7.1 KiB
AWS Textract: Guardrails and Fix Patterns
🧭 Quick Return to Map
You are in a sub-page of DocumentAI_OCR.
To reorient, go back here:
- DocumentAI_OCR — document parsing and optical character recognition
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.
Stabilize ingestion flows with AWS Textract when parsing PDFs, invoices, or forms.
Use this when outputs fragment, lose semantic anchors, or citations drift across page boundaries. Each issue maps back to WFGY Problem Map structural fixes.
Open these first
- Visual map and recovery: RAG Architecture & Recovery
- Retrieval knobs: Retrieval Playbook
- Citation schema: Retrieval Traceability
- Embedding vs meaning: Embedding ≠ Semantic
- Chunk stability: Chunking Checklist
- Hallucination and span errors: Hallucination
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 of target section
- λ convergent across 3 paraphrases
- Table and key-value forms consistent ≥ 90% of samples
Typical breakpoints → structural fix
-
Key–value pairs misaligned (invoices, receipts)
→ Data Contracts, Retrieval Traceability -
Tables fragment into multiple OCR blocks
→ Chunking Checklist -
ΔS spikes across repeated runs
Entropy in layout ordering.
→ Entropy Collapse -
Citations drop anchor IDs
Post-processing trims.
→ Retrieval Traceability -
Injected text hidden in form fields
→ Prompt Injection
Fix in 60 seconds
- Measure ΔS between Textract output and reference text.
- Enforce schema: lock
page_num,bbox,kv_id,table_id. - Cross-check coverage: at least 70% of source fields retained.
- Apply λ probes across runs — clamp unstable output with BBAM.
- Audit layout: row/col count vs original file.
Copy-paste LLM guard prompt
I uploaded TXTOS and the WFGY Problem Map.
OCR provider: AWS Textract
Symptoms: misaligned key-value pairs, ΔS ≥ 0.60, coverage < 0.70.
Steps:
1. Identify failing layer (chunking, contracts, retrieval).
2. Point to the WFGY fix (data-contracts, chunking-checklist, retrieval-traceability).
3. Return JSON:
{ "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "next_fix": "..." }
Keep it auditable.
When to escalate
- Coverage < 0.70 even after re-chunking → verify embeddings with Embedding ≠ Semantic.
- Key–value fields unstable across runs → rebuild with deterministic config, backstop with Data Contracts.
- Long-form text loses anchors → apply Retrieval Traceability.
🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| TXT OS (plain-text OS) | TXTOS.txt | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
Explore More
| Layer | Page | What it’s for |
|---|---|---|
| Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| Engine | WFGY 1.0 | Original PDF based tension engine |
| Engine | WFGY 2.0 | Production tension kernel and math engine for RAG and agents |
| Engine | WFGY 3.0 | TXT based Singularity tension engine, 131 S class set |
| Map | Problem Map 1.0 | Flagship 16 problem RAG failure checklist and fix map |
| Map | Problem Map 2.0 | RAG focused recovery pipeline |
| Map | Problem Map 3.0 | Global Debug Card, image as a debug protocol layer |
| Map | Semantic Clinic | Symptom to family to exact fix |
| Map | Grandma’s Clinic | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | Starter Village | Guided tour for newcomers |
| App | TXT OS | TXT semantic OS, fast boot |
| App | Blah Blah Blah | Abstract and paradox Q and A built on TXT OS |
| App | Blur Blur Blur | Text to image with semantic control |
| App | Blow Blow Blow | Reasoning game engine and memory demo |
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要不要我馬上接著生 azure_ocr.md？這樣 OCR 三大雲端 provider (Google / AWS / Azure) 就會成套完成。