Deleting Vectors

1. Deleting Vectors by ID

Example: Delete by IDs

index.delete(ids=["doc1", "doc2"], namespace="docs")

Param	Description
`ids`	List of IDs to delete (up to 1000)
`namespace`	Target namespace (default `""`)
`filter`	Metadata filter (pod only)
`delete_all`	Wipe namespace

2. Deleting by Metadata Filter

Example: Delete by Filter

index.delete(filter={"source": {"$eq": "archive"}}, namespace="docs")

Warning: Filter-based delete is supported on pod indexes only. For serverless, query+collect IDs, then delete by ID.

3. Deleting All Vectors in Namespace

Example: Wipe Namespace

index.delete(delete_all=True, namespace="temp")

Effect	Detail
Removes all vectors	Namespace remains (empty)
Irreversible	No undo

4. Deleting Entire Namespace

Example: Delete Namespace

index.delete_namespace("temp")  # serverless v5+

Method	Behavior
`delete_namespace`	Removes namespace entirely (serverless)
`delete(delete_all=True)`	Empties namespace

5. Batch Deleting Vectors

Example: Chunked Delete

for i in range(0, len(ids), 1000):
    index.delete(ids=ids[i:i+1000])

6. Verifying Deletion Success

Example: Verify

index.delete(ids=["d1"])
res = index.fetch(ids=["d1"])
assert "d1" not in res["vectors"]

Note: Deletes are eventually consistent. Wait briefly before verifying or use stats.

7. Handling Delete Failures

Error	Action
404 Index missing	Confirm index name
400 Empty IDs	Add at least one ID or use `delete_all`
429	Backoff and retry

8. Implementing Soft Deletes

Example: Soft Delete via Metadata

index.update(id="d1", set_metadata={"deleted": True})
# Filter out at query time:
res = index.query(vector=qv, top_k=10, filter={"deleted": {"$ne": True}})

Approach	Tradeoff
Soft delete	Recoverable, but still stored/billed
Hard delete	Immediate cost reduction, irreversible

9. Scheduling Automated Deletions

Example: TTL Job (Python)

# Cron: nightly
cutoff = int(time.time()) - 30 * 86400
# Query stale, collect IDs, delete in batches
stale = index.query(vector=[0]*1536, top_k=10000,
                    filter={"ts": {"$lt": cutoff}})
ids = [m["id"] for m in stale["matches"]]
index.delete(ids=ids)

10. Understanding Deletion Performance

Operation	Speed
Delete by ID (1000)	~50–200 ms
Delete by filter	O(matches); slower
`delete_all`	Near-instant
Eventual consistency	1–2 s typical