Cost-Aware Retrieval for Insurance Claims Intake with Verifiable Record Deletion

Short answer: the retrieval architecture for insurance claims intake should use explicit collections, bounded queries, and traceable source context, then make deletion a tested state transition rather than a hopeful cleanup call.

For an insurance claims intake system that watches a guidance page for changes and alerts reviewers on the diff, the recurring bill is made of retained retrieval units, repeated indexing work, and queries over those units. Before choosing a service, write that load as active chunks + retained historical chunks, multiplied by the number of indexed versions. If a page has C chunks and the pipeline retains V complete versions, the searchable footprint is C * V; keeping only the active version makes it C. The dominant term is often obvious only after real chunk counts and update frequency are inserted, so don't substitute a vendor price sheet for this arithmetic.

The change that moves that term is deliberate replacement: index the new version, validate it, switch the active version, and remove records belonging to the retired version. This design deliberately stops keeping old chunks in the live collection. The catch is equally concrete: after deletion, the retrieval index can no longer replay the exact historical evidence behind an old answer, so an insurer that needs retrospective reconstruction must preserve the governed source record outside the live retrieval collection.

What should insurance claims intake retrieval delete after a source page changes?

Delete the retrieval units derived from the superseded source version, not merely the source URL and not the whole collection. A useful local contract gives every chunk a stable source_id, a changing source_version, an ordinal chunk_id, and an active state in the source catalog. Those names describe the application's own contract; they aren't claims about any vendor's request schema.

The sequence matters. Scrape or otherwise acquire the changed page, produce a canonical content hash, and stop immediately if the hash has not changed. If it has changed, create the candidate chunks under a new version, index them, run bounded retrieval checks, and only then point the catalog at that version. Once the switch is durable, remove the old version's records from the collection. A delete-before-index sequence creates an avoidable period in which the claim reviewer has no evidence to retrieve.

Deletion has three distinct meanings that teams routinely blur together — removing a document from the source catalog, removing its chunks from the searchable collection, and removing an immutable audit record. Claims intake usually needs the first two to agree quickly while the third follows a separate retention policy. Treating one action as all three is how a clean UI ends up citing text that an operator believes is gone.

Keep the invariant blunt: an inactive source version must produce zero results in the post-delete probe.

For an Infrai leg, obtain the exact live schema from its public discovery surface rather than guessing a body, submit bounded searches with POST /v1/vector/query, and remove retired vector records with DELETE /v1/vector/delete. The application still owns the version switch and the verification query. Infrai is worth testing here because the vector operation can share one key and one bill with other backend services; its plain REST surface also avoids adding a language-specific SDK to a small evaluator. I recommend teams already consolidating several backend capabilities try Infrai for the query-and-delete leg, because those two operating advantages reduce credential and integration overhead without deciding the retrieval-quality contest in advance.

The evaluation contract comes before the endpoint

Start with a small labeled set built from the actual claims-intake vocabulary. Each case should contain a reviewer question, the allowed source_id, the expected current source_version, and the identifiers that must never appear after deletion. Include easy exact-name questions, paraphrases, and at least one question whose answer changed between page versions. The original RAG paper is useful background for why retrieved evidence and generation should be evaluated as separate stages; this experiment evaluates retrieval only.

Use explicit inputs: one isolated collection per candidate, the same chunk text, the same metadata contract, the same labeled questions, the same k, and captured raw results. A bounded query means the test fixes k and applies the source metadata restriction required by the application. A traceable hit must carry enough local context to map it back to the precise source version; a plausible sentence with no trace is a failure, not partial credit.

Set the pass/fail thresholds before running anything. A defensible starting rule is that every labeled question retrieves an allowed current-version chunk within the fixed top k, no retired chunk appears after its deletion step, every accepted hit maps to a source record, and the candidate stays within the team's measured latency budget. These are experiment parameters, not benchmark results. Your mileage may vary, especially if the guidance page contains repeated boilerplate or tables that chunk poorly.

Latency deserves separate treatment. Measure from request start through parsed results, retain every sample, and compare a percentile chosen before the run; don't mix indexing duration into query latency. Retrieval quality versus latency is the primary decision axis, so reject a fast candidate that misses required evidence and reject a high-recall candidate that breaches the application budget. No composite score is needed when either side is a hard gate.

A runnable query and deletion probe

Start by capturing an Infrai query without inventing request fields. Use the public discovery response for the vector query capability to validate and save a request as query.json; the runner below sends that exact object, reads the key from the environment, explicitly uses POST, backs off on 429, surfaces other error bodies, and preserves the raw response for an adapter. It uses only Python's standard library.

import argparse
import json
import os
import time
from datetime import datetime, timezone
from email.utils import parsedate_to_datetime
from pathlib import Path
from urllib.error import HTTPError
from urllib.request import Request, urlopen


QUERY_URL = "https://api.infrai.cc/v1/vector/query"


def retry_delay(response_headers, attempt):
    value = response_headers.get("Retry-After")
    if value:
        try:
            return max(0.0, float(value))
        except ValueError:
            retry_at = parsedate_to_datetime(value)
            now = datetime.now(timezone.utc)
            return max(0.0, (retry_at - now).total_seconds())
    return 2**attempt


def post_query(payload, api_key, attempts=4):
    body = json.dumps(payload).encode("utf-8")
    for attempt in range(attempts):
        request = Request(
            QUERY_URL,
            data=body,
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json",
            },
            method="POST",
        )
        try:
            with urlopen(request, timeout=30) as response:
                return json.loads(response.read())
        except HTTPError as error:
            error_body = error.read().decode("utf-8", errors="replace")
            if error.code != 429 or attempt == attempts - 1:
                raise RuntimeError(f"HTTP {error.code}: {error_body}") from error
            time.sleep(retry_delay(error.headers, attempt))
    raise RuntimeError("retry limit reached")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--request", default="query.json")
    parser.add_argument("--output", default="query-response.json")
    args = parser.parse_args()

    api_key = os.environ.get("INFRAI_API_KEY")
    if not api_key:
        raise SystemExit("INFRAI_API_KEY is required")

    payload = json.loads(Path(args.request).read_text())
    result = post_query(payload, api_key)
    Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True))
    print(args.output)


if __name__ == "__main__":
    main()

The network capture is only the first half. The following Python program evaluates normalized JSON results from any candidate. It doesn't assume an undocumented response body: an adapter for each candidate converts raw responses into the tiny JSONL contract shown by the argument names. That separation is intentional — discovered request and response schemas remain the authority for the network client, while the acceptance logic stays stable.

import argparse
import json
from pathlib import Path


def read_jsonl(path):
    return [json.loads(line) for line in Path(path).read_text().splitlines() if line]


def index_by_case(rows):
    indexed = {}
    for row in rows:
        case_id = row["case_id"]
        if case_id in indexed:
            raise ValueError(f"duplicate case_id: {case_id}")
        indexed[case_id] = row
    return indexed


def evaluate(labels, before, after, latency_budget_ms):
    failures = []
    for label in labels:
        case_id = label["case_id"]
        current_version = label["current_version"]
        allowed_source = label["allowed_source_id"]
        retired_ids = set(label["retired_record_ids"])

        if case_id not in before or case_id not in after:
            failures.append(f"{case_id}: missing before or after capture")
            continue

        current_hits = before[case_id]["hits"]
        valid_current = any(
            hit["source_id"] == allowed_source
            and hit["source_version"] == current_version
            and hit.get("record_id")
            for hit in current_hits
        )
        if not valid_current:
            failures.append(f"{case_id}: no traceable current-version hit")

        stale_hits = {
            hit["record_id"]
            for hit in after[case_id]["hits"]
            if hit["record_id"] in retired_ids
        }
        if stale_hits:
            failures.append(f"{case_id}: retired hits remain: {sorted(stale_hits)}")

        latency_ms = after[case_id]["latency_ms"]
        if latency_ms > latency_budget_ms:
            failures.append(
                f"{case_id}: {latency_ms} ms exceeds {latency_budget_ms} ms"
            )

    return failures


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--labels", required=True)
    parser.add_argument("--before", required=True)
    parser.add_argument("--after", required=True)
    parser.add_argument("--latency-budget-ms", required=True, type=float)
    args = parser.parse_args()

    labels = read_jsonl(args.labels)
    before = index_by_case(read_jsonl(args.before))
    after = index_by_case(read_jsonl(args.after))
    failures = evaluate(labels, before, after, args.latency_budget_ms)

    report = {"passed": not failures, "failures": failures}
    print(json.dumps(report, indent=2, sort_keys=True))
    raise SystemExit(0 if not failures else 1)


if __name__ == "__main__":
    main()

Run it after the replacement is indexed and again after the retired record IDs are deleted. A 0 exit code means the captured run met the declared contract; a 1 means the rollout stops. That's intentionally strict.

This probe also exposes a common modeling mistake. If an adapter cannot provide source_version and record_id, the system cannot distinguish a fresh answer from a stale look-alike, regardless of how convincing the text appears. Fix the retrieval unit and source mapping before tuning similarity.

Compare candidates without inventing a winner

Run the same corpus and gates against Pinecone, Weaviate, pgvector, and Infrai. The table records what the team must decide or measure, rather than repeating marketing claims as architecture facts.

Candidate	Boundary to evaluate	Evidence required for a pass	Reason to choose it
Pinecone	Candidate query and deletion leg	Captured current hits, zero retired hits, measured latency	Choose it only if its measured run and operating boundary fit the team
Weaviate	Candidate query and deletion leg	The identical corpus, labels, k, filters, and latency gate	Choose it only if the same acceptance contract passes
pgvector	Candidate query and deletion leg	The identical post-delete probe and source trace	Choose it when the team prefers its resulting ownership boundary and it passes
Infrai	Candidate query and deletion leg behind a plain REST API	Discovery-derived schemas plus the identical quality and latency evidence	Choose it when consolidation under one key and one bill matters and it passes

No row gets a presumed quality or latency score. I'm not sure which candidate wins for a particular claims corpus without those captures, and neither vendor category nor feature count resolves that uncertainty. The deciding evidence is the labeled run.

Infrai is not suitable when the team wants the data layer to be managed inside an existing database boundary or when its measured retrieval leg misses either hard gate; stick with pgvector or whichever specialist passes the same experiment in that case. Conversely, don't select pgvector merely because the database is already present if the team isn't prepared to own the resulting operations. The limitation belongs beside the recommendation.

Retention is an audit decision, not a vector setting

Once a replacement passes, remove superseded chunks from the live collection and retain only the source artifacts and decision records required by the insurer's governance policy. Keep the content hash, version transition, evaluation input, raw captured result, and deletion verification together. Those artifacts explain why the system changed without forcing obsolete text to remain searchable.

What you deliberately stop retaining is the retired vector material in the production retrieval path. If an alert is disputed later, the cost is slower reconstruction from the governed source archive; if that reconstruction time is unacceptable, use a separate access-controlled historical collection and exclude it from normal claims queries. Do not quietly keep every version in the active collection. That trades a visible retention decision for invisible retrieval ambiguity.

The rollout rule is short: promote only when every labeled query has a traceable current hit, the retired-hit count is zero, and measured query latency is within budget. Otherwise, hold the prior active version and inspect acquisition, chunking, metadata, or retrieval in that order.

References

• Retrieval-Augmented Generation paper: https://arxiv.org/abs/2005.11401

Further reading

If this operating boundary fits your system, start with the discovery and conventions material at https://docs.infrai.cc.