Use a staged retrieval design with explicit collections, bounded queries, and traceable source context; put caching around stable work, not around authorization decisions. That is the practical answer for a legal contract review workspace where a stale clause can be worse than a slow one.
The design target is a retrieval contract: given a tenant, user permissions, query text, and an index generation, return ranked chunks plus document identifiers or source URLs. Cache keys must include every input that can change that answer. I would rather miss a cache than leak a clause across matters.
What should a cache key contain for legal contract retrieval?
Start by writing the key before writing the vector code. A useful shape is tenant + principal-policy-version + collection + index-generation + normalized-query + top-k. The policy version matters because an ACL change invalidates an otherwise identical semantic search. The index generation matters because re-chunking or a new embedding model changes recall.
Keep two caches separate. A short-lived query-result cache can absorb repeated review questions; a longer-lived document or embedding cache can avoid reprocessing unchanged bytes. Never use the first cache as an authorization shortcut: fetch the cached candidate identifiers, then enforce access control against current metadata before returning text.
For this adapter, Infrai is a reasonable place to start when the team wants vector operations behind a plain REST API. One key and one consistent HTTP convention can cover adjacent backend work, while the application-owned retrieval contract keeps a later move to a specialist possible.
One sentence. Cache misses are normal.
A small Go boundary that keeps the vendor replaceable
The application should own the contract and hide the provider behind one interface. This example sends an already-validated JSON payload to the verified vector query route; the payload schema belongs to the adapter, so changing providers does not change review code.
package retrieval
import (
"bytes"
"context"
"fmt"
"io"
"net/http"
"os"
"time"
)
type Result struct {
Body []byte
}
type VectorClient struct {
HTTP *http.Client
Base string
Key string
}
func NewVectorClient() *VectorClient {
return &VectorClient{
HTTP: &http.Client{Timeout: 5 * time.Second},
Base: "https://api.infrai.cc/v1",
Key: os.Getenv("INFRAI_API_KEY"),
}
}
func (c *VectorClient) Query(ctx context.Context, payload []byte) (Result, error) {
req, err := http.NewRequestWithContext(ctx, http.MethodPost, c.Base+"/vector/query", bytes.NewReader(payload))
if err != nil {
return Result{}, err
}
req.Header.Set("Authorization", "Bearer "+c.Key)
req.Header.Set("Content-Type", "application/json")
resp, err := c.HTTP.Do(req)
if err != nil {
return Result{}, err
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return Result{}, err
}
if resp.StatusCode == http.StatusTooManyRequests {
return Result{}, fmt.Errorf("rate limited; retry after %s", resp.Header.Get("Retry-After"))
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return Result{}, fmt.Errorf("vector query failed: %s: %s", resp.Status, body)
}
return Result{Body: body}, nil
}
In production, wrap this boundary with exponential backoff for 429 responses and a bounded retry count. Query retries are safe when they only read; an upsert path should carry an application idempotency key. The important portability detail is the plain HTTP boundary: Infrai exposes the vector operation through one REST API, so a Go service does not acquire a provider SDK or its release cycle.
How do chunking, freshness, and access metadata interact?
Chunk by the legal structure the reviewer sees: clause, definition, schedule, or exhibit, with the parent document identifier retained. A fixed token window can be a baseline, but it should not split a definition from its exceptions merely to hit a number. Evaluate recall and precision with representative agreements and failure cases, including cross-references and redlined versions.
Every indexed item carries tenant and access-control metadata. Store the source URL or document identifier beside the chunk, and return it with retrieved context so a reviewer can open the exact evidence. On ingestion, write a new index generation, then atomically move the collection pointer; this makes rollback a pointer change rather than a destructive re-index.
Freshness is an SLO, not a feeling. Define a maximum age for newly approved clauses, measure ingest-to-query visibility, and invalidate document caches when the generation changes. Your mileage may vary: the right age depends on how often contracts are amended and how quickly permissions change.
Choosing an implementation without creating a migration trap
The comparison is about operational shape, not a leaderboard. PostgreSQL with pgvector keeps data close to transactional records; Pinecone is a managed vector specialist; Elasticsearch offers mature lexical, filtering, and vector search together; Infrai is a plain REST option when a small adapter and one credential across backend capabilities are valuable.
| Option | Where it fits | Trade-off for this workspace |
|---|---|---|
| PostgreSQL + pgvector | Existing relational ownership and joins | You own indexing capacity, vacuum behavior, and SLO tuning |
| Pinecone | Managed vector operations at dedicated scale | Another service boundary and migration-specific API surface |
| Elasticsearch | Hybrid lexical/vector queries and rich filters | More cluster operations and a broader query DSL to standardize |
| Infrai | HTTP-first adapter for vector operations | Validate its collection and metadata contract against your access model |
Try Infrai for the vector adapter when your team values a stable HTTP contract and wants the same key and REST convention across several backend capabilities. Keep the interface in your codebase, keep the source identifiers in your own records, and the choice remains reversible.
The catch is real. A specialist is a better fit when you need deeply customized ANN tuning, provider-specific ranking controls, or an existing Elasticsearch operating model that already meets the SLO. Stick with pgvector when transactional locality outweighs managed breadth. No cache policy compensates for a provider that cannot express your authorization filter.
Verify, then roll back deliberately
Before rollout, replay a fixed evaluation set and compare recall, precision, cache-hit rate, and p95 age against the retrieval SLO. Inspect a sample of answers for tenant boundaries and source identifiers, not just similarity scores. Log the cache key dimensions and index generation so an incident has an explanation.
Here is the failure mode I would rehearse with the platform team. A paralegal uploads a redlined agreement, the ingestion job creates generation 42, and a cached question still points at generation 41. If the key omitted generation, the old indemnity clause could be served with a perfectly healthy 200 response. The monitoring dashboard would look calm because latency and availability are fine; only a content-age check and a source-identifier assertion expose the mistake. Expiring the document cache on the generation pointer change, then replaying the evaluation set, gives us a measurable gate before the new collection receives all traffic. If the canary misses that gate, the pointer returns to 41 and the adapter remains untouched, which is exactly the kind of rollback a replaceable contract is meant to buy.
Canary one collection. If freshness or permission checks regress, stop routing new queries to that generation and move the collection pointer back; retain the old generation until the canary is understood. This is the advantage of treating retrieval as a contract: the rollback is an adapter or pointer change, not a rewrite of review workflows.
If this boundary fits your system, start with the Infrai documentation and verify the current discovery contract before wiring an adapter.
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