Merge pull request #91 from enoch3712/90-small-change-to-documentation

pointer to examples removed

Author: enoch3712

docs/core-concepts/classification/mom.md

@@ -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. 

docs/core-concepts/classification/vision.md

@@ -98,5 +98,3 @@ result = process.classify(
     image=True
 )
 ```
-
-For more examples and advanced usage, check out the [examples directory](examples/) in the repository. 

docs/core-concepts/contracts/index.md

@@ -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.

docs/core-concepts/document-loaders/aws-textract.md

@@ -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. 

docs/core-concepts/document-loaders/azure-form.md

@@ -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. 

docs/core-concepts/document-loaders/google-document-ai.md

@@ -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. 

docs/core-concepts/document-loaders/tesseract.md

@@ -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.

docs/examples/local-processing.md

@@ -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. 
+   ```

docs/getting-started/index.md

@@ -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>