> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reducto.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Deep Extract
> Achieve near-perfect accuracy on complex and long extractions with our agentic loop
## What is Deep Extract?
Deep Extract is an agentic extraction mode that iteratively refines its output to achieve near-perfect accuracy. Unlike standard extraction which makes a single pass over the document, Deep Extract runs an agentic loop that verifies and corrects its results against the source document until a quality threshold is met.
This is especially powerful for complex documents where a single extraction pass may miss values, misalign table rows, or produce inconsistencies. Deep Extract catches these issues by checking its own work and re-extracting until the results are accurate.
***
## When to Use It
Deep Extract is designed for extractions where accuracy is critical and the cost of errors is high. Common use cases include:
* **Invoice line item extraction** — Ensuring every line item is captured and that totals reconcile with the sum of individual amounts
* **Financial statement processing** — Extracting balance sheets, income statements, or cash flow data where numbers must be internally consistent
* **Legal document extraction** — Pulling clauses, dates, and party information from contracts where missing a single field has real consequences
* **Medical and insurance forms** — Capturing patient data, procedure codes, and billing amounts that must be complete and correct
* **Multi-page tables** — Documents with hundreds of rows spanning many pages where standard extraction may truncate or skip entries
If your extraction is simple (a few scalar fields from a short document), standard extraction is sufficient. Use Deep Extract when you need high reliability on complex or lengthy documents.
***
## How to Use It
Enable Deep Extract by setting `deep_extract` to `true` in the `settings` object:
```python Python theme={null}
result = client.extract.run(
input=upload.file_id,
instructions={
"schema": schema,
"system_prompt": "Extract all line items from this invoice. Iterate until the line items sum up to the total listed in the document."
},
settings={
"deep_extract": True
}
)
```
```javascript Node.js theme={null}
const result = await client.extract.run({
input: upload.file_id,
instructions: {
schema,
system_prompt: 'Extract all line items from this invoice. Iterate until the line items sum up to the total listed in the document.'
},
settings: {
deep_extract: true
}
});
```
```bash cURL theme={null}
curl -X POST https://platform.reducto.ai/extract \
-H "Authorization: Bearer $REDUCTO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "reducto://your-file-id",
"instructions": {
"schema": {...},
"system_prompt": "Extract all line items from this invoice. Iterate until the line items sum up to the total listed in the document."
},
"settings": {
"deep_extract": true
}
}'
```
***
## Best Practices
### Add verification criteria to your system prompt
The agentic loop uses your system prompt to determine when extraction is "good enough." Including explicit verification criteria gives the agent a concrete goal to iterate toward.
```python theme={null}
instructions={
"schema": invoice_schema,
"system_prompt": (
"Extract all line items from this invoice. "
"Iterate until the line items sum up to the total listed in the document."
)
}
```
Other examples of effective verification criteria:
* **Financial documents:** "Verify that the sum of all transaction amounts equals the statement total."
* **Multi-page tables:** "Ensure every row in the table is captured. The document states there are N entries — verify the count matches."
* **Contracts:** "Confirm that all parties listed in the signature block are captured in the parties array."
### Use with a well-defined schema
Deep Extract works best when your schema has clear field names and descriptions. The agent uses these to understand what it's looking for during each iteration. See [Extract Best Practices](/extraction/best-practices-extract) for schema design guidance.
### Pair with Parse configuration
Deep Extract can only verify and refine what Parse sees. If the underlying parse output is missing data (e.g., a table isn't detected), Deep Extract won't be able to find it either. Consider enabling [agentic mode](/configs/parse/agentic-modes) for tables or using [HTML table output](/configs/parse/table-output-formats) for complex documents.
***
## Related
Endpoint basics and parameters.
Schema design and prompt writing tips.
Link values to source locations.
Understand how credits are calculated.