forked from cleveragents/cleveragents-core
brent.edwards
17fe46d925
There had been over 100 behave tests failing. There should be none failing now.
166 lines
6.3 KiB
Gherkin
166 lines
6.3 KiB
Gherkin
Feature: Context Service Analysis Integration
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As a developer using CleverAgents
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I want to analyze context files using LangGraph workflows
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So that I can understand the codebase better before making changes
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# TODO: Uncomment when step definitions are implemented
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# Background:
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# Given I have initialized a CleverAgents project
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# And I have a context service with LangGraph integration
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#
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# Scenario: Analyze context when no files are loaded
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# Given the current plan has no context files
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# When I analyze the context
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# Then the analysis result should have empty documents
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# And the analysis summary should indicate no files to analyze
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# And there should be no error in the analysis
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#
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# Scenario: Analyze context with a single Python file
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# Given I have a Python file "main.py" with content:
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# """
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# import os
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# import sys
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# from pathlib import Path
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#
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# def main():
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# print("Hello, World!")
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# """
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# And I have added "main.py" to the LangGraph context
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# When I analyze the context
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# Then the analysis result should have 1 document
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# And the dependencies for "main.py" should include "os"
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# And the relevance score for "main.py" should be between 0.0 and 1.0
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# And the summary should not be empty
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#
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# Scenario: Analyze context with multiple files
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# Given I have a Python file "utils.py" with content:
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# """
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# def helper():
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# return 42
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# """
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# And I have a Python file "app.py" with content:
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# """
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# from utils import helper
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# print(helper())
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# """
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# And I have added "utils.py" to the LangGraph context
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# And I have added "app.py" to the LangGraph context
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# When I analyze the context
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# Then the analysis result should have 2 documents
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# And the dependencies should include entries for both files
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# And the relevance scores should have entries for both files
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#
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# Scenario: Get context summary
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# Given I have a Python file "sample.py" with content:
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# """
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# # Sample module
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# class Sample:
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# pass
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# """
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# And I have added "sample.py" to the LangGraph context
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# When I get the context summary
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# Then the summary should be a non-empty string
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# And the summary should not indicate an error
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#
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# Scenario: Get context dependencies
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# Given I have a Python file "deps.py" with content:
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# """
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# import json
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# import requests
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# from typing import Dict
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# """
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# And I have added "deps.py" to the LangGraph context
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# When I get the context dependencies
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# Then the dependencies dict should have an entry for "deps.py"
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# And the dependencies should be a non-empty list
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#
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# Scenario: Get relevant files with threshold
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# Given I have a Python file "important.py" with content:
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# """
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# # Core business logic
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# def critical_function():
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# return "important"
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# """
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# And I have a Python file "trivial.py" with content:
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# """
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# # Just comments
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# pass
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# """
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# And I have added "important.py" to the LangGraph context
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# And I have added "trivial.py" to the LangGraph context
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# When I get relevant files with threshold 0.0
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# Then the result should include both files with scores
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# And all LangGraph scores should be between 0.0 and 1.0
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#
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# Scenario: Stream context analysis
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# Given I have a Python file "streaming.py" with content:
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# """
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# print("test streaming")
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# """
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# And I have added "streaming.py" to the LangGraph context
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# When I stream the context analysis
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# Then I should receive multiple events
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# And the events should include node execution results
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#
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# Scenario: Stream context analysis when no files are loaded
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# Given the current plan has no context files
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# When I stream the context analysis
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# Then the streaming output should report no files to analyze
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#
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# Scenario: Analyze context asynchronously
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#
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# Given I have a Python file "async_test.py" with content:
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# """
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# async def async_func():
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# return "async"
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# """
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# And I have added "async_test.py" to the LangGraph context
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# When I analyze the context asynchronously
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# Then the async result should have documents
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# And the async result should have a summary
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# And there should be no error in the async result
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#
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# Scenario: Analyze context asynchronously when no files are loaded
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# Given the current plan has no context files
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# When I analyze the context asynchronously
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# Then the async analysis result should have empty documents
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# And the async analysis summary should indicate no files to analyze
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#
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# Scenario: Stream context analysis asynchronously with loaded files
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# Given I have a Python file "async_stream.py" with content:
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# """
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# print("async stream")
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# """
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# And I have added "async_stream.py" to the LangGraph context
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# When I stream the context analysis asynchronously
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# Then the async streaming events should include node execution results
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#
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# Scenario: Stream context analysis asynchronously with no files
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# Given the current plan has no context files
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# When I stream the context analysis asynchronously
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# Then the async streaming output should report no files to analyze
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#
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# Scenario: Handle non-existent files gracefully
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# Given I have added a non-existent file path to the analysis
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#
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#
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# When I analyze the context with the non-existent path
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# Then the analysis should complete without raising an exception
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# And the error field should contain file not found information
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#
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# Scenario: Get context agent instance
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# When I request a context analysis agent
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# Then I should receive a ContextAnalysisAgent instance
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# And the agent should have the standard workflow nodes
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#
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# Scenario: Prepare LangSmith metadata when tracing is enabled
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# Given LangSmith tracing is enabled for context analysis metadata
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# And I have a Python file "langsmith.py" with content:
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# """
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# print("langsmith tracing")
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# """
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# And I have added "langsmith.py" to the LangGraph context
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# When I prepare the LangGraph analysis config for run "LangSmithRun" in "stream" mode
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# Then the LangSmith config should include the current project metadata
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# And the LangSmith config should record 1 context file path
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