Refactors the GTM orchestrator prompts (phases 2-9) to use a question-based strategic framework derived from the internal marketing blueprint. This new 'Meta-Framework' approach ensures strategic depth and prevents content pollution from irrelevant examples when analyzing new product categories.
- Updates orchestrator prompts in .
- Adds documentation in explaining how to modify the new strategy logic.
- Includes minor fixes to the Node.js and dependency updates in .
- Fixed TypeError in SessionBrowser by adding defensive checks for the sessions array.
- Implemented mandatory URL persistence: The research URL is now saved in DB, shown in UI, and included in reports.
- Added 'Start New Analysis' button to the session browser for better UX flow.
- Updated documentation to reflect v2.6.1 changes.
- Replaced the basic session list with a dedicated, card-based Session Browser page.
- Each session card now displays product name, category, description, and a thumbnail placeholder for better usability.
- Updated the backend DB manager to extract this rich information from the existing JSON data store.
- Refactored the frontend (App.tsx, types.ts) to support the new UI and data structure.
- Added new component SessionBrowser.tsx and its corresponding CSS.
- Updated documentation to reflect the v2.6 changes.
- Modifies docker-compose.yml to use for injecting the Gemini API key, which is more robust than volume mounting.
- Updates helpers.py to prioritize reading the API key from the environment variable.
- Removes the now-redundant file-based key loading logic from config.py and the Dockerfile.
- This change completely bypasses the problematic file system interactions within the container, providing a definitive fix for the 'API Key missing' error.
- Updates requirements.txt to use the new 'google-genai' package.
- Updates import statements and error messages in helpers.py to use the new library.
- Ensures the gemini_api_key.txt is copied into the Docker container to resolve API key errors.
- Refactors the gtm-architect Dockerfile for a flat, more efficient build process.
- Implements robust web scraping via BeautifulSoup in helpers.py for URL analysis in phase1.
- Makes shared library imports (gspread, pandas, etc.) in helpers.py optional to prevent ModuleNotFoundErrors in microservices.
- Implements the main execution logic in the orchestrator to handle command-line arguments.
- Updates documentation to reflect the new architecture, scraping feature, and dependency handling.
- Refactor Docker build process for gtm-app to ensure reliability
- Correct volume mounts and build context in docker-compose.yml to prevent stale code
- Fix frontend 404 error by using relative paths in index.html
- Ensure API key is correctly mounted into the container
- Stabilize Node.js to Python data passing via explicit --data argument
- Update gtm_architect_documentation.md with extensive troubleshooting guide
