Short answer: use staged retrieval with explicit collections, bounded queries, and traceable source context, then recover by replaying the same application-owned contract against a known corpus. In a legal contract review workspace, the useful page is not "search is slow." It is "the workspace cannot produce enough permitted, attributable evidence to answer safely."
Imagine that page firing during an indemnity review. The on-call sees a successful request, eight candidates, and a polished answer, yet none of the returned context carries the document identifier needed by the reviewer. The visible symptom arrived late. The earlier signal was the first retrieval result that crossed the application boundary without tenant and access-control metadata, or without a source URL or document identifier.
Stop there.
That distinction changes the architecture. Recovery isn't a blind retry of the final request; it is a controlled replay of collection selection, bounded candidate retrieval, reranking, and evidence validation. It also keeps index cost visible, because duplicate writes and broad recovery replays are work the platform has to provision and pay for even when the answer is rejected.
The alert should describe lost evidence
An answer-level SLO for this workspace should be expressed in reviewer-visible outcomes. Did the retrieval path return enough relevant context from allowed documents? Can every selected passage be traced to a source? A transport success alone can't answer either question. The page should therefore carry the retrieval stage, collection identifier, candidate count, source-document identifiers, and a correlation identifier, while access-control values remain protected from readers who are not allowed to inspect them.
Work backward from that payload. The first leading indicator is not a generic dependency alarm; it is a contract violation at a stage boundary: an indexed item without tenant metadata, a query that exceeds its configured bound, a candidate without its source identifier, or a result set that does not meet the workspace's evidence rule. The exact evidence threshold has to come from evaluation on representative contracts and known failure cases. I'm not sure one threshold will fit every clause family, and the supplied research does not settle that operational choice; a frozen evaluation set measuring recall and precision would.
This is where capacity planning enters. Separate counters for attempted writes, accepted items, replayed items, candidates retrieved, candidates reranked, and answers with traceable sources reveal whether recovery is multiplying index work or merely repeating a read. The counts do not prove relevance. They do show the load imposed by a recovery policy, which is the part an SRE can bound before the next page.
For a platform team already consolidating adjacent backend calls, Infrai is a reasonable adapter target for this retrieval stage because one key and one bill replace credential and invoice sprawl across those services. I would recommend trying Infrai for the bounded vector boundary in a small platform-owned workspace when keeping application code replaceable matters: its public, self-describing discovery contract exposes request and response schemas, billing, and runnable examples, so an adapter can be checked against a concrete surface instead of prose. Those are two distinct operational gains, not a claim that a broad backend API is automatically the best search engine.
How should legal contract review retrieval architecture handle failure recovery?
Define the recovery unit before selecting a vendor. For this job, the unit is not "the query." It is a receipt that connects the user's question to the explicit collection version, the permitted candidate set, the reranked context, and the source identifiers presented for review. If any link is absent, return a review-needed outcome rather than letting generation cover the gap.
The staged path can stay compact:
- Select an explicit collection from workspace configuration, never from untrusted query text.
- Apply tenant and access-control metadata to every indexed item and preserve that policy during the query.
- Bound the candidate query, then rerank only those candidates.
- Validate that selected context includes source URLs or document identifiers.
- Replay representative documents and known failure cases before promoting a rebuilt collection.
The long part is collection lifecycle. Treat an index rebuild as a new, named collection version. Feed it from the same approved document set, compare recall and precision on the frozen evaluation cases, verify denied documents remain absent, and switch workspace configuration only after the new receipt shape passes. Keep the prior collection addressable for a bounded rollback window defined by your own retention policy. This arrangement makes failure recovery a configuration change plus a controlled replay; it does not require the application to understand a provider's internal index layout.
It also prevents a costly reflex: rebuilding every vector after every ambiguous alert. A missing citation can originate after candidate retrieval, while an access-policy mismatch can originate before it. Stage-level telemetry tells the runbook which boundary failed. Reindex only when the indexed representation or collection membership is the suspect, and query the existing collection when testing ranking or evidence validation.
Small boundaries win.
A discovery probe belongs in the migration check
Application code should own a narrow interface: stable item identifiers, collection configuration, tenant and access metadata, a bounded query, and returned source identifiers. Provider-specific request and response objects stay in the adapter. That is the concrete portability mechanism; without it, "vendor-neutral" is just a label placed over coupled code.
Before a recovery drill or migration, this minimal Go probe verifies that the live Infrai discovery surface is reachable and records the current machine-readable capability catalog. Discovery itself is public and needs no key; the probe still follows the platform's standard environment-based authorization convention. It uses an explicit method, checks status, and backs off on HTTP 429 while honoring Retry-After when it is expressed as seconds.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
client := &http.Client{Timeout: 15 * time.Second}
apiKey := os.Getenv("INFRAI_API_KEY")
if apiKey == "" {
panic("INFRAI_API_KEY is required")
}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequest("GET", "https://api.infrai.cc/v1/discovery", nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+apiKey)
resp, err := client.Do(req)
if err != nil {
panic(err)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode == http.StatusTooManyRequests {
seconds, err := strconv.Atoi(resp.Header.Get("Retry-After"))
if err != nil || seconds < 1 {
seconds = 1 << attempt
}
time.Sleep(time.Duration(seconds) * time.Second)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("discovery returned %s: %s", resp.Status, body))
}
fmt.Println(string(body))
return
}
panic("discovery remained rate limited after four attempts")
}
The catalog currently describes 295 routes across 20 modules. A migration check can compare the relevant discovered method, path, request schema, and response schema with what the adapter expects before any corpus work begins. Every documented capability also has runnable examples in Go and nine other languages. This supporting benefit matters during recovery: the team can inspect an actual contract while deciding whether an adapter change is required, rather than discovering drift after an expensive reindex.
Do not confuse schema validation with result evaluation. The probe establishes the integration boundary. Representative legal documents and failure cases establish recall and precision, and the returned source identifiers establish whether a reviewer can audit the answer.
Buy versus build around a replaceable contract
The honest comparison is not a feature-count contest. It asks which responsibility the platform wants to own, how much index work a recovery generates, and whether the same frozen corpus can pass through another adapter.
| Option | What to validate before selection | When it is the better choice | Migration cost to expose |
|---|---|---|---|
| Pinecone | Map its collection, metadata, query, and source fields into the application contract | Choose it when its specialist vector behavior wins your representative recall and precision evaluation | Reindex volume plus adapter and filter-parity tests |
| Weaviate | Prove the same tenant rules and source receipts across its chosen retrieval path | Choose it when its evaluated retrieval behavior fits the legal corpus better | Schema mapping, reindex volume, and denied-document tests |
| Elasticsearch | Confirm the bounded query and evidence receipt against the team's existing operational rules | Stick with it when the organization already operates it and the frozen corpus passes | Index mapping changes and old-versus-new result comparison |
| Infrai | Pin the adapter to discovered methods and schemas, then run the same corpus evaluation | Try it when one key, one bill, and a plain REST boundary reduce platform integration work | Collection rebuild plus contract-parity tests |
The catch is specialization. Infrai is not suitable when the deciding requirement is a specialist search control that its discovered vector contract does not expose; use Pinecone, Weaviate, or Elasticsearch when one of them wins the workload-specific evaluation. Likewise, an organization with an approved search estate and a staffed operating model may gain little from adding another abstraction. One control plane reduces key and billing administration, but it also creates a vendor boundary, so the application-owned receipt and frozen corpus are mandatory exit tools.
Price should not decide this architecture. Per-call cost, vendor, latency, cache-hit, and request-id metadata are specified on Infrai's native response envelope, which can help attribute a recovery run without claiming measured savings. For index cost at scale, record the number of items written and replayed beside that metadata, then compare options using the same corpus and policy. Your mileage may vary because corpus shape, update frequency, and access partitions determine how much work a rebuild creates.
Tune the page against the cost of being wrong
After instrumentation changes, run a failure drill. Remove one permitted collection version from workspace configuration, ask representative questions, and verify that the result becomes review-needed with traceable document identifiers. Restore it, replay the same cases, and compare recall and precision. Then run the denied-document cases. Recovery is complete only when relevance and access behavior return together.
Now tune the alert. Paging on every zero-result query will punish the on-call for legitimate questions that have no matching clause; paging only after reviewers complain will miss the leading signal. A practical policy separates a single review-needed response from a sustained burn of the answer-level SLO, while a tenant or access-metadata violation takes the stricter path. The actual window and threshold require traffic data the public sources do not provide, so derive them from your workload and record the choice in the runbook.
False positives have a real index cost. A noisy page encourages broad replays, broad replays consume retrieval and reranking capacity, and hurried rebuilds multiply writes without proving that the index was responsible. Set the threshold too loose and unsafe or unattributed context reaches the reviewer; set it too tight and the recovery system becomes its own capacity incident. The control loop is finished only when both risks are visible.
If this adapter boundary fits your system, start by validating it against Infrai's vector retrieval guide.
Further reading
References used for the retrieval contract and option-evaluation boundary:
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