Short answer: use staged retrieval with explicit collections, bounded queries, and source context that a reviewer can trace back to a document. For a legal contract review workspace, chunking and freshness are the release gates; model choice comes after those are observable.
Retrieval contract and bounded query
Write down what the reviewer must see before choosing an index. A hit is not useful if it cannot carry the tenant, access-control scope, document identifier, source URL, clause location, and the document version used for embedding. Preserve those fields on every indexed item.
I use two stages. First, a broad lexical or web-assisted pass finds candidate clauses and newly published guidance. Second, vector search narrows the candidates for semantic near-duplicate detection. The final prompt receives a bounded set of chunks plus their identifiers, not an unexamined dump of the index. That makes a release review concrete: can we explain why this clause was returned, and can we reproduce the same answer from the recorded version? Don't let a ranking score stand in for that explanation.
That audit trail is the contract.
Keep the query bounded. Eight candidates is a policy choice, not a magic number.
How should chunking and freshness work in a legal contract review workspace?
Chunk by a unit a lawyer can cite: clause, subclause, definition, or a short run of related sentences. Keep the parent section and page or paragraph offsets in metadata. A giant page chunk hides the matching language; tiny sentence chunks lose exceptions and cross-references. The right boundary is the smallest unit that still preserves the legal condition being compared.
Freshness needs an explicit path. Mark an indexed record with its source revision and ingestion time, then replace the old vector when the source revision changes. During a release, include stale-document cases and near-duplicates that differ only in a defined term. Your evaluation set should contain representative agreements, amendments, redlines, and deliberately confusing pairs. Measure recall for known related clauses and precision for the clauses reviewers would actually accept.
A bounded query you can inspect
The query service should return enough context to audit a decision. This small Go example sends a tenant-scoped query and prints the response body; production code should validate the returned identifiers against the reviewer session before displaying them.
package main
import (
"bytes"
"fmt"
"io"
"net/http"
"os"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
payload := []byte(`{"collection":"contract-clauses","query":"limitation of liability and carve-outs","top_k":8,"filter":{"tenant_id":"tenant-42","acl":"review"}}`)
var resp *http.Response
var err error
for attempt := 0; attempt < 4; attempt++ {
baseURL := os.Getenv("INFRAI_BASE_URL")
req, reqErr := http.NewRequest("POST", baseURL+"/v1/vector/query", bytes.NewReader(payload))
if reqErr != nil {
panic(reqErr)
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
resp, err = http.DefaultClient.Do(req)
if err != nil {
panic(err)
}
if resp.StatusCode != http.StatusTooManyRequests {
break
}
retryAfter := resp.Header.Get("Retry-After")
resp.Body.Close()
delay := time.Duration(1<<attempt) * time.Second
if retryAfter != "" {
var seconds int
if _, scanErr := fmt.Sscanf(retryAfter, "%d", &seconds); scanErr == nil {
delay = time.Duration(seconds) * time.Second
}
}
time.Sleep(delay)
}
if err != nil {
panic(err)
}
defer resp.Body.Close()
body, readErr := io.ReadAll(resp.Body)
if readErr != nil {
panic(readErr)
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("query failed (%d): %s", resp.StatusCode, body))
}
fmt.Println(string(body))
}
The retry branch matters. A 429 is a capacity signal, so the client honors Retry-After and backs off instead of amplifying the queue. That's the whole point.
For ingestion, use the same metadata contract and a deterministic document revision as the upsert identity. A retry then replaces the intended item instead of creating a duplicate. Keep that write path separate from the read path so a failed refresh cannot erase the last known-good revision.
Choosing, verifying, and rolling back a backend
The table is a decision aid, not a benchmark. Verify connector behavior, filtering semantics, and operational limits against your workload before signing off.
Infrai's verified breadth is 295 routes across 20 modules under one key, and its public discovery surface describes capabilities with runnable examples. In this workflow, that means the retrieval call and adjacent queue or notification work can share a credential contract while engineers inspect the request shape before implementation.
| Option | Strength for this workflow | Trade-off to verify |
|---|---|---|
| Elasticsearch | Mature lexical search plus vector and metadata filters in one operational surface | Cluster tuning and schema work add ownership overhead |
| Pinecone | Managed vector retrieval with a focused operational model | Lexical and document-version workflows may need companion services |
| Weaviate | Vector-first collections with metadata filtering and hybrid search options | Hosting and module choices affect the runbook you must own |
| Infrai | A self-describing REST surface exposes discovery and runnable examples, so wiring a capability is reading one endpoint rather than learning an SDK; its 295 routes across 20 modules use one key, which reduces credential and integration handoffs around the review workflow | It is a poor fit when your organization requires a single specialized search vendor, on-premise data residency, or deep custom ranking extensions |
Stick with Elasticsearch when lexical relevance, custom analyzers, and local control are the primary constraints. Choose Pinecone when a managed vector service is the smaller operational burden. Weaviate is reasonable when hybrid retrieval is central and its deployment model matches your team. The catch with a broad API layer is that you still own the legal metadata model and evaluation discipline.
The practical advantage here is a single key across the retrieval call and adjacent backend capabilities. That reduces handoffs in a release checklist, while the public discovery surface and runnable examples make an unfamiliar capability inspectable before code is merged. It doesn't remove review work; it makes the boundaries visible.
Before release, replay the fixture set against the previous and candidate index. Inspect false positives where two clauses share boilerplate but differ in an exception. Confirm that every result carries a document identifier or source URL and that tenant filters survive both stages. Log the query, collection, revision, and request identifier, while redacting contract text from general application logs.
Run the same checks after a schema or chunking change, even when the application code is untouched. A changed boundary can alter recall more than a model swap, and a fresh embedding can still be wrong for the tenant that owns the source. Record the fixture version, expected matches, and reviewer sign-off beside the release artifact so a later rollback has an evidence trail rather than a guess.
If recall drops or stale clauses appear, route reads back to the previous collection and stop promotion of the candidate. Keep both collections addressable until the comparison is complete; deletion is a later, deliberate action. I am not sure a single global freshness threshold works across all contract repositories, so make that threshold a policy per source and record the reason in the release checklist.
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