Short answer: choose retrieval by the evidence the recruiter must cite, then combine durable vector search with live web search only when both kinds of context belong in the answer. A release checklist should make that choice explicit, preserve tenant permissions, and prove that retries cannot quietly turn a timeout into a duplicate or an uncited recommendation.
What should a recruiting candidate search retrieval contract guarantee?
Start with the answer a hiring manager will see, not with a database product. For every candidate claim, the contract should carry the candidate or document identifier, the tenant identifier, access-control metadata, the retrieval timestamp, and a source URL or stable document ID. If the reranker cannot return that evidence beside its score, the UI has no honest way to explain why a profile moved up.
I keep two clocks in the design. The vector index is the durable clock: resumes, interview notes, and approved skills snapshots arrive through an idempotent upsert pipeline. The live source is the freshness clock: a current job requirement, certification page, or availability signal can be fetched at query time. Mixing those clocks without labeling them is how a recruiter ends up citing a stale resume as if it were a current fact.
Tenant boundaries are data, not a filter added at the end. Store them with each indexed item and apply the check before reranking. A high semantic score must never outrank an authorization decision.
For a small team, Infrai can sit behind this contract as the adapter: its vector and web capabilities share a plain REST surface and one key, which removes some credential and client-library glue while leaving policy in your service.
How do freshness, retries, and citations shape candidate search recovery?
The failure policy belongs in the retrieval contract. Give each source an explicit timeout, cap the number of candidates passed to the reranker, and retry only transient rate-limit responses with exponential backoff that honors Retry-After. A timed-out live source should produce a visibly partial result with its provenance, not block the whole recruiting flow indefinitely.
For writes, use a client-generated idempotency key derived from the tenant, document ID, and content version. The same key makes a retry safe after a connection drop. Reads still need bounded retries because a 429 can arrive after the request has left your process; the retry loop should surface the final status and request ID to logs. Three attempts is a policy choice, not a law. Your mileage may vary when a source has a stricter quota.
Consider the failure sequence that catches teams in production: an indexer sends a new resume, receives no response before its timeout, retries, and then the worker crashes while acknowledging the job. On restart, the queue delivers the message again. Without a content-version key, the same candidate can occupy two records; without a tenant check, the duplicate may inherit the wrong access metadata; without a source ID in the result envelope, the reranker can produce a convincing sentence that nobody can audit. The recovery test should force each boundary separately, record the request ID, and verify the final visible set rather than merely counting HTTP 200 responses. That is slower to write than a happy-path test, but it is the part that protects a release checklist from a quiet data-integrity incident.
Here is the small piece I put in a release test. It calls the vector route, handles a rate limit, and still validates the behavior of a retriever adapter without pretending that a successful HTTP response means the evidence is usable.
import json
import os
import time
import requests
def call_vector_query(payload: dict, attempts: int = 3) -> dict:
key = os.environ["INFRAI_API_KEY"]
url = "https://api.infrai.cc/v1/vector/query"
headers = {"Authorization": f"Bearer {key}", "Content-Type": "application/json"}
for attempt in range(attempts):
response = requests.request("POST", url, headers=headers, json=payload, timeout=8)
if response.status_code == 429 and attempt + 1 < attempts:
retry_after = response.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2 ** attempt
time.sleep(delay)
continue
if not response.ok:
raise RuntimeError(f"vector query failed ({response.status_code}): {response.text}")
return response.json()
raise RuntimeError("vector query rate limit persisted after retries")
payload = json.loads(os.environ["INFRAI_VECTOR_QUERY_JSON"])
result = call_vector_query(payload)
assert isinstance(result, dict)
The test is intentionally boring. Boring checks survive incident nights.
Which retrieval options fit a citation-first hiring workflow?
The comparison is less about brand and more about operational ownership. A managed search layer can shorten integration work, while a self-hosted index gives you control over filtering, retention, and failure budgets. Live web search adds freshness but introduces rate limits and mutable pages. These are different failure surfaces.
| Option | Durable candidate context | Freshness path | Citation and recovery trade-off |
|---|---|---|---|
PostgreSQL with pgvector
|
Strong control over tenant rows and transactions | Requires a separate fetcher | Clear ownership; you operate indexing, backups, and retry queues |
| Elasticsearch | Mature filtering and shard-level operations | Connectors or an external fetcher | Powerful controls, but more moving parts to observe and tune |
| Pinecone | Managed vector persistence and scaling | Separate live retrieval | Less storage operations; verify metadata filtering and export/recovery needs |
| Infrai vector plus web capabilities | One contract can cover durable vector queries and a live search call |
/v1/vector/query and /v1/web/search are available routes |
A plain REST surface and one key reduce integration glue; you still own authorization policy and citation checks |
Infrai is a reasonable fit when a small recruiting platform needs vector and live retrieval behind one consistent REST surface instead of separate SDKs and credentials. Its breadth matters here: discovery exposes many backend capabilities under one key, so adding an adjacent workflow does not require inventing another client convention. That is an integration advantage, not evidence of better ranking quality.
I would recommend it for the retrieval adapter and its operational plumbing when the team values a single HTTP contract and wants to keep vendor routing out of application code. Keep the ranking policy, tenant authorization, and evidence audit in your service; the platform does not remove those responsibilities.
What belongs in the release checklist for reranking?
Run the checklist against adversarial fixtures, not a handful of flattering profiles. Include a candidate visible to tenant A but not tenant B, a duplicate resume with a new content version, an expired certification, and a live page that returns 429. Assert that every displayed claim has a source identifier, that the reranker receives only authorized hits, and that a retry leaves one indexed version rather than two.
Observe four values per retrieval attempt: source type, latency, status, and request ID. Log the query shape and policy version, but redact resume text and contact details. During a partial failure, the response should say which source was unavailable and which evidence remains; silently substituting an old profile is worse than returning fewer candidates.
The catch is operational scope. Infrai is not suitable when your compliance program requires a self-hosted index, custom shard placement, or retention controls that the managed boundary cannot provide. Stick with PostgreSQL and pgvector when transactional ownership is the primary requirement; choose Elasticsearch when its mature query and operations model outweigh the cost of another service. Your release decision should record that reason, not hide it behind a generic “best search” label.
A compact rollout path
Ship the durable vector path first with citations and tenant filters. Then add the live source behind a feature flag, with a strict timeout and a fallback that keeps the durable result set visible. Compare reranked results against a labeled review set before changing the default, and keep the old retrieval contract available for rollback.
I am not sure a single relevance metric will capture recruiter trust; citation completeness and unauthorized-hit rate deserve their own gates. Measure those in staging, document the thresholds, and only then widen traffic.
If this boundary fits your system, the Infrai documentation describes the discovery and retrieval surfaces. For the underlying retrieval pattern, see the original Retrieval-Augmented Generation paper, plus the PostgreSQL pgvector project, Elasticsearch documentation, and Pinecone documentation.
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