21530c6408
This is a major refactoring of the MCP Server to support multiple providers through a YAML-based configuration system with factory pattern implementation. ## Key Changes ### Architecture Improvements - Modular configuration system with YAML-based settings - Factory pattern for LLM, Embedder, and Database providers - Support for multiple database backends (Neo4j, FalkorDB, KuzuDB) - Clean separation of concerns with dedicated service modules ### Provider Support - **LLM**: OpenAI, Anthropic, Gemini, Groq - **Embedders**: OpenAI, Voyage, Gemini, Anthropic, Sentence Transformers - **Databases**: Neo4j, FalkorDB, KuzuDB (new default) - Azure OpenAI support with AD authentication ### Configuration - YAML configuration with environment variable expansion - CLI argument overrides for runtime configuration - Multiple pre-configured Docker Compose setups - Proper boolean handling in environment variables ### Testing & CI - Comprehensive test suite with unit and integration tests - GitHub Actions workflows for linting and testing - Multi-database testing support ### Docker Support - Updated Docker images with multi-stage builds - Database-specific docker-compose configurations - Persistent volume support for all databases ### Bug Fixes - Fixed KuzuDB connectivity checks - Corrected Docker command paths - Improved error handling and logging - Fixed boolean environment variable expansion Co-authored-by: Claude <noreply@anthropic.com>
2.4 KiB
2.4 KiB
Instructions for Using Graphiti's MCP Tools for Agent Memory
Before Starting Any Task
- Always search first: Use the
search_nodestool to look for relevant preferences and procedures before beginning work. - Search for facts too: Use the
search_factstool to discover relationships and factual information that may be relevant to your task. - Filter by entity type: Specify
Preference,Procedure, orRequirementin your node search to get targeted results. - Review all matches: Carefully examine any preferences, procedures, or facts that match your current task.
Always Save New or Updated Information
- Capture requirements and preferences immediately: When a user expresses a requirement or preference, use
add_memoryto store it right away.- Best practice: Split very long requirements into shorter, logical chunks.
- Be explicit if something is an update to existing knowledge. Only add what's changed or new to the graph.
- Document procedures clearly: When you discover how a user wants things done, record it as a procedure.
- Record factual relationships: When you learn about connections between entities, store these as facts.
- Be specific with categories: Label preferences and procedures with clear categories for better retrieval later.
During Your Work
- Respect discovered preferences: Align your work with any preferences you've found.
- Follow procedures exactly: If you find a procedure for your current task, follow it step by step.
- Apply relevant facts: Use factual information to inform your decisions and recommendations.
- Stay consistent: Maintain consistency with previously identified preferences, procedures, and facts.
Best Practices
- Search before suggesting: Always check if there's established knowledge before making recommendations.
- Combine node and fact searches: For complex tasks, search both nodes and facts to build a complete picture.
- Use
center_node_uuid: When exploring related information, center your search around a specific node. - Prioritize specific matches: More specific information takes precedence over general information.
- Be proactive: If you notice patterns in user behavior, consider storing them as preferences or procedures.
Remember: The knowledge graph is your memory. Use it consistently to provide personalized assistance that respects the user's established preferences, procedures, and factual context.