brent.edwards
3eecb79003
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TDD expected-fail tests proving bug #822 exists: CheckpointService.rollback_to_checkpoint() returns a successful RollbackResult but does not execute git reset --hard. Files modified after the checkpoint remain unchanged after rollback. Also fixes Robot Framework timeout robustness across the entire test suite: all Run Process calls now use on_timeout=kill (prevents SIGTERM-induced -15 exit codes under CI load) and timeouts increased to 120s (prevents premature kills during heavy parallel execution). ISSUES CLOSED: #839
443 lines
22 KiB
Plaintext
443 lines
22 KiB
Plaintext
*** Settings ***
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Documentation Integration tests for PlanGenerationGraph workflow
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Resource ${CURDIR}/common.resource
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Library Process
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Library OperatingSystem
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Library Collections
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Library String
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Suite Setup Setup Test Environment
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Suite Teardown Cleanup Test Environment
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*** Variables ***
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${PYTHON} python
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${SRC_DIR} ${CURDIR}/../src
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${TEST_PROJECT} test_plan_generation_project
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*** Test Cases ***
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Plan Generation Graph Module Can Be Imported
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[Documentation] Verify the plan generation module can be imported
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${result}= Run Process ${PYTHON} -c
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... import sys; sys.path.insert(0, '${SRC_DIR}'); from cleveragents.agents.plan_generation import PlanGenerationGraph; print('SUCCESS')
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... shell=True
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Should Contain ${result.stdout} SUCCESS
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Should Be Equal As Integers ${result.rc} 0
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Plan Generation Graph Can Be Instantiated With Default Parameters
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[Documentation] Create PlanGenerationGraph with defaults
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... assert graph is not None
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... assert graph.max_retries == 3
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... assert graph.llm is not None
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... print('Graph initialized successfully')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} initialized successfully
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Should Be Equal As Integers ${result.rc} 0
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Plan Generation Graph Can Be Instantiated With Custom Max Retries
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[Documentation] Create PlanGenerationGraph with custom max_retries
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3), max_retries=5)
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... assert graph.max_retries == 5
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... print('Max retries: ' + str(graph.max_retries))
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Max retries: 5
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Should Be Equal As Integers ${result.rc} 0
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Plan Generation Graph Creates Prompt Templates
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[Documentation] Verify prompt templates are created
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... assert hasattr(graph, 'analyze_prompt')
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... assert hasattr(graph, 'generate_prompt')
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... assert hasattr(graph, 'validate_prompt')
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... assert 'prompt' in graph.analyze_prompt.input_variables
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... assert 'context_summary' in graph.analyze_prompt.input_variables
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... print('All prompts configured')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} All prompts configured
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Should Be Equal As Integers ${result.rc} 0
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Plan Generation Graph Builds Workflow With Correct Nodes
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[Documentation] Verify workflow graph has correct nodes
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... nodes = graph.graph.nodes
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... assert 'load_context' in nodes
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... assert 'analyze_requirements' in nodes
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... assert 'generate_plan' in nodes
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... assert 'validate' in nodes
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... print(f'Graph has {len(nodes)} nodes')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Graph has 4 nodes
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Should Be Equal As Integers ${result.rc} 0
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LangGraph Graphs Package Exports Workflow Classes
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[Documentation] Ensure cleveragents.agents.graphs exports LangGraph workflows
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.graphs import PlanGenerationGraph, ContextAnalysisAgent
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... from cleveragents.agents.graphs import __all__
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... from cleveragents.agents import PlanGenerationGraph as ExportedPlanGraph
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... from cleveragents.agents import ContextAnalysisAgent as ExportedContextAgent
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... assert 'PlanGenerationGraph' in __all__
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... assert 'ContextAnalysisAgent' in __all__
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... assert PlanGenerationGraph is ExportedPlanGraph
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... assert ContextAnalysisAgent is ExportedContextAgent
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... print('LangGraph graphs exports verified')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} LangGraph graphs exports verified
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Should Be Equal As Integers ${result.rc} 0
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Format Context Summary With No Files Returns Appropriate Message
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[Documentation] Test _format_context_summary with empty list
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... summary = graph._format_context_summary([])
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... assert summary == 'No context files provided'
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... print('Empty context handled correctly')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Empty context handled correctly
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Should Be Equal As Integers ${result.rc} 0
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Format Context Summary With Multiple Files
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[Documentation] Test _format_context_summary with multiple Context objects
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Context
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... contexts = [
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... Context(plan_id=1, path='file1.py', content='# File 1 content'),
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... Context(plan_id=1, path='file2.py', content='# File 2 content'),
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... ]
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... summary = graph._format_context_summary(contexts)
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... assert 'file1.py' in summary
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... assert 'file2.py' in summary
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... assert 'File:' in summary
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... print('Multiple files formatted correctly')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Multiple files formatted correctly
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Should Be Equal As Integers ${result.rc} 0
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Format Context Summary Limits To Five Files
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[Documentation] Test that _format_context_summary limits to first 5 files
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Context
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... contexts = [Context(plan_id=1, path=f'file{i}.py', content='content') for i in range(8)]
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... summary = graph._format_context_summary(contexts)
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... assert 'file0.py' in summary
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... assert 'file4.py' in summary
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... assert 'and 3 more files' in summary
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... print('File limiting works correctly')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} File limiting works correctly
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Should Be Equal As Integers ${result.rc} 0
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Load Context Node Initializes State
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[Documentation] Test _load_context node execution
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Project, Plan
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... state = {'project': None, 'plan': None, 'contexts': []}
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... result = graph._load_context(state)
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... assert result['retry_count'] == 0
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... assert result['error'] is None
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... assert result['context_summary'] == 'No context files provided'
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... assert result['context_dependencies'] == {}
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... assert result['context_relevance'] == {}
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... assert result['context_analysis_error'] is None
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... print('Load context node works')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Load context node works
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Should Be Equal As Integers ${result.rc} 0
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Load Context Node Generates Summary With Sample Contexts
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[Documentation] Ensure context analysis metadata is populated
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${result}= Run Process ${PYTHON} ${CURDIR}/helper_plan_generation.py
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Should Contain ${result.stdout} Context analysis summary ready
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Should Be Equal As Integers ${result.rc} 0
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Should Retry Returns Retry When Validation Fails And Retries Available
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[Documentation] Test _should_retry returns "retry" appropriately
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3), max_retries=3)
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... state = {
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... 'validation_result': {'status': 'FAIL'},
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... 'retry_count': 0
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... }
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... decision = graph._should_retry(state)
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... assert decision == 'retry'
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... assert state['retry_count'] == 1
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... print('Should retry: retry decision correct')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Should retry: retry decision correct
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Should Be Equal As Integers ${result.rc} 0
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Should Retry Returns End When Validation Passes
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[Documentation] Test _should_retry returns "end" when validation succeeds
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3), max_retries=3)
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... state = {
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... 'validation_result': {'status': 'PASS'},
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... 'retry_count': 0
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... }
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... decision = graph._should_retry(state)
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... assert decision == 'end'
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... print('Should retry: end decision correct')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Should retry: end decision correct
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Should Be Equal As Integers ${result.rc} 0
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Should Retry Returns End When Max Retries Reached
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[Documentation] Test _should_retry returns "end" when max retries reached
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3), max_retries=3)
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... state = {
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... 'validation_result': {'status': 'FAIL'},
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... 'retry_count': 3
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... }
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... decision = graph._should_retry(state)
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... assert decision == 'end'
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... print('Should retry: max retries respected')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Should retry: max retries respected
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Should Be Equal As Integers ${result.rc} 0
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Plan Generation State TypedDict Has Correct Structure
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[Documentation] Verify PlanGenerationState TypedDict structure
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationState
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... annotations = PlanGenerationState.__annotations__
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... assert 'project' in annotations
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... assert 'plan' in annotations
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... assert 'contexts' in annotations
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... assert 'context_summary' in annotations
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... assert 'context_dependencies' in annotations
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... assert 'context_relevance' in annotations
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... assert 'context_analysis_error' in annotations
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... assert 'prompt' in annotations
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... assert 'analyzed_requirements' in annotations
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... assert 'generated_changes' in annotations
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... assert 'validation_result' in annotations
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... assert 'retry_count' in annotations
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... assert 'error' in annotations
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... print('PlanGenerationState structure verified')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} PlanGenerationState structure verified
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Should Be Equal As Integers ${result.rc} 0
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Validate Node Fails When No Changes Provided
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[Documentation] Test _validate with no changes
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... state = {'generated_changes': []}
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... result = graph._validate(state)
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... assert result['validation_result']['status'] == 'FAIL'
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... assert 'No changes to validate' in result['validation_result']['message']
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... print('Validate correctly handles empty changes')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Validate correctly handles empty changes
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Should Be Equal As Integers ${result.rc} 0
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Generate Plan Handles Missing Requirements
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[Documentation] Test _generate_plan with no requirements
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... state = {'analyzed_requirements': {}}
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... result = graph._generate_plan(state)
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... assert result['generated_changes'] == []
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... assert 'No requirements to generate from' in result['error']
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... print('Generate plan handles missing requirements')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Generate plan handles missing requirements
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Should Be Equal As Integers ${result.rc} 0
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Generate Plan Infers Test File Name From Prompt
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[Documentation] Test file name inference for test-related prompts
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${script}= Catenate SEPARATOR=\n
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... import sys
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... from pathlib import Path
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Project, Plan
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... state = {
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... 'project': Project(id=1, name='test', path=Path('/tmp/test_project')),
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... 'plan': Plan(id=1, project_id=1, name='Unit Test Plan', prompt='Create unit tests'),
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... 'contexts': [],
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... 'prompt': 'Create unit tests',
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... 'analyzed_requirements': {
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... 'description': 'tests',
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... 'files_to_modify': [],
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... 'operation': 'create'
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... }
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... }
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... result = graph._generate_plan(state)
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... assert len(result['generated_changes']) > 0
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... assert result['generated_changes'][0].file_path == 'test_generated.py'
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... print('Test file name inferred correctly')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Test file name inferred correctly
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Should Be Equal As Integers ${result.rc} 0
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Generate Plan Infers Error Handler File Name From Prompt
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[Documentation] Test file name inference for error/exception prompts
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${script}= Catenate SEPARATOR=\n
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... import sys
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... from pathlib import Path
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Project, Plan
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... state = {
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... 'project': Project(id=1, name='test', path=Path('/tmp/test_project')),
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... 'plan': Plan(id=1, project_id=1, name='Error Handling Plan', prompt='Add error handling'),
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... 'contexts': [],
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... 'prompt': 'Add error handling',
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... 'analyzed_requirements': {
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... 'description': 'error handling',
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... 'files_to_modify': [],
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... 'operation': 'create'
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... }
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... }
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... result = graph._generate_plan(state)
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... assert result['generated_changes'][0].file_path == 'error_handler.py'
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... print('Error handler file name inferred')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Error handler file name inferred
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Should Be Equal As Integers ${result.rc} 0
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Workflow Invoke Method Returns Complete State
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[Documentation] Test that invoke() returns complete workflow state
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${script}= Catenate SEPARATOR=\n
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... import sys
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... from pathlib import Path
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Project, Plan, Context
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... project = Project(id=1, name='test_project', path=Path('/tmp/test_project'))
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... plan = Plan(id=1, project_id=1, name='Logging Plan', prompt='Add logging')
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... contexts = [Context(plan_id=plan.id, path='app.py', content='def main(): pass')]
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... result = graph.invoke(project, plan, contexts, thread_id='test-123')
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... assert result['plan'].name == plan.name
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... assert 'project' in result
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... assert 'plan' in result
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... assert 'contexts' in result
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... assert 'context_summary' in result
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... assert 'context_dependencies' in result
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... assert 'context_relevance' in result
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... assert 'context_analysis_error' in result
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... assert 'generated_changes' in result
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... assert 'validation_result' in result
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... assert 'retry_count' in result
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... print('Workflow invoke completes successfully')
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${result}= Run Process ${PYTHON} -c ${script} shell=True timeout=120s on_timeout=kill
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Should Contain ${result.stdout} Workflow invoke completes successfully
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Should Be Equal As Integers ${result.rc} 0
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Workflow Stream Method Yields Events
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[Documentation] Test that stream() yields workflow events
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${script}= Catenate SEPARATOR=\n
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... import sys
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... from pathlib import Path
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from cleveragents.domain.models.core import Project, Plan, Context
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... project = Project(id=1, name='test_project', path=Path('/tmp/test_project'))
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... plan = Plan(id=1, project_id=1, name='Feature Plan', prompt='Add feature')
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... contexts = [Context(plan_id=plan.id, path='app.py', content='# app')]
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... events = list(graph.stream(project, plan, contexts))
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... assert len(events) > 0
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... assert all(isinstance(e, dict) for e in events)
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... print(f'Stream yielded {len(events)} events')
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${result}= Run Process ${PYTHON} -c ${script} shell=True timeout=120s on_timeout=kill
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Should Contain ${result.stdout} Stream yielded
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Should Contain ${result.stdout} events
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Should Be Equal As Integers ${result.rc} 0
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Graph Has Checkpointer For State Persistence
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[Documentation] Verify checkpointer is configured
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${script}= Catenate SEPARATOR=\n
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... import sys
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... sys.path.insert(0, '${SRC_DIR}')
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... from cleveragents.agents.plan_generation import PlanGenerationGraph
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... from langchain_community.llms import FakeListLLM
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... from langgraph.checkpoint.memory import MemorySaver
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... graph = PlanGenerationGraph(llm=FakeListLLM(responses=['test']*3))
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... assert graph.checkpointer is not None
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... assert isinstance(graph.checkpointer, MemorySaver)
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... assert graph.app is not None
|
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... print('Checkpointer configured correctly')
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${result}= Run Process ${PYTHON} -c ${script} shell=True
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Should Contain ${result.stdout} Checkpointer configured correctly
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Should Be Equal As Integers ${result.rc} 0
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*** Keywords ***
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Create Test Project
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[Arguments] ${project_name}
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Create Directory /tmp/${project_name}
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Set Suite Variable ${TEST_PROJECT} /tmp/${project_name}
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