Weaviate

AI & ML

Search, store, and manage your Weaviate vector database — Neotask handles every operation through OpenClaw.

What You Can Do

Semantic and Hybrid Search

Ask Neotask to find objects in Weaviate using natural language. It will build the nearText, nearVector, or hybrid query, execute it, and return results in a readable format.

Schema Design and Management

Describe your data model in plain language and Neotask will create Weaviate classes with the right properties, data types, and vectorizer configuration. Modify schemas without downtime risk.

Object Import and Updates

Batch-import objects from JSON, CSV, or plain description. Update individual properties or run bulk patches across a class — Neotask handles the Weaviate client calls.

Generative Search

Use Weaviate's generate module to retrieve relevant objects AND generate a response in a single query. Describe the task and Neotask constructs the generative search call end-to-end.

Cross-Reference Management

Create and query cross-references between Weaviate classes. Model complex relationships — articles linked to authors, products linked to categories — without writing BeaconIDs by hand.

Try Asking

  • "Search my Weaviate 'Articles' class for content about climate policy using semantic search"
  • "Add this JSON array of 200 products to the 'Products' class in Weaviate"
  • "Create a Weaviate schema for a movie database with Title, Genre, Year, and Director"
  • "Run a hybrid search for 'machine learning optimization' and return the top 5 results with scores"
  • "Generate a summary of the most relevant support articles for 'password reset issues'"
  • "How many objects are in each class in my Weaviate instance?"
  • "Update the 'status' property to 'archived' for all Articles published before 2022"
  • "Show me the schema for the 'CustomerFeedback' class"

    • Pro Tips

  • Use hybrid search (BM25 + vector) for enterprise search use cases — Neotask will tune the alpha parameter based on your description of the tradeoff you want
  • Describe your vectorizer requirements (OpenAI, Cohere, or local) and Neotask will configure the module during schema creation
  • Request object counts by class weekly to monitor database growth and plan capacity upgrades
  • Use generative search for customer-facing Q&A — one query retrieves and answers simultaneously, reducing latency
  • Always include a certainty or distance threshold when querying — ask Neotask to filter out low-confidence results automatically

    • Works Well With