- Implemented a "Re-evaluate Wikipedia" button in the UI.
- Added a backend endpoint to trigger targeted Wikipedia metric extraction.
- Hardened the LLM metric extraction prompt to prevent hallucinations.
- Corrected several database path errors that caused data loss.
- Updated application version to 0.6.4 and documented the ongoing issue.
- Implements a 3-tier database architecture (Canonical Products, Portfolio, Companies) to separate product master data from company-specific portfolio information.
- Upgrades import_competitive_radar.py to an intelligent "upsert" script that prevents duplicates by checking for existing entries before importing.
- This enables detailed GTM strategy tracking for RoboPlanet products while monitoring competitor portfolios.
- Updates documentation to reflect the new architecture and import process.
- Fixed a critical in the company-explorer by forcing a database re-initialization with a new file (). This ensures the application code is in sync with the database schema.
- Documented the schema mismatch incident and its resolution in MIGRATION_PLAN.md.
- Restored and enhanced BUILDER_APPS_MIGRATION.md by recovering extensive, valuable content from the git history that was accidentally deleted. The guide now again includes detailed troubleshooting steps and code templates for common migration pitfalls.
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 .
- Implemented semantic classification for Products (e.g. 'Cleaning', 'Logistics') and Battlecards (e.g. 'Price', 'Support').
- Created 'import_competitive_radar.py' for full 4-database relational import to Notion.
- Updated Orchestrator with new prompts for structured output.
- Cleaned up obsolete scripts.
- Extended import_relational_radar.py to include a 'Products' database.
- Implemented full dual-way relations for Companies <-> Landmines, References, Products.
- Updated documentation to reflect the 4-database architecture.
- Added import_relational_radar.py for bidirectional database structure in Notion.
- Added refresh_references.py to populate analysis data with grounded facts via scraping.
- Updated documentation for Competitive Radar v2.0.
Improves the competitor reference analysis (Step 8) by replacing the previous LLM-only approach with a grounded, scraping-based method.
- Implemented a new scraper to actively search for and parse competitor reference/case study pages.
- The analysis is now based on actual website content, significantly increasing the accuracy and reliability of the results and preventing model hallucinations.
- Updated documentation to reflect the new 'Grounded References' architecture.
Resolved multiple issues preventing the 'competitor-analysis' app from running and serving its frontend:
1. **Fixed Python SyntaxError in Prompts:** Corrected unterminated string literals and ensure proper multi-line string formatting (using .format() instead of f-strings for complex prompts) in .
2. **Addressed Python SDK Compatibility (google-generativeai==0.3.0):**
* Removed for and by adapting the orchestrator to pass JSON schemas as direct Python dictionaries, as required by the older SDK version.
* Updated with detailed guidance on handling / imports and dictionary-based schema definitions for older SDKs.
3. **Corrected Frontend Build Dependencies:** Moved critical build dependencies (like , , ) from to in .
* Updated to include this pitfall, ensuring frontend build tools are installed in Docker.
4. **Updated Documentation:**
* : Added comprehensive lessons learned regarding dependencies, Python SDK versioning (specifically and imports for ), and robust multi-line prompt handling.
* : Integrated specific details of the encountered errors and their solutions, making the migration report a more complete historical record and guide.
These changes collectively fix the 404 error by ensuring the Python backend starts correctly and serves the frontend assets after a successful build.
