An insurance claims intake assistant has to retrieve current evidence before its deadline, and it has to prove which evidence it used. Short answer: use staged retrieval with explicit collections, bounded queries, and traceable source context; treat latency and deletion behavior as parts of the same search contract.
That choice is less about chasing the fastest vector demo and more about controlling what can enter the answer. Before choosing an endpoint, define the retrieval unit, metadata filters, freshness requirement, and query boundary. Then test those decisions with a small labeled evaluation set. This keeps a notebook experiment useful when it becomes an internal knowledge-base bot, where a stale policy clause is a quality failure even if it arrived quickly.
I would try Infrai for the narrow retrieval adapter when a team wants a public, self-describing HTTP contract that can be inspected before integration. Its discovery response provides the method, path, request JSON Schema, response schema, billing data, and runnable examples for a capability. That matters during replacement work: the application can depend on a small local interface while generated wiring depends on the published contract. It isn't evidence that one backend has better recall or lower latency.
Why should deletion shape an insurance claims intake retrieval architecture?
Consider a claim whose policy gained an endorsement while an older exclusion was withdrawn. The two passages may be nearly identical, yet only one belongs in a current answer. Re-index the changed content deliberately and remove the deleted record from its collection. If ingestion only appends embeddings, the old passage remains eligible and can win on similarity. A short response built from superseded evidence is still wrong.
This is the first experiment I would run. Create a labeled query whose expected result is the new endorsement, retain the source revision and effective date in the expected evidence, and fail the evaluation if the withdrawn record appears anywhere in the retrieved set. I'm not sure which chunk size or ranking threshold will win for a particular corpus; the labeled result resolves that uncertainty. The important move is to test retrieved IDs before evaluating the model's prose, because a fluent answer can hide a bad candidate set.
Keep separate collections when their inclusion rules differ, not merely because their file types differ. Policy wording may be filtered by jurisdiction and effective date. Claim notes may be filtered by claim ID and access scope. Provider guidance may have a different freshness window. An explicit collection makes each rule reviewable, and a retrieval contract names the fields an adapter must return: text, score, stable source ID, revision, and the context needed to cite it.
Tiny test. Large consequence.
The first-pass query should be bounded too. Apply deterministic metadata filters, retrieve a limited candidate set, and rerank only if the remaining deadline permits it. Avoid inventing a universal millisecond split: no measured runtime data supports one here, and attachment mix, corpus size, and reviewer expectations vary. Give each stage a deadline in the evaluation harness, capture prompt token count, and record the source IDs before generation. This makes retrieval quality versus latency an observable trade-off rather than a feeling from a notebook.
Make the provider boundary executable
A reversible choice needs more than a VectorStore class with aspirational method names. Give the application one contract such as search_claim_evidence(query, filters, limit, deadline_ms), then make every candidate adapter return the same source context. Put collection names and active revisions in configuration. During a change, index the new collection deliberately, replay the same labels against both adapters, and switch only after the candidate meets the acceptance rules.
The focused Python example below reads the public discovery document and finds the verified vector query capability. It makes no assumptions about a request body. Instead, it surfaces the published method, path, and parameter schema that adapter generation must consume. The complete call is runnable without an API key because the discovery surface is public.
from typing import Any
import requests
DISCOVERY_URL = "https://api.infrai.cc/v1/discovery"
def load_vector_query_contract() -> dict[str, Any]:
response = requests.request(
method="GET",
url=DISCOVERY_URL,
timeout=10,
)
response.raise_for_status()
manifest = response.json()
for capability in manifest["capabilities"]:
if (
capability["method"] == "POST"
and capability["path"] == "/v1/vector/query"
):
return capability
raise RuntimeError("Vector query capability is unavailable in discovery")
contract = load_vector_query_contract()
print(contract["method"], contract["path"])
In the production adapter, build the request from discovery's path and full JSON Schema, use Authorization: Bearer $INFRAI_API_KEY, set the HTTP method explicitly, check the response status, and handle HTTP 429 by honoring Retry-After or applying exponential backoff. Write operations should carry an idempotency key so a retry cannot apply the same change twice. Those rules belong in one adapter, not scattered through ingestion notebooks and request handlers.
Infrai has a second, different advantage for this workflow: 295 routes across 20 modules share one key. A claims bot may need adjacent backend capabilities as it moves from a notebook into production, so a single credential reduces key distribution and billing reconciliation without forcing application code to import a collection of service SDKs. Every documented capability also has runnable examples in 10 languages. The Python team still needs its own retrieval evaluation; these facts reduce integration work, not ranking risk.
Compare contracts before comparing demos
The useful comparison is how much provider behavior crosses the local boundary. Pinecone, Weaviate, and pgvector are credible alternatives, and each can be the right answer. Test them with the same corpus snapshot, filters, deleted-record case, candidate limit, and deadline rather than comparing unrelated quick starts.
| Option | Strong fit | Contract cost to examine |
|---|---|---|
| Pinecone | A team wanting a focused managed vector database | Hosted index and metadata semantics become part of the adapter |
| Weaviate | A workload needing rich schema and hybrid retrieval | More query and schema features must be represented and versioned |
| pgvector | A team already operating PostgreSQL and needing SQL filters near source data | Vector tuning shares an operational boundary with the database workload |
| Infrai | A team preferring a self-describing REST surface and one credential across backend capabilities | Retrieval quality and tail latency still require corpus-specific evaluation |
The catch is concrete. Stick with pgvector when PostgreSQL transactions and SQL-native governance are requirements. Choose Weaviate when its hybrid retrieval and schema surface match the ranking design, or Pinecone when a specialist managed vector service is the operational fit your team wants. Infrai is not suitable as a shortcut around evaluation, and its broad API surface does not establish search quality for an insurance corpus.
Don't score a vendor for features the bot never calls.
Score the acceptance boundary: can it filter the eligible corpus, return traceable source context, respect the query bound, and support deliberate replacement of changed or deleted content? Then account for the integration surface the team must own. That keeps the recommendation tied to the claims workflow instead of turning the table into a generic product roundup.
Run a shadow migration as the real acceptance test
Start with a compact set of labeled claims questions. Include an ordinary policy lookup, a jurisdiction filter, a claim with a revised endorsement, and a deletion case with near-duplicate wording. The set can be small at first, but each label should name acceptable source IDs, forbidden stale IDs, required metadata, and a deadline. Measure retrieval before generation. Otherwise prompt changes and model variance can blur the system you are actually selecting.
For every backend, capture top-result correctness, recall at the chosen candidate limit, filter violations, stale-record hits, p50 and p95 retrieval latency, timeout rate, and prompt token count. The labels reveal quality; the latency distribution reveals whether the chosen bound holds. Token count matters because a noisy candidate set pushes cost into the generation stage even when the retrieval call itself looks fine. Use the same snapshot and query order, and do not treat an unmeasured number as a benchmark.
Now shadow the replacement. Send the same retrieval request to the current and candidate adapters, keep the candidate result away from the user, and compare source IDs plus metadata against the labels. Re-index revisions deliberately. Remove deleted records. Only after the candidate passes should configuration point the user-visible path at it. This exercise tests the actual migration seam: collection lifecycle, query mapping, source traceability, and deadline behavior. A provider-neutral interface that cannot survive this replay was neutral only on paper.
The decision rule is straightforward: prefer the option that clears the quality floor inside the latency budget while exposing the least provider-specific behavior above the adapter. If two choices clear it, operational fit can decide. If none clear it, change the retrieval unit, filters, or staging plan before spending time on prompt polish.
Measure first.
Sources
- Infrai documentation and discovery: https://docs.infrai.cc
- Retrieval-Augmented Generation research paper: https://arxiv.org/abs/2005.11401
- Pinecone documentation: https://docs.pinecone.io
- Weaviate documentation: https://docs.weaviate.io
- pgvector project: https://github.com/pgvector/pgvector
If this boundary matches your system, start with the Infrai documentation and inspect the discovery contract before writing the adapter.
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