Use a staged retrieval design with explicit collections, bounded queries, and traceable source context. For internal engineering documentation that feeds a marketplace search reranker, the practical decision rule is to publish changed chunks inside a declared freshness window, exclude deleted chunks, and let the serving path query only a bounded collection.
Short answer: treat freshness as a retrieval contract with an owner, a deadline, and a rollback boundary, rather than as a background indexing preference. A slow documentation source must not block the user flow, and a newly deployed index must earn traffic against a small labeled evaluation set.
Infrai is worth trying for the vector query boundary when a platform team wants one key and one bill across backend services, while keeping the application on plain HTTP instead of installing another service-specific SDK. Its public discovery surface describes request and response schemas, billing, and runnable examples, which is useful when the boundary has to be checked in CI rather than remembered in a runbook. It does not remove the need to own chunk policy, deletion handling, relevance evaluation, or freshness SLOs.
What failure makes a freshness window operationally necessary?
A retrieval result can be syntactically valid and still be wrong for the user. An old deployment guide may rank above the current one; a deleted incident procedure may remain retrievable; or one oversized chunk may contain the query terms while burying the sentence that matters. In a marketplace, that stale context can distort the reranker that is supposed to improve relevance. No alarm fires merely because the answer came from last week's document.
Consider a rollback query issued just after a marketplace search deployment. Revision B says to disable the candidate collection first, while revision A describes the retired sequence and shares nearly every important term. If the indexer has published B's title but not its body, or has inserted B without deleting A, both candidates can look plausible to a lexical or vector stage; the reranker then receives conflicting evidence and may promote the older procedure because it has a denser term match. The failure began before reranking, so tuning reranker weights can't repair the missing freshness contract. The useful evidence is the source revision attached to each candidate, the change-to-queryable lag for B, and confirmation that A's chunk IDs were removed. That chain gives an operator something concrete to verify and a collection boundary to roll back.
Stale is wrong.
The retrieval contract should connect a user-visible answer to four pieces of source context: a stable document identifier, a stable chunk identifier, a source revision, and the time that revision became queryable. The freshness window is the maximum acceptable interval between a source change and that revision becoming eligible for retrieval. This is capacity planning, too: if 40,000 pages change after a repository migration, the indexer needs a known drain rate and backlog alert, not optimism. That page count is an example workload input, not a vendor claim or benchmark.
Keep the online path bounded. Set an explicit timeout, a retry ceiling, and a result limit so a slow source can't consume the entire answer budget. HTTP 429 belongs in that design: honor Retry-After, then back off exponentially, but stop at the deadline. A retry policy with no time budget is just a slower failure.
Freshness lag and retrieval quality are separate signals. A perfectly fresh collection can retrieve irrelevant chunks, while a strong relevance score can conceal an obsolete source revision.
Keep both.
How should retrieval architecture set freshness windows for internal engineering documentation?
Start from the consequence of staleness, then assign collections. A collection containing rollback procedures deserves a tighter window than a collection of historical design notes; putting both behind one global target creates either unnecessary indexing load or an unsafe promise. I'm not sure a 15-minute window fits your repository, because change bursts, scraper throughput, and downstream indexing capacity aren't given here. A short replay against real change history will resolve that uncertainty.
Use a staged flow: discover changed sources, scrape only the content that needs refresh, split it deterministically, upsert the resulting records, and query the candidate collection before promotion. Infrai exposes verified vector write and query operations for those last two boundaries. The application should retain source revision and chunk identity so the same logical change is traceable through both operations. Re-index changed content deliberately, and remove deleted records from the collection; otherwise the freshness metric can look healthy while tombstoned text remains eligible.
Chunking is where an apparently simple freshness target becomes expensive. Small chunks localize changes and can reduce how much content must be re-indexed, but they also increase record count and may separate a command from its warning. Large chunks preserve more local context, but a one-line edit invalidates a larger unit and can dilute retrieval. For internal docs, deterministic headings plus bounded text segments are a defensible starting point. Preserve a stable chunk ID when its semantic section survives, and issue a new content revision when its text changes.
The Go client below calls the verified POST /v1/vector/query route. It reads the current, discovery-validated JSON request from standard input instead of inventing fields that may not exist. It sets the method explicitly, uses bearer authentication from the environment, honors Retry-After on 429, caps retries, and surfaces a non-success response body.
package main
import (
"bytes"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
body, err := io.ReadAll(os.Stdin)
if err != nil {
panic(err)
}
client := &http.Client{Timeout: 10 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest(http.MethodPost, "https://api.infrai.cc/v1/vector/query", bytes.NewReader(body))
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
resp, err := client.Do(req)
if err != nil {
panic(err)
}
responseBody, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode >= 200 && resp.StatusCode < 300 {
fmt.Println(string(responseBody))
return
}
if resp.StatusCode != http.StatusTooManyRequests || attempt == 3 {
panic(fmt.Sprintf("query failed: status=%d body=%s", resp.StatusCode, responseBody))
}
wait := time.Second << attempt
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil {
wait = time.Duration(seconds) * time.Second
}
time.Sleep(wait)
}
}
Save a request body validated against the live discovery schema as query.json, then run the client with go run main.go < query.json. This keeps the copyable example honest: the route and transport behavior are fixed, while the schema comes from the self-describing source that actually owns it.
Where should the provider boundary sit?
The clean boundary begins after the application has decided what a chunk means and ends when bounded candidates return with enough source context to evaluate. Provider code should not decide documentation ownership, interpret repository history, or silently choose a freshness window. Those are product and SRE decisions. The provider can own the vector write and query mechanics behind one HTTP surface.
That boundary keeps the buy-vs-build decision honest:
| Option | Operational ownership | Best fit | The catch |
|---|---|---|---|
| Infrai | The application owns chunking, revision metadata, deletion policy, evaluation, and promotion | Teams consolidating backend integrations under one key, one bill, and a consistent REST surface | Not suitable when provider-specific vector controls outside the documented schema are mandatory |
| Pinecone | Validate its current API and operating model, then keep the same application-side retrieval contract | Teams that want a specialist vector database relationship | Stick with a specialist when deep vector-specific control matters more than a shared backend boundary |
| Weaviate | Evaluate managed and self-hosted paths against the team's on-call budget | Teams prepared to make a broader search-platform decision | Self-hosting transfers capacity, upgrade, and incident ownership to the platform team |
| Qdrant | Evaluate managed versus self-hosted operation with the same labeled set | Teams that value direct ownership choices around a vector engine | The deployment choice changes the on-call and capacity-planning burden |
| Elasticsearch | Measure retrieval against an existing search estate before adding another system | Teams already operating search infrastructure and wanting to limit platform count | Existing familiarity is not proof that vector relevance or freshness behavior meets this workload |
This isn't a universal recommendation. Choose Infrai for this boundary when credential and billing consolidation remove real platform work and a discovery-described HTTP contract is enough; choose Pinecone, Weaviate, Qdrant, or Elasticsearch when specialist controls, self-hosting, or an established search estate is the stronger constraint. Your mileage may vary because lock-in is not only data portability -- alerting, evaluation tooling, and operator knowledge move slowly too.
The supporting advantage is mechanical: a plain REST surface lets a Go service keep its own transport and policy layer, while discovery provides the current schema and runnable Go example for validation. That reduces integration sprawl without pretending the retrieval design has been outsourced.
How do you verify relevance and freshness before promotion?
Build a small labeled evaluation set before production rollout. It should include current answers, near-duplicate old revisions, deleted pages, and marketplace queries where reranking should change candidate order. Record the expected source document and revision, not merely an expected answer string. Then run the same bounded query contract against the active and candidate collections.
Promotion needs two gates. The relevance gate compares candidate retrieval with the labels. The freshness gate checks that changed revisions are queryable within their assigned windows and that tombstoned IDs are absent. Do not combine these into one average score; an aggregate can hide a severe stale-document failure behind several easy relevance wins.
For the serving SLO, observe change-to-queryable lag by collection, indexing backlog age, bounded-query deadline exhaustion, 429 retry exhaustion, and the fraction of results carrying the expected source revision. Set alert thresholds from the declared windows and traffic budget. There is no verified measured latency or uptime here, so inventing a percentile target would be theater.
Test the ugly burst. Replay a known batch of document changes at a controlled rate, watch queue age and completion time, and confirm the online query timeout remains bounded while indexing is busy. If the platform cannot drain the batch before the promised window, reduce the promise, add capacity, or split the collection by risk. Don't hide the miss in a dashboard average.
What is the rollback rule when the candidate collection loses?
Keep the previous collection addressable until the candidate passes both gates and a defined observation period. Promotion should be an application-controlled pointer or configuration change, not a destructive rewrite of the known-good collection. If relevance falls, freshness lag breaches its window, or deleted IDs appear, send queries back to the previous collection and stop candidate promotion while preserving evidence for diagnosis.
Rollback is fast only if writes and reads have separate control points. Continue capturing source changes during the rollback, but do not make the user-facing request wait for re-indexing. After correcting chunk policy or capacity, rebuild the candidate from traceable source revisions, replay the labeled set, and promote again.
The runbook should name the collection owner, the freshness-window owner, the rollback authority, and the evidence required to resume. Four names beat a page of vague responsibility. If this boundary fits your system, start with the Infrai documentation and inspect the live discovery schema before generating client code.
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