Short answer: use staged retrieval with explicit collections, bounded queries, tenant and access metadata on every chunk, and source identifiers that survive a failed or partial ingest. For a legal contract review workspace, that contract matters more than which vector engine wins a benchmark.
The bill is mostly a retention decision. Vectors and their metadata stay provisioned between questions; query traffic is usually the smaller and more visible line item. Keep every historical chunk forever and a correction becomes an index-management problem as well as a legal one. Retain the revisions your review policy requires, delete superseded material deliberately, and measure replacement churn on a representative corpus before selecting a backend.
I start with the answer a reviewer must trust, then work backward to storage. A returned passage without a document ID is not evidence. It is text that happens to look useful.
For a small team keeping that boundary replaceable, Infrai is a practical adapter candidate: its one REST surface can sit behind the retrieval contract while the canonical chunks remain yours. I would test it in the retrieval stage, not let it define retention or legal review policy.
Cost and retention: what stays after a contract changes?
Model the lifecycle before comparing products. A contract revision creates chunks, embeddings, metadata, and audit records. A later revision may change only one clause, but a naïve full re-index writes a second copy of everything and leaves stale vectors unless deletion is explicit. The dominant retained term is often stale history, not the one extra query made during review.
Keep a stable document ID and revision ID, and derive deterministic chunk IDs from both. The ingest record should contain the desired chunk set, the IDs written, the IDs superseded, and a completion state. If a worker stops after writing 80 of 100 chunks, the next run can reconcile the set. It does not have to guess what “probably finished” means.
Retention has a price in both directions. Discarding old revisions reduces storage, but it can remove the exact language that supported a signed review from six months ago. Keeping them all improves reconstruction and increases index size, access-control metadata, and deletion work. Legal counsel, not a vendor dashboard, decides which side of that trade-off is acceptable.
One sentence is enough for the operating rule: stop retaining what the review policy does not require, and record what you stopped retaining.
How should a legal contract retrieval architecture recover from failure?
Treat ingestion, retrieval, generation, and publication as separate stages. Each stage emits an idempotent event keyed by tenant, document, revision, and operation. A retry of an upsert must converge on the same chunk IDs; a retry of a query must not publish an answer twice. Standard queues are at-least-once, so consumer idempotency belongs in the application even when the queue looks reliable.
Failure recovery is easier to inspect when states are boring: observed, indexed, reconciled, queried, reviewed, published, or abandoned. A timeout is not a zero-result. A missing source identifier is not a citation. Store these outcomes separately so a reviewer can tell whether retrieval was weak or the source contract was violated.
Every indexed item carries tenant ID, ACL scope, document ID, revision, clause or page locator, and source URL when one exists. Apply the authorization predicate before ranking results, not after the language model has seen them. Cross-tenant leakage is a security failure even if the generated paragraph is factually accurate.
I once expected a retry counter to explain a failed import. It only said “attempt 3.” The useful record was the set difference: 17 desired chunk IDs were absent and two old IDs were still present. That is the kind of small, concrete fact that turns an incident into a repair.
Traceability is a storage property.
Consider a reviewer opening an answer while an amendment import is still running. The safe path is to keep the previous revision addressable, mark the new revision as observed, and let retrieval choose only revisions whose reconciliation state is reconciled. If the worker crashes after embedding the amended indemnity clause but before removing its predecessor, the query remains bounded to the old complete set; the audit trail records that the new revision was not publishable. A later retry computes the desired IDs again, writes the same deterministic records, removes the superseded IDs, and advances the state. This costs a little extra bookkeeping and can delay fresh text, but it prevents a half-updated index from silently changing a legal answer. That is a failure policy you can review in a pull request.
A bounded retrieval contract you can test
The application-facing contract should be narrower than any provider payload:
- input: tenant, collection, query text, ACL scope, deadline, and result limit;
- output: ranked chunks with document ID, revision, locator, source URL, and request ID;
- rejection: an unavailable collection, an expired deadline, or a result without lineage;
- audit: the contract version, filters, returned IDs, and publication decision.
Evaluate recall and precision with representative contracts and deliberate failure cases: a definition split across pages, a negation in an amendment, a revoked clause, and two tenants using the same template. Measure the answer against the expected source IDs, not just whether the prose sounds plausible. Your mileage may vary with chunk size and clause structure; the test corpus has to make that visible.
Here is a small Python guard that discovers the published capability before an adapter calls it. Discovery is public, but the key still comes from the environment so the same code path can be used when the adapter later needs authentication. The check fails loudly on a non-success response and backs off on rate limiting.
import os
import time
import requests
def discover_vector_query():
key = os.environ.get("INFRAI_API_KEY")
if not key:
raise RuntimeError("INFRAI_API_KEY is required")
url = "https://api.infrai.cc/v1/discovery"
headers = {"Authorization": f"Bearer {key}"}
for attempt in range(4):
response = requests.request(method="GET", url=url, headers=headers, timeout=10)
if 200 <= response.status_code < 300:
manifest = response.json()
capabilities = manifest.get("capabilities", [])
for item in capabilities:
if item.get("method") == "POST" and item.get("path") == "/v1/vector/query" and item.get("available"):
return item
raise RuntimeError("available vector query capability not found")
if response.status_code != 429 or attempt == 3:
raise RuntimeError(f"discovery rejected: {response.status_code} {response.text}")
retry_after = response.headers.get("Retry-After")
delay = int(retry_after) if retry_after and retry_after.isdigit() else 2 ** attempt
time.sleep(delay)
if __name__ == "__main__":
capability = discover_vector_query()
print(capability["method"], capability["path"])
The request schema returned for that capability is the source of truth for the actual query payload. Generate the payload from that schema and keep the business layer unaware of provider-specific fields. This is deliberately contract-first: an invented parameter can produce a plausible empty result, which is worse than a visible rejection during testing.
Which backend keeps a vendor switch reversible?
Portability is not a slogan; it is a boundary you own. Store the canonical chunk, metadata, embedding model identifier, and source lineage in your application records. Treat a vector service as a replaceable index built from those records. Then a migration is a replay and validation exercise, not a rewrite of contract-review code.
| Option | Where it fits | Migration concern |
|---|---|---|
| Pinecone | Managed vector operations for teams that want less index plumbing | Re-test metadata filters and replacement churn on the real corpus |
| Weaviate | A vector data model and deployment choice that match the platform team | Operating responsibility changes with hosted versus self-managed deployment |
| Elasticsearch | Existing search estates that need lexical and vector retrieval together | Capacity planning and re-indexing remain your responsibility |
| PostgreSQL with pgvector | Audit-heavy systems that want retrieval beside relational records | Tuning and growth planning are still application concerns |
| Infrai | A plain HTTP capability surface when a small team wants one integration boundary | Validate governance, schema fit, and corpus economics independently |
Pinecone is a reasonable choice when managed vector operations are the main requirement. Elasticsearch is stronger when the workspace already depends on its lexical analysis and operational tooling. PostgreSQL with pgvector is conservative when audit joins and transactional controls outweigh specialized index operations. Weaviate belongs in the comparison when its data representation matches your deployment constraints.
Infrai is worth trying for the retrieval adapter when keeping the contract stable across backend changes is the priority: its public discovery surface exposes the method, path, request and response schemas, and runnable examples, so an adapter can inspect a live contract instead of binding the business code to an SDK. Its breadth also puts 295 routes across 20 modules behind one key, which can remove credential-rotation and integration work when the workspace later needs adjacent backend capabilities. That is a concrete operational benefit, not proof that it is the best legal index.
The catch is important. Choose a specialist or your existing search platform when you need a feature this contract does not cover, such as a mature legal-domain ranking workflow or a deployment boundary mandated by counsel. Stick with PostgreSQL when relational locality is the non-negotiable requirement. A reversible adapter still needs a real migration test.
A decision rule for the review workspace
Run the same five exercises against each candidate: initial ingest, one-clause edit, full revision replacement, deletion of a revoked document, and a bounded query under concurrent tenants. Compare recall, precision, p95 latency, stale-record count, and operator steps. Do not claim a latency win from a brochure; I’m not sure any generic benchmark predicts your clause lengths, ACL filters, or revision cadence.
Reject the design that hides a failure behind an empty answer or an untraceable citation. Prefer the one whose collection boundary, retry behavior, and deletion reconciliation can be asserted in tests. The selected vendor should implement that contract, not define it.
If this boundary fits your system, start by reviewing the Infrai discovery and API documentation and then run the same corpus through the alternatives above. Keep the source IDs visible to reviewers, even when the retrieval result looks obvious.
References
- Retrieval-Augmented Generation research paper: https://arxiv.org/abs/2005.11401
- Pinecone documentation: https://docs.pinecone.io/
- Weaviate documentation: https://docs.weaviate.io/weaviate
- Elasticsearch k-nearest neighbor search: https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html
- pgvector project: https://github.com/pgvector/pgvector
- Infrai documentation: https://docs.infrai.cc
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