SDOF Knowledge Base

# SDOF Knowledge Base Auto-Save Functionality ## Overview We've enhanced the SDOF Knowledge Base MCP Server with automatic plan saving functionality. This feature allows the SDOF Orchestrator to automatically save plans, analyses, implementations, evaluations, and other artifacts to both the filesystem and the vector database. ## Implementation Components We've created the following components: 1. **Plan Auto-Save Service** - `src/services/plan-auto-save.service.ts`: Core service that handles saving plans to both filesystem and knowledge base - Organizes plans by type in appropriate directories - Generates vector embeddings for semantic search 2. **MCP Tool Integration** - `src/tools/plan-auto-save.tool.ts`: Exposes the auto-save functionality as an MCP tool - Defines the input schema and handler for the `store_sdof_plan` tool - Provides structured response with file paths and entry IDs 3. **Server Configuration Update** - `src/index.ts.update`: Shows how to register the new tool with the MCP server 4. **Documentation** - `docs/SETUP_GUIDE.md`: Guide for setting up MongoDB with vector search and configuring language models - `docs/AUTO_SAVE_GUIDE.md`: Guide for using the auto-save functionality - `docs/CUSTOM_MODE_EXAMPLE.md`: Example of how to modify the SDOF Orchestrator custom mode ## Directory Structure The auto-save functionality organizes plans in a structured directory hierarchy: ``` sdof_knowledge_base/ ├── docs/ │ ├── plans/ │ │ ├── explorations/ │ │ ├── analyses/ │ │ ├── implementations/ │ │ ├── evaluations/ │ │ ├── integrations/ │ │ └── syntheses/ ``` Each plan is saved with a date prefix (YYYY-MM-DD) and a slug derived from the title. ## How It Works 1. **SDOF Orchestrator** receives results from a phase (e.g., Explorer, Analyzer) 2. Orchestrator processes the results as usual 3. Orchestrator calls the `store_sdof_plan` MCP tool with: - Title: "SDOF [PlanType]: [Brief Problem Description]" - Content: The Markdown content of the plan - Plan Type: The phase type (exploration, analysis, etc.) - Tags: Relevant tags for categorization 4. **Plan Auto-Save Service**: - Saves the plan as a Markdown file in the appropriate directory - Generates an embedding for the plan content - Stores the plan in the knowledge base with metadata - Returns the file path and entry ID 5. **Orchestrator** informs the user that the plan has been saved ## Benefits This functionality provides several key benefits: 1. **Persistent Knowledge Repository**: All SDOF plans are automatically saved and organized 2. **Semantic Search**: Vector embeddings enable finding relevant past plans by concept 3. **Structured Organization**: Plans are categorized by type and date 4. **Automatic Metadata**: Tags and categories make filtering and retrieval easier 5. **Knowledge Evolution**: Track how solutions evolve through the SDOF process ## Next Steps To complete the implementation: 1. **Update Dependencies**: - Add @types/node to package.json: `npm install --save-dev @types/node` - Update tsconfig.json to include Node.js types 2. **Integrate with MCP Server**: - Copy the code from index.ts.update to your main index.ts file - Make sure to import the plan-auto-save.tool.ts file 3. **Create Directory Structure**: - Create the docs/plans directory and subdirectories 4. **Update SDOF Orchestrator Custom Mode**: - Follow the instructions in CUSTOM_MODE_EXAMPLE.md - Modify your custom_modes.json file to include the auto-save instructions 5. **Test the Integration**: - Run the MCP server - Use the SDOF Orchestrator to solve a problem - Verify that plans are being saved to both the filesystem and knowledge base ## Customization Options The auto-save functionality can be customized in several ways: 1. **Directory Structure**: Modify the directory paths in plan-auto-save.service.ts 2. **File Naming**: Change the file naming convention in saveToFilesystem method 3. **Metadata**: Add additional metadata fields in saveToKnowledgeBase method 4. **Embedding Models**: Configure different embedding models as outlined in SETUP_GUIDE.md By implementing this auto-save functionality, your SDOF workflow will build a growing knowledge repository that can be leveraged for future problem-solving, creating a powerful feedback loop of continuous improvement.