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pointer to examples removed #91

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2 changes: 0 additions & 2 deletions docs/core-concepts/classification/mom.md
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Expand Up @@ -93,5 +93,3 @@ result = process.classify(
- Set appropriate confidence thresholds based on your use case
- Consider using different model providers for better diversity
- Monitor and log classification results for each model

For more examples and advanced usage, check out the [examples directory](examples/) in the repository.
2 changes: 0 additions & 2 deletions docs/core-concepts/classification/vision.md
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Expand Up @@ -98,5 +98,3 @@ result = process.classify(
image=True
)
```

For more examples and advanced usage, check out the [examples directory](examples/) in the repository.
2 changes: 0 additions & 2 deletions docs/core-concepts/contracts/index.md
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Expand Up @@ -30,5 +30,3 @@ class InvoiceContract(Contract):
```python
--8<-- "extract_thinker/models/contract.py"
```

For more examples and advanced usage, check out the [examples directory](examples/) in the repository.
2 changes: 1 addition & 1 deletion docs/core-concepts/document-loaders/aws-textract.md
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Expand Up @@ -68,4 +68,4 @@ The loader returns a dictionary with the following structure:
- Process pages individually for large documents
- Monitor API quotas and costs

For more examples and implementation details, check out the [examples directory](examples/) in the repository.
For more examples and implementation details, check out the [AWS Stack](../../examples/aws-textract) in the repository.
2 changes: 1 addition & 1 deletion docs/core-concepts/document-loaders/azure-form.md
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Expand Up @@ -61,4 +61,4 @@ Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`,
- Handle tables and paragraphs separately for better accuracy
- Process documents page by page for large files

For more examples and advanced usage, check out the [examples directory](examples/) in the repository.
For more examples and implementation details, check out the [Azure Stack](../../examples/azure-form.md) in the repository.
2 changes: 1 addition & 1 deletion docs/core-concepts/document-loaders/google-document-ai.md
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Expand Up @@ -79,4 +79,4 @@ with open("document.pdf", "rb") as f:

Document AI supports PDF, TIFF, GIF, JPEG, PNG with a maximum file size of 20MB or 2000 pages.

For more examples and implementation details, check out the [examples directory](examples/) in the repository.
For more examples and implementation details, check out the [Google Stack](../../examples/google-document-ai) in the repository.
2 changes: 1 addition & 1 deletion docs/core-concepts/document-loaders/tesseract.md
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Expand Up @@ -61,4 +61,4 @@ Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`
- Consider image preprocessing for better accuracy
- Set appropriate page segmentation mode based on document layout

For more examples and advanced usage, check out the [examples directory](examples/) in the repository.
For more examples and advanced usage, check out the [Local Stack](../../examples/local-processing) in the repository.
14 changes: 1 addition & 13 deletions docs/examples/local-processing.md
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Expand Up @@ -96,16 +96,4 @@ result = extractor.extract("document.pdf", Contract)
)
except Exception as e:
print(f"Processing error: {e}")
```

## Performance Comparison

| Aspect | Cloud | Local |
|--------|-------|-------|
| Speed | Faster | Depends on hardware |
| Cost | Pay per use | Free |
| Privacy | Data leaves network | Complete privacy |
| Setup | Simple | More complex |
| Maintenance | None | Required |

For more examples and implementation details, check out the [examples directory](https://github.com/enoch3712/ExtractThinker/tree/main/examples) in the repository.
```
12 changes: 5 additions & 7 deletions docs/getting-started/index.md
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Expand Up @@ -67,24 +67,22 @@ print(f"Total: ${result.total_amount}")
<li>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14 2H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h12c1.1 0 2-.9 2-2V8l-6-6zM6 20V4h7v5h5v11H6z"></path></svg></span> <strong>Extraction with Pydantic</strong></p>
<p>Extract structured data from any document type using Pydantic models for validation, custom features, and prompt engineering capabilities.</p>
<p><a href="../examples/extraction"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p>
<!-- <p><a href="../examples/extraction"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p> -->
</li>
<li>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2L4.5 20.29l.71.71L12 18l6.79 3 .71-.71z"></path></svg></span> <strong>Classification & Split</strong></p>
<p>Intelligent document classification and splitting with support for consensus strategies, eager/lazy splitting, and confidence thresholds.</p>
<p><a href="../examples/classification"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p>
<!-- <p><a href="../examples/classification"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p> -->
</li>
<li>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 1 3 5v6c0 5.55 3.84 10.74 9 12 5.16-1.26 9-6.45 9-12V5l-9-4zm0 10.99h7c-.53 4.12-3.28 7.79-7 8.94V12H5V6.3l7-3.11v8.8z"></path></svg></span> <strong>PII Detection</strong></p>
<p>Automatically detect and handle sensitive personal information in documents with privacy-first approach and advanced validation.</p>
<p><a href="../examples/pii"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p>
<!-- <p><a href="../examples/pii"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p> -->
</li>
<li>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M21.7 18.6-6.9-6.9q-.275-.275-.637-.425-.363-.15-.763-.15-.425 0-.787.15-.363.15-.638.425L8.1 15.5q-.275.275-.425.638-.15.362-.15.762 0 .425.15.788.15.362.425.637l6.9 6.9q.275.275.638.425.362.15.787.15.425 0 .788-.15.362-.15.637-.425l4.9-4.9q.275-.275.425-.637.15-.363.15-.788 0-.425-.15-.787-.15-.363-.425-.638Z"></path></svg></span> <strong>LLM and OCR Agnostic</strong></p>
<p>Freedom to choose and switch between different LLM providers and OCR engines based on your needs and cost requirements.</p>
<p><a href="../examples/integrations"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p>
<!-- <p><a href="../examples/integrations"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8.22 2.97a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.75.75 0 0 1-1.042-.018.75.75 0 0 1-.018-1.042l2.97-2.97H3.75a.75.75 0 0 1 0-1.5h7.44L8.22 4.03a.75.75 0 0 1 0-1.06"></path></svg></span> Learn More</a></p> -->
</li>
</ul>
</div>

Check out our [advanced usage guide](./advanced-usage.md) for more complex scenarios like document classification, batch processing, and custom LLM integration.
</div>