Add Graph and DataSetGenerator integration tests

[lukehinds]

• Create test_graph_integration.py with 4 tests covering Graph.build_async, save/load JSON roundtrip, and cycle detection for OpenAI and Gemini
• Create test_generator_integration.py with 6 tests covering basic generation, topic model integration, async generation, chain-of-thought, and file saving
• Add Makefile targets: test-integration-graph, test-integration-generator
• Use shared fixtures from conftest.py for provider configuration

[gemini-code-assist]

Summary of Changes

Hello @lukehinds, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the project's test coverage by introducing a comprehensive suite of integration tests for the Graph and DataSetGenerator components. These new tests ensure the robust functionality and reliability of these core features, particularly when interacting with real OpenAI and Gemini API services, and provide dedicated Makefile targets for streamlined execution.

Highlights

• Graph Integration Tests: Introduced 4 new integration tests for the Graph component, covering asynchronous building, JSON save/load roundtrips, and cycle detection for both OpenAI and Gemini providers.
• DataSetGenerator Integration Tests: Added 6 new integration tests for DataSetGenerator, validating basic data generation, integration with topic models, asynchronous generation, chain-of-thought conversations, and dataset saving to files.
• New Makefile Targets: Added test-integration-graph and test-integration-generator targets to the Makefile for easy execution of the new integration test suites.
• Shared Fixture Utilization: Leveraged existing shared fixtures from conftest.py for consistent provider configuration across the new tests.

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[gemini-code-assist]

Code Review

This pull request adds valuable integration tests for the Graph and DataSetGenerator components, covering core functionalities like building, saving/loading, and data generation with different providers (OpenAI and Gemini). The tests are well-structured and cover important use cases. I've made a few suggestions to improve code clarity and consistency in the new test files, mainly around how async operations are handled and ensuring test data is consistent.