Design schemas, construct OpenCypher graph queries, and implement Locy logic rules.
npx skills add https://github.com/rustic-ai/uni-db --skill uni-db
Please help me install this Agent Skill. Skill: uni-db Source: https://github.com/rustic-ai/uni-db/tree/main/skills/uni-db Command: npx skills add https://github.com/rustic-ai/uni-db --skill uni-db
This Skill solves the complexity of managing disparate databases for graph, vector, and document data by providing a unified, embedded engine that runs locally within your application process.
Use the uni-db skill to initialize a new database, define a schema with vector and scalar properties, and execute a transaction to insert nodes and edges.
High-intent search queries and answers about installing and using this skill.
An embedded multi-model graph and vector database combines OpenCypher graph queries, vector search, and columnar storage in one library to build local RAG pipelines and recommendation engines without a separate database server.
You can combine semantic vector similarity and graph relationship traversal by using a multi-model engine that supports both vector search and OpenCypher queries within a single transactional library.
Yes, you can define a schema with vector and scalar properties in a local database by initializing the embedded engine, structuring the multimodal schema, and executing transactions to insert nodes and edges.
No, an embedded graph database runs locally within your application process, eliminating the overhead of managing a separate database server while providing high-performance multimodal data access.
The best way to manage graph, vector, and document data without disparate databases is using a unified embedded engine that simplifies schema management, transaction handling, and bulk data loading for complex datasets.
Locy logic programming integrates with OpenCypher graph queries by operating within a single multi-model engine, allowing developers to implement recursive logic rules alongside complex graph-based relationship traversals.
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Mohamed-ElkahefDevelop multi-agent RAG systems with vector search and knowledge graphs.
insanalaminExecute unified graph, vector, spatial, and full-text queries in-process.
