Integration · Vector Database

Vald

Scale vector similarity search across distributed infrastructure with Vald. Handle billion-scale vector workloads for recommendation and search applications.

Talk to our team Back to the catalog

SchedulerWorkflow

What you can do

What you can do with Vald.

Index embedding vectors from ML models for approximate nearest neighbor search
Search similar items across large vector datasets for recommendation engines
Manage distributed vector indexes with automated rebalancing and replication

Operations

Vald operations.

Insert

Update

Remove

How it works

How it works.

Add it
Drop the integration onto the Workflow canvas or attach it to a Scheduler job — no code.
Configure
Pick the operation and fill in the fields; credentials are stored encrypted and resolved only at run time.
Run
Execute it inline as part of an automation, or on a cron or interval at fleet scale.

More Vector Database integrations.

Build and query approximate nearest neighbor indexes using the Annoy library

Store, query, and manage vector embeddings in ChromaDB collections

Store, query, and manage document vectors and embeddings with DocArray

Perform vector similarity searches and manage indexes using FAISS library

Build document search and question-answering pipelines with Haystack

Generate embeddings and run vector search queries using Jina AI services

Wire Vald into your operation.

AccuOSS deploys AccuOps and builds the automations that put integrations like this to work.

Talk to our team Browse the catalog