Pinecone

Data

Neotask manages your Pinecone vector infrastructure through OpenClaw — search records, manage indexes, and build AI retrieval pipelines without writing code.

What You Can Do

Pinecone through Neotask provides 10 actions covering vector operations, index management, and intelligent retrieval:

Vector Operations

  • Search records — query your indexes using vector embeddings for similarity search
  • Upsert records — add or update records with vectors and metadata
  • Rerank documents — re-score search results for improved relevance
  • Cascading search — multi-stage retrieval that progressively narrows results
  • Assistant context — retrieve contextual data from Pinecone Assistant for RAG workflows

    • Index Management

  • List indexes — browse all indexes in your Pinecone project
  • Describe index — get configuration, dimension, and metric details for any index
  • Describe index stats — check record counts, namespace distribution, and storage usage
  • Create index for model — spin up an index optimized for a specific embedding model
  • Search docs — query Pinecone documentation without leaving the conversation

    • Every action runs autonomously or requires your approval — you decide.

    Try Asking

  • "Search our product index for items similar to 'wireless noise-canceling headphones'"
  • "How many records are in the knowledge-base index?"
  • "Create a new index optimized for the text-embedding-3-large model"
  • "Upsert these 100 product records into the catalog namespace"
  • "Rerank the top 50 search results for better relevance"
  • "What indexes do we have and how much storage is each using?"
  • "Run a cascading search for customer support queries related to billing"

    • Pro Tips

  • Use cascading search for high-precision retrieval — it is more accurate than a single-pass search for complex queries.
  • Create model-specific indexes to get optimal performance for your embedding provider.
  • Combine Pinecone with your content management system in an app group so new content is automatically embedded and indexed.
  • Reranking after initial retrieval significantly improves result quality for RAG applications.
  • Monitor index stats through scheduled automations to catch unexpected growth or namespace imbalances early.
  • Multi-agent teams can search multiple indexes in parallel and merge results for cross-domain retrieval.

    • Works Well With