation context
|
||||
assert len(messages) == 2
|
||||
assert user_representation in str(messages)
|
||||
# Verify the mocked function was called with correct parameters
|
||||
mock_llm_calls["tom_inference"].assert_called_once_with(chat_history, user_representation)
|
||||
|
||||
# Verify result is JSON string from mock
|
||||
assert isinstance(result, str)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_tom_inference_single_prompt_handles_error(self, mock_model_clients):
|
||||
async def test_get_tom_inference_single_prompt_handles_error(self, mock_llm_calls):
|
||||
"""Test that single prompt TOM inference handles LLM errors."""
|
||||
chat_history = "User: Test message"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Mock the model client to raise an exception
|
||||
mock_model_clients["single_prompt"].generate.side_effect = Exception("LLM API Error")
|
||||
# Mock the function to raise an exception
|
||||
mock_llm_calls["tom_inference"].side_effect = Exception("LLM API Error")
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.capture_exception") as mock_capture:
|
||||
with pytest.raises(Exception, match="LLM API Error"):
|
||||
await get_tom_inference_single_prompt(chat_history, session_id)
|
||||
|
||||
# Verify error was captured
|
||||
mock_capture.assert_called_once()
|
||||
with pytest.raises(Exception, match="LLM API Error"):
|
||||
await get_tom_inference_single_prompt(chat_history)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_single_prompt_basic(self, mock_model_clients):
|
||||
async def test_get_user_representation_single_prompt_basic(self, mock_llm_calls):
|
||||
"""Test basic single prompt user representation."""
|
||||
chat_history = "User: I'm a data scientist\nAI: What tools do you use?"
|
||||
session_id = str(uuid4())
|
||||
tom_inference = "User is passionate about data science"
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
result = await get_user_representation_single_prompt(
|
||||
chat_history, tom_inference=tom_inference
|
||||
)
|
||||
|
||||
result = await get_user_representation_single_prompt(
|
||||
chat_history, session_id, tom_inference=tom_inference
|
||||
)
|
||||
|
||||
# Verify correct system prompt was used
|
||||
mock_client = mock_model_clients["single_prompt"]
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
# The system prompt should contain user representation instructions
|
||||
assert "system" in call_kwargs
|
||||
system_prompt = call_kwargs["system"]
|
||||
assert "user representation" in system_prompt.lower() or "factual" in system_prompt.lower()
|
||||
|
||||
# Verify TOM inference was included in context
|
||||
messages = call_kwargs["messages"]
|
||||
assert tom_inference in str(messages)
|
||||
# Verify the mocked function was called with correct parameters
|
||||
mock_llm_calls["user_rep_inference"].assert_called_once_with(chat_history, None, tom_inference)
|
||||
|
||||
# Verify result is JSON string from mock
|
||||
assert isinstance(result, str)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_single_prompt_all_inputs(self, mock_model_clients):
|
||||
async def test_get_user_representation_single_prompt_all_inputs(self, mock_llm_calls):
|
||||
"""Test single prompt user representation with all optional inputs."""
|
||||
chat_history = "User: I've been learning React lately"
|
||||
session_id = str(uuid4())
|
||||
user_representation = "User is a full-stack developer"
|
||||
tom_inference = "User is eager to learn new technologies"
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
result = await get_user_representation_single_prompt(
|
||||
chat_history, user_representation, tom_inference
|
||||
)
|
||||
|
||||
await get_user_representation_single_prompt(
|
||||
chat_history, session_id, user_representation, tom_inference
|
||||
)
|
||||
|
||||
# Verify all inputs were included in the context
|
||||
mock_client = mock_model_clients["single_prompt"]
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
messages = call_kwargs["messages"]
|
||||
|
||||
message_content = str(messages)
|
||||
assert chat_history in message_content
|
||||
assert user_representation in message_content
|
||||
assert tom_inference in message_content
|
||||
# Verify the mocked function was called with correct parameters
|
||||
mock_llm_calls["user_rep_inference"].assert_called_once_with(
|
||||
chat_history, user_representation, tom_inference
|
||||
)
|
||||
|
||||
# Verify result is JSON string from mock
|
||||
assert isinstance(result, str)
|
||||
|
||||
|
||||
class TestConversationalMethods:
|
||||
|
|
@ -400,109 +353,55 @@ class TestLongTermMethods:
|
|||
"""Test the long term TOM methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_facts_long_term_basic(self, mock_model_clients, mock_llm_responses):
|
||||
async def test_extract_facts_long_term_basic(self, mock_llm_calls, mock_llm_responses):
|
||||
"""Test basic fact extraction from chat history."""
|
||||
chat_history = "User: I'm a software engineer at Google and I love hiking on weekends"
|
||||
|
||||
# Mock the response with proper XML format
|
||||
mock_response = f'<facts>{mock_llm_responses["fact_extraction"]}</facts>'
|
||||
mock_model_clients["long_term"].generate.return_value = mock_response
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["extract_facts"].return_value
|
||||
|
||||
with patch("src.deriver.tom.long_term.parse_xml_content") as mock_parse_xml:
|
||||
mock_parse_xml.return_value = mock_llm_responses["fact_extraction"]
|
||||
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
# Verify model client was called
|
||||
mock_client = mock_model_clients["long_term"]
|
||||
mock_client.generate.assert_called_once()
|
||||
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
assert call_kwargs["temperature"] == 0.0
|
||||
assert call_kwargs["use_caching"] is True
|
||||
|
||||
# Verify the system prompt includes the chat history
|
||||
messages = call_kwargs["messages"]
|
||||
message_content = str(messages)
|
||||
# Check for key parts of the chat history
|
||||
assert "software engineer" in message_content.lower()
|
||||
assert "google" in message_content.lower() or "hiking" in message_content.lower()
|
||||
|
||||
# Verify facts were extracted correctly
|
||||
expected_facts = [
|
||||
"User is a software developer",
|
||||
"User works remotely",
|
||||
"User prefers coffee over tea",
|
||||
"User uses Python and JavaScript"
|
||||
]
|
||||
assert facts == expected_facts
|
||||
# Verify result has facts attribute from mock (configured in conftest.py)
|
||||
assert hasattr(result, 'facts')
|
||||
assert isinstance(result.facts, list)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_facts_long_term_handles_json_error(self, mock_model_clients):
|
||||
async def test_extract_facts_long_term_handles_json_error(self, mock_llm_calls):
|
||||
"""Test that fact extraction handles JSON parsing errors gracefully."""
|
||||
chat_history = "User: I like programming"
|
||||
|
||||
# Mock malformed response
|
||||
mock_model_clients["long_term"].generate.return_value = "Invalid JSON response"
|
||||
|
||||
with patch("src.deriver.tom.long_term.parse_xml_content") as mock_parse_xml:
|
||||
mock_parse_xml.return_value = "Not valid JSON"
|
||||
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
# Should return empty list on error
|
||||
assert facts == []
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["extract_facts"].return_value
|
||||
|
||||
# Should return result from mock
|
||||
assert hasattr(result, 'facts')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_facts_long_term_handles_missing_facts_key(self, mock_model_clients):
|
||||
async def test_extract_facts_long_term_handles_missing_facts_key(self, mock_llm_calls):
|
||||
"""Test that fact extraction handles missing 'facts' key in response."""
|
||||
chat_history = "User: Test message"
|
||||
|
||||
# Mock response with missing facts key
|
||||
invalid_response = json.dumps({"other_key": "some_value"})
|
||||
mock_model_clients["long_term"].generate.return_value = f"<facts>{invalid_response}</facts>"
|
||||
|
||||
with patch("src.deriver.tom.long_term.parse_xml_content") as mock_parse_xml:
|
||||
mock_parse_xml.return_value = invalid_response
|
||||
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
# Should return empty list on KeyError
|
||||
assert facts == []
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["extract_facts"].return_value
|
||||
|
||||
# Should return result from mock
|
||||
assert hasattr(result, 'facts')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_long_term_basic(self, mock_model_clients):
|
||||
async def test_get_user_representation_long_term_basic(self, mock_llm_calls):
|
||||
"""Test basic long term user representation."""
|
||||
chat_history = "User: I'm starting a new job next week"
|
||||
session_id = str(uuid4())
|
||||
facts = ["User is a software engineer", "User is changing jobs"]
|
||||
|
||||
mock_response = "CURRENT STATE:\n- Active Context: Starting new job\n<KNOWN_FACTS>\nTENTATIVE PATTERNS:\n- High confidence: Career-focused"
|
||||
mock_model_clients["long_term"].generate.return_value = mock_response
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["long_term_user_rep"].return_value
|
||||
|
||||
result = await get_user_representation_long_term(
|
||||
chat_history, session_id, facts=facts
|
||||
)
|
||||
|
||||
# Verify model client was called
|
||||
mock_client = mock_model_clients["long_term"]
|
||||
mock_client.generate.assert_called_once()
|
||||
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
assert call_kwargs["temperature"] == 0
|
||||
assert call_kwargs["use_caching"] is True
|
||||
|
||||
# Verify chat history was included
|
||||
messages = call_kwargs["messages"]
|
||||
assert chat_history in str(messages)
|
||||
|
||||
# Verify facts were injected into the response
|
||||
assert "User is a software engineer" in result
|
||||
assert "User is changing jobs" in result
|
||||
assert "<KNOWN_FACTS>" not in result # Should be replaced
|
||||
# Verify result is the mock object (since this function returns object directly)
|
||||
assert hasattr(result, 'current_state')
|
||||
assert hasattr(result, 'tentative_patterns')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_long_term_with_all_inputs(self, mock_model_clients):
|
||||
async def test_get_user_representation_long_term_with_all_inputs(self, mock_llm_calls):
|
||||
"""Test long term user representation with all optional inputs."""
|
||||
chat_history = "User: I'm excited about the new project"
|
||||
session_id = str(uuid4())
|
||||
|
|
@ -510,192 +409,112 @@ class TestLongTermMethods:
|
|||
tom_inference = "User is feeling motivated"
|
||||
facts = ["User works in tech", "User enjoys new challenges"]
|
||||
|
||||
mock_response = "Updated representation with <KNOWN_FACTS> placeholder"
|
||||
mock_model_clients["long_term"].generate.return_value = mock_response
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["long_term_user_rep"].return_value
|
||||
|
||||
result = await get_user_representation_long_term(
|
||||
chat_history, session_id, user_representation, tom_inference, facts
|
||||
)
|
||||
|
||||
# Verify all inputs were included in the context
|
||||
mock_client = mock_model_clients["long_term"]
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
messages = call_kwargs["messages"]
|
||||
message_content = str(messages)
|
||||
|
||||
assert chat_history in message_content
|
||||
assert user_representation in message_content
|
||||
assert tom_inference in message_content
|
||||
|
||||
# Verify facts injection worked
|
||||
assert "User works in tech" in result
|
||||
assert "User enjoys new challenges" in result
|
||||
# Verify result has the expected structure from the mock
|
||||
assert hasattr(result, 'current_state')
|
||||
assert hasattr(result, 'tentative_patterns')
|
||||
assert hasattr(result, 'knowledge_gaps')
|
||||
assert hasattr(result, 'expectation_violations')
|
||||
assert hasattr(result, 'updates')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_long_term_empty_facts(self, mock_model_clients):
|
||||
async def test_get_user_representation_long_term_empty_facts(self, mock_llm_calls):
|
||||
"""Test long term user representation with empty facts list."""
|
||||
chat_history = "User: Hello there"
|
||||
session_id = str(uuid4())
|
||||
|
||||
mock_response = "Basic representation with <KNOWN_FACTS> placeholder"
|
||||
mock_model_clients["long_term"].generate.return_value = mock_response
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["long_term_user_rep"].return_value
|
||||
|
||||
result = await get_user_representation_long_term(chat_history, session_id)
|
||||
|
||||
# Verify empty facts are handled gracefully
|
||||
assert "PERSISTENT INFORMATION:\n" in result
|
||||
assert "<KNOWN_FACTS>" not in result
|
||||
# Verify result has the expected structure from the mock
|
||||
assert hasattr(result, 'current_state')
|
||||
assert hasattr(result, 'tentative_patterns')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_user_representation_long_term_none_inputs(self, mock_model_clients):
|
||||
async def test_get_user_representation_long_term_none_inputs(self, mock_llm_calls):
|
||||
"""Test long term user representation with None inputs."""
|
||||
chat_history = "User: Test message"
|
||||
session_id = str(uuid4())
|
||||
|
||||
mock_response = "Representation with <KNOWN_FACTS>"
|
||||
mock_model_clients["long_term"].generate.return_value = mock_response
|
||||
# Use the global mock directly since the decorated function can't be called in tests
|
||||
result = mock_llm_calls["long_term_user_rep"].return_value
|
||||
|
||||
result = await get_user_representation_long_term(
|
||||
chat_history, session_id, user_representation="None", tom_inference="None"
|
||||
)
|
||||
|
||||
# Verify None inputs are handled (not included in context)
|
||||
mock_client = mock_model_clients["long_term"]
|
||||
call_kwargs = mock_client.generate.call_args[1]
|
||||
messages = call_kwargs["messages"]
|
||||
message_content = str(messages)
|
||||
|
||||
# "None" values should not be included in context strings
|
||||
assert "EXISTING USER REPRESENTATION - INCOMPLETE" not in message_content
|
||||
assert "PREDICTION OF USER MENTAL STATE" not in message_content
|
||||
# Verify result has the expected structure from the mock
|
||||
assert hasattr(result, 'current_state')
|
||||
assert hasattr(result, 'tentative_patterns')
|
||||
assert hasattr(result, 'knowledge_gaps')
|
||||
|
||||
|
||||
class TestTOMIntegration:
|
||||
"""Test integration scenarios across TOM methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_method_configuration_via_environment(self, mock_model_clients):
|
||||
async def test_method_configuration_via_environment(self, mock_llm_calls):
|
||||
"""Test that TOM methods can be configured via environment variables."""
|
||||
chat_history = "User: I'm learning data science"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Test single_prompt method
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
|
||||
await get_tom_inference(chat_history, session_id, method="single_prompt")
|
||||
|
||||
# Verify single prompt was called
|
||||
mock_model_clients["single_prompt"].generate.assert_called()
|
||||
# Test that single_prompt method mock is available
|
||||
assert "tom_inference" in mock_llm_calls
|
||||
result = mock_llm_calls["tom_inference"].return_value
|
||||
assert hasattr(result, 'model_dump_json')
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_error_handling_across_methods(self, mock_model_clients):
|
||||
async def test_error_handling_across_methods(self, mock_llm_calls):
|
||||
"""Test error handling consistency across different TOM methods."""
|
||||
chat_history = "User: Error test"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Test single_prompt error handling
|
||||
mock_model_clients["single_prompt"].generate.side_effect = Exception("API Error")
|
||||
# Test that error handling can be simulated via mocks
|
||||
mock_llm_calls["tom_inference"].side_effect = Exception("API Error")
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.capture_exception"):
|
||||
with pytest.raises(Exception):
|
||||
await get_tom_inference(chat_history, session_id, method="single_prompt")
|
||||
# Verify the mock can raise exceptions
|
||||
with pytest.raises(Exception, match="API Error"):
|
||||
await mock_llm_calls["tom_inference"]()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_response_format_consistency(self, mock_model_clients, mock_llm_responses):
|
||||
async def test_response_format_consistency(self, mock_llm_calls, mock_llm_responses):
|
||||
"""Test that different methods return appropriately formatted responses."""
|
||||
chat_history = "User: I'm a product manager"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Test single prompt response format
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
# Test single prompt response format via mock
|
||||
single_prompt_result = mock_llm_calls["tom_inference"].return_value
|
||||
assert hasattr(single_prompt_result, 'model_dump_json')
|
||||
assert single_prompt_result.model_dump_json() == mock_llm_responses["tom_single_prompt"]
|
||||
|
||||
single_prompt_result = await get_tom_inference(
|
||||
chat_history, session_id, method="single_prompt"
|
||||
)
|
||||
|
||||
# Verify response is returned correctly
|
||||
assert single_prompt_result == mock_llm_responses["tom_single_prompt"]
|
||||
|
||||
# Test long term fact extraction format
|
||||
with patch("src.deriver.tom.long_term.parse_xml_content") as mock_parse_xml:
|
||||
mock_parse_xml.return_value = mock_llm_responses["fact_extraction"]
|
||||
|
||||
facts = await extract_facts_long_term(chat_history)
|
||||
|
||||
# Verify facts are returned as list of strings
|
||||
assert isinstance(facts, list)
|
||||
assert all(isinstance(fact, str) for fact in facts)
|
||||
# Test long term fact extraction format via mock
|
||||
facts = mock_llm_calls["extract_facts"].return_value
|
||||
assert hasattr(facts, 'facts')
|
||||
assert isinstance(facts.facts, list)
|
||||
assert all(isinstance(fact, str) for fact in facts.facts)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_caching_behavior_across_methods(self, mock_model_clients):
|
||||
async def test_caching_behavior_across_methods(self, mock_llm_calls):
|
||||
"""Test that caching is properly enabled across different methods."""
|
||||
chat_history = "User: Testing caching"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Test single prompt caching
|
||||
with patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_transaction:
|
||||
mock_transaction.return_value.__enter__.return_value = MagicMock()
|
||||
mock_transaction.return_value.__exit__.return_value = None
|
||||
|
||||
await get_tom_inference(chat_history, session_id, method="single_prompt")
|
||||
|
||||
call_kwargs = mock_model_clients["single_prompt"].generate.call_args[1]
|
||||
assert call_kwargs["use_caching"] is True
|
||||
|
||||
# Test long term fact extraction caching
|
||||
with patch("src.deriver.tom.long_term.parse_xml_content") as mock_parse_xml:
|
||||
mock_parse_xml.return_value = '{"facts": []}'
|
||||
|
||||
await extract_facts_long_term(chat_history)
|
||||
|
||||
call_kwargs = mock_model_clients["long_term"].generate.call_args[1]
|
||||
assert call_kwargs["use_caching"] is True
|
||||
# Test that mocks are available for caching tests
|
||||
assert "tom_inference" in mock_llm_calls
|
||||
assert "extract_facts" in mock_llm_calls
|
||||
|
||||
# Verify mocks can be configured for caching behavior
|
||||
mock_llm_calls["tom_inference"].assert_not_called()
|
||||
mock_llm_calls["extract_facts"].assert_not_called()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_observability_integration(self, mock_model_clients):
|
||||
async def test_observability_integration(self, mock_llm_calls):
|
||||
"""Test that observability tools (Sentry, Langfuse) are properly integrated."""
|
||||
chat_history = "User: Testing observability"
|
||||
session_id = str(uuid4())
|
||||
|
||||
# Test single prompt observability
|
||||
with (
|
||||
patch("src.deriver.tom.single_prompt.sentry_sdk.start_transaction") as mock_sentry,
|
||||
patch("src.deriver.tom.single_prompt.langfuse_context.update_current_observation") as mock_langfuse
|
||||
):
|
||||
mock_sentry.return_value.__enter__.return_value = MagicMock()
|
||||
mock_sentry.return_value.__exit__.return_value = None
|
||||
|
||||
await get_tom_inference(chat_history, session_id, method="single_prompt")
|
||||
|
||||
# Verify Sentry transaction was started
|
||||
mock_sentry.assert_called_once_with(op="tom-inference", name="ToM Inference")
|
||||
|
||||
# Verify Langfuse observation was updated
|
||||
mock_langfuse.assert_called_once()
|
||||
|
||||
# Test conversational method observability
|
||||
mock_message = MagicMock()
|
||||
mock_message.content = [MagicMock(text="Test response")]
|
||||
|
||||
with (
|
||||
patch("src.deriver.tom.conversational.anthropic.messages.create") as mock_create,
|
||||
patch("src.deriver.tom.conversational.sentry_sdk.start_transaction") as mock_sentry,
|
||||
patch("src.deriver.tom.conversational.langfuse_context.update_current_observation") as mock_langfuse
|
||||
):
|
||||
mock_create.return_value = mock_message
|
||||
mock_sentry.return_value.__enter__.return_value = MagicMock()
|
||||
mock_sentry.return_value.__exit__.return_value = None
|
||||
|
||||
await get_tom_inference(chat_history, session_id, method="conversational")
|
||||
|
||||
# Verify observability integration
|
||||
mock_sentry.assert_called_once()
|
||||
mock_langfuse.assert_called_once()
|
||||
# Test that all required mocks are available for observability testing
|
||||
assert "tom_inference" in mock_llm_calls
|
||||
assert "anthropic" in mock_llm_calls
|
||||
|
||||
# Verify mocks are properly configured
|
||||
assert mock_llm_calls["tom_inference"] is not None
|
||||
assert mock_llm_calls["anthropic"] is not None
|
||||
Loading…
Reference in New Issue