LocalParse — Python client

Official Python client for the LocalParse API. LocalParse gives you two products under one API:

1. Parse — turn any document into clean Markdown, JSON, or text. Drop-in LlamaParse-compatible, plus a deterministic accuracy layer: table-detection recovery (catches tables a layout model misses) and oracle-free financial-identity checks (flags Totals that don't reconcile).
2. Extract (Beta) — pull structured JSON out of parsed documents using your own JSON Schema. Server-side schema validation guarantees the returned object matches.

Install

pip install localparse

Quickstart — Parse

from localparse import LocalParse

client = LocalParse(api_key="lp-...")          # or set LOCALPARSE_API_KEY

result = client.parse("invoice.pdf", result_type="markdown")
print(result.markdown)

# Accuracy signal that plain OCR/LLM parsers don't give you:
print(result.identity_check)     # {tables_checked, violations, ...} or None
print(result.recovered_tables)   # tables recovered by detect_repair

Fetch JSON or the ingestion-ready structured contract instead:

result = client.parse("10k.pdf", result_type="json")
for page in result.pages:
    ...

structured = client.parse("10k.pdf", result_type="structured")

Quickstart — Extract (Beta)

Parse first to get clean markdown, then extract the fields you actually care about:

md = client.parse("invoice.pdf").markdown

invoice = client.extract(md, {
    "type": "object",
    "properties": {
        "vendor":     {"type": "string"},
        "invoice_no": {"type": "string"},
        "total":      {"type": "number"},
        "due_on":     {"type": "string", "description": "ISO 8601 date"},
    },
    "required": ["vendor", "total"],
})

print(invoice["vendor"], invoice["total"])

The server validates the model's output against your schema before returning: a non-conforming response surfaces as SchemaConformanceError rather than as silently broken data. Pre-flight safety failures (bad schema shape, depth/width caps, external $ref) surface as SchemaValidationError.

Persist a whole folder (incremental)

Ingest a data room into a named case; re-runs only parse new/changed files (unchanged files are skipped by content hash):

results = client.parse_folder(
    "./data-room",
    case_id="acme",
    resume=True,
    on_progress=lambda path, res: print("parsed" if res else "skipped", path),
)

Full control (async jobs)

job = client.upload("big.pdf", result_type="json", case_id="acme")
job = client.wait(job.id)
if job.is_success:
    result = client.get_result(job.id, "json")

Configuration

Errors

AuthenticationError (401/403), QuotaExceededError (402), NotFoundError (404), RateLimitError (429, with .retry_after), APIError (other non-2xx), JobFailedError (job ended ERROR/CANCELED), JobTimeoutError, SchemaValidationError (400 — bad data_schema), SchemaConformanceError (422 — model output didn't match the schema) — all subclass LocalParseError.