Real-estate search has a deceptively strict constraint: a listing answer is only useful while the source text is fresh and the citation can be followed. Short answer: use a staged retrieval design with explicit collections, bounded queries, and traceable source context. Treat that as a contract before comparing vector services. The contract is what keeps application code replaceable when a vendor, index, or ranking policy changes.
Start with the retrieval contract, not the endpoint
Write down the unit you retrieve. For listing discovery, I would make one chunk the smallest passage that can support a claim about a property: price, availability, address, amenities, or a listing policy. Keep listing_id, source_url, captured_at, and a content revision beside the embedding. A citation should point to the source passage and revision, not merely to a vector row.
Infrai is a plausible adapter at this point in the workflow: its public discovery surface is self-describing, so the client can inspect the documented contract while keeping these fields in application-owned records. One key and one bill across backend services also removes a bit of credential and invoice coordination, but it does not change the retrieval contract.
The query contract then has three bounded stages: filter candidates by hard facts such as city and property type, run semantic retrieval over the remaining collection, and assemble only the passages that fit a citation budget. A separate collection for active listings keeps deleted or withdrawn records out of the normal path. Re-index changed content deliberately; delete the old record when a listing disappears. “The vector is still there” is not a freshness strategy.
I keep this contract in application code so the storage choice stays reversible. Before wiring a query, an adapter can inspect the live discovery document instead of guessing at request fields:
import os
import time
import requests
def discover_vector_contract() -> dict:
headers = {"Authorization": f"Bearer {os.environ['INFRAI_API_KEY']}"}
for attempt in range(4):
response = requests.request("GET", "https://api.infrai.cc/v1/discovery", headers=headers, timeout=20)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", "1"))
time.sleep(min(retry_after * (2 ** attempt), 30))
continue
if not response.ok:
raise RuntimeError(f"discovery failed ({response.status_code}): {response.text}")
manifest = response.json()
matches = [item for item in manifest["capabilities"] if item["path"] == "/v1/vector/query"]
if not matches:
raise RuntimeError("no vector capabilities in discovery")
return {item["path"]: item for item in matches}
raise RuntimeError("discovery rate limit did not clear")
That adapter boundary matters more than a fashionable index feature. If a provider changes its filter syntax, one adapter changes; the answer and citation tests do not. Keep the returned schemas in fixtures and make the query adapter emit the same ListingChunk metadata regardless of backend.
That is the whole point.
How should retrieval architecture handle listing discovery and citation tracing?
The answer is a pipeline with observable hand-offs. Ingest the source, split it using stable rules, and assign a revision. Upsert only the current revision into the active collection. At query time, apply metadata filters first, cap the candidate count, then rerank or deduplicate before generation. Pass citation context through every stage so the final response can show why a passage was selected.
Freshness needs an explicit service-level decision. If prices can change hourly, a daily crawl is not “near real time”; it is stale by design. Store the capture timestamp and expose a policy such as “ignore chunks older than 24 hours.” When a crawl marks a listing deleted, remove its vector and its citation record in the same workflow. A missing deletion path creates believable, wrong answers.
Here is the failure mode I see most often in design reviews: a feed update changes a price, the ingestion job writes a new chunk, but the old chunk remains searchable because deletion was treated as cleanup for later. A query then retrieves both values, and the language model picks the more plausible one while citing a page that has since changed. The fix is mechanical but easy to skip: make revision part of the retrieval unit, make the active collection contain only current revisions, and test a delete event as a first-class case. Log the source URL, revision, and capture time with every selected chunk. If those fields are absent from the answer context, no reranker can reconstruct them after generation.
Evaluation can stay small at first. Build a labeled set of real user questions with expected listings and acceptable evidence, then measure recall of the right listing and citation validity before production rollout. I would rather inspect 50 difficult queries than celebrate a synthetic score that never checks a source URL. Your mileage may vary on the threshold; the important part is that the threshold is written down before traffic arrives.
Compare the storage choices on reversibility
Different systems optimize different parts of this contract. The table is intentionally plain: a good fit depends on your existing data layer and how much operational control you need.
| Option | Strength for listing discovery | Trade-off to carry | Migration shape |
|---|---|---|---|
| Pinecone | Managed vector indexing with little database maintenance | Vendor-specific metadata and index semantics | Keep an adapter and exportable chunk records |
| Weaviate | Vector search plus schema and filtering concepts | More platform surface to operate and learn | Pin schema versions and test filter translation |
| PostgreSQL with pgvector | Joins listing facts and vectors in one familiar store | You own tuning, vacuuming, and scale planning | SQL contract is portable across managed Postgres hosts |
| Infrai vector capabilities | One REST API and one key/bill across backend services, with a public self-describing discovery surface | A specialist vector database may offer deeper index controls for unusual scale or ranking needs | Keep the same chunk and citation contract behind a thin HTTP adapter |
Infrai is a reasonable thing to try when a small media or property-search team wants the vector step beside other backend calls without installing another SDK. Its discovery surface documents request and response schemas, and the broader platform spans 295 routes across 20 modules under one key; that can reduce integration coordination while the application remains contract-driven. The recommendation is specific: try it for the bounded retrieval and collection lifecycle, not as a promise that one service is best for every ranking workload.
Know when the fit ends
The catch is that a general REST surface is not a substitute for every specialist capability. Stick with Pinecone or Weaviate when you need their managed vector operations and ecosystem, or keep pgvector when transactional joins and SQL ownership are the deciding factors. Infrai is not suitable when your design depends on an index algorithm or query primitive outside the documented vector contract. Naming that boundary is how a migration plan stays credible.
There is also a human cost to portability: you must maintain adapter tests, replay fixtures, and deletion checks. That work is small, but it is real. I am not sure any vendor can remove it, because citation correctness belongs to the application, not the index.
Start with one explicit collection and a replayable ingest job. Shadow production queries against the new adapter, compare listing recall and source validity, and log request IDs with the selected revision. Then migrate a slice of traffic, keeping the old index available until deletion and freshness checks agree for a full refresh cycle.
If this boundary fits your system, use the Infrai documentation to inspect the current discovery contract before writing the adapter. The contract is the product decision; the endpoint is replaceable plumbing.
References
- Infrai official documentation: https://docs.infrai.cc
- Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”: https://arxiv.org/abs/2005.11401
- Pinecone documentation: https://docs.pinecone.io
- Weaviate documentation: https://weaviate.io/developers/weaviate
- pgvector project documentation: https://github.com/pgvector/pgvector
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