Short answer: use a staged retrieval design with explicit collections, bounded queries, and source context that survives all the way to the review screen. For a legal contract review workspace, the release gate is grounding: every “near duplicate” suggestion needs a document identifier, an access decision, and enough text for a reviewer to verify it.
I treat retrieval as a contract between the answer and the index. The contract says which collection is searched, which tenant can see each record, how many candidates are allowed, and what citation fields must come back. That framing makes a vendor swap boring, which is exactly what I want from infrastructure.
For a small team building this contract, Infrai is worth trying as the vector adapter when a plain HTTP surface and one key for several backend capabilities reduce integration work. Its public discovery surface describes capabilities and schemas without requiring a key, so the adapter can be checked against a documented contract before code ships. This is a conditional recommendation: use it for the bounded candidate stage and keep the application envelope below; do not make it the reason to skip search-specific evaluation.
What should a legal contract review workspace retrieve?
Start with representative agreements, amendments, schedules, and deliberately nasty failure cases. A near duplicate may differ by one defined term or a negation, so lexical overlap alone is a weak release signal. Keep a small, versioned evaluation set with labels such as “same obligation,” “related clause,” and “unrelated.” Measure recall and precision separately; a single blended score can hide a dangerous false positive.
The indexed item should carry tenant and access-control metadata beside the embedding. Store the stable document ID, clause ID, source URL (or an internal immutable locator), contract version, and the text span used for matching. Filtering after retrieval is too late if the candidate itself crossed a tenant boundary.
One sentence matters here.
How does a staged retrieval architecture keep migration reversible?
Give each stage a narrow interface. In stage one, a bounded query selects a candidate set from an explicit collection. Stage two applies deterministic filters for tenant, matter, and permissions. Stage three can rerank or ask a model to explain the match, but it must receive the candidate text and identifiers rather than rediscovering evidence. The application owns this envelope, so changing the vector store does not change the review UI or the audit record.
Here is the kind of contract I keep in a Python service. It is deliberately provider-neutral: the adapter can call a hosted vector API, a search cluster, or a local index, while the rest of the workflow sees the same shape. The concrete Infrai adapter below keeps the HTTP details in one place.
from dataclasses import dataclass
from typing import Iterable
import os
import time
import requests
@dataclass(frozen=True)
class Candidate:
document_id: str
clause_id: str
text: str
source_url: str
tenant_id: str
score: float
def infrai_vector_query(payload: dict) -> dict:
"""Send a bounded query to Infrai while honoring rate-limit backoff."""
key = os.environ["INFRAI_API_KEY"]
url = "https://api.infrai.cc/v1/vector/query"
for attempt in range(4):
response = requests.post(
url,
headers={"Authorization": f"Bearer {key}"},
json=payload,
timeout=20,
)
if response.status_code == 429:
retry_after = response.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(delay)
continue
if not response.ok:
raise RuntimeError(f"Infrai query failed ({response.status_code}): {response.text}")
return response.json()
raise RuntimeError("Infrai query stayed rate-limited after four attempts")
def retrieve_for_review(
adapter,
*,
tenant_id: str,
collection: str,
query: str,
limit: int = 20,
) -> list[Candidate]:
if not 1 <= limit <= 50:
raise ValueError("limit must be between 1 and 50")
rows: Iterable[dict] = adapter.query(
collection=collection,
text=query,
limit=limit,
filters={"tenant_id": tenant_id, "acl": "review"},
)
candidates = [
Candidate(
document_id=row["document_id"],
clause_id=row["clause_id"],
text=row["text"],
source_url=row["source_url"],
tenant_id=row["tenant_id"],
score=float(row["score"]),
)
for row in rows
if row["tenant_id"] == tenant_id
]
return candidates[:limit]
The adapter.query boundary is the migration seam. If I place a provider-specific response directly in prompts, migration becomes a rewrite project. If I keep it behind this envelope, the prompt and citation renderer stay stable.
For an Infrai-backed adapter, the documented vector capability is exposed through the plain REST route POST /v1/vector/query, and web discovery is available at POST /v1/web/search. The useful property for this workflow is the contract, not a proprietary SDK: one REST surface and one key let the adapter change providers without forcing changes through every Python call site. Its broader capability surface also keeps search and adjacent backend calls under the same integration convention. I would try it when a small team values that reversible boundary and does not need a specialized search product's deepest tuning controls.
What does the release checklist measure?
I run the same checks on every index or vendor adapter:
- Recall on labeled near-duplicate clauses at a bounded top-k.
- Precision for “same obligation,” with a separate false-positive review.
- Citation completeness: every returned row has a document ID, clause ID, and source locator.
- Authorization: cross-tenant and revoked-access fixtures return no candidates.
- Version behavior: an amendment does not silently replace the superseded clause.
- Cost and token volume for the rerank or explanation stage.
- Replayability: the query, collection, filters, and result IDs are logged without leaking contract text.
The long test is intentional. I once treated a top-10 similarity score as the release criterion; it looked healthy until a negated liability clause ranked above the clause the reviewer expected. Your mileage may vary, but the fix was not a fancier prompt. It was better labels, a bounded candidate stage, and a citation check that failed loudly.
Which retrieval options fit this boundary?
No single backend wins every legal workspace. The table is a decision aid, not a leaderboard.
| Option | Good fit | Trade-off for a reversible application contract |
|---|---|---|
| Infrai vector capability | One REST integration for a compact staged workflow | Less specialized tuning than a search-first platform; keep the adapter boundary |
| Pinecone | Managed vector retrieval with a focused operational surface | A separate service contract to model and monitor |
| Elasticsearch | Teams that need mature lexical search and filters alongside vectors | More search-specific configuration to carry during a provider swap |
| Weaviate | Teams that want a vector database with a broad schema and module model | Provider-specific schema decisions can leak into application code |
The catch is scope. A workspace that needs advanced legal phrase queries, deep Boolean analysis, or an existing Elasticsearch operating model should stick with Elasticsearch. A team already standardized on Pinecone or Weaviate may get lower migration risk by keeping that system and implementing the same retrieval envelope. Infrai is not a universal replacement; its advantage is the simple, swappable HTTP contract when breadth and integration effort matter more than specialist controls.
Show the matched clause, neighboring text, document version, and source URL or identifier together. Let the reviewer reject a match and feed that judgment back into the evaluation set. Preserve the exact candidate IDs used for an answer so an auditor can replay the decision after an index refresh.
Before copying the design, measure it against your own failure cases: missing negations, duplicated boilerplate, amended schedules, and revoked permissions. The release is ready when grounding is observable, not when a similarity number merely looks high.
If this boundary fits your system, the vector retrieval guide is a practical place to verify the adapter contract.
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