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Unified graph and vector database for low-latency intelligent search and memory workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "NornicDB" yet — see the docs or source repo.
Design a knowledge base solution with NornicDB: use Neo4j Bolt/Cypher to connect existing graph data, and add vector search, embedding management, and reranking for internal enterprise Q&A. Provide the data model, query flow, and sample Cypher/search requests.
A graph-plus-vector retrieval design with schema, query pipeline, and example requests.
I currently use Neo4j and a separate vector database. Explain how to migrate to NornicDB while keeping Bolt/Cypher and existing access patterns unchanged as much as possible. List compatibility points, migration steps, risks, and a validation checklist.
A migration plan covering compatibility, rollout steps, risks, and validation methods.
Use NornicDB to design a long-term memory system for an AI assistant. It should support temporal versioning, policy-based memory decay, vector recall, and graph traversal. Provide core entities and relations, write/retrieval logic, and interface recommendations for an MCP server.
An AI long-term memory architecture with data relationships, update rules, and retrieval interface guidance.
Give AI coding agents local-first persistent memory management and recall.
Query Neo4j graph databases with natural language for fast relationship insights.
Discover Neo4j vector indexes and run semantic search with metadata prefiltering.
Turn natural language into SQL across databases with document QA and semantic caching.
Lets AI query markdown note graphs and extract linked entities and relationships.
Query databases in natural language with automatic schema discovery and SQL generation.