/v1/search

​

GET /v1/threads/:id/search — Within one thread

curl -G 'https://api.qlaud.ai/v1/threads/$THREAD_ID/search' \
  --data-urlencode 'q=what did we discuss about Rust' \
  --data-urlencode 'limit=10' \
  -H "x-api-key: $QLAUD_API_KEY"

​

GET /v1/search — Across every thread you own

curl -G 'https://api.qlaud.ai/v1/search' \
  --data-urlencode 'q=refund policy questions' \
  --data-urlencode 'end_user_id=user_42' \
  --data-urlencode 'limit=10' \
  -H "x-api-key: $QLAUD_API_KEY"

​

Query

​

Response

{
  "object": "list",
  "query": "what did we discuss about Rust",
  "data": [
    {
      "thread_id": "39e6f5ac-...",
      "seq": 4,
      "role": "assistant",
      "score": 0.732,
      "snippet": "Rust's ownership model is a compile-time memory management system…",
      "created_at": 1777262998000
    },
    {
      "thread_id": "39e6f5ac-...",
      "seq": 3,
      "role": "user",
      "score": 0.666,
      "snippet": "Now explain Rust's ownership model in one sentence.",
      "created_at": 1777262995000
    }
  ]
}

​

Score

​

Snippets

​

What gets embedded

• Every user turn (the content you sent).
• Every FINAL assistant turn (the model’s text response).
• Tool-loop intermediate turns (the tool_use + tool_result blocks inside a multi-turn dispatch) are NOT embedded — they’re internal state and would pollute search with non-prose noise.
• Image/file/tool blocks within content are stripped at embed time; only text blocks contribute.

​

Tenant isolation

• user_id (your qlaud account)
• thread_id
• seq, role, created_at
• end_user_id if the thread was tagged with one at create time

​

Errors

​

Limits (v1)

• Embedding takes a few seconds after a turn persists; very recent turns may not be searchable yet.
• Newly-tagged end_user_id filter requires the Vectorize metadata index — already created at the qlaud account level, no setup on your end.
• Cross-account search is impossible by design.