Build the endpoint as a reviewable extraction pipeline: validate an uploaded image, ask a supported DEVUP AI model for structured fields, parse the response, validate it with Zod, run checks your application can prove, and send uncertain records to a person. A passing result means only that the named checks passed—not that the invoice is authentic, payable, or correctly read.
What the API should return
Treat the model’s response as a proposed interpretation of image pixels, not as verified bookkeeping data. Keep the extraction schema narrow and make uncertainty explicit: use null for a value that is absent or unreadable instead of asking the model to guess.
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A practical starting shape might include seller name, invoice number, issue date, due date, currency, line items, subtotal, tax, and total. These are example fields, not requirements imposed by DEVUP AI. Adapt them to the documents and business rules your application supports. For money, choose a representation that avoids floating-point surprises, such as integer minor units when the currency’s precision is known, or a decimal-safe representation. State assumptions about currency and rounding in your application logic.
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Set up the Next.js and DEVUP AI integration
DEVUP AI’s Vercel AI SDK integration guide targets Next.js and Node.js. Its prerequisites list Node.js 22.0.0 or later, and its installation command is:
npm install devupai ai @ai-sdk/openai-compatible zodPackage and SDK interfaces can change, so check the current integration guide when implementing. DEVUP AI’s Quickstart gives the OpenAI-compatible base URL as https://api.devupai.com/v1 and requires an API key and model identifier. Keep the key in a server-side environment variable; do not include it in browser code or return it in an API response.
The integration guide demonstrates creating a provider with createDevupAI and using it through the Vercel AI SDK in an App Router route handler. The exact SDK call depends on the versions you install; follow the current guide rather than copying an outdated method signature. Keep this endpoint on a server runtime compatible with the documented integration.
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Validate the request before calling the model
Accept only the request formats your endpoint explicitly supports. For an image upload, check that the request is well-formed, that the file exists, that its media type is allowed, and that its size is within a limit chosen for your application. Reject invalid input before spending a model request. Do not treat a client-supplied content type alone as proof that a file contains a valid image.
- Return a clear client error for a missing file, unsupported type, oversized upload, or malformed request.
- Set limits that fit your supported documents and infrastructure; the DEVUP documentation cited here does not prescribe upload limits.
- Keep credentials and model calls on the server. Avoid logging raw images or extracted personal and commercial data unless your policy and controls explicitly permit it.
Choose JSON mode or JSON Schema mode
DEVUP AI distinguishes JSON formatting from schema-constrained output in its Structured Outputs guide. JSON mode requests a JSON object, but it does not define the application’s required object shape. JSON Schema mode adds a requested shape constraint when the selected model supports it.
| Mode | What it requests | Application responsibility |
|---|---|---|
| JSON mode | JSON-formatted output | Parse the response and validate required fields and types locally. |
| JSON Schema mode | Output constrained toward a supplied schema, if supported by the selected model | Still parse and validate locally; schema-constrained generation does not eliminate incomplete, truncated, refused, or otherwise unusable output. |
Check model capability before relying on either mode. Keep the model-side schema small and explicit, and make it consistent with the prompt. Schema support and strictness vary by model; a complex schema or conflicting prompt can lead to a mismatch. In either mode, validate the final result with Zod before using it.
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Parse and validate the model response with Zod
After generation, inspect the completion metadata and response content before parsing. Reject a refusal, missing content, or a response whose finish reason indicates length truncation. Do not try to turn partial JSON into a complete invoice. DEVUP’s structured-output guidance says to check finish_reason for truncation and recommends retrying with a higher output limit or a simpler schema.
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Use a bounded, observable retry policy if you retry at all. A retry should have a clear limit and a recorded outcome; never loop indefinitely. Then parse the complete text as JSON and validate the parsed value against the application schema. If parsing or validation fails, do not fill missing values with guesses or silently accept a partial object.
const parsed = JSON.parse(modelText);
const result = InvoiceSchema.safeParse(parsed);
if (!result.success) {
return { status: "needs_review", reason: "schema_validation_failed" };
}This illustrates the validation gate, not a complete route handler: the endpoint must also handle the provider response, request validation, failures, and any required audit or privacy controls. DEVUP’s guidance is explicit: “Applications should always validate model output before using it, even when using json_schema.”
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Run deterministic checks, then assign a review state
Once the object passes schema validation, run only checks whose rules you understand for the document types you support. For example, compare line-item arithmetic with the subtotal, then check whether subtotal, tax, and adjustments reconcile with the total. These checks can flag inconsistencies, but invoice conventions differ: discounts, tax treatment, and rounding may change the expected arithmetic.
Return a state that names the outcome without overstating what it proves. Route a record to review when required information is missing, validation fails, arithmetic disagrees, or the document falls outside declared assumptions. A state such as auto_checked should mean only that the explicitly named structural and arithmetic checks passed. It is not evidence that the values were read correctly, that the invoice is genuine, or that it is payable.
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Handle unsupported modes and other failures safely
DEVUP AI’s API Error Reference documents response_format_not_supported when a model cannot provide JSON mode or JSON Schema, and structured_output_mismatch when strict output still fails the schema after a retry. Use these errors to guide a safe fallback rather than to invent data.
- Unsupported output mode: select a model documented as supporting the mode, or change the request design.
- Schema mismatch: simplify the schema or resolve conflicting prompt instructions; keep Zod as the final local gate.
- Truncation: reject the incomplete response. If using a retry, raise the output limit or simplify the schema within a bounded policy.
- Malformed request: return a clear client error before model invocation where possible. The error reference lists malformed JSON and unsupported request parameters among 400-class causes.
- Provider or transport failure: return a clear failure or route the item to review. Do not represent an API error as an empty invoice.
Do not claim an extraction accuracy rate without a labeled evaluation set and reproducible metrics for the documents and conditions you support.
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