This MCP implementation, built with Python, provides a versatile framework for integrating with ChromaDB, a vector database for AI applications. It utilizes sentence transformers for efficient text embedding and tokenization, enabling advanced natural language processing capabilities. The implementation supports websocket connections, allowing for real-time data exchange and updates. By combining these technologies, it offers a robust solution for building AI-powered applications that require semantic search, document similarity comparisons, and content recommendation systems. This implementation is particularly well-suited for use cases such as intelligent document retrieval, content categorization, and personalized information delivery across various domains.
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Highlight AIStore new information with optional tags. Parameters: content (string), tags (array of strings)
Perform semantic search for relevant memories. Parameters: query (string)
Retrieve memories using natural language time expressions. Parameters: time_expression (string)
Find memories using specific tags. Parameters: tags (array of strings)
Find memories with exact content match. Parameters: content (string)
Retrieve memories with similarity scores. Parameters: query (string)
Create database backup.
Get memory statistics.
Optimize database performance.
Get database health metrics.
Verify model status.
Delete specific memory by hash. Parameters: memory_hash (string)
Delete memories with specific tag(s). Supports both single tags and multiple tags. Parameters: tags (array of strings)
Explicitly delete memories containing any of the specified tags (OR logic). Parameters: tags (array of strings)
Delete memories containing all specified tags (AND logic). Parameters: tags (array of strings)
Remove duplicate entries.
Manually trigger consolidation for any time horizon. Parameters: time_horizon (string)
Monitor system health and performance.
View processing statistics and insights.
Configure autonomous scheduling. Parameters: schedule (string)
Explore discovered memory connections.
Browse semantic memory clusters.
Get AI-powered memory management advice.