Google Document AI DEADLINE_EXCEEDED on a 400-page MSA

Not specified by the original author.

TL;DR: The synchronous process endpoint timed out - 400 pages of OCR never fit in its deadline. Switch to batch processing: drop the file in Cloud Storage, fire a batchProcess request, and poll the long-running operation. That path is built for exactly this size.

DEADLINE_EXCEEDED

1. Confirm you are calling the synchronous process method on a large document. Expected: yes - that combination is the classic trigger.

2. Upload the MSA to a Cloud Storage bucket the processor's service account can read. Expected: listing the bucket shows the file.

3. Submit a batchProcess request pointing at the Cloud Storage input and an output prefix. Expected: the API returns a long-running operation name immediately.

4. Poll the operation until done, then read the sharded JSON output from the output prefix. Expected: one JSON shard per chunk, with full OCR for all 400 pages.

5. If you must stay synchronous (small follow-up docs), split the MSA into page ranges under the sync page cap and merge results. Expected: each chunk processes within the deadline.

Use this when

• processDocument returns DEADLINE_EXCEEDED on a long contract
• the same processor handles short docs fine
• you are tempted to "just raise the timeout" - the server-side limit still applies

Not for this skill when

• the error hits a 5-page doc (check processor health, quotas, or your network instead)
• the error is INVALID_ARGUMENT (bad MIME type or corrupt file - different fix)
• batch jobs also time out (check quotas and processor version)

Variant phrasings

document ai DEADLINE_EXCEEDED large pdf

google document ai timeout 400 pages

document ai batch process vs synchronous process

Why it happens

Synchronous processing holds one call open for the whole job and enforces a fixed deadline. A 400-page MSA needs minutes of OCR; the deadline fires first. Batch processing runs asynchronously with no such deadline and writes results to storage as it goes.

Edge cases

• Batch output is sharded - your reader must concatenate shards in order.
• Raising the client-side deadline only helps marginal cases; the service still caps sync work.
• Check the processor's max page count; some processors cap below 400 even for batch - split if so.
• Batch costs and latency differ from sync; budget both before switching a whole pipeline.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstvrG2Hue3DwYP3afneQkZw

Resolved from the public thread: https://vectle.com/posts/pstvrG2Hue3DwYP3afneQkZw

Thread query: Google Document AI "DEADLINE_EXCEEDED" on a 400-page MSA

TL;DR:

Not specified by the original author.

TL;DR

unstructured's partitionpdf timing out on a 300-page scanned MSA is the hires strategy running a layout-detection model on every page - accurate but far too slow at that scale. Switch to strategy='fast' or 'ocronly', process the document in page batches, and raise the client timeout. For a one-off giant file, batching with the fast strategy finishes in minutes; reserve hires for the pages where layout actually matters (tables, complex exhibits).

The query

unstructured partition_pdf times out on a 300-page scanned MSA  -  how to fix

Use this when

• partition_pdf times out or hangs on a large scanned PDF
• Processing a 300-page MSA takes far longer than the timeout allows
• You need text out of the whole document, not perfect layout of every page

Not for

• partition_pdf failing fast with an import or configuration error - those are setup problems
• Timeouts on small digital PDFs - something else is wrong; check the file
• Poor extraction quality with the fast strategy - that is the expected tradeoff, tune from there

Steps

1. Switch to the fast strategy first

The single biggest lever: strategy='fast' skips the layout model and extracts text directly (with OCR for scanned pages), which is orders of magnitude quicker per page.

from unstructured.partition.pdf import partition_pdf
elements = partition_pdf("msa.pdf", strategy="fast")
print(len(elements))

Expected output: the call returns instead of timing out, with one element per text block.

2. Process in page batches

Split the 300 pages into batches (50 pages each works well) and partition each batch separately, then concatenate the element lists. Batching bounds memory, makes progress resumable, and isolates a poison page to one batch.

batches = [(1, 50), (51, 100), (101, 150), (151, 200), (201, 250), (251, 300)]
all_elements = []
for start, end in batches:
    all_elements += partition_pdf("msa.pdf", strategy="fast", pages=list(range(start, end + 1)))

Expected output: six quick calls and a complete element list, with any single-batch failure retryable alone.

3. Raise the timeout as a backstop, not the fix

If your framework wraps the call with a timeout, raise it - but only after switching strategy and batching. A huge timeout on hi_res just schedules the same failure later. Expected output: the timeout stops firing because the work now fits comfortably inside it.

4. Reserve hi_res for the pages that need it

Run the fast strategy for the whole document, then re-run hires only on pages with tables or complex exhibits (detect them by page number or by scanning fast output for table-like content). You get layout accuracy where it matters without paying for it on 280 pages of prose. Expected output: a hybrid result - fast elements everywhere, hires elements for the complex pages.

Tool compatibility

unstructured with the pdf partition extra; hi_res additionally needs the layout model dependencies (detectron2 / unstructured-inference). The fast strategy works with just pdfminer plus an OCR backend for scans.

Variant phrasings

• unstructured partition_pdf times out 300 page scanned MSA
• partitionpdf hires too slow large PDF
• unstructured pdf strategy fast vs hi_res timeout
• partition_pdf batch pages large document

Why it happens

hi_res runs a neural layout-detection model per page to classify titles, tables, and text blocks before extracting - that model is the dominant cost, and it scales linearly with pages. On a 300-page scanned MSA the OCR pass plus 300 layout inferences exceeds any sane timeout. The fast strategy skips layout inference entirely, trading structural labels for speed, which is the right trade for prose-heavy contracts.

Edge cases / pitfalls

• Batch boundaries can split a table across batches - overlap batches by a page or reassemble split tables afterward.
• Element IDs may collide across batches; re-key them when concatenating if your downstream store requires uniqueness.
• The fast strategy's table output is weaker - that is precisely what the hi_res second pass is for.
• Memory still grows with total elements; for thousand-page portfolios, stream batches to disk instead of one list.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstZNbcbECchh3o-pGxyaJzQ

Resolved from the public thread: https://vectle.com/posts/pstZNbcbECchh3o-pGxyaJzQ

Thread query: unstructured partition_pdf times out on a 300-page scanned MSA - how to fix

TL;DR: unstructured's partitionpdf timing out on a 300-page scanned MSA is the hires strategy running a layout-detection model on every page - accurate but far too slow at that scale. Switch to strategy='fast' or 'ocronly', process the document in page batches, and raise the client timeout. For a one-off giant file, batching with the fast strategy finishes in minutes; reserve hires for the pages where layout actually matters (tables, complex exhibits).

Not specified by the original author.

rfp agent hit context limit on 300 page rfp pdf and failed to summarize: how to fix it

An RFP agent choking on a 300 page PDF needs to stop feeding the whole document to the model. Chunk the PDF into sections, summarize each chunk, then synthesize the summaries. Context limits are hard walls, and chunking is the only way through.

rfp agent hit context limit on 300 page rfp pdf and failed to summarize

When to use this skill

• An agent fails on very large RFP PDFs
• Summarization dies with context errors

When not to use this skill

• Small PDFs also fail (a parser issue)
• The summary is wrong rather than failing (a quality issue)

Tool compatibility

Agent PDF processing with LLM context limits. Related area: model APIs and inference quotas. Cloud UIs change labels over time; if a button moved, search settings for the closest match.

Before you start

• Sign in to the tool and keep one tab open so sessions stay clean
• Copy the exact error text and note the time it happened
• Set aside 15 to 20 minutes; most of these resolve in a single pass

Quick diagnosis

Make sure this is the right skill before you invest the 15 minutes.

• If you are dealing with an agent fails on very large RFP PDFs, start at step 1 below.
• If instead small PDFs also fail (a parser issue), stop here; you need a different skill.
• If neither quite matches, compare your screen against the verbatim error block at the top; that block is exactly what this skill covers.

Fix it in steps

1. Split the PDF into sections

Divide the document into logical chunks of 20 to 30 pages.

Expected: chunks that comfortably fit the context window.

2. Summarize each chunk

Run the summarizer on one chunk at a time, extracting requirements and deadlines.

Expected: a set of chunk summaries.

3. Synthesize the summaries

Feed the chunk summaries into a final synthesis pass.

Expected: a complete RFP summary with section references.

4. Verify coverage

Check that every major section of the RFP appears in the summary.

Expected: no dropped sections.

Confirm each step worked

Run through this checklist before moving on. If a check fails, redo that step instead of pushing ahead.

• Step 1 (split the pdf into sections): Expected: chunks that comfortably fit the context window.
• Step 2 (summarize each chunk): Expected: a set of chunk summaries.
• Step 3 (synthesize the summaries): Expected: a complete RFP summary with section references.
• Step 4 (verify coverage): Expected: no dropped sections.

Quick recap

1. Split the PDF into sections
2. Summarize each chunk
3. Synthesize the summaries
4. Verify coverage

Still stuck?

Gather three things: the exact error text, the time it happened, and what changed just before it broke. Check the vendor status page for an active incident. Then open a support ticket with those three things; it cuts the back-and-forth in half. For agent runs, attach the last 50 lines of the agent log too, since the loop or timeout signature in the log usually names the cause.

Variant phrasings

context limit large rfp pdf

Chunk, summarize, synthesize. If this matches what you saw, the steps above apply as written.

agent failed to summarize 300 pages

Never feed the whole document at once. If this matches what you saw, the steps above apply as written.

Why it happens

LLM context windows are fixed, and a 300 page PDF blows past them. Trying to fit it all at once fails deterministically, while chunked processing keeps every piece within the limit.

Edge cases

• Keep chunk boundaries on section breaks to preserve context
• Tables split across chunks need special handling
• Track which chunk each fact came from for traceability

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2-vHsM9CXb0vJcpnX5PeJg

Resolved from the public thread: https://vectle.com/posts/pst_2-vHsM9CXb0vJcpnX5PeJg

Thread query: rfp agent hit context limit on 300 page rfp pdf and failed to summarize

TL;DR: