Parsyn

Pipeline Steps

Validation

Extraction Data (editable — change values and click Reprocess)

CSV Preview

Extraction History

Previous extractions for this document. Select two to compare.

Extraction Comparison

Results

Per-File Results

File	Status	Rows	Cost	Duration

Actions

AI Improvement Suggestions

Draft rules from corrections

Analyses your recent corrections across documents and drafts the rules that would fix the recurring patterns. Preview each draft against recent documents before creating it.

Draft rules (AI ~$0.02)

Results

Document	Score	Status	Mismatches

Parsyn — Documentation

Extract structured data from any PDF using the Parsyn engine. Supports multiple document types — each with its own schema, prompt, enrichment rules, validation rules, and output formats — all configurable without code changes.

1. Tabs Overview

Tab	Purpose
Documents	Upload PDFs (or ZIPs), select document type, view status
Extract	Run extraction, edit JSON results, reprocess (no re-extraction), download CSV. Select document type or auto-classify. Load Previous reloads from DB without re-running extraction.
Bulk	Upload multiple PDFs or ZIPs, extract all, download combined CSV. Supports ZIP archives (ignores hidden/dot files from Mac).
Rules	Configure keywords, thresholds, enrichment rules, validation rules, and prompts — per document type
Doc Types	Create, edit, clone, publish, and Generate from PDF (AI builds schema + rules from a sample)
Review	Compare extraction vs ground truth, save corrections for regression testing
Regression	Run extraction on all ground-truth documents and compare results automatically

2. Pipeline Flow

PDF Upload
  |
  v
1. Classification — auto-detects document type from all active types in DB
  Uses: type descriptions + classification hints keywords
  |
  v
2. Segmentation (free) — splits multi-account PDFs into separate segments
  |
  v
3. Extraction — reads the PDF natively, extracts structured JSON per schema
  |
  v
4. Enrichment (free) — 16 rule types for post-processing
  |
  v
5. Validation (free) — 17 rule types for quality checks
  |
  v
6. Recovery (only if validation fails) — re-extracts failed fields
  |
  v
7. Output (free) — CSV via output templates + JSON
        

Typical cost: ~$0.04 per PDF. Steps 4-7 can be re-run without re-extracting via the Reprocess button.

3. Creating a New Document Type

Two approaches — manual or AI-assisted:

Clone: Copy an existing type (including all rules) as a starting point.
Schema field types: string, float, int, bool, date, array (with nested item_fields)

4. Enrichment Rules (16 types)

Run after extraction to clean, transform, and normalize data. All free — no API calls.

Rule Type	What It Does	Example Parameters
field_transform	Transform a field value in-place	strip_chars, trim, to_uppercase, regex_replace, pad_left, date_format
value_conversion	Convert numeric values	divide_by, multiply_by, round, negate, to_number
row_filter	Exclude/include array items	exclude_if_name_matches, include_only_if, exclude_if_value_null
field_injection	Set or copy field values	set_if_null, set_always, copy_if_null, calculate_if_null
rename_value	Normalize field values	replace_exact (mapping table), replace_contains
calculation	Cross-field calculations	sum_and_set, evaluate_expression ("qty * price")
currency_detect	Detect currency from symbols	source_field: scan for currency symbols, set GBP/USD/EUR
date_normalize	Parse dates from any format	15 format patterns: "13 Mar 2026", "03/13/26", "13.03.2026" etc.
lookup_map	Map values via lookup table	mappings: {"uk": "United Kingdom"}, case_insensitive, output_field
array_sort	Sort array items by field	sort_by: "amount", descending: true
array_aggregate	Sum/count/min/max/avg across array	source_path: "items[].amount", operation: "sum"
string_concat	Concatenate multiple fields	fields: ["first_name", "last_name"], separator: " "
conditional_set	Set field based on if/else chain	conditions: [{condition: {field, op, value}, set_value}], default
array_deduplicate	Remove duplicate array items	compare_fields: ["name", "amount"] (or all fields)
array_sort (pin)	Keep matching items in their printed order but pin some to the end	pin_last_field: "charge_type", pin_last_values: ["Grand Total"]
builtin_operation	Invoke a named, parameterised backend operation (reusable across types)	operation: "utility_bill.merge_reconciliation_bills" + params

Target paths: vendor_name, items[].amount, bills[].charges[].total
Conditions: {"field": "measure", "op": "equals", "value": "MWh"} — operators: equals, not_equals, contains, gt, lt, gte, lte, is_null, is_not_null, matches_regex, in_set
Priority: lower = runs first. Use 10, 20, 30... for easy reordering.
keywords_ref: Reference a shared keyword set instead of inline keywords.

5. Validation Rules (17 types)

Check extracted data for errors and warnings. Run after enrichment.

Rule Type	What It Checks	Parameters
required	Field not null/empty	(none)
format	Regex pattern match	pattern: "^\d{4}-\d{2}-\d{2}$"
range	Numeric min/max bounds	min: 0, max: 100000
length	String length bounds	min: 1, max: 200
cross_field	Compare two fields	other_field, op: lt/gt/eq/ne/lte/gte
calculation	Verify computed relationship	expression: "qty * price", tolerance_pct, tolerance_min
conditional	Nested check when condition met	condition + check (inner rule)
unique	No duplicates in array field	(none)
not_empty_array	Array has min items	min_items: 1
email_format	Valid email address	(none)
sum_check	Array items sum to total	source_path: "items[].amount", tolerance_pct
date_range	Date within allowed range	min_date/max_date: "today", "-365d", "+30d", "2025-01-01"
depends_on	Required when another field set	trigger_field, trigger_op: "gt", trigger_value: 0
one_of	Value from allowed set	allowed: ["GBP", "USD", "EUR"], case_insensitive: true
count_check	Array item count range	min_items: 1, max_items: 100
consistency	Same value across array items	(none) — checks all resolved values match

Severity: error (blocks) or warning (informational).
Error codes: Custom identifiers for reports (e.g. MISSING_REQUIRED, SUM_MISMATCH).
= new rule types

6. Keywords & Shared Variables

Keyword sets, numeric thresholds, and config entries are named values stored per document type. Enrichment rules reference keyword sets via keywords_ref so you can change a list in one place without editing every rule. These work for any document type — open the Rules → Keywords & Config tab, pick your type, and use “+ New keyword set / threshold / config entry” to author them from scratch, or edit/override the defaults inline. (A completely new type starts empty; you add the named lists and knobs your own documents need.)

Step 1: Create a keyword set (Rules tab or CLI):
  POST /api/cli/keyword/add
  { "document_type_id": "travel_expense",
    "category": "enrichment_keyword_set",
    "key": "exclude_descriptions",
    "value": "cancelled" }

Step 2: Reference it in an enrichment rule:
  { "rule_type": "row_filter",
    "parameters_json": {
      "action": "exclude_if_name_matches",
      "field": "destination",
      "keywords_ref": "exclude_descriptions"
    }}

Change the keyword list → all referencing rules pick up the change automatically.

7. Output Formats

Each document type can have multiple named output templates. The same extracted JSON can produce different layouts and formats — CSV, JSON, XLSX, and XML.

• Default CSV — auto-flattens JSON. Arrays become rows with header fields repeated.
• Custom templates — define column mappings: Output Name = source_path
• Array fields — use items[].amount to create one row per array item
• Multiple row-streams — map two sibling arrays (e.g. bills[].charges[] AND bills[].readings[]) and the template emits a row per charge and a row per reading under the same header. Use group_priority to order the streams (e.g. readings before a terminating total row).
• Transforms — per-column date_format, round, strip_currency, pad_left, upper/lower, etc.
• Aggregations — sum / count / avg footer rows.
• Hardcoded values — add static columns (e.g. "Company" = "Optima Energy")
• Concatenation — origin + ' to ' + destination

Manage via Manage Formats button in the Extract tab. Drag fields from the JSON to build your output visually.

8. Bulk Processing

The Bulk tab processes multiple PDFs in one go and produces a combined CSV output.

• Accepts .pdf and .zip files (PDFs are extracted from zips automatically)
• Hidden/dot files ignored (handles Mac __MACOSX and ._ files)
• Upload progress bar with per-file extraction log
• Combined CSV includes a _source_file column identifying which PDF each row came from
• Download as combined CSV or individual JSON files

9. Walkthrough: Adding a New Document Type

1. Doc Types → Generate from PDF or + New — create the type with schema, prompt, hints
2. Rules → select your type → Enrichment Rules — add post-processing:
   • Strip currency symbols (field_transform)
   • Normalize dates (date_normalize)
   • Calculate subtotals (array_aggregate)
   • Map vendor codes (lookup_map)
3. Rules → Validation Rules — add quality checks:
   • Required fields (required)
   • Email format (email_format)
   • Subtotal matches line items (sum_check)
   • Currency in allowed set (one_of)
4. Extract tab — upload a PDF, select your type, run extraction
5. Edit JSON + Reprocess — iterate on enrichment/validation rules without re-extracting
6. Manage Formats — create custom CSV layouts from the extracted JSON
7. Publish — version-stamp the type for production use

10. REST API Reference

All operations are available via REST API. Authenticate with X-API-Key header. Interactive docs at /docs (Swagger) and /redoc (ReDoc).

Authentication

# Login (returns session cookie)
curl -X POST http://localhost:8080/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username": "admin", "password": "your-password"}' \
  -c cookies.txt

# Or use API key (recommended for automation)
curl http://localhost:8080/api/documents/ \
  -H "X-API-Key: ce_your_api_key_here"

Upload & Extract (Single PDF)

# 1. Upload a PDF
curl -X POST http://localhost:8080/api/documents/ \
  -H "X-API-Key: ce_xxx" \
  -F "[email protected]" \
  -F "document_type_id=utility_bill"

# Response:
{ "id": "abc123", "filename": "invoice.pdf", "status": "pending" }

# 2. Run extraction (~$0.04, 30-60 seconds)
curl -X POST http://localhost:8080/api/extractions/run/abc123 \
  -H "X-API-Key: ce_xxx"

# Response:
{
  "extraction_id": "ext_789",
  "success": true,
  "steps": [
    {"name": "classify", "success": true, "duration_ms": 1200, "cost": {"total_cost": 0.002}},
    {"name": "extract",  "success": true, "duration_ms": 28000, "cost": {"total_cost": 0.038}},
    {"name": "enrich",   "success": true, "duration_ms": 15},
    {"name": "validate", "success": true, "duration_ms": 3}
  ],
  "extraction_data": { "supplier_name": "SSE", "bills": [...] },
  "validation_result": { "errors": [], "warnings": [...] },
  "total_cost": { "total_cost": 0.042 },
  "total_duration_ms": 29500,
  "_confidence": { "overall": 0.87, "low_confidence_fields": [...] }
}

# 3. Reprocess (free — re-run enrichment + validation after editing JSON)
curl -X POST http://localhost:8080/api/extractions/tools/reprocess \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{"extraction_data": {"supplier_name": "SSE", ...}, "document_type_id": "utility_bill"}'

# Response: enriched data + validation + CSV rows (all free)

Direct File Extraction (No Pre-Upload)

# Option A: Multipart file upload (simplest)
curl -X POST http://localhost:8080/api/extractions/tools/extract-file \
  -H "X-API-Key: ce_xxx" \
  -F "[email protected]" \
  -F "document_type_id=utility_bill" \
  -F "store_result=true"

# Option B: Base64-encoded bytes (for programmatic use)
curl -X POST http://localhost:8080/api/extractions/tools/extract-bytes \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "pdf_base64": "'$(base64 -w0 invoice.pdf)'",
    "document_type_id": "utility_bill",
    "store_result": false
  }'

# Both return the same response format as /run/{doc_id}
# Use store_result=false for stateless extraction (no DB records)
# Use store_result=true to save for later review/comparison

# Python example:
import base64, requests
with open("invoice.pdf", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()
resp = requests.post("http://localhost:8080/api/extractions/tools/extract-bytes",
    json={"pdf_base64": b64, "document_type_id": "utility_bill"},
    headers={"X-API-Key": "ce_xxx"})
data = resp.json()["extraction_data"]

Batch Processing (50% Cost Savings)

# 1. Submit batch job (up to 10,000 PDFs)
curl -X POST http://localhost:8080/api/batch/submit?document_type_id=utility_bill \
  -H "X-API-Key: ce_xxx" \
  -F "[email protected]" \
  -F "[email protected]" \
  -F "[email protected]"

# Response:
{
  "job_id": "job_abc123",
  "batch_api_id": "msgbatch_xxx",
  "total_files": 15,
  "status": "processing",
  "message": "Batch submitted with 15 files. Results may take up to 24 hours."
}

# 2. Check status (poll every few minutes)
curl http://localhost:8080/api/batch/status/job_abc123 \
  -H "X-API-Key: ce_xxx"

# Response (processing):
{
  "id": "job_abc123",
  "status": "processing",
  "api_status": "in_progress",
  "total_files": 15,
  "succeeded": 8,
  "failed": 0,
  "processing": 7
}

# Response (completed):
{ "id": "job_abc123", "status": "completed", "succeeded": 14, "failed": 1 }

# 3. Retrieve results
curl http://localhost:8080/api/batch/results/job_abc123 \
  -H "X-API-Key: ce_xxx"

# Response:
{
  "job_id": "job_abc123",
  "status": "completed",
  "results": [
    { "custom_id": "a1b2c3_0", "filename": "bill1.pdf", "success": true,
      "extraction_data": { "supplier_name": "SSE", "bills": [...] } },
    { "custom_id": "d4e5f6_1", "filename": "bill2.pdf", "success": true,
      "extraction_data": { ... } },
    { "custom_id": "g7h8i9_2", "filename": "corrupt.pdf", "success": false,
      "error": "JSON parse error" }
  ]
}

# 4. List all batch jobs
curl http://localhost:8080/api/batch/ -H "X-API-Key: ce_xxx"

Webhooks (Event Notifications)

# Create a webhook
curl -X POST http://localhost:8080/api/webhooks/ \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Slack Notification",
    "url": "https://hooks.slack.com/services/xxx/yyy/zzz",
    "events": "extraction.complete,batch.complete"
  }'

# Events: extraction.complete, extraction.failed, batch.complete,
#          ground_truth.saved, regression.complete

# Webhook payload (sent as POST to your URL):
{
  "event": "extraction.complete",
  "timestamp": "2026-03-31T14:30:00",
  "data": {
    "document_id": "abc123",
    "filename": "invoice.pdf",
    "document_type": "utility_bill",
    "success": true,
    "cost": 0.042,
    "duration_ms": 29500
  }
}

# Test a webhook
curl -X POST http://localhost:8080/api/webhooks/webhook_id/test \
  -H "X-API-Key: ce_xxx"

# List / update / delete
curl http://localhost:8080/api/webhooks/ -H "X-API-Key: ce_xxx"
curl -X PUT  http://localhost:8080/api/webhooks/id -d '{"is_active": false}'
curl -X DELETE http://localhost:8080/api/webhooks/id

Folder Watcher (Auto-Extraction)

# Start watching a folder (PDFs dropped here are auto-extracted)
curl -X POST http://localhost:8080/api/watcher/start \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "watch_dir": "/data/incoming-pdfs",
    "document_type_id": "utility_bill",
    "poll_interval": 10
  }'

# Files are moved to /data/incoming-pdfs/processed/ or /data/incoming-pdfs/failed/

# Check status
curl http://localhost:8080/api/watcher/status -H "X-API-Key: ce_xxx"

# Stop watcher
curl -X POST http://localhost:8080/api/watcher/stop -H "X-API-Key: ce_xxx"

Output Templates & Export

# Create an output template with transforms and aggregations
curl -X POST http://localhost:8080/api/output-templates/ \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "document_type_id": "utility_bill",
    "name": "BDL Import Format",
    "format": "csv",
    "field_mappings": [
      {"output_name": "Supplier",    "source": "supplier_name", "transform": "upper"},
      {"output_name": "Bill Date",   "source": "bill_date", "transform": "date_format",
       "transform_args": {"format": "%d/%m/%Y"}},
      {"output_name": "Charge Name", "source": "bills[].charges[].name"},
      {"output_name": "Amount",      "source": "bills[].charges[].total",
       "transform": "round", "transform_args": {"decimals": 2}},
      {"output_name": "Calculated",  "source": "{quantity} * {rate}"}
    ],
    "aggregations": [
      {"column": "Amount", "function": "sum", "label": "Grand Total"},
      {"column": "Charge Name", "function": "count", "label": "Row Count"}
    ]
  }'

# Apply template to extraction data
curl -X POST http://localhost:8080/api/output-templates/apply/template_id \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{"extraction_data": {...}, "document_type_id": "utility_bill"}'

# Export as Excel (direct download)
curl -X POST http://localhost:8080/api/output-templates/template_id/export-xlsx \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{"extraction_data": {...}}' -o output.xlsx

# Export as XML
curl -X POST http://localhost:8080/api/output-templates/template_id/export-xml \
  -H "X-API-Key: ce_xxx" \
  -d '{"extraction_data": {...}}' -o output.xml

# Available transforms
curl http://localhost:8080/api/output-templates/available-transforms

# AI: Generate template from sample CSV
curl -X POST http://localhost:8080/api/output-templates/generate-from-sample \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "document_type_id": "utility_bill",
    "sample_columns": ["Supplier", "Bill Date", "Amount", "VAT"],
    "available_fields": ["supplier_name", "bill_date", "bills[].charges[].total"]
  }'

Document Types & Schema

# List all document types
curl http://localhost:8080/api/document-types/ -H "X-API-Key: ce_xxx"

# Create a new type
curl -X POST http://localhost:8080/api/document-types/ \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "type_id": "purchase_order",
    "display_name": "Purchase Order",
    "description": "Standard PO documents",
    "schema_fields": [
      {"name": "po_number", "type": "string", "required": true},
      {"name": "vendor", "type": "string"},
      {"name": "line_items", "type": "array", "items": [
        {"name": "description", "type": "string"},
        {"name": "quantity", "type": "number"},
        {"name": "unit_price", "type": "number"}
      ]}
    ],
    "extraction_prompt": "Extract all data from this purchase order..."
  }'

# AI: Generate type from a sample PDF
curl -X POST http://localhost:8080/api/document-types/generate-from-pdf \
  -H "X-API-Key: ce_xxx" \
  -F "[email protected]" \
  -F "description=Hotel invoice with room charges, taxes, and guest details" \
  -F "requirements=Extract guest name, dates, room charges, taxes, total"

# Clone a type
curl -X POST http://localhost:8080/api/document-types/utility_bill/clone \
  -d '{"new_type_id": "utility_bill_v2", "new_display_name": "Utility Bill v2"}'

# Export/Import config (for sharing between environments)
curl http://localhost:8080/api/document-types/utility_bill/export -o config.json
curl -X POST http://localhost:8080/api/document-types/import \
  -H "Content-Type: application/json" -d @config.json

Quality: Ground Truth & Regression

# Save corrected data as ground truth
curl -X POST http://localhost:8080/api/ground-truth/doc_abc123 \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{"corrected_data": {"supplier_name": "SSE Energy", ...}}'

# Response: auto-computed diff against latest extraction
{ "id": "gt_xyz", "correction_count": 3 }

# Run regression test (re-extracts all docs with ground truth)
curl -X POST http://localhost:8080/api/regression/run \
  -H "X-API-Key: ce_xxx" \
  -H "Content-Type: application/json" \
  -d '{"document_type_id": "utility_bill"}'

# Response:
{
  "total_documents": 12,
  "passed": 11,
  "failed": 1,
  "summary": { "pass_rate": 0.917, "avg_score": 0.95 },
  "results": [
    { "filename": "sse_bill.pdf", "score": 1.0, "status": "pass" },
    { "filename": "edf_bill.pdf", "score": 0.8, "status": "fail",
      "comparison": { "mismatch_count": 2, "mismatches": [...] } }
  ]
}

# AI: Suggest improvements based on corrections
curl -X POST http://localhost:8080/api/ground-truth/doc_abc123/suggest-improvements \
  -d '{"document_type_id": "utility_bill"}'

Integration Workflow Example

# Complete automation flow:
# 1. Set up webhook for notifications
curl -X POST /api/webhooks/ -d '{
  "name": "Process Complete", "url": "https://your-system.com/webhook",
  "events": "extraction.complete"
}'

# 2. Start folder watcher (or submit via API)
curl -X POST /api/watcher/start -d '{
  "watch_dir": "/incoming", "document_type_id": "utility_bill"
}'

# 3. Drop PDFs into /incoming/ → auto-extracted → webhook fires
# 4. Your system receives webhook, fetches results:
curl /api/documents/                     # list docs
curl /api/extractions/document/doc_id    # get extraction
curl -X POST /api/output-templates/apply/template_id \
  -d '{"extraction_data": ...}'          # format output
curl -X POST /api/output-templates/template_id/export-xlsx \
  -d '{"extraction_data": ...}' -o out.xlsx  # Excel export