docs: add Docling integration guide, clear code example, and fix parser consistency issues

docs: update changelog with model switching fixes

CHANGELOG.md

@@ -8,6 +8,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Fixed
+- Fixed model switching bug in `TextEmbedder` where internal state was not cleared, preventing dynamic updates between `fastembed` and `sentence_transformers` (#160).
+- Implemented model-intrinsic embedding dimension detection in `TextEmbedder` to ensure consistency between models and vector databases.
+- Updated `set_model` to properly refresh configuration and dimensions during model switches.
+
+### Added
+- Added comprehensive unit test suite `tests/embeddings/test_model_switching.py` for verifying dynamic model transitions and dimension updates.
 - Fixed `TypeError: unhashable type: 'Entity'` in `GraphAnalyzer` when processing graphs with raw `Entity` objects or dictionaries in relationships (#159).
 - Robustified ID extraction across `CentralityCalculator`, `CommunityDetector`, and `ConnectivityAnalyzer` to handle various entity formats.
 - Improved `Entity` class hashability and equality logic in `utils/types.py`.

README.md

@@ -271,9 +271,13 @@ parsed = parser.parse("document.pdf", format="auto")
 
 # Enhanced parsing with Docling (recommended for complex layouts/tables)
 # Requires: pip install docling
-docling_parser = DoclingParser()
-docling_result = docling_parser.parse("complex_table.pdf")
-print(f"Extracted {len(docling_result.tables)} tables")
+docling_parser = DoclingParser(enable_ocr=True)
+result = docling_parser.parse("complex_table.pdf")
+
+print(f"Text (Markdown): {result['full_text'][:100]}...")
+print(f"Extracted {len(result['tables'])} tables")
+for i, table in enumerate(result['tables']):
+    print(f"Table {i+1} headers: {table.get('headers', [])}")
 
 # Normalize text
 normalizer = TextNormalizer()

docs/CodeExamples.md

@@ -138,6 +138,39 @@ async for item in feed_processor.stream_items():
     knowledge_graph.add_triplets(core.generate_triplets(semantics))
 ```
 
+### 🦆 Docling Clear Code Example
+
+High-accuracy document parsing with structural understanding:
+
+```python
+from semantica.parse import DoclingParser
+
+# 1. Initialize DoclingParser
+# Docling provides superior table extraction and structure understanding
+# Requires: pip install docling
+parser = DoclingParser(
+    enable_ocr=True,          # Enable OCR for scanned documents
+    export_format="markdown"  # Options: "markdown", "html", "json"
+)
+
+# 2. Parse a complex document
+# Supports PDF, DOCX, PPTX, XLSX, HTML, and images
+result = parser.parse("complex_invoice.pdf")
+
+# 3. Access structured content
+print(f"Content (Markdown):\n{result['full_text']}")
+
+# 4. Extract and iterate over tables with high precision
+for i, table in enumerate(result['tables']):
+    print(f"\nTable {i+1}:")
+    print(f"Headers: {table.get('headers', [])}")
+    print(f"Data rows: {len(table.get('rows', []))}")
+
+# 5. Get document metadata
+metadata = result['metadata']
+print(f"\nMetadata: {metadata.get('title')} ({result.get('total_pages')} pages)")
+```
+
 ### 📊 Structured Data Processing Module
 
 Handle structured and semi-structured data formats:

docs/integrations/docling.md

@@ -0,0 +1,107 @@
+# Docling Integration
+
+Semantica features a native integration with **Docling**, the powerful document parsing library that excels at extracting structured data from complex documents like PDFs, DOCX, and PPTX.
+
+## Overview
+
+Docling is integrated into Semantica's `parse` module via the `DoclingParser`. This allows you to seamlessly convert unstructured documents into semantic structures that can be indexed, searched, and analyzed within the Semantica framework.
+
+- 📖 **Semantica Docling Integration Docs**: [Reference Guide](../reference/parse.md)
+- 💻 **Semantica Docling Integration GitHub**: [Source Code](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py)
+- 🧑🏽‍🍳 **Semantica Docling Integration Example**: [Docling Clear Code Example](../CodeExamples.md#docling-clear-code-example)
+- 📦 **Semantica Docling Integration PyPI**: [Installation Guide](../installation.md)
+
+---
+
+## 📖 Integration Documentation
+
+The `DoclingParser` provides a high-level interface for document processing. It supports:
+
+*   **Multi-format support**: PDF, DOCX, PPTX, HTML, and more.
+*   **Table Extraction**: High-fidelity table extraction with header detection.
+*   **OCR Support**: Built-in Optical Character Recognition for scanned documents.
+*   **Markdown Export**: Clean markdown output optimized for LLM consumption.
+
+### Basic Usage
+
+```python
+from semantica.parse import DoclingParser
+
+# Initialize with OCR enabled
+parser = DoclingParser(enable_ocr=True)
+
+# Parse a complex document
+result = parser.parse("financial_report.pdf")
+
+# Access the structured data
+print(f"Content: {result['full_text'][:200]}...")
+print(f"Found {len(result['tables'])} tables")
+```
+
+For more details, see the [Parse Reference](../reference/parse.md).
+
+---
+
+## 🧑🏽‍🍳 Integration Example
+
+We provide a detailed cookbook and clear code examples to help you get started quickly.
+
+### Docling Clear Code Example
+
+```python
+from semantica.parse import DoclingParser
+import json
+
+# 1. Initialize the Docling Parser with advanced config
+parser = DoclingParser(
+    enable_ocr=True, 
+    export_format="markdown"
+)
+
+# 2. Parse a complex document (PDF, DOCX, etc.)
+result = parser.parse("complex_invoice.pdf")
+
+# 3. Access the clean Markdown text
+print(f"--- Document Content ---\n{result['full_text']}")
+
+# 4. Iterate through extracted tables
+for i, table in enumerate(result['tables']):
+    print(f"\nTable {i+1} headers: {table.get('headers', [])}")
+    # Access table rows as a list of lists
+    for row in table.get('rows', [])[:3]:  # Print first 3 rows
+        print(f"  Row: {row}")
+
+# 5. Get document metadata
+metadata = result['metadata']
+print(f"\n--- Metadata ---\nTitle: {metadata.get('title')}")
+print(f"Total Pages: {result.get('total_pages')}")
+```
+
+See more in our [Code Examples](../CodeExamples.md).
+
+---
+
+## 💻 GitHub Source
+
+The integration is open-source and available on GitHub. You can explore the implementation, contribute improvements, or report issues.
+
+- [docling_parser.py](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py) - The core implementation of the Docling integration.
+
+---
+
+## 📦 PyPI & Installation
+
+Docling is an optional but highly recommended dependency for Semantica. You can install it along with Semantica or as a separate requirement.
+
+### Install via Semantica
+```bash
+pip install semantica
+```
+
+### Install Docling manually
+If you are working in a custom environment:
+```bash
+pip install docling
+```
+
+For full installation details, see the [Installation Guide](../installation.md).

docs/reference/parse.md

@@ -159,11 +159,11 @@ parser = DoclingParser()
 result = parser.parse("complex_table.pdf")
 
 # Access high-accuracy tables
-for table in result.tables:
-    print(table.headers)
+for table in result["tables"]:
+    print(table["headers"])
     
 # Get markdown representation
-print(result.markdown)
+print(result["full_text"])
 ```
 
 ### WebParser

mkdocs.yml

@@ -139,6 +139,8 @@ nav:
     - examples.md
     - Code Examples: CodeExamples.md
     - learning-more.md
+  - Integrations:
+    - Docling: integrations/docling.md
   - Cookbook: cookbook.md
   - Resources:
     - community-projects.md

semantica/parse/parse_usage.md

@@ -170,17 +170,17 @@ result = parser.parse("complex_invoice.pdf")
 
 # 2. Extract structured content
 # result contains the full Docling document object if available
-print(f"Extracted Text (Markdown): {result.markdown}")
+print(f"Extracted Text (Markdown): {result['full_text']}")
 
 # 3. Access extracted tables with high accuracy
-for i, table in enumerate(result.tables):
-    print(f"Table {i+1} headers: {table.headers}")
-    print(f"Table {i+1} row count: {len(table.rows)}")
+for i, table in enumerate(result['tables']):
+    print(f"Table {i+1} headers: {table.get('headers', [])}")
+    print(f"Table {i+1} row count: {len(table.get('rows', []))}")
 
 # 4. Extract metadata
-metadata = result.metadata
-print(f"Title: {metadata.title}")
-print(f"Page Count: {metadata.page_count}")
+metadata = result['metadata']
+print(f"Title: {metadata.get('title')}")
+print(f"Page Count: {metadata.get('page_count')}")
 ```
 
 #### Advanced Configuration
@@ -198,7 +198,7 @@ parser = DoclingParser(
 
 # Parse with specific export format
 result = parser.parse("scanned_document.pdf")
-print(f"HTML Content: {result.html}")
+print(f"HTML Content: {result['full_text']}")
 
 # Batch processing
 results = parser.parse_batch(["doc1.pdf", "doc2.docx"])
@@ -504,23 +504,22 @@ pdf_parser = PDFParser()
 pdf_data = pdf_parser.parse("document.pdf", extract_text=True, extract_tables=True)
 
 # Access pages
-for page_dict in pdf_data.get("pages", []):
-    page = PDFPage(**page_dict)
-    print(f"Page {page.page_number}: {len(page.text)} characters")
-    print(f"  Tables: {len(page.tables)}")
-    print(f"  Images: {len(page.images)}")
+for page in pdf_data.get("pages", []):
+    print(f"Page {page['page_number']}: {len(page['text'])} characters")
+    print(f"  Tables: {len(page['tables'])}")
+    print(f"  Images: {len(page['images'])}")
 
 # Access metadata
-metadata = PDFMetadata(**pdf_data.get("metadata", {}))
-print(f"Title: {metadata.title}")
-print(f"Author: {metadata.author}")
-print(f"Page Count: {metadata.page_count}")
+metadata = pdf_data.get("metadata", {})
+print(f"Title: {metadata.get('title')}")
+print(f"Author: {metadata.get('author')}")
+print(f"Page Count: {metadata.get('page_count')}")
 ```
 
 ### DOCX Parser
 
 ```python
-from semantica.parse import DOCXParser, DocxSection, DocxMetadata
+from semantica.parse import DOCXParser
 
 docx_parser = DOCXParser()
 
@@ -528,15 +527,14 @@ docx_parser = DOCXParser()
 docx_data = docx_parser.parse("document.docx", extract_tables=True)
 
 # Access sections
-for section_dict in docx_data.get("sections", []):
-    section = DocxSection(**section_dict)
-    print(f"Section: {section.heading} (Level {section.level})")
-    print(f"  Content: {section.content[:100]}...")
+for section in docx_data.get("sections", []):
+    print(f"Section: {section['heading']} (Level {section['level']})")
+    print(f"  Content: {section['content'][:100]}...")
 
 # Access metadata
-metadata = DocxMetadata(**docx_data.get("metadata", {}))
-print(f"Title: {metadata.title}")
-print(f"Author: {metadata.author}")
+metadata = docx_data.get("metadata", {})
+print(f"Title: {metadata.get('title')}")
+print(f"Author: {metadata.get('author')}")
 ```
 
 ### JSON Parser

tests/parse/test_docling_parser.py

@@ -0,0 +1,78 @@
+import unittest
+from unittest.mock import MagicMock, patch
+from pathlib import Path
+from semantica.parse.docling_parser import DoclingParser, DoclingMetadata
+
+class TestDoclingParser(unittest.TestCase):
+    def setUp(self):
+        # Patch DOCLING_AVAILABLE to True for testing logic
+        self.available_patcher = patch('semantica.parse.docling_parser.DOCLING_AVAILABLE', True)
+        self.available_patcher.start()
+        
+        # Mock the DocumentConverter
+        self.mock_converter_cls = patch('semantica.parse.docling_parser.DocumentConverter').start()
+        self.mock_converter = self.mock_converter_cls.return_value
+        
+        self.parser = DoclingParser()
+
+    def tearDown(self):
+        patch.stopall()
+
+    def test_parse_returns_dict(self):
+        # Mock the result of converter.convert
+        mock_result = MagicMock()
+        mock_result.document.export_to_markdown.return_value = "# Test Content"
+        mock_result.document.tables = []
+        mock_result.document.pages = []
+        
+        # Mock metadata
+        mock_result.input.file.name = "test.pdf"
+        mock_result.document.name = "test.pdf"
+        
+        self.mock_converter.convert.return_value = mock_result
+        
+        # Create a dummy file for Path.exists()
+        with patch.object(Path, 'exists', return_value=True):
+            result = self.parser.parse("test.pdf")
+            
+            # Verify result is a dict and has expected keys
+            self.assertIsInstance(result, dict)
+            self.assertIn("full_text", result)
+            self.assertIn("tables", result)
+            self.assertIn("metadata", result)
+            self.assertIn("total_pages", result)
+            
+            # Verify we are using dict access for tables (as per our doc fix)
+            self.assertIsInstance(result["tables"], list)
+            self.assertEqual(result["full_text"], "# Test Content")
+
+    def test_extract_text_uses_dict_access(self):
+        # Mock parse to return a dict
+        mock_parse_result = {
+            "full_text": "Extracted Text",
+            "tables": [],
+            "metadata": {},
+            "total_pages": 1
+        }
+        
+        with patch.object(DoclingParser, 'parse', return_value=mock_parse_result):
+            text = self.parser.extract_text("test.pdf")
+            self.assertEqual(text, "Extracted Text")
+
+    def test_extract_tables_uses_dict_access(self):
+        # Mock parse to return a dict
+        mock_tables = [{"headers": ["Col1"], "rows": [["Val1"]]}]
+        mock_parse_result = {
+            "full_text": "Text",
+            "tables": mock_tables,
+            "metadata": {},
+            "total_pages": 1
+        }
+        
+        with patch.object(DoclingParser, 'parse', return_value=mock_parse_result):
+            tables = self.parser.extract_tables("test.pdf")
+            self.assertEqual(tables, mock_tables)
+            self.assertEqual(tables[0]["headers"], ["Col1"])
+
+if __name__ == '__main__':
+    unittest.main()