WFGY/ProblemMap/GlobalFixMap/DocumentAI_OCR/google_docai.md

# Google Document AI OCR: Guardrails and Fix Patterns

<details>
  <summary><strong>🧭 Quick Return to Map</strong></summary>

<br>

  > You are in a sub-page of **DocumentAI_OCR**.  
  > To reorient, go back here:  
  >
  > - [**DocumentAI_OCR** — document parsing and optical character recognition](./README.md)  
  > - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)  
  > - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)  
  >
  > 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.
</details>


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 + \<your question>”   |
| **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.

> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="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).

<div align="center">

[![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)
 

</div>


要不要我直接幫你下一步補 **aws\_textract.md**？這樣 OCR MVP 會更快成形。