Milvus

Data

Neotask connects to your Milvus vector database - search, index, and manage embeddings through natural conversation instead of writing query code.

• Run text, vector, hybrid, and multi-vector searches across your collections without writing code
• Create collections, insert data, and build indexes through plain English instructions
• Monitor collection stats and manage your vector infrastructure conversationally

What You Can Do

The Milvus integration provides 11 actions covering search, data management, and collection administration:

Search Operations

• Text search - full-text search across collection fields for keyword matching
• Vector search - similarity search using embeddings for semantic retrieval
• Hybrid search - combine text and vector search for best-of-both-worlds results
• Multi-vector search - search across multiple vector fields simultaneously
• Query - filter and retrieve records using boolean expressions
• Count - count records matching filter conditions

Collection Management

• List all collections in your instance
• Get schema, index, and statistics for any collection
• Create new collections with defined schemas
• Insert data records into collections
• Build indexes on vector or scalar fields

Every action runs autonomously or requires your approval - you decide.

Try Asking

• "Search our product catalog for items similar to this description"
• "How many records are in the customer embeddings collection?"
• "Create a new collection called 'support_tickets' with a 768-dimension vector field"
• "Run a hybrid search combining keyword 'pricing' with the semantic meaning of 'cost reduction'"
• "Show me the schema and index details for the knowledge_base collection"
• "Insert these 50 product records into the catalog collection"

Pro Tips

• Hybrid search typically outperforms pure vector or pure text search - default to it for production retrieval use cases.
• Build indexes before running large-scale searches; unindexed collections scan every record.
• Use multi-vector search when your records have separate embeddings for title, body, and metadata.
• Pair Milvus with your content management system in an app group so agents can automatically embed and store new documents as they are published.

Plans

Works Well With

• OpenAI - Connect Milvus and OpenAI with Neotask to build AI-powered semantic search, vector storage, and retrieval-augmented gene...
• Synapse - Connect Milvus vector search with Synapse biomedical datasets. Index research embeddings, run similarity search across p...

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