140 lines
5.4 KiB
Python
140 lines
5.4 KiB
Python
import pytest
|
|
import yaml
|
|
from unittest.mock import MagicMock, patch
|
|
from langchain_core.messages import AIMessage
|
|
from ea_chatbot.graph.workflow import app
|
|
from ea_chatbot.graph.nodes.query_analyzer import QueryAnalysis
|
|
from ea_chatbot.schemas import TaskPlanResponse, TaskPlanContext, CodeGenerationResponse
|
|
|
|
@pytest.fixture
|
|
def mock_llms():
|
|
with patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model") as mock_qa_llm, \
|
|
patch("ea_chatbot.graph.nodes.planner.get_llm_model") as mock_planner_llm, \
|
|
patch("ea_chatbot.graph.nodes.coder.get_llm_model") as mock_coder_llm, \
|
|
patch("ea_chatbot.graph.nodes.summarizer.get_llm_model") as mock_summarizer_llm, \
|
|
patch("ea_chatbot.graph.nodes.researcher.get_llm_model") as mock_researcher_llm, \
|
|
patch("ea_chatbot.graph.nodes.summarize_conversation.get_llm_model") as mock_summary_llm, \
|
|
patch("ea_chatbot.utils.database_inspection.get_data_summary") as mock_get_summary:
|
|
mock_get_summary.return_value = "Data summary"
|
|
|
|
# Mock summary LLM to return a simple response
|
|
mock_summary_instance = MagicMock()
|
|
mock_summary_llm.return_value = mock_summary_instance
|
|
mock_summary_instance.invoke.return_value = AIMessage(content="Turn summary")
|
|
|
|
yield {
|
|
"qa": mock_qa_llm,
|
|
"planner": mock_planner_llm,
|
|
"coder": mock_coder_llm,
|
|
"summarizer": mock_summarizer_llm,
|
|
"researcher": mock_researcher_llm,
|
|
"summary": mock_summary_llm
|
|
}
|
|
|
|
def test_workflow_data_analysis_flow(mock_llms):
|
|
"""Test full flow: QueryAnalyzer -> Planner -> Coder -> Executor -> Summarizer."""
|
|
|
|
# 1. Mock Query Analyzer (routes to plan)
|
|
mock_qa_instance = MagicMock()
|
|
mock_llms["qa"].return_value = mock_qa_instance
|
|
mock_qa_instance.with_structured_output.return_value.invoke.return_value = QueryAnalysis(
|
|
data_required=["2024 results"],
|
|
unknowns=[],
|
|
ambiguities=[],
|
|
conditions=[],
|
|
next_action="plan"
|
|
)
|
|
|
|
# 2. Mock Planner
|
|
mock_planner_instance = MagicMock()
|
|
mock_llms["planner"].return_value = mock_planner_instance
|
|
mock_planner_instance.with_structured_output.return_value.invoke.return_value = TaskPlanResponse(
|
|
goal="Get results",
|
|
reflection="Reflect",
|
|
context=TaskPlanContext(initial_context="Ctx", assumptions=[], constraints=[]),
|
|
steps=["Step 1"]
|
|
)
|
|
|
|
# 3. Mock Coder
|
|
mock_coder_instance = MagicMock()
|
|
mock_llms["coder"].return_value = mock_coder_instance
|
|
mock_coder_instance.with_structured_output.return_value.invoke.return_value = CodeGenerationResponse(
|
|
code="print('Execution Success')",
|
|
explanation="Explain"
|
|
)
|
|
|
|
# 4. Mock Summarizer
|
|
mock_summarizer_instance = MagicMock()
|
|
mock_llms["summarizer"].return_value = mock_summarizer_instance
|
|
mock_summarizer_instance.invoke.return_value = AIMessage(content="Final Summary: Success")
|
|
|
|
# Initial state
|
|
initial_state = {
|
|
"messages": [],
|
|
"question": "Show me 2024 results",
|
|
"analysis": None,
|
|
"next_action": "",
|
|
"plan": None,
|
|
"code": None,
|
|
"error": None,
|
|
"plots": [],
|
|
"dfs": {}
|
|
}
|
|
|
|
# Run the graph
|
|
result = app.invoke(initial_state, config={"recursion_limit": 15})
|
|
|
|
assert result["next_action"] == "plan"
|
|
assert "Execution Success" in result["code_output"]
|
|
assert "Final Summary: Success" in result["messages"][-1].content
|
|
|
|
def test_workflow_research_flow(mock_llms):
|
|
"""Test flow: QueryAnalyzer -> Researcher -> Summarizer."""
|
|
|
|
# 1. Mock Query Analyzer (routes to research)
|
|
mock_qa_instance = MagicMock()
|
|
mock_llms["qa"].return_value = mock_qa_instance
|
|
mock_qa_instance.with_structured_output.return_value.invoke.return_value = QueryAnalysis(
|
|
data_required=[],
|
|
unknowns=[],
|
|
ambiguities=[],
|
|
conditions=[],
|
|
next_action="research"
|
|
)
|
|
|
|
# 2. Mock Researcher
|
|
mock_researcher_instance = MagicMock()
|
|
mock_llms["researcher"].return_value = mock_researcher_instance
|
|
# Researcher node uses bind_tools if it's ChatOpenAI/ChatGoogleGenerativeAI
|
|
# Since it's a MagicMock, it will fallback to using the base instance
|
|
mock_researcher_instance.invoke.return_value = AIMessage(content="Research Results")
|
|
|
|
# Also mock bind_tools just in case we ever use spec
|
|
mock_llm_with_tools = MagicMock()
|
|
mock_researcher_instance.bind_tools.return_value = mock_llm_with_tools
|
|
mock_llm_with_tools.invoke.return_value = AIMessage(content="Research Results")
|
|
|
|
# 3. Mock Summarizer (not used in this flow, but kept for completeness)
|
|
mock_summarizer_instance = MagicMock()
|
|
mock_llms["summarizer"].return_value = mock_summarizer_instance
|
|
mock_summarizer_instance.invoke.return_value = AIMessage(content="Final Summary: Research Success")
|
|
|
|
# Initial state
|
|
initial_state = {
|
|
"messages": [],
|
|
"question": "Who is the governor of Florida?",
|
|
"analysis": None,
|
|
"next_action": "",
|
|
"plan": None,
|
|
"code": None,
|
|
"error": None,
|
|
"plots": [],
|
|
"dfs": {}
|
|
}
|
|
|
|
# Run the graph
|
|
result = app.invoke(initial_state, config={"recursion_limit": 10})
|
|
|
|
assert result["next_action"] == "research"
|
|
assert "Research Results" in result["messages"][-1].content
|