# Google Document AI OCR: Guardrails and Fix Patterns A compact guide to stabilize ingestion flows using **Google Cloud Document AI OCR**. Use this when PDF or scanned document parsing produces unstable tokens, missing tables, or broken citations. Each failure is mapped to a structural fix in the WFGY Problem Map. --- ## Open these first - Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) - End-to-end knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) - Citation schema: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) - Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) - OCR text boundaries: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) - Injection and schema locks: [Prompt Injection](https://github.com/onestardao/WFGY/blob/main/ProblemMap/prompt-injection.md) --- ## Core acceptance - ΔS(question, retrieved) ≤ 0.45 - Coverage ≥ 0.70 of target section - λ remains convergent across three paraphrases and two seeds - Table and form layout preserved in ≥ 85% of samples --- ## Typical breakpoints → structural fix - **Lost tables or merged columns** Payload schema drift. → [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md), [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) - **OCR output differs across runs of the same PDF** Non-deterministic layout parse. → [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md), [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md) - **Citations drop page anchors** Post-processing trims. → [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) - **Injection vectors inside scanned forms** Malicious text embedded in OCR’d images. → [Prompt Injection](https://github.com/onestardao/WFGY/blob/main/ProblemMap/prompt-injection.md) --- ## Fix in 60 seconds 1. **Measure ΔS** on OCR’d snippets vs reference text. 2. **Lock schemas** with Data Contracts (force `page_num`, `bbox`, `tokens`). 3. **Enforce cite-then-explain** at retrieval time. 4. **Add λ probes** across multiple OCR calls — if divergent, clamp with BBAM. 5. **Audit tables**: cross-check row count and column headers against source PDF. --- ## Copy-paste LLM guard prompt ```txt I uploaded TXTOS and the WFGY Problem Map. OCR provider: Google Document AI Symptoms: lost tables, ΔS ≥ 0.60, λ diverges across 3 paraphrases. Steps: 1. Identify which structural fix applies (chunking-checklist, data-contracts, retrieval-traceability). 2. Return a JSON plan: { "citations": [...], "answer": "...", "λ_state": "<>", "ΔS": 0.xx, "next_fix": "..." } Keep it auditable and short. ```` --- ## When to escalate * ΔS stays ≥ 0.60 even after chunk / schema fixes → rebuild pipeline with [Semantic Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md). * Coverage < 0.70 across paraphrases → verify embeddings with [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md). * Inconsistent runs across identical files → enforce deterministic parser config, or switch to dual-engine validation (DocAI + Tesseract). --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | | **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” | | **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly | --- ### 🧭 Explore More | Module | Description | Link | | ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) | | Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) | | Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) | | Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) | | Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) | | Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) | | 🧙‍♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) | --- > 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** — > Engineers, hackers, and open source builders who supported WFGY from day one. > GitHub stars ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
[![WFGY Main](https://img.shields.io/badge/WFGY-Main-red?style=flat-square)](https://github.com/onestardao/WFGY)   [![TXT OS](https://img.shields.io/badge/TXT%20OS-Reasoning%20OS-orange?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS)   [![Blah](https://img.shields.io/badge/Blah-Semantic%20Embed-yellow?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)   [![Blot](https://img.shields.io/badge/Blot-Persona%20Core-green?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)   [![Bloc](https://img.shields.io/badge/Bloc-Reasoning%20Compiler-blue?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)   [![Blur](https://img.shields.io/badge/Blur-Text2Image%20Engine-navy?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)   [![Blow](https://img.shields.io/badge/Blow-Game%20Logic-purple?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)  
要不要我直接幫你下一步補 **aws\_textract.md**？這樣 OCR MVP 會更快成形。