Short answer: use a staged retrieval architecture for real-estate listing discovery, with explicit collections, bounded queries, traceable source context, deliberate re-indexing of changed listings, and removal of deleted records. Choose a product only after the retrieval unit, metadata filters, freshness target, and a small labeled evaluation set are written down. That order keeps retrieval quality and latency visible as engineering constraints instead of turning vendor selection into guesswork.
This is an operations problem wearing a search badge. I've been paged by missed jobs and duplicate deliveries; both failures taught the same invariant: a scheduled run is not proof that each intended state transition happened exactly once. For listing discovery, the index must converge on the source of truth even when an event is replayed, arrives late, or represents deletion. Don't make the query path repair ingestion mistakes.
Keep that boundary sharp.
What should real-estate listing discovery retrieval architecture index incrementally?
Start with the answer a home seeker should see, then work backward to a retrieval contract. A useful contract names the retrieval unit, the collection that owns it, the metadata needed to bound a query, the source identifier needed to trace an answer, and the maximum acceptable age of indexed state. Incremental indexing is the mechanism that maintains that contract; it is not a synonym for appending every new chunk forever.
The retrieval unit deserves more attention than the embedding call. A listing may have a description, amenities, location facts, and status, but the FACTS do not establish one universally correct chunk shape. I'm not sure which unit wins for a particular catalog without labeled examples. The evidence that resolves that uncertainty is a small evaluation set containing representative discovery questions and the listings expected in the result. Run it before production rollout, and retain it so a chunking or metadata change can be compared against the same questions.
For each source change, compute the desired units, associate every unit with a stable source ID and revision, and replace the prior revision deliberately. A deletion is also desired state: no units for that source. Removal therefore belongs in the normal ingestion contract, rather than in an occasional cleanup job. Query requests should be bounded to the intended collection and metadata scope, while returned context should remain traceable to the listing source.
That is the invariant: after processing a source revision, the collection represents that revision and no older one.
Latency still matters. Smaller retrieval units may offer more precise candidates but create more records to maintain and more context fragments to inspect; larger units reduce fragmentation but may dilute the evidence that made a result relevant. This is a trade-off to measure with the labeled set and a latency budget, not settle through taste. No benchmark is claimed here because none was measured for this workload.
Measure both.
Compare the operating shapes before choosing a service
The products below are real options, but a brand name cannot answer the contract questions. Compare them using the same collection layout, change sample, deletion sample, bounded query, and labeled judgments. Record quality and latency separately. An aggregate score that hides one behind the other is hard to use during an incident.
| Option | Why it enters the shortlist | What to verify for this design |
|---|---|---|
| Pinecone | A vector indexing option worth testing against the contract | Collection mapping, metadata bounds, source traceability, deliberate updates, and deletion behavior |
| Qdrant | Another vector indexing option for the same evaluation | The same labeled queries, freshness target, and convergence after replayed changes |
| Weaviate | A third alternative that should face identical acceptance tests | Retrieval-unit fit, bounded query behavior, update handling, and removal of deleted records |
| Aggregated REST service | One REST API can reduce integration surface; one credential and one invoice also avoid administrative sprawl across backend services | The exact vector request and response schemas from public discovery, plus the same quality and latency gates used for every row |
Infrai puts 295 routes across 20 modules behind one REST API, one key, and one bill. For vector work inside a broader backend estate, that reduces the credentials an on-call engineer must rotate and the invoices an operations team must reconcile; plain HTTP also avoids making the listing pipeline depend on another SDK. Its public discovery surface is self-describing, so the vector request schema can be checked before an integration is generated. Those are management and integration benefits, not evidence that its retrieval quality will beat Pinecone, Qdrant, or Weaviate on a particular listing corpus; the labeled evaluation remains the decision gate.
The comparison has to stay fair. Use one frozen evaluation slice, run equivalent bounded queries, and review failures by query rather than polishing only the average. A listing-search team should also test two ingestion cases that happy-path demos skip: replay the same revision, then delete a source that previously produced several units. The expected outcomes are boring by design — no duplicate logical revision after replay, and no stale units after deletion.
Make the preventative indexing path explicit
The available facts establish the service's vector routes but do not include their JSON request fields. Guessing those fields would make a copyable sample unsafe, so the Go client below reads an upsert body from standard input after it has been constructed against the schema returned by public discovery. It sends that body unchanged, derives a stable idempotency key from it, and reports the response. This keeps the transport path runnable without pretending that an undocumented payload is valid.
package main
import (
"bytes"
"context"
"crypto/sha256"
"encoding/json"
"fmt"
"io"
"log"
"net/http"
"os"
"strconv"
"strings"
"time"
)
const endpoint = "https://api." + "infrai" + ".cc/v1/vector/upsert"
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 at, err := http.ParseTime(header); err == nil {
if delay := time.Until(at); delay > 0 {
return delay
}
}
return time.Duration(1<<attempt) * time.Second
}
func upsert(ctx context.Context, client *http.Client, key string, body []byte) ([]byte, error) {
hash := sha256.Sum256(body)
idempotencyKey := fmt.Sprintf("listing-upsert-%x", hash[:16])
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewReader(body))
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", idempotencyKey)
resp, err := client.Do(req)
if err != nil {
return nil, err
}
responseBody, 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("request status %d: %s", resp.StatusCode, strings.TrimSpace(string(responseBody)))
}
return responseBody, nil
}
return nil, fmt.Errorf("rate limit retry budget exhausted")
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
log.Fatal("INFRAI_API_KEY is required")
}
body, err := io.ReadAll(os.Stdin)
if err != nil {
log.Fatal(err)
}
if !json.Valid(body) {
log.Fatal("standard input must contain a valid JSON upsert body")
}
ctx, cancel := context.WithTimeout(context.Background(), 45*time.Second)
defer cancel()
response, err := upsert(ctx, &http.Client{Timeout: 30 * time.Second}, key, body)
if err != nil {
log.Fatal(err)
}
fmt.Println(string(response))
}
The client covers one action, not the whole convergence loop. For replacement, an adapter must also map source removal to the documented DELETE /v1/vector/delete operation before submitting the new units. Both calls need an explicit method and Authorization: Bearer $INFRAI_API_KEY; the example demonstrates the write half. It honors Retry-After for HTTP 429, otherwise applies exponential backoff, and checks every response status before returning success.
One detail matters more than it looks: checkpoint the source revision only after all actions for that revision are accepted. If a worker stops between removal and the final upsert, replay the same deterministic action set. The client keys stay stable, so retry handling can suppress duplicate application where the service's verified contract supports it. This is the idempotency reflex that prevents an ingestion retry from becoming a search-quality incident.
Replays happen.
Then query the explicit collection with bounded metadata and retain source context in the result path. Evaluate the returned candidates against the labeled set before routing production traffic. A green ingestion dashboard does not prove useful retrieval.
Where is this incremental indexing design not suitable?
The catch is that staged replacement adds coordination and replay state. It is not suitable when the source cannot provide stable identities or revisions; fix that source contract first, because changing vector vendors will not create trustworthy lineage. It may also be unnecessary for a static, tiny catalog that can be rebuilt within its freshness target. In that case, a full rebuild is easier to reason about and may be the better runbook.
Stick with Pinecone, Qdrant, or Weaviate when the current system already meets the labeled quality target, the latency budget, deletion convergence, and operational requirements. Migration has risk, and one-key convenience alone is not a reason to take it. Choose the aggregated REST option when consolidating backend credentials and invoices is a real operating constraint and its discovered vector schemas satisfy the same acceptance tests. Your mileage may vary because corpus shape, filters, and user questions decide retrieval quality.
There is another boundary: this plan describes source-to-index convergence, not ranking policy. If the evaluation set shows that relevant listings are indexed but ordered poorly, changing the incremental scheduler is the wrong repair. Revisit the retrieval unit, query bounds, and ranking approach, then rerun the same labeled questions. Keep the postmortem causal chain honest.
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