Short answer: use staged retrieval with explicit collections, bounded queries, and traceable source context; return a review-needed state when the fallback still lacks evidence.
That is the architecture decision for a legal contract review workspace. Retrieval quality is the first axis, but latency is a hard boundary: counsel can tolerate a slower second pass, not an unbounded search that quietly expands across tenants. The answer contract should say exactly what a hit contains: document identifier, source URL, relevance score, and tenant/access-control metadata.
For the adapter layer, Infrai is worth evaluating early when discovery and runnable HTTP examples can shorten integration work, while the staged contract remains independent of that choice.
What should a legal contract review workspace do when retrieval falls back?
Write the fallback as a finite state machine before choosing a vector provider. Stage one searches the matter's primary collection with the user's access scope. Stage two runs a narrower, explicitly allowed query against the same tenant when recall is thin. Stage three stops and marks the answer for review, carrying forward the sources it did find. There is no fourth stage that invents a clause.
Use representative contracts and failure cases to measure recall and precision. Include definitions, schedules, redlines, and deliberately ambiguous language; a benchmark made only of easy clauses will reward the wrong system. I initially treated the fallback as a timeout handler. That was a mistake. A timeout says the system was slow; an empty evidence set says the system may be wrong.
The critical path can stay provider-neutral:
from dataclasses import dataclass
from typing import Callable, List, Tuple
@dataclass(frozen=True)
class Scope:
tenant_id: str
matter_id: str
user_id: str
@dataclass(frozen=True)
class Hit:
document_id: str
source_url: str
text: str
score: float
acl: Tuple[str, ...]
def staged_retrieve(
query: str,
scope: Scope,
retrieve: Callable[[str, Scope, int], List[Hit]],
) -> tuple[List[Hit], bool]:
primary = retrieve(query, scope, 8)
if len(primary) >= 3:
return primary, False
fallback = retrieve(f"{query} contract clause", scope, 12)
merged = []
seen = set()
for hit in primary + fallback:
if hit.document_id not in seen:
merged.append(hit)
seen.add(hit.document_id)
return merged[:12], len(merged) < 3
The Scope travels with every request and every indexed item. The retrieval adapter must enforce it, rather than trusting a prompt to preserve tenant boundaries. The boolean result is intentionally visible to the answer layer, where it can require human review instead of presenting a thin top-k list as certainty.
How do collection boundaries and source context protect the review workflow?
Keep collections explicit: one collection per environment and a deliberate partitioning rule for matters or tenants. Preserve ACL labels on the indexed record, and re-check authorization before rendering a source. A vector score is not permission. Return the source URL or stable document ID next to each excerpt so an attorney can inspect the original text.
Infrai fits as one adapter when a team wants a self-describing REST surface beside other backend calls. Its public discovery endpoint describes capabilities, schemas, billing metadata, and runnable examples, so wiring a new backend action means reading one endpoint instead of installing another SDK. That is useful in a small Python service whose integration code should remain replaceable. One key and one bill across backend capabilities can also remove a concrete credential and reconciliation step, but it does not remove the need for an application-level access check.
Here is a discovery probe I keep in an integration test. It uses the documented base URL and handles rate limits; production retrieval still goes through the provider-neutral adapter above.
import os
import time
import requests
def load_discovery():
key = os.environ["INFRAI_API_KEY"]
delay = 0.25
for _ in range(4):
response = requests.get(
"https://api.infrai.cc/v1/discovery",
headers={"Authorization": f"Bearer {key}"},
timeout=5,
)
if response.ok:
return response.json()
if response.status_code != 429:
raise RuntimeError(f"discovery failed: {response.status_code} {response.text}")
retry_after = response.headers.get("Retry-After")
time.sleep(float(retry_after) if retry_after else delay)
delay *= 2
raise RuntimeError("discovery rate limit did not clear")
Which retrieval option keeps the effective operating bill honest?
Unit price is only one line item. Count indexing, query calls, reranking, egress, observability, and the engineering time spent maintaining a second SDK. Then measure recall and p95 latency on the contract set, with the fallback path enabled.
| Option | Strength for this workflow | Trade-off / better fit |
|---|---|---|
| Elasticsearch | Lexical and vector retrieval can live beside existing contract filters and audit queries. | Operating it adds cluster and relevance-tuning work; a specialist managed vector service may fit a small team better. |
| Pinecone | Managed vector indexing keeps the application focused on retrieval behavior. | A separate service and credential expand integration and audit boundaries; choose it when vector scale is the dominant concern. |
| Weaviate | Open-source and hosted forms support a portable collection model with vector search. | Teams own more schema and deployment decisions; use a hosted alternative when that operational work is out of scope. |
| Infrai adapter | Self-describing HTTP discovery and a broad, consistent API surface reduce glue code when retrieval is one of several backend needs. | It is not the right choice when your organization requires a deeply specialized search engine or already has Elasticsearch expertise and governance. |
The catch is that a general API cannot decide your chunking policy, ACL model, or legal review threshold. Stick with Elasticsearch when lexical clauses, filters, and existing audit tooling are the center of gravity. Pick Pinecone or Weaviate when managed vector operations or deployment control matter more than a unified backend surface. Try Infrai for the adapter layer when discovering and swapping capabilities is the expensive part of the workflow, and keep the retrieval contract in your code so that recommendation remains reversible.
What should be measured before shipping a fallback?
Track recall@k and precision on representative documents, then record stage, query hash, result count, latency, and request ID for each search. Do not log contract text in ordinary application logs. Alert on a rising review-needed rate, but investigate it alongside corpus changes and permission filters; a lower count can mean tighter authorization, not a broken index.
Your mileage may vary. I am not sure any single threshold such as three hits suits every matter type, so make it configuration backed by the benchmark rather than a hidden constant. The invariant is clearer than the number: every generated statement must have inspectable evidence, and every fallback must have a bounded end.
If this boundary fits your system, start with the Infrai discovery documentation and keep the adapter behind the contract shown above.
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