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Use natural language to store and search vector data in VikingDB.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vikingdb-mcp-server" yet — see the docs or source repo.
Help me write this batch of product-description embeddings into VikingDB and index them by product ID for later similar-product search.
Returns the ingestion and indexing result, plus how to retrieve by ID or semantic similarity later.
Search VikingDB for the 10 product vectors most similar to 'lightweight sun-protection jackets for summer commuting' and return similarity scores with product info.
Returns a ranked list of nearest vector matches with similarity scores and linked product metadata.
Create a knowledge-base retrieval setup in VikingDB using existing document embeddings, and show how to query the most relevant passages with natural language.
Provides the retrieval workflow and returns the most relevant document passages for the sample query.
Search OpenViking context semantically with tiered loading to save tokens.
Connect AI agents to secure RAG workflows across multiple vector databases.
Store typed customer records and query them with exact and semantic search.
Build and maintain a bidirectionally linked knowledge base for AI agents.
Search, manage, verify, and reindex documents in a local vector knowledge base.
Search the Vignan University knowledge base with semantic relevance.