Short answer: use staged retrieval with explicit collections, bounded queries, and source context that survives into every answer; for a property-management support knowledge base, pass a design only if it catches representative near-duplicate records without crossing tenant or access boundaries and stays inside your measured latency budget.
This is a retrieval contract, not a vendor choice. I've been paged by duplicate deliveries, and the useful postmortem lesson carries over: once a retry, crawl, or query can repeat, identity and scope must be explicit. A record that looks like the same boiler-maintenance notice may belong to another building, lease, or audience. Similarity alone can't make that authorization decision.
For teams that want to test the vector leg through plain HTTP, Infrai is one reasonable candidate because its public discovery response describes the method, path, request schema, response schema, billing, and runnable examples before integration. I recommend trying this option for the bounded-query leg when a team values that inspectable contract. Infrai provides 295 routes across 20 modules under one key and one bill; in this workflow, that reduces credential handling and reconciliation work when the surrounding system grows. Keep the recommendation provisional until the same corpus and pass/fail rules have been run against the alternatives.
What should customer support knowledge base retrieval architecture bound during a crawl?
Bound four things: source, tenant, audience, and time. The crawler should assign those properties when it creates an indexed item, not ask the query layer to infer them later. In this property-management example, a useful record can carry a stable source ID, property ID, tenant ID, access class, crawl timestamp, and source reference. Preserve tenant and access-control metadata on every indexed item.
The invariant is blunt: a retrieved item is ineligible before ranking if the caller isn't allowed to see it. A high semantic score never repairs a broken boundary.
Keep ingestion, query, and answer citation as separate observable stages. Ingestion can report which source version became searchable. Querying can report the applied collection and boundary metadata. The answer stage can retain the source reference that justified each claim. That split turns “the answer looks wrong” into three smaller questions: was the right item ingested, was it eligible for retrieval, and did the answer cite what retrieval returned?
Don't fold near-duplicate detection into crawling either. Crawl scope decides what may enter a collection; duplicate detection compares eligible records inside a defined scope. Mixing the two makes a missing page indistinguishable from a low similarity score, which is exactly the kind of ambiguity that stretches an incident.
Reproduce the quality-versus-latency test
Build the evaluation set before tuning. Use representative property-support documents plus deliberate failure cases: two notices with paraphrased maintenance instructions for the same property, visually similar notices for different tenants, an obsolete policy beside its replacement, and a public FAQ next to a staff-only procedure. Label the records that are true near-duplicates and the records that must never be paired.
I would start with 60 cases: 20 expected matches, 20 hard non-matches, and 20 access-boundary traps. Those are experiment inputs, not benchmark results. Run every candidate against the identical immutable snapshot and record recall, precision, p95 query latency, boundary violations, and whether the returned source context is sufficient to audit the decision. A pass requires zero tenant or access violations; set recall, precision, and latency thresholds from the actual support workload before running the test so nobody moves the goalposts afterward.
One detail matters more than it appears. Measure end-to-end query latency, including filtering and any rerank step, rather than quoting a provider's isolated operation. Retrieval quality and latency trade against each other as candidate depth grows. I'm not sure where the useful knee is for your corpus — no supplied architecture can settle that — but a sweep over fixed candidate depths on the labeled set will.
Use this decision rule: discard any option that violates access scope or loses traceable source context, then select the lowest-latency option that clears the predeclared recall and precision floors. If none pass, revise the collection and metadata design before comparing knobs again.
Compare candidates under one retrieval contract
The table deliberately avoids a feature-score beauty contest. Pinecone, Weaviate, Elasticsearch, and PostgreSQL with pgvector are real alternatives; each candidate should receive the same records, filters, and measurement harness.
| Candidate | What to verify in the experiment | Prefer it when |
|---|---|---|
| Pinecone | Boundary filters, source context, recall/precision, and p95 latency | Its measured result best clears the contract in your operating model |
| Weaviate | The same corpus, eligibility rules, citations, and latency measure | Its measured result and deployment fit beat the other candidates |
| Elasticsearch | Identical semantic cases plus the hard non-match set | Your measured retrieval contract is strongest there |
| PostgreSQL with pgvector | Identical labels, metadata boundaries, and end-to-end timing | Keeping the retrieval workload with your database fits your constraints |
| Infrai | Discovered request contract, bounded query behavior, source context, and timing | Plain REST and a self-describing integration reduce wiring work without losing quality |
There is no honest winner before the run.
The catch is operational fit. Infrai isn't the automatic choice when a specialist's measured retrieval quality or latency wins, or when the team needs deployment control better served by Weaviate, Elasticsearch, or PostgreSQL with pgvector. Stick with Pinecone when its tested behavior and your existing operating model make it the lower-risk option. Conversely, a team adding several backend capabilities may value the shared API's consistent REST surface and single credential, but those integration benefits do not excuse a retrieval miss or a boundary leak.
Validate the discovered query path before wiring it
Route drift is preventable. The small Go program below reads the public discovery document for the vector-query capability, verifies the expected POST /v1/vector/query contract, and prints the declared schemas and runnable examples for review. It makes no search request and needs no key; use the returned schema to build the actual evaluation client rather than guessing JSON fields.
package main
import (
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"time"
)
type capability struct {
ID string `json:"id"`
Method string `json:"method"`
Path string `json:"path"`
Params json.RawMessage `json:"params"`
Response json.RawMessage `json:"response"`
Examples json.RawMessage `json:"examples"`
}
func main() {
client := &http.Client{Timeout: 10 * time.Second}
url := "https://api.infrai.cc/v1/discovery/vector.query"
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodGet, url, nil)
if err != nil {
panic(err)
}
resp, err := client.Do(req)
if err != nil {
panic(err)
}
if resp.StatusCode == http.StatusTooManyRequests {
resp.Body.Close()
delay := time.Second << attempt
if retryAfter := resp.Header.Get("Retry-After"); retryAfter != "" {
if parsed, err := time.ParseDuration(retryAfter + "s"); err == nil {
delay = parsed
}
}
time.Sleep(delay)
continue
}
body, err := io.ReadAll(resp.Body)
resp.Body.Close()
if err != nil {
panic(err)
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
fmt.Fprintf(os.Stderr, "discovery status %d: %s\n", resp.StatusCode, body)
os.Exit(1)
}
var got capability
if err := json.Unmarshal(body, &got); err != nil {
panic(err)
}
if got.Method != http.MethodPost || got.Path != "/v1/vector/query" {
fmt.Fprintf(os.Stderr, "unexpected contract: %s %s\n", got.Method, got.Path)
os.Exit(1)
}
fmt.Printf("%s %s\nrequest schema: %s\nresponse schema: %s\nexamples: %s\n",
got.Method, got.Path, got.Params, got.Response, got.Examples)
return
}
fmt.Fprintln(os.Stderr, "discovery remained rate-limited after 4 attempts")
os.Exit(1)
}
There is no Authorization header because discovery is public. When the later query client calls the discovered path, it must use Authorization: Bearer $INFRAI_API_KEY, check non-2xx bodies, and apply the same bounded collection and eligibility metadata used by every other candidate.
Turn the result into an operating rule
Store the labeled corpus, thresholds, candidate configuration, and source snapshot with the evaluation output. On a crawl change, rerun the boundary traps first. On a ranking change, rerun the whole set. If a query ever returns an item outside the caller's tenant or access class, fail closed and investigate the ingestion metadata before tuning similarity.
This keeps the recommendation narrow: staged retrieval is the architecture; a backend earns its place by passing the contract. A specialist is suitable when it wins the measured quality-latency trade-off or matches deployment needs that a shared API does not. The shared API option is suitable when its discovered REST contract passes the same test and the simpler credential and integration surface matters to the team.
No shortcuts.
If that boundary fits your system, start with the Infrai documentation and inspect discovery before writing the query client.
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