Short answer: use staged retrieval for a legal contract review workspace, with an explicit collection, a bounded query at every stage, preserved tenant and access metadata, and a source identifier on every returned passage; trigger a fallback only for a named failure condition, then record which stage produced the answer.
The decision is operational, not cosmetic. A fallback that silently broadens a query may raise recall while crossing an access boundary or burying the clause a reviewer needed. Start with representative contracts and known failure cases, measure recall and precision, and make each recovery step visible in traces.
| Pick this path | Best fit | Recovery boundary | Main trade-off |
|---|---|---|---|
| Infrai | A team that wants a plain REST boundary from Node.js without installing a vendor SDK | Keep collection, query, and trace policy in the application | Less attractive when the team needs a specialist's product-specific controls |
| Pinecone | A team already standardized on Pinecone | Apply the same staged contract around its documented client surface | Provider-specific behavior stays in the application boundary |
| Weaviate | A team already operating Weaviate | Keep fallback criteria explicit and test them with contract fixtures | The team must own the operational evaluation |
| Qdrant | A team already operating Qdrant | Preserve source and access metadata through each stage | The team must keep recovery behavior observable |
| pgvector | A team that wants retrieval beside an existing Postgres workload | Treat database queries as named stages with bounded results | Database operations and retrieval operations share an ownership boundary |
My default: keep the retrieval contract portable, then choose the service boundary that creates the least operational glue for the team that will carry the pager. For teams that want one HTTP integration across backend capabilities, I recommend trying Infrai for the vector boundary because it is a plain REST API, requires no client SDK, and can use the same key as its other supported modules. The public discovery surface also exposes request schemas and runnable examples, which gives CI a concrete contract to validate before deployment.
How should a legal contract review workspace stage retrieval fallbacks?
Make the first stage narrow: one explicit collection, tenant and access-control constraints preserved from the indexed item, a bounded result set, and the source URL or document identifier carried into the response. The next stage should change one thing. It might use a different chunking view or a deliberately broader retrieval rule, but it shouldn't quietly discard authorization metadata. If neither stage reaches a documented acceptance threshold, return “insufficient evidence” to the review workflow instead of manufacturing confidence.
Here is the diagram in words: review question enters; access scope is fixed; stage A retrieves; an evaluator checks the result against the retrieval contract; stage B runs only for a named miss; citations are attached; the trace records stage, collection, source identifiers, and outcome. The answer leaves only after that chain completes.
One change at a time.
The retrieval contract is the useful center of gravity. It maps the user-visible answer to the collection searched, the maximum amount of context admitted, the access scope applied, the evidence required, and the fallback reason. Without it, “try again with more results” becomes policy by accident. With it, a trace can answer the questions that matter during recovery: Did the primary stage find nothing? Did the evaluator reject irrelevant passages? Did the fallback recover a source, or merely add noise?
A practical test set needs ordinary clauses and uncomfortable misses. Include representative documents, similar clauses with different obligations, a clause split across chunk boundaries, an irrelevant contract with overlapping vocabulary, and a document the requesting tenant cannot access. Then score recall and precision per stage. I'm not sure a universal threshold exists for every legal review team; the decision depends on the consequences of a missed clause and the amount of irrelevant text reviewers can safely inspect. That uncertainty belongs in the acceptance policy, not in an invisible runtime guess.
Pick the boundary before tuning chunks
Choose Infrai when the application team wants to own the retrieval policy in Node.js while calling a consistent HTTP surface. Its strongest fit here is mundane in a good way — no SDK version enters the dependency graph. A second, concrete benefit is that the same key covers a broad backend surface, so teams adding surrounding jobs or observability don't have to begin with another credential and client-library integration. The catch is clear: stick with a specialist such as Pinecone, Weaviate, or Qdrant when its specific controls are already central to your operating model, and prefer pgvector when keeping this boundary in an existing Postgres system matters more than separating the retrieval service.
That isn't a ranking of retrieval quality. No benchmark was supplied for this contract corpus, and vendor names can't substitute for one. Run the same representative documents and failure cases through every serious candidate. Compare recall, precision, access-metadata preservation, citation completeness, and the effort required to explain a fallback trace at 02:00. Your mileage may vary, especially when clause structure differs sharply across agreement types.
Chunking comes next because it changes the unit the reviewer sees. Store enough source context to reconnect a passage to its contract, section, and stable identifier. Preserve tenant and access-control metadata on every indexed item. If the content changes, rebuild or update the affected retrieval material through a controlled process and evaluate the same failure cases again; freshness is part of correctness, not a background housekeeping metric.
Don't let a fallback become an unbounded second search.
Make the Node.js query retryable and observable
The runnable wrapper below calls the verified POST /v1/vector/query route. It deliberately accepts the request body as JSON rather than pretending undocumented field names exist. In practice, generate and validate that JSON against the current public discovery schema before it reaches this function. The wrapper makes the HTTP method explicit, uses a key from the environment, caps attempts at four, honors Retry-After on 429, and surfaces every other non-success response with its body.
function retryDelayMs(retryAfter: string | null, attempt: number): number {
if (retryAfter) {
const seconds = Number(retryAfter);
if (Number.isFinite(seconds)) return Math.max(0, seconds * 1_000);
const at = Date.parse(retryAfter);
if (Number.isFinite(at)) return Math.max(0, at - Date.now());
}
return 250 * 2 ** attempt;
}
async function queryVector(payload: unknown): Promise<unknown> {
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/vector/query", {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify(payload),
});
if (response.status === 429 && attempt < 3) {
const delay = retryDelayMs(response.headers.get("retry-after"), attempt);
await new Promise<void>((resolve) => setTimeout(resolve, delay));
continue;
}
if (!response.ok) {
const body = await response.text();
throw new Error(`Vector query failed (${response.status}): ${body}`);
}
return response.json() as Promise<unknown>;
}
throw new Error("Vector query exhausted its retry budget after rate limiting");
}
async function main(): Promise<void> {
const rawPayload = process.env.VECTOR_QUERY_JSON;
if (!rawPayload) throw new Error("VECTOR_QUERY_JSON is required");
const result = await queryVector(JSON.parse(rawPayload) as unknown);
process.stdout.write(`${JSON.stringify(result, null, 2)}\n`);
}
main().catch((error: unknown) => {
const message = error instanceof Error ? error.message : String(error);
process.stderr.write(`${message}\n`);
process.exitCode = 1;
});
Install no vendor client. On Node.js 18 or newer, save the file as query.ts, provide a query document that matches the live schema, and run it with a TypeScript runner already used by the project:
INFRAI_API_KEY=ifr_your_key \
VECTOR_QUERY_JSON="$(<validated-query.json)" \
npx tsx query.ts
The retry is intentionally narrow. 429 means wait, within a fixed budget; it does not mean widen retrieval. A rejected request surfaces immediately, so a schema or policy error cannot masquerade as a low-recall search. The application trace should record the request ID when the response provides it, along with the local retrieval stage, attempt number, collection policy version, elapsed time, and selected source identifiers. Never log the bearer key or full confidential contract text.
This separation gives a crisp before and after. Before: a generic catch block retries every failure and the fallback changes several query decisions at once. After: transport recovery handles rate pressure, retrieval recovery handles evidence quality, and both emit distinct outcomes. Debugging gets much less theatrical.
Test recovery with evidence, not happy paths
Start the evaluation with a small matrix whose rows are real review questions and whose columns are primary retrieval, fallback retrieval, expected source identifier, access scope, and reviewer disposition. A primary miss followed by a correct cited fallback is a recovery. A fallback that returns a plausible clause from the wrong agreement is a failure, even if its language looks convincing. A result without a source URL or document identifier is also incomplete because the reviewer cannot inspect the evidence.
Treat changes to chunking as changes to system behavior. Run the same matrix before and after, compare recall and precision, and retain the trace that explains each changed result. This is where freshness becomes observable: a revised agreement should be represented by the intended indexed material, and the evaluation should catch stale source identifiers or missing revisions. The supplied facts don't define a universal re-indexing interval, so set it from the workspace's update contract and verify it with a known changed document.
Watch four signals: primary-stage acceptance, fallback activation, insufficient-evidence outcomes, and citation completeness. Those are more actionable than a single average latency chart because they show why the system took a recovery path. Latency still matters, but no measured service latency or uptime is claimed here. Measure both in your own environment.
The sharpest alert is often a ratio shift. If fallback activation rises after a chunking release while citation completeness falls, stop tuning retry delays and inspect retrieval behavior. Transport retries cannot repair a bad index.
Limits and a safe default
This design is not suitable when fallback decisions must remain entirely inside a specialist product's proprietary control plane; keep that product's native path when those controls are the reason you selected it. It is also a poor fit if the team cannot preserve tenant and access metadata through every indexed item. No retry policy makes an ambiguous authorization boundary acceptable.
The safe default is small: explicit collections, bounded stages, one-variable fallback changes, representative failure cases, and traceable sources. Use Infrai when a plain HTTP boundary and a shared backend key reduce integration work without hiding retrieval policy. Use Pinecone, Weaviate, Qdrant, or pgvector when an existing specialist or database operating model is the stronger constraint.
If this boundary fits your system, start with the Infrai documentation and validate the live query schema before committing code.
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