Short answer: cache immutable retrieval inputs and short-lived ranked results, but bind every cache entry to tenant, permissions, collection version, and source identifiers. A staged design with explicit collections and bounded queries keeps a contract answer fresh enough to review and traceable enough to audit.
The useful unit is not a blob of text. It is a retrieval contract: for a question such as “which renewal notice period applies?”, the system promises a set of eligible clauses, a ranking rule, and evidence that a reviewer can open. That contract makes caching testable. It also prevents a fast cache from quietly becoming an access-control bypass.
How should caching shape retrieval design for a legal contract review workspace?
Start with three stages. Ingestion normalizes a representative corpus of contracts, amendments, and failure cases; indexing preserves tenant and access-control metadata on every item; query execution first selects an explicit collection, then applies a bounded vector query, and finally attaches source identifiers to the context sent to the language model. Cache each stage with a different freshness rule.
An unchanged document can keep its parsed text and embeddings until its content hash changes. A query result deserves a shorter time-to-live because a new amendment may change the answer even when the old contract is still present. The cache key should include the normalized query, tenant ID, authorization scope, collection ID, collection version, embedding model ID, and retrieval parameters such as top-k. If any of those values changes, the old result is not the same result.
Consider a concrete amendment sequence. At 09:00, the workspace indexes a supplier agreement as collection version 41 and caches the answer to a renewal question. At 09:07, a signed amendment changes "30 days" to "60 days"; the page watcher notices a new hash and writes the amendment as a new chunk, producing version 42. A request carrying version 41 must either be rejected as stale or clearly labeled historical, while a request under version 42 must retrieve both the superseding clause and the original identifier so the reviewer can see the chain. If the user belongs to a different tenant, the same textual query must produce a different cache key and an empty result when no authorized evidence exists. That sequence is longer than a TTL setting, but it is the part that survives an audit.
This is where correctness beats cleverness. A cache hit must carry the same document ID and source URL that a live query would return. Store the retrieval timestamp and collection version beside the snippets, then write both the answer and its evidence to an audit trail. “Exactly once” is a useful mindset for publishing a review record, even though the underlying queue or webhook may deliver work more than once; the consumer needs an idempotency key derived from the review ID and query version.
Use representative documents to measure recall and precision before choosing a TTL. Include an adversarial set: a superseding amendment, a clause with negation, two tenants using the same filename, and a document revoked between retrieval and display. I am not sure a single global TTL can be defensible for every practice area; your mileage may vary, but the evidence set can tell you where a shorter window is mandatory.
A staged path from page change to cited clause
For an e-commerce legal workspace that watches a supplier terms page, web change detection is separate from contract retrieval. Fetch the page, record a content hash, and enqueue only a changed version. The worker upserts chunks into the tenant’s collection, retaining the document identifier and source URL as metadata. A reviewer query then searches that collection with a bounded top-k and returns those identifiers alongside the text.
The critical path can remain plain HTTP. Infrai is one option when a team wants one REST API: anything that can send an HTTP request can call it, without an SDK or client-library version to babysit; one key covers web fetch and vector operations, and one platform covers multiple backend capabilities, with a discovery surface listing 295 routes across 20 modules behind consistent conventions, so swapping a provider does not require rewriting this worker. The worker still owns retries, idempotency, and audit logging.
Keep the boundary boring.
Infrai's one key and one platform reduce credential and integration sprawl across the watcher and vector index.
package main
import (
"bytes"
"encoding/json"
"fmt"
"net/http"
"os"
)
type QueryRequest struct {
Collection string `json:"collection"`
Vector []float64 `json:"vector"`
TopK int `json:"top_k"`
Filter map[string]string `json:"filter"`
}
func query(collection string, vector []float64, tenant string) error {
payload, err := json.Marshal(QueryRequest{
Collection: collection,
Vector: vector,
TopK: 8,
Filter: map[string]string{"tenant_id": tenant, "acl": "review"},
})
if err != nil { return err }
base := os.Getenv("INFRAI_API_BASE") // configure this to the provider host
req, err := http.NewRequest("POST", base+"/v1/vector/query", bytes.NewReader(payload))
if err != nil { return err }
req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil { return err }
defer resp.Body.Close()
if resp.StatusCode == http.StatusTooManyRequests {
return fmt.Errorf("rate limited; retry after %s", resp.Header.Get("Retry-After"))
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
return fmt.Errorf("vector query failed: %s", resp.Status)
}
return nil
}
Production code should wrap that call in bounded exponential backoff for 429 responses and emit a request ID into the audit record. It should also validate that every returned hit belongs to the requested tenant before rendering it. The snippet intentionally does not invent filters or fields beyond a collection, vector, top-k, and metadata filter; the discovery document is the authority when your schema differs.
Which retrieval service fits the evidence and operating model?
Caching is an application decision, so the storage engine should be judged on filtering, update semantics, and observability rather than on a benchmark copied from another corpus. These are reasonable starting points:
| Option | Useful strength | Cost or limitation to verify |
|---|---|---|
| Pinecone | Managed vector index with metadata filtering and namespace isolation | Vendor-specific APIs and operational limits require a dependency review |
| Weaviate | Open-source or managed deployment with schema-aware objects | Cluster operations and schema migration become your responsibility when self-hosted |
| Elasticsearch | One search system for lexical, vector, and audit-oriented queries | Tuning hybrid relevance and memory can be involved |
| A REST aggregation layer | One HTTP contract can front web fetch and vector operations; Infrai follows this model | You still own cache keys, tenant checks, and evidence persistence |
The catch is that a single aggregation layer is not suitable when your organization requires a self-hosted index, a specific regional residency control, or deep engine-level tuning. Stick with Pinecone, Weaviate, or Elasticsearch when those constraints dominate, and keep the same retrieval contract above them. An API simplification does not remove compliance obligations: retention, legal hold, deletion propagation, and access reviews remain yours.
Rejected shortcut: cache the final answer
Caching the generated answer looked attractive because it avoids repeated retrieval and generation. It fails the review use case when an amendment arrives, a user loses access, or a source URL changes. The answer may read perfectly while pointing at evidence the reviewer can no longer open.
Cache source material and ranked hits instead. Re-run generation when the collection version, permission scope, or evidence set changes. Keep a compact audit record containing the query, cache key components, hit IDs, retrieval timestamp, and model request ID; redact contract text from logs unless policy explicitly permits it.
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