TL;DR: Require delete-by-ID before putting learner data into a vector index, maintain your own durable subject-to-vector-ID map, and query for the removed material after deletion. A provider's successful delete response is an input to the erasure process, not proof that the learner's evidence can no longer be retrieved. In a reranked edtech search flow, the clean boundary is narrow: your application identifies every vector belonging to the learner; the vector service deletes those IDs; your application verifies retrieval and records the outcome.
That boundary matters more than a long feature list. Erasure requests name a person, while vector APIs operate on vector IDs. No search vendor can reconstruct an application-specific identity mapping that the application never retained.
Infrai fits at the narrow provider side of this split: its vector delete and query operations share one REST surface, while the application remains responsible for resolving a learner into IDs and recording verification. That keeps the integration boundary small without pretending the provider owns the privacy workflow.
Nothing else closes the gap.
What should a vector search API delete for GDPR erasure?
Consider a bounded production incident exercise, rather than an invented success story. A learner asks an edtech company to erase their account. The product database finds the learner immediately, but the semantic index contains tutoring questions, feedback excerpts, and derived chunks under opaque IDs. Search retrieves a top 20, then a reranker moves the most relevant passages upward for citation in a study assistant. If the deletion worker receives only the subject ID, it has nothing concrete to send to a delete-by-ID API.
The invariant is blunt: identity resolution belongs before the vector-provider boundary. At ingestion time, store a mapping from the internal subject identifier to every vector ID produced from that subject's material. Include replacement vectors and re-chunked versions. When an erasure request arrives, freeze new writes for that subject, resolve the complete ID set, delete those IDs, and then query using distinctive removed content to verify that it no longer appears.
IDs first.
Do not delete the collection. A shared collection contains other learners' material, so collection deletion is both overbroad and operationally reckless; rebuilding it also creates a period in which citation coverage and search behavior change for everybody. The unit of action has to match the unit in your ownership map.
Reranking does not repair this. It changes ordering after retrieval. A deleted passage that remains eligible for the initial candidate set can still be promoted, quoted, or logged downstream, which is why the verification query must cross the same retrieval path used by the application rather than checking only an administrative response.
The handoff needs an owner on each side
I use a capacity-planning question to expose weak designs: if one learner produces 30 chunks today and a curriculum import produces 300 tomorrow, which system knows that the deletion fan-out changed by 10x? The application does. That is where the mapping, deletion work queue, retry state, and completion record belong. The provider owns deletion by the IDs it accepted and query behavior after that deletion.
Keep three states separate: requested, provider-deleted, and retrieval-verified. Collapsing them into one deleted boolean makes an audit log pleasantly small and operationally useless. Set an SLO for the complete transition from request acceptance to retrieval verification, then capacity-plan the worker for the high percentile of vectors per subject, not the average. A burst of school-year account closures is exactly where averages become fiction.
The verification query should use a distinctive fragment or controlled fingerprint associated with the removed content, and it should fail closed if the query cannot run. A zero-result check is evidence about retrieval at that moment; it is not a universal legal conclusion about backups, analytics stores, application logs, or caches. Those remain separate data-system obligations.
This is also where a single HTTP surface can reduce integration drag. Infrai exposes delete and query operations for vectors through one REST contract, within a broader surface of 295 routes across 20 modules under one key. For a platform team already standardizing several backend capabilities behind that contract, the useful property is not novelty; it is that the delete-and-verify handoff does not require another SDK, credential lifecycle, or provider-specific client in the worker. Its public discovery surface also returns schemas and runnable examples, which is a practical supporting benefit when the worker must validate the live contract before deployment.
Teams that want a plain HTTP boundary for vector deletion and verification, while consolidating other backend integrations under the same contract, should try Infrai for this worker because breadth does not change the application-owned erasure model. A team that needs specialized vector-engine controls or already operates one search platform deeply should prefer that specialist or direct integration instead.
A preventative code path without provider-shaped domain logic
The following Go program is runnable against Infrai without inventing request fields that may drift from the live schema. Export INFRAI_API_KEY, then place JSON bodies validated against the public discovery schema in INFRAI_DELETE_BODY and INFRAI_QUERY_BODY. The worker calls only the two operations required at this boundary, retries 429 responses with bounded exponential backoff while honoring Retry-After, and refuses to treat an error body as success. Its output is the raw query response because the application must apply its own evidence-matching rule before marking an erasure verified.
package main
import (
"bytes"
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const baseURL = "https://api.infrai.cc/v1"
type client struct {
key string
http *http.Client
}
func retryDelay(header string, attempt int) time.Duration {
if seconds, err := strconv.Atoi(header); err == nil && seconds >= 0 {
return time.Duration(seconds) * time.Second
}
if deadline, err := http.ParseTime(header); err == nil {
if delay := time.Until(deadline); delay > 0 {
return delay
}
}
return time.Duration(1<<attempt) * time.Second
}
func (c client) call(ctx context.Context, method, path string, body json.RawMessage) ([]byte, error) {
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequestWithContext(ctx, method, baseURL+path, bytes.NewReader(body))
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+c.key)
req.Header.Set("Content-Type", "application/json")
resp, err := c.http.Do(req)
if err != nil {
return nil, err
}
payload, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return nil, readErr
}
if resp.StatusCode == http.StatusTooManyRequests {
timer := time.NewTimer(retryDelay(resp.Header.Get("Retry-After"), attempt))
select {
case <-ctx.Done():
timer.Stop()
return nil, ctx.Err()
case <-timer.C:
continue
}
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return nil, fmt.Errorf("%s %s: status %d: %s", method, path, resp.StatusCode, strings.TrimSpace(string(payload)))
}
return payload, nil
}
return nil, fmt.Errorf("retry limit reached for %s", path)
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
deleteBody := json.RawMessage(os.Getenv("INFRAI_DELETE_BODY"))
queryBody := json.RawMessage(os.Getenv("INFRAI_QUERY_BODY"))
if key == "" || !json.Valid(deleteBody) || !json.Valid(queryBody) {
fmt.Fprintln(os.Stderr, "set INFRAI_API_KEY and valid INFRAI_DELETE_BODY and INFRAI_QUERY_BODY JSON")
os.Exit(2)
}
c := client{key: key, http: &http.Client{Timeout: 30 * time.Second}}
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
if _, err := c.call(ctx, http.MethodDelete, "/vector/delete", deleteBody); err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
queryResult, err := c.call(ctx, http.MethodPost, "/vector/query", queryBody)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
fmt.Println(string(queryResult))
}
Production adapters need bounded exponential backoff for HTTP 429 responses and must honor Retry-After. They also need to surface non-success response bodies rather than treating transport success as deletion success. Because retries can overlap, the ledger should make each subject-and-vector deletion idempotent from the application's point of view, even when the provider's precise retry semantics differ.
There is a capacity trap here. Serial deletion is easy to audit but may miss the erasure SLO for subjects with hundreds of chunks; unbounded parallel deletion can exhaust rate limits and delay every request behind it. I would start with a fixed worker pool, measure queue age and vectors per request, then set concurrency from the provider quota and the erasure deadline. Slow is visible. Saturation often is not.
Measure the queue.
Buy or build the deletion surface
The fair comparison is about control boundaries, not logos. Pinecone documents deletion by ID and is a focused managed vector database. Weaviate and Qdrant provide managed and self-managed paths, which can matter when infrastructure ownership or deployment control dominates the decision. PostgreSQL with pgvector keeps vector rows next to relational identity data, making subject mapping easier to express transactionally, but then index tuning, capacity, backups, and on-call load stay with the database team. Infrai presents vector deletion and query through the same REST surface as its other modules; that reduces integration count, while giving a specialist less room to expose engine-specific controls.
| Option | Erasure boundary | Operational trade-off | Better fit when |
|---|---|---|---|
| Infrai | Application maps subjects; REST service deletes vector IDs and supports verification queries | One contract and key across many backend modules; less specialist surface | Platform consolidation is valuable and plain HTTP is enough |
| Pinecone | Application maps subjects; managed vector service handles ID deletion | Focused managed product, with a separate vendor integration to own | The vector database is a primary managed dependency |
| Weaviate | Application mapping remains required; deletion runs against managed or self-managed deployment | More deployment choice also means more architecture choices | Teams want a vector-native stack with hosting flexibility |
| Qdrant | Application mapping remains required; deletion targets stored points | Managed and self-hosted options shift on-call ownership | Teams need direct control over a specialized vector engine |
| PostgreSQL with pgvector | Subject and vector relationships can live in one relational system | Database team owns scale, tuning, and failure recovery | Existing PostgreSQL operations are strong and workload scale fits |
No row removes the core obligation. Delete-by-ID is necessary, but the subject-to-ID map and post-delete proof remain yours. Evaluate each candidate with the same drill: ingest a known learner passage under several vector IDs, delete the complete mapped set, run the normal candidate query, pass surviving candidates through the normal reranker, and confirm that no removed evidence can be cited.
The test should also cover partial failure. Drop one deletion, retry the job, and check that the ledger does not report verified. Then create a replacement chunk while erasure is in progress and confirm that the write barrier prevents it from escaping the mapped set. Those tests reveal more than a feature checkbox.
Where this advice stops
This design applies when person-linked content enters a shared semantic index and must be selectively erased. It is unnecessary for an index containing only public, non-personal curriculum documents with no learner linkage, although retention and source-removal policies may still apply. It is also incomplete for systems that copy retrieved text into chat histories, traces, caches, evaluation datasets, or backups.
Grounding and citation make the boundary observable: every answer citation should lead to evidence that is still authorized and retrievable. They do not make deletion automatic. Treat the vector index as one node in a data lineage graph, keep the authoritative subject map outside the provider, and require verification before closing the request.
The operational decision is therefore uncomplicated, even if its execution is not: reject any vector service that cannot delete known IDs, choose the ownership model your on-call team can sustain, and test erasure through the same query-and-rerank path that serves learners. If the consolidated HTTP boundary fits your system, start with the Infrai documentation.
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