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Use Amazon Bedrock Knowledge Bases when PDFs must become a searchable corpus. Choose the default parser for selectable-text PDFs, Bedrock Data Automation (BDA) or a foundation-model parser when figures, tables, charts, images, or layout matter, and an OCR pipeline for scans. For a single document, a direct model request can be simpler than creating a vector store. The right choice depends on the document, whether the answer is one-off or recurring, and how much visual meaning must be preserved.
Choose the extraction path first
“PDF extraction” in Bedrock is not one API or one parser. AWS provides several paths with different capabilities and billing models.
| Path | Best fit | What it handles | Billing and limits |
|---|---|---|---|
| Knowledge Bases default parser | Selectable text in a repeat-use corpus | Extracts text for chunking, embeddings, and retrieval; does not interpret visual content in charts, figures, tables, or images | AWS says parsing has no usage charge |
| Bedrock Data Automation | Managed multimodal ingestion | Extracts and represents figures, charts, tables, images, and text without extra extraction prompting | Priced by pages or images processed; applies to every PDF in that data source |
| Foundation-model parser | Complex documents requiring an adjustable extraction instruction | Model-based multimodal parsing with a customizable default prompt | Priced by input and output tokens; applies to every PDF in that data source |
| Textract plus Bedrock | Scanned pages and OCR-oriented workflows | Textract can extract printed text, handwriting, layout elements, and data; Bedrock interprets the result | Check current regional Textract and Bedrock prices and use the correct synchronous or asynchronous operation |
| Direct model request | One document or a small application-controlled job | Ask a supported model to interpret submitted content, subject to that model’s document-input limits | Token and model charges; support for PDF bytes and formats differs by model |
Advanced parsing is a data-source setting. If you select BDA or a foundation-model parser, Bedrock uses it for every PDF in that source, including text-only files. Separate text-only and visually rich collections when that makes cost and operations easier to control.
Text-only PDFs: build a Knowledge Base
A Knowledge Base is the appropriate default when people or applications will ask many questions over the same documents. Bedrock ingests the source, parses documents, chunks content, creates embeddings, and writes vectors to a configured vector store. Later, Retrieve returns relevant chunks; RetrieveAndGenerate retrieves chunks and asks a model to produce a grounded answer with source attribution.
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Set up the corpus
- Place the PDFs in a supported unstructured data source, such as an Amazon S3 location.
- Create an IAM role that allows Bedrock to read the source, invoke the selected embedding and foundation models, and write to the chosen vector store. Scope permissions to the specific bucket, prefixes, knowledge base, and model resources.
- Create the Knowledge Base and select an embedding model and vector store.
- Choose the default parser for selectable text. If any PDFs require visual interpretation, decide whether to move them to a separate data source and use BDA or a foundation-model parser there.
- Configure chunking, then start ingestion or synchronization.
Sync after additions, edits, or deletions so the vector index reflects the source. Treat extracted fields as candidates, not unquestionable truth; verify important values against the original page.
Retrieve chunks in Python
import boto3
bedrock_agent = boto3.client("bedrock-agent-runtime", region_name="us-east-1")
response = bedrock_agent.retrieve(
knowledgeBaseId="YOUR_KNOWLEDGE_BASE_ID",
retrievalQuery={"text": "What is the contract termination date?"},
retrievalConfiguration={
"vectorSearchConfiguration": {"numberOfResults": 5}
},
)
for result in response.get("retrievalResults", []):
text = result.get("content", {}).get("text", "")
score = result.get("score")
location = result.get("location", {})
print(f"score={score}n{text}n{location}n")Use Retrieve when your application must control ranking, filtering, validation, or its own response format. Use RetrieveAndGenerate when Bedrock should compose the answer from retrieved material and return citations. Keep the returned source metadata with the answer so users can inspect the supporting page or chunk.
Visually rich PDFs: BDA or a foundation-model parser
Text extraction alone can lose the meaning of a chart, table structure, figure, or image. BDA is the managed option when you want multimodal extraction without writing an extraction prompt. A foundation-model parser is useful when you need to customize instructions—for example, asking for a table as normalized records or requesting a particular field schema.
Control cost at the data-source boundary
Both advanced parsers process every PDF in the selected data source. A folder containing 10,000 text-only PDFs and 20 diagram-heavy PDFs can therefore incur advanced-parser processing for all 10,020 files. Splitting sources by parsing need can prevent that mismatch, but it adds synchronization and query-routing work. Confirm current regional pricing with your page count, parser, model, and output volume before committing.
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Inspect layout-sensitive answers
Ask for page references or source attribution, then compare critical totals, labels, and units with the rendered page. Low-resolution scans, overlapping text, handwriting, and complex tables can produce plausible but incorrect values.
One-off extraction with a direct model request
For one document or a small, application-controlled workload, creating a Knowledge Base and vector store may be unnecessary. Bedrock’s Converse API supplies a common message interface for supported models. However, the Converse reference does not establish that every model accepts PDF bytes or the same document formats. Check the selected model’s input support, regional availability, size limits, and required permissions before sending a PDF directly. If direct document input is unavailable, extract text or render page images first, then submit those representations.
Minimal Converse pattern
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="YOUR_SUPPORTED_MODEL_ID",
messages=[
{
"role": "user",
"content": [
{"text": "Extract the invoice number, date, supplier, and total. Return JSON and mark uncertain fields."}
# Add a document or image content block only when this model's
# current documentation confirms support for your input format.
],
}
],
)
print(response["output"]["message"]["content"])The runtime caller needs permission to invoke the model (including the appropriate bedrock:InvokeModel access). Validate the response against a schema, preserve the source page, and require human review for compliance, financial, or legal decisions.
Scanned PDFs and OCR
A scanned PDF is usually an image of a page, not a text document. OCR or visual interpretation must happen before reliable text retrieval. AWS’s Bedrock/Textract hands-on tutorial demonstrates DetectDocumentText with a single-page JPG or PNG. It explicitly excludes the different asynchronous Textract workflow required for multi-page PDFs, so that tutorial is not a complete multi-page implementation.
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For production scans, choose the current asynchronous Textract document-processing path, verify its input constraints and output format in your region, collect all page results, and then send the extracted text or structured blocks to Bedrock. Preserve page numbers and confidence information. Review skewed pages, stamps, handwriting, columns, and tables manually when the value matters.
Return the original or parsed document
When an interface needs to display or download the document behind a retrieved result, call GetDocumentContent with the Knowledge Base, data-source, and document identifiers. The response provides a MIME type and a pre-signed URL that expires after five minutes. The caller needs both bedrock:Retrieve and bedrock:GetDocumentContent. If ACL-based access control is enabled, pass the user’s identity context so document permissions are enforced.
Operational checklist
- Document classification: selectable text, scanned pages, or visual layout?
- Workload shape: one answer, occasional batches, or a repeatedly queried corpus?
- Parser scope: will every PDF in this source need multimodal parsing?
- Model constraints: is the chosen model available in the target region and able to accept the submitted format?
- Evidence: will responses retain chunk metadata, page numbers, and links to source content?
- Security: are S3, model, vector-store, and document-content permissions least-privilege?
- Validation: are low-confidence OCR and critical extracted fields routed for review?
Common failures and fixes
Answers ignore a chart or table
Cause: the default parser extracted text only. Fix: use BDA or a foundation-model parser for that data source, or process the relevant pages as images.
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Parsing costs are higher than expected
Cause: an advanced parser runs on every PDF in its source. Fix: separate text-only and multimodal collections, reduce unnecessary re-syncs, and check current page- or token-based prices.
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A PDF cannot be sent to Converse
Cause: the selected model or request format does not support PDF input, or the file exceeds a limit. Fix: verify the model documentation and region; extract text or render pages before invoking the model.
Scanned pages return empty retrieval results
Cause: the source contains images without an OCR stage. Fix: run the appropriate asynchronous multi-page Textract workflow, preserve page output, and ingest the resulting text.
A source link no longer works
Cause: GetDocumentContent URLs expire after five minutes. Fix: request a new URL when the user opens the document rather than storing the signed URL permanently.
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Retrieved text is stale
Cause: the source changed without a synchronization or direct ingestion/deletion operation. Fix: sync the data source and monitor ingestion completion before serving the new version.
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
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Frequently Asked Questions
Can Bedrock extract handwriting from a PDF?
Use an OCR-oriented Textract workflow for scanned handwriting, then pass the extracted material to Bedrock for interpretation. Validate low-confidence results against the page.
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Should I store every PDF in one Knowledge Base?
Not necessarily. Separate sources when parser requirements, access controls, update schedules, or cost profiles differ; query multiple sources only when your application can route and merge results safely.
How long does a GetDocumentContent download link last?
AWS documents a five-minute lifetime for the pre-signed URL, so generate it close to the time the user opens the document.
Quick Recap
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