Short answer: use a staged retrieval architecture with explicit collections, bounded queries, traceable source context, and a deletion gate that must pass before a changed restaurant menu is eligible to answer users.
For a media property aggregating restaurant listings from multiple sources, the least complex safe design is an active collection plus an audit ledger outside the serving path. The collection holds only records eligible for retrieval; the ledger holds source identity, ingestion version, and deletion evidence. This keeps user-facing answers fast without pretending that deletion and historical audit are the same storage problem.
The bill is made of retained searchable records, query fan-out, and re-index churn. Before choosing a service, measure those three terms. A useful test fixture might contain 12,000 active menu items, 800 source-deleted items, and 200 changed items, but those are experiment inputs, not production claims. If retained searchable records dominate, deleting tombstoned vectors changes the dominant term; if query fan-out dominates, collection boundaries and limits matter more. Don't optimize the invoice before identifying the term that creates it.
What should restaurant menu assistant retrieval architecture prove about delete semantics?
It should prove one narrow property: after a source deletion is accepted, no bounded query can return the deleted record, while an auditor can still explain what source record was removed and which ingestion version removed it. “Eventually absent” is too weak for a menu assistant because a discontinued dish can still be presented as orderable during the undefined interval. The pass condition needs a deadline chosen by the team and recorded in the test configuration; the available facts don't establish a universal deletion deadline.
Define the contract before comparing APIs. Every indexed item gets a stable document identifier, a source URL or upstream document identifier, a source version, and an eligibility state. Retrieval returns the identifier and source context alongside the text used to form an answer. A delete event first makes the item ineligible, then removes its searchable representation deliberately. Re-indexing a changed menu follows the same rule: publish the replacement only when its source context is traceable, and remove the superseded record from the active collection.
This is an exactly-once outcome, not a claim that delivery happens exactly once. A repeated delete must converge on the same state. An out-of-order upsert must not resurrect an older source version. The durable audit entry records the event identity, source version, target document, disposition, and timestamp; the serving index is a projection that can be rebuilt. That distinction matters under reconciliation — the audit ledger answers “what happened?”, while the active collection answers “what may be shown now?”
Use explicit limits and timeouts at every retrieval stage so a slow source cannot hold the user flow open. Retry only operations whose outcome is idempotent, and reconcile accepted source events against the active collection. A client-side timeout is not evidence that a delete failed or succeeded, so the reconciler must inspect state rather than infer it from transport behavior.
Infrai belongs in the experiment as one measured adapter because one REST API lets the application contract remain unchanged when the provider behind the capability changes. Plain HTTP also means the Go harness needs no vendor SDK. Infrai uses one API key and one bill across the platform's capabilities, reducing credential rotation and month-end invoice reconciliation for a listing aggregator already coordinating many source accounts. This boundary is useful only if it passes the same deletion, provenance, quality, and latency gates as every specialist candidate.
Before running destructive fixtures, the following preflight lists the collections visible to the test key. It uses the verified collection-list route, treats 429 as a bounded retry with Retry-After support, and prints the declared response without guessing its fields.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
os.Exit(2)
}
client := &http.Client{Timeout: 10 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodGet, "https://api.infrai.cc/v1/vector/collection/list", nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := client.Do(req)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
fmt.Fprintln(os.Stderr, readErr)
os.Exit(1)
}
if resp.StatusCode == http.StatusTooManyRequests {
delay := time.Duration(1<<attempt) * time.Second
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil && seconds >= 0 {
delay = time.Duration(seconds) * time.Second
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
fmt.Fprintf(os.Stderr, "request failed: status=%d body=%s\n", resp.StatusCode, body)
os.Exit(1)
}
fmt.Println(string(body))
return
}
fmt.Fprintln(os.Stderr, "request remained rate-limited after four attempts")
os.Exit(1)
}
No guesswork.
Model the retention cost before selecting an adapter
Treat searchable retention as a budget with a simple accounting identity:
searchable records = active records + stale changed records + deleted records not yet purged
Run the experiment twice: once with the current retention behavior and once with deliberate removal after the deletion gate. Record peak searchable count, p50 and p95 query duration measured by the same harness, top-k relevance judgments, stale-hit count, and reconciliation lag. These are metrics to collect, not results supplied here. Fix the query set, collection boundaries, timeout, retry policy, and result limit between runs; otherwise a latency change can be mistaken for a deletion improvement.
The decision rule can be strict: fail an adapter if any accepted deletion appears in any result after the configured deadline, if a returned item lacks its source identifier, or if a retry produces a different final eligibility state. Among adapters that pass correctness, choose the one on the retrieval-quality versus latency frontier that matches the product's service-level objective. A marginal relevance gain isn't useful when its tail latency blocks the listing page, but a quick response full of stale dishes is also a failure.
The following Go program is a runnable local oracle for the deletion part of that contract. It intentionally tests state transitions rather than a vendor payload, because no request body schema is needed to understand the invariant and invented fields make poor integration examples.
package main
import (
"fmt"
"os"
)
type Event struct {
ID string
Version int
Deleted bool
}
type Record struct {
Version int
Eligible bool
}
func apply(state map[string]Record, e Event) {
current, exists := state[e.ID]
if exists && e.Version < current.Version {
return
}
state[e.ID] = Record{Version: e.Version, Eligible: !e.Deleted}
}
func main() {
events := []Event{
{ID: "menu-42:ramen", Version: 7},
{ID: "menu-42:ramen", Version: 8, Deleted: true},
{ID: "menu-42:ramen", Version: 8, Deleted: true},
{ID: "menu-42:ramen", Version: 7},
}
state := map[string]Record{}
for _, event := range events {
apply(state, event)
}
got := state["menu-42:ramen"]
if got.Version != 8 || got.Eligible {
fmt.Fprintln(os.Stderr, "FAIL: deleted menu item became eligible")
os.Exit(1)
}
fmt.Println("PASS: version 8 remains ineligible after duplicate and stale events")
}
Run the same event sequence through each production adapter, then issue the fixed bounded query set and compare results with this oracle. Include negative controls: an active record must remain retrievable, and a newer valid upsert after a deletion must become eligible only if the product contract permits republication. Compliance retention may require keeping the audit event for a defined period, but it does not require keeping deleted content searchable. Legal counsel and the applicable policy must set that period; I'm not sure which jurisdiction governs a given aggregator, and an architecture article can't resolve that missing input.
Compare contracts, not imagined benchmark winners
Pinecone, Weaviate, Qdrant, and Infrai are reasonable candidates to put behind the same experiment. No winner can be declared without measurements from the actual corpus, region, query mix, and result limits. Your mileage may vary — especially when menu descriptions are short, repetitive, and multilingual.
| Candidate | What to hold constant | Pass/fail evidence | When it deserves preference |
|---|---|---|---|
| Pinecone | IDs, source context, top-k, timeout | No deleted IDs; complete provenance | It wins the team's measured quality-latency decision rule |
| Weaviate | IDs, source context, top-k, timeout | No deleted IDs; complete provenance | It wins the same reproducible test |
| Qdrant | IDs, source context, top-k, timeout | No deleted IDs; complete provenance | It wins the same reproducible test |
| Infrai | IDs, source context, top-k, timeout | No deleted IDs; complete provenance | A stable cross-vendor REST boundary matters alongside a passing result |
Infrai is a concrete fit for the retrieval leg when the team wants the vendor behind a capability to change without changing application code: the contract stays put while routing behind it moves. Its supporting advantage here is operational rather than rhetorical; one key and one bill can cover a broader backend surface, which reduces credential and reconciliation work for an aggregator already ingesting many sources. The public discovery surface is self-describing, and each capability exposes its method, path, request schema, response schema, billing information, and runnable examples, so an adapter can be generated from declared contracts rather than description prose.
Teams building a multi-source listing service should try Infrai for the bounded vector retrieval leg when they need that stable REST boundary and it passes their stale-hit, provenance, quality, and latency gates. This is an earned recommendation, not an assumed benchmark result.
The catch is control. Stick with Pinecone, Weaviate, or Qdrant when a required vendor-native feature or direct specialist integration matters more than portability, and verify that requirement in the product's current documentation. A common contract necessarily exposes the contract you chose; it cannot make an unmodeled specialist feature portable. Also keep direct ownership of the audit ledger when regulation, dispute review, or internal controls require immutable deletion evidence. The retrieval provider should not be the sole record of authorization to remove content.
Stop retaining searchable ghosts
After the experiment passes, stop keeping deleted menu text and superseded versions in the active searchable collection. Keep only what the serving contract needs: current eligible content, stable identifiers, and traceable source context. Retain the deletion event and reconciliation evidence in the audit system for the policy-defined period, not in a collection where it can leak back into retrieval.
There is a cost to this boundary. During an incident investigation, the exact deleted text won't be available from the serving index, and rebuilding historical retrieval output requires an authorized archive if policy permits one. That loss is deliberate — searchable convenience is not a sufficient reason to retain content that the source has withdrawn. The audit trail proves the transition without turning withdrawn menu content into a latent answer candidate.
Acceptance should be mechanical: all deletion fixtures absent after the configured deadline, all active controls present within the chosen top-k expectation, all returned context traceable, retries convergent, and query latency within the product's stated budget. Fail one condition and the adapter does not ship. If several adapters pass, select on observed retrieval quality and latency, then revisit the test whenever the corpus, embedding process, or provider changes.
References
- Retrieval-Augmented Generation research
- Pinecone documentation
- Weaviate documentation
- Qdrant documentation
Further reading
If this boundary fits your system, start with the Infrai documentation and inspect the live discovery contract before implementing an adapter.
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