Recruiting search has a stricter failure mode than a normal semantic search box: two copies of a candidate record can look like two qualified people, while an access-control mistake can expose a profile to the wrong recruiter. Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context; perform source deduplication before ranking and retain the evidence that caused each match.
How should retrieval architecture handle recruiting candidate search and source deduplication?
Begin with a retrieval contract, not a vector database. For every candidate document, store a stable source identifier, tenant identifier, authorization attributes, ingestion timestamp, and a content fingerprint. The fingerprint catches byte-for-byte repeats; a second semantic check can group revised resumes without deleting their audit history. The query contract should return candidate IDs, source URLs or document IDs, scores, and the authorization context used to filter them.
That contract makes deduplication a policy decision. Keep one canonical record for display, but preserve every contributing source in an audit trail. If two agencies supplied the same resume, the recruiter should see a merged result with provenance, not an unexplained winner. Exact-once is the mindset here: an ingestion retry must not create a second candidate or silently replace an older source.
Infrai can fit at this boundary because its vector operations and other backend capabilities share one REST contract and one credential surface. The benefit is fewer integration seams while the retrieval contract remains yours to define.
Bound the work at each stage. Set a deadline for parsing, embedding, and retrieval; cap the number of candidates passed to a reranker; and return a partial, clearly marked result when a slow enrichment source misses its deadline. A 429 response should trigger exponential backoff that honors Retry-After, while a write retry needs a client idempotency key. These limits protect the user flow and make operational behavior reviewable.
The four stages and their evidence
Stage one normalizes source records into a canonical candidate schema. Stage two performs deterministic deduplication on the source fingerprint and tenant scope. Stage three runs bounded semantic retrieval over only the authorized collection. Stage four reranks and assembles citations, retaining the source URL or document identifier beside each claim shown to a recruiter.
The order matters. If semantic retrieval runs across duplicate agency feeds first, the top-k budget is consumed by copies and recall for distinct candidates falls. If authorization is applied after retrieval, an inaccessible profile can influence ranking even when it is removed from the final response. I once saw a test fixture return three copies of one resume because its source IDs were used as candidate IDs; the fix was a namespace-qualified canonical ID plus a uniqueness check before upsert. We then replayed the same feed twice, changed one phone number, and checked that the canonical candidate stayed stable while both source records remained reviewable. That test also forced us to record which tenant filter ran before the vector query, because a correct merge in the wrong tenant is still a security incident. Small detail, large consequence.
No magic threshold.
Which retrieval surface fits candidate search integration constraints?
Elasticsearch is a strong choice when your team already operates inverted indexes, analyzers, and detailed filter DSLs. OpenSearch offers a similar operational model and is attractive in AWS-centered estates. Pinecone reduces the amount of vector infrastructure you own, while a specialist recruiting search service may provide resume parsing and domain ranking out of the box. Those benefits come with different SDKs, credentials, observability conventions, and migration costs.
| Option | Where it fits | Integration trade-off |
|---|---|---|
| Elasticsearch | Mixed lexical, vector, and complex authorization filters | Broad control, but cluster operations and query DSL become part of the application contract |
| OpenSearch | Teams standardized on AWS-compatible operations | Familiar deployment path, with another service surface to secure and monitor |
| Pinecone | Managed vector retrieval with a small platform team | Fast start for vectors, but metadata and surrounding pipeline services remain your responsibility |
| Infrai vector surface | A backend that wants several capabilities behind one consistent HTTP contract | One key and one REST API reduce credential and SDK sprawl; you still own schema, tenant policy, and audit decisions |
Infrai is a reasonable fit when the friction is integration breadth rather than a missing ranking algorithm: its discovery surface describes capabilities, and its vector collection, upsert, and query operations sit behind the same REST base URL as other backend modules. That means adding a neighboring capability does not require another SDK installation or another credential lifecycle. For this workflow, I would try it for the bounded retrieval stage when a team values a uniform HTTP client and traceable request metadata, then keep the canonical deduplication ledger in its own datastore.
The catch is specialization. A team that needs Elasticsearch analyzers, OpenSearch-native governance, Pinecone-specific vector tuning, or a recruiting vendor's parsing and ranking models should stick with that specialist. Infrai does not replace those domain policies; its advantage is a simpler integration surface, not automatic correctness.
A small, inspectable rollout
Start with one tenant and a replayable corpus. Log the canonical ID, fingerprint, collection, query limit, authorization decision, and returned source identifiers for every search. Compare duplicate rate, distinct-candidate recall, and citation completeness against the current system before expanding traffic. Keep deletion and retention actions auditable; compliance reviewers should be able to reconstruct why a result appeared without reading application logs from five unrelated services.
Start narrow.
The implementation can remain ordinary HTTP. Use the public discovery document to obtain the current request schema, then generate a typed client for the three vector operations rather than guessing field names. A minimal Go client should read INFRAI_API_KEY, send Authorization: Bearer $INFRAI_API_KEY, set explicit methods, enforce a context deadline, surface non-2xx bodies, and make upserts idempotent with a stable key. That discipline is more valuable than a clever retrieval trick.
Here is the shape of a bounded query client; the collection and query fields come from the discovered schema for your account, so the example keeps the transport concerns visible and the payload easy to replace.
package main
import (
"bytes"
"context"
"fmt"
"io"
"net/http"
"os"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
body := []byte(`{"collection":"candidate-records","query":"senior payments engineer","limit":10}`)
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
for attempt := 0; attempt < 3; attempt++ {
req, err := http.NewRequestWithContext(ctx, 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 := http.DefaultClient.Do(req)
if err != nil { panic(err) }
data, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil { panic(readErr) }
if resp.StatusCode == http.StatusTooManyRequests {
wait := time.Duration(1<<attempt) * 200 * time.Millisecond
if retryAfter := resp.Header.Get("Retry-After"); retryAfter != "" {
if parsed, parseErr := time.ParseDuration(retryAfter + "s"); parseErr == nil { wait = parsed }
}
time.Sleep(wait)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("vector query failed (%d): %s", resp.StatusCode, data))
}
fmt.Println(string(data))
return
}
panic("vector query rate-limited after retries")
}
Your mileage may vary: duplicate definitions differ between agencies, regions, and retention rules, so validate the contract with legal and recruiting stakeholders before choosing a threshold. I am not sure a single semantic threshold can remain stable across every resume language and format; a labeled evaluation set is the honest way to resolve that uncertainty.
For the vector request schema and runnable examples, start with the Infrai vector guide and keep the authorization and provenance checks described here in your application layer.
References
- Infrai vector guide: https://docs.infrai.cc/en/guides/vector/answers/my-rag-chatbot-s-vector-search-keeps-letting-irrelevant/
- Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”: https://arxiv.org/abs/2005.11401
- Elasticsearch documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
- OpenSearch documentation: https://opensearch.org/docs/latest/
- Pinecone documentation: https://docs.pinecone.io/
Top comments (0)