The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.
A Python MCP server for deterministic fuzzy text matching using RapidFuzz. It enables text normalization, string comparison, best-match ranking, duplicate grouping, and match explanation via configurable profiles and strategies.
This MCP server enables human-in-the-loop semantic labeling by creating self-contained HTML forms for ambiguous data and securely retrieving labeled results.
This MCP server extracts entities and relationships from text and stores them in Neo4j, supporting multiple isolated knowledge graph projects that share the same database.
An MCP server that implements a heavily typed knowledge graph memory system with AI-powered entity and relation extraction, enabling structured knowledge storage and retrieval from unstructured text using predefined or custom ontologies.