* fix(SPEC_11): address CodeRabbit review feedback
- Update run_full.py docstring to reference sexual health pipeline (not drug repurposing)
- Update run_full.py help text to use sexual health query example
- Fix app.py domain display to show "Sexual Health" (not "Sexual_Health")
- Update test_nodes.py mock data to use sexual health terms (Testosterone, Androgen Receptor)
- Add @pytest.mark.unit markers to test_judges.py test classes
- Add specific assertion for next_search_queries in test_judges.py
- Add missing @pytest.mark.asyncio marker to test_mcp_tools.py
- Update test_mcp_tools.py and test_clinicaltrials.py to use sexual health queries
- Fix SPEC_12 markdown bare code fence (MD040)
* fix(SPEC_11): comprehensive domain alignment audit
Second pass of CodeRabbit review fixes - found additional domain mismatches:
- examples/search_demo/run_search.py: Update docstring from "drug repurposing" to "sexual health"
- examples/orchestrator_demo/run_agent.py: Update help text from "metf…
feat(SPEC_11): finalize transition to Sexual Health Research Specialist
This commit completes the transition of DeepBoner to a dedicated Sexual Health Research Agent by removing all references to "general" and "drug repurposing" domains. Key changes include:
- Updated domain defaults to exclusively use "sexual_health".
- Replaced all example queries and documentation to reflect a focus on sexual health topics.
- Removed any lingering references to non-sexual health concepts in code and tests.
- Ensured all tests are aligned with the new domain focus, passing successfully.
This change reinforces the project's identity and simplifies the codebase, enhancing clarity and usability for users focused on sexual health research.
Closes #89.
fix: Update test for judge node initialization with improved mocking
- Replaced the mock for the pydantic_ai Agent with a mock for get_model to avoid real API key requirements.
- Enhanced the test by mocking the Agent class entirely to ensure proper behavior during initialization.
- Adjusted the test setup to maintain functionality while improving isolation from external dependencies.
feat: Enhance research workflow with embedding service integration
- Updated graph nodes to accept an optional EmbeddingService for improved evidence handling and deduplication.
- Refactored search, judge, resolve, and synthesize nodes to utilize the embedding service for enhanced functionality.
- Modified the research graph creation to bind the embedding service to worker nodes, ensuring seamless integration.
- Added logging for better traceability during node execution.
- Expanded unit and integration tests to cover new embedding service interactions and ensure robust functionality.
feat: Integrate LangGraph for enhanced research orchestration
- Added LangGraph dependencies to pyproject.toml and uv.lock for improved state management and orchestration capabilities.
- Implemented LangGraph-based orchestrator in src/orchestrators/langgraph_orchestrator.py, enabling structured research workflows.
- Developed graph nodes and state management in src/agents/graph, including search, judge, and synthesis functionalities.
- Updated app.py to support new "god" mode for advanced orchestration.
- Enhanced documentation for active bugs and architectural gaps related to LangGraph integration.
- Added unit and integration tests to ensure functionality and reliability of the new features.
