Search

Verida’s Search APIs let you perform powerful keyword-based searches across multiple data sources. Each search endpoint uses the specified credit amount per call.

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1. Chat Thread Search

• HTTP Method & Endpoint: GET /search/chat-threads

• Summary: Search through all chat threads for matching keywords.

• Credit Usage: 2 credits

• Scope: api:search-chat-threads

• Example:

  bashCopycurl -X GET "https://user-apis.verida.network/search/chat-threads?keywords=urgent" \
       -H "Authorization: Bearer YOUR_AUTH_TOKEN"

• Full Documentation: Search: Chat Threads

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2. Datastore Search

• HTTP Method & Endpoint: GET /search/ds or POST /search/ds

• Summary: Perform a keyword search across a specific datastore.

• Credit Usage: 1 credit

• Scope: api:search-ds

• Example:

  bashCopy# Using GET
  curl -X GET "https://user-apis.verida.network/search/ds?keywords=invoice&datastore=social-email" \
       -H "Authorization: Bearer YOUR_AUTH_TOKEN"

• Full Documentation: Search: Datastore

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3. Universal Search

• HTTP Method & Endpoint: GET /search/universal

• Summary: Perform a keyword search across all user data (datastores, databases, etc.) the user has granted access to.

• Credit Usage: 2 credits

• Scope: api:search-universal

• Example:

  bashCopycurl -X GET "https://user-apis.verida.network/search/universal?keywords=meeting+agenda" \
       -H "Authorization: Bearer YOUR_AUTH_TOKEN"

• Full Documentation: Search: Universal

Vector database?

As an AI developer you may be asking, does Verida offer a Vector Database over user data?

We currently don't, because from our testing Vector Databases require more resources to create than a traditional high performance keyword index and produces sub-par results when working with user data.

We are happy to re-assess this if there's a use case that specifically requires a Vector Database.