fix: remove file extension from DocumentMetadata.display_name

requirements.txt

@@ -11,8 +11,7 @@ uvicorn[standard]==0.34.0
 python-multipart==0.0.20
 
 # Document Processing - Extractors
-PyPDF2==3.0.1          # PDF extraction
-python-docx==1.1.2     # DOCX extraction
+docling                # Unified document extraction (PDF, DOCX, Excel)
 
 # Cloud Storage
 boto3==1.35.94         # AWS S3 integration

src/adapters/incoming/api_routes.py

@@ -18,7 +18,11 @@ from pathlib import Path
 from typing import Iterator, List, Optional
 
 from fastapi import APIRouter, Depends, FastAPI, File, Form, HTTPException, UploadFile, status
+from fastapi.openapi.docs import get_swagger_ui_html, get_redoc_html
 from fastapi.responses import JSONResponse
+from fastapi.security import HTTPBasicCredentials
+
+from .auth import check_docs_credentials, validate_api_key
 
 from ...core.config import get_settings
 from ...core.domain.exceptions import (
@@ -41,11 +45,6 @@ from .api_schemas import (
 
 logger = logging.getLogger(__name__)
 
-
-# =============================================================================
-# Application Setup
-# =============================================================================
-
 # Load settings
 settings = get_settings()
 
@@ -53,12 +52,19 @@ app = FastAPI(
     title="Text Processor API",
     description="Text extraction and chunking system using Hexagonal Architecture",
     version="1.0.0",
-    docs_url="/docs",
-    redoc_url="/redoc",
+    # docs_url=None,
+    # redoc_url=None,
 )
 
-router = APIRouter(prefix="/api/v1", tags=["Text Processing"])
+router = APIRouter(
+    prefix="/api/v1",
+    tags=["Text Processing"],
+    dependencies=[Depends(validate_api_key)]
+)
 
+public_router = APIRouter(
+    tags=["System"],
+)
 
 # =============================================================================
 # Global Exception Handler
@@ -101,7 +107,7 @@ def get_service() -> ITextProcessor:
 
 def get_chunking_strategy(
     strategy_name: ChunkingMethod = Form(..., description="Chunking method"),
-    chunk_size: int = Form(..., description="Target chunk size in characters", ge=1, le=10000),
+    chunk_size: int = Form(512, description="Target chunk size in characters", ge=1, le=10000),
     overlap_size: int = Form(0, description="Overlap between chunks", ge=0),
     respect_boundaries: bool = Form(True, description="Respect text boundaries"),
 ) -> ChunkingStrategy:
@@ -210,8 +216,6 @@ def to_chunk_responses(chunks: List[Chunk]) -> List[ChunkResponse]:
             document_id=str(chunk.document_id),
             content=chunk.content,
             sequence_number=chunk.sequence_number,
-            start_char=chunk.start_char,
-            end_char=chunk.end_char,
             length=chunk.get_length(),
         )
         for chunk in chunks
@@ -231,8 +235,8 @@ def to_chunk_responses(chunks: List[Chunk]) -> List[ChunkResponse]:
 )
 async def perform_chunking(
     file: Optional[UploadFile] = File(None, description="Markdown file (.md) to upload"),
-    text: Optional[str] = Form(None, description="Markdown text to process", json_schema_extra={"x-textarea": True}),
-    title: str = Form("markdown_input", description="Optional title for the document"),
+    text: Optional[str] = Form('', description="Markdown text to process"),
+    title: Optional[str] = Form('', description="Optional title for the document"),
     strategy: ChunkingStrategy = Depends(get_chunking_strategy),
     service: ITextProcessor = Depends(get_service),
 ) -> ChunkListResponse:
@@ -339,7 +343,7 @@ async def process_file(
     )
 
 
-@router.get(
+@public_router.get(
     "/health",
     response_model=HealthCheckResponse,
     status_code=status.HTTP_200_OK,
@@ -356,21 +360,29 @@ async def health_check() -> HealthCheckResponse:
     )
 
 
+# =============================================================================
+# Protected Documentation Routes
+# =============================================================================
+
+# @app.get("/docs", include_in_schema=False)
+# def api_docs(_: HTTPBasicCredentials = Depends(check_docs_credentials)):
+#     return get_swagger_ui_html(
+#         openapi_url="/openapi.json",
+#         title="Protected Text-Processor API Docs"
+#     )
+#
+#
+# @app.get("/redoc", include_in_schema=False)
+# def api_docs(_: HTTPBasicCredentials = Depends(check_docs_credentials)):
+#     return get_redoc_html(
+#         openapi_url="/openapi.json",
+#         title="Protected Text-Processor API Docs"
+#     )
+
 # =============================================================================
 # Application Setup
 # =============================================================================
 
-# Include router in app
+# Include routers in app
 app.include_router(router)
-
-
-@app.get("/")
-async def root():
-    """Root endpoint with API information."""
-    return {
-        "name": "Text Processor API",
-        "version": "1.0.0",
-        "description": "Text extraction and chunking system using Hexagonal Architecture",
-        "docs_url": "/docs",
-        "api_prefix": "/api/v1",
-    }
+app.include_router(public_router)

src/adapters/incoming/api_schemas.py

@@ -101,8 +101,6 @@ class ChunkResponse(BaseModel):
     document_id: str
     content: str
     sequence_number: int
-    start_char: int
-    end_char: int
     length: int
 
 

src/adapters/incoming/auth.py

@@ -0,0 +1,34 @@
+import secrets
+from fastapi import Depends, HTTPException, Security, status
+from fastapi.security import APIKeyHeader, HTTPBasic, HTTPBasicCredentials
+from ...core.config import get_settings
+
+settings = get_settings()
+# This allows Swagger UI to detect the "Authorize" button
+api_key_header = APIKeyHeader(name=settings.API_KEY_NAME, auto_error=False)
+http_basic = HTTPBasic()
+
+async def validate_api_key(api_key: str = Security(api_key_header)):
+    """
+    Validates the X-API-Key header.
+    Using secrets.compare_digest protects against timing attacks.
+    """
+    if not api_key or not secrets.compare_digest(api_key, settings.API_KEY):
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Could not validate credentials. Invalid or missing API Key.",
+        )
+    return api_key
+
+
+security = HTTPBasic()
+
+def check_docs_credentials(credentials: HTTPBasicCredentials = Depends(security)):
+    is_correct_user = secrets.compare_digest(credentials.username, settings.DOCS_USERNAME)
+    is_correct_password = secrets.compare_digest(credentials.password, settings.DOCS_PASSWORD)
+
+    if not (is_correct_user and is_correct_password):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            headers={"WWW-Authenticate": "Basic"},
+        )

src/adapters/outgoing/chunkers/fixed_size_chunker.py

@@ -70,8 +70,8 @@ class FixedSizeChunker(IChunker):
                 chunks = self._chunk_by_sections(document, strategy)
             else:
                 # Standard chunking: process entire raw_markdown
-                segments = self._split_into_segments(document.raw_markdown, strategy)
-                chunks = self._create_chunks(segments, document.id)
+                chunk_texts = self._split_into_segments(document.raw_markdown, strategy)
+                chunks = self._create_chunks(chunk_texts, document.id)
 
             logger.info(f"Created {len(chunks)} fixed-size chunks")
             return chunks
@@ -136,7 +136,7 @@ class FixedSizeChunker(IChunker):
         self,
         text: str,
         strategy: ChunkingStrategy,
-    ) -> List[tuple[str, int, int]]:
+    ) -> List[str]:
         """
         Split text into fixed-size segments.
 
@@ -145,7 +145,7 @@ class FixedSizeChunker(IChunker):
             strategy: Chunking strategy configuration
 
         Returns:
-            List of (chunk_text, start_position, end_position) tuples
+            List of chunk text strings
         """
         segments = []
         text_length = len(text)
@@ -155,7 +155,7 @@ class FixedSizeChunker(IChunker):
         position = 0
 
         while position < text_length:
-            segment = self._extract_segment(
+            chunk_text = self._extract_segment(
                 text=text,
                 position=position,
                 chunk_size=chunk_size,
@@ -163,10 +163,8 @@ class FixedSizeChunker(IChunker):
                 respect_boundaries=strategy.respect_boundaries,
             )
 
-            if segment:
-                chunk_text, start_pos, end_pos = segment
-                if chunk_text.strip():
-                    segments.append((chunk_text, start_pos, end_pos))
+            if chunk_text and chunk_text.strip():
+                segments.append(chunk_text)
 
             position += step_size
 
@@ -183,7 +181,7 @@ class FixedSizeChunker(IChunker):
         chunk_size: int,
         text_length: int,
         respect_boundaries: bool,
-    ) -> tuple[str, int, int] | None:
+    ) -> str:
         """
         Extract a single segment from text.
 
@@ -195,16 +193,15 @@ class FixedSizeChunker(IChunker):
             respect_boundaries: Whether to respect boundaries
 
         Returns:
-            Tuple of (chunk_text, start_pos, end_pos) or None
+            Chunk text string
         """
         end_pos = min(position + chunk_size, text_length)
         chunk_text = text[position:end_pos]
 
         if respect_boundaries and end_pos < text_length:
             chunk_text = self._adjust_to_boundary(text, position, end_pos)
-            end_pos = position + len(chunk_text)
 
-        return (chunk_text, position, end_pos)
+        return chunk_text
 
     def _adjust_to_boundary(
         self,
@@ -258,17 +255,15 @@ class FixedSizeChunker(IChunker):
         global_sequence = 0
 
         for section_index, section in enumerate(document.sections):
-            # Split this section's content into segments
-            segments = self._split_into_segments(section.content, strategy)
+            # Split this section's content into chunks
+            chunk_texts = self._split_into_segments(section.content, strategy)
 
             # Create chunks for this section
-            for text, start_char, end_char in segments:
+            for text in chunk_texts:
                 chunk = Chunk(
                     document_id=document.id,
                     content=text,
                     sequence_number=global_sequence,
-                    start_char=start_char,
-                    end_char=end_char,
                     section_title=section.title,
                     section_index=section_index,
                 )
@@ -282,16 +277,16 @@ class FixedSizeChunker(IChunker):
 
     def _create_chunks(
         self,
-        segments: List[tuple[str, int, int]],
+        chunk_texts: List[str],
         document_id,
         section_title: Optional[str] = None,
         section_index: Optional[int] = None,
     ) -> List[Chunk]:
         """
-        Create Chunk entities from text segments.
+        Create Chunk entities from text strings.
 
         Args:
-            segments: List of (text, start_pos, end_pos) tuples
+            chunk_texts: List of chunk text strings
             document_id: ID of parent document
             section_title: Optional section title
             section_index: Optional section index
@@ -301,13 +296,11 @@ class FixedSizeChunker(IChunker):
         """
         chunks = []
 
-        for sequence_number, (text, start_char, end_char) in enumerate(segments):
+        for sequence_number, text in enumerate(chunk_texts):
             chunk = Chunk(
                 document_id=document_id,
                 content=text,
                 sequence_number=sequence_number,
-                start_char=start_char,
-                end_char=end_char,
                 section_title=section_title,
                 section_index=section_index,
             )

src/adapters/outgoing/chunkers/paragraph_chunker.py

@@ -70,8 +70,8 @@ class ParagraphChunker(IChunker):
                 chunks = self._chunk_by_sections(document, strategy)
             else:
                 # Standard chunking: process entire raw_markdown
-                segments = self._split_and_group_paragraphs(document.raw_markdown, strategy)
-                chunks = self._create_chunks(segments, document.id)
+                chunk_texts = self._split_and_group_paragraphs(document.raw_markdown, strategy)
+                chunks = self._create_chunks(chunk_texts, document.id)
 
             logger.info(f"Created {len(chunks)} paragraph-based chunks")
             return chunks
@@ -136,7 +136,7 @@ class ParagraphChunker(IChunker):
         self,
         text: str,
         strategy: ChunkingStrategy,
-    ) -> List[tuple[str, int, int]]:
+    ) -> List[str]:
         """
         Split text into paragraphs and group them into chunks.
 
@@ -145,14 +145,14 @@ class ParagraphChunker(IChunker):
             strategy: Chunking strategy configuration
 
         Returns:
-            List of (chunk_text, start_position, end_position) tuples
+            List of chunk text strings
         """
         # Split into paragraphs
         paragraphs = logic_utils.split_into_paragraphs(text)
 
         if not paragraphs:
             # No paragraphs found, return whole text as single chunk
-            return [(text, 0, len(text))]
+            return [text]
 
         # Group paragraphs into chunks
         return self._group_paragraphs(paragraphs, strategy)
@@ -161,7 +161,7 @@ class ParagraphChunker(IChunker):
         self,
         paragraphs: List[str],
         strategy: ChunkingStrategy,
-    ) -> List[tuple[str, int, int]]:
+    ) -> List[str]:
         """
         Group paragraphs into chunks based on target size.
 
@@ -170,12 +170,11 @@ class ParagraphChunker(IChunker):
             strategy: Chunking strategy
 
         Returns:
-            List of (chunk_text, start_pos, end_pos) tuples
+            List of chunk text strings
         """
         segments = []
         current_paragraphs = []
         current_size = 0
-        current_start = 0
 
         for paragraph in paragraphs:
             para_size = len(paragraph)
@@ -185,13 +184,11 @@ class ParagraphChunker(IChunker):
                 current_size, para_size, strategy.chunk_size, current_paragraphs
             ):
                 # Create chunk from accumulated paragraphs
-                segment = self._create_segment(
-                    current_paragraphs, current_start
-                )
+                segment = self._create_segment(current_paragraphs)
                 segments.append(segment)
 
                 # Handle overlap
-                current_paragraphs, current_start, current_size = (
+                current_paragraphs, current_size = (
                     self._handle_overlap(
                         segment, paragraph, para_size, strategy.overlap_size
                     )
@@ -203,7 +200,7 @@ class ParagraphChunker(IChunker):
 
         # Add final chunk
         if current_paragraphs:
-            segment = self._create_segment(current_paragraphs, current_start)
+            segment = self._create_segment(current_paragraphs)
             segments.append(segment)
 
         logger.debug(
@@ -237,56 +234,49 @@ class ParagraphChunker(IChunker):
     def _create_segment(
         self,
         paragraphs: List[str],
-        start_pos: int,
-    ) -> tuple[str, int, int]:
+    ) -> str:
         """
         Create a segment from paragraphs.
 
         Args:
             paragraphs: List of paragraph strings
-            start_pos: Starting position
 
         Returns:
-            Tuple of (chunk_text, start_pos, end_pos)
+            Chunk text string
         """
-        chunk_text = "\n\n".join(paragraphs)
-        end_pos = start_pos + len(chunk_text)
-        return (chunk_text, start_pos, end_pos)
+        return "\n\n".join(paragraphs)
 
     def _handle_overlap(
         self,
-        previous_segment: tuple[str, int, int],
+        previous_segment: str,
         new_paragraph: str,
         new_para_size: int,
         overlap_size: int,
-    ) -> tuple[List[str], int, int]:
+    ) -> tuple[List[str], int]:
         """
         Handle overlap between chunks.
 
         Args:
-            previous_segment: Previous chunk segment
+            previous_segment: Previous chunk text
             new_paragraph: New paragraph to start with
             new_para_size: Size of new paragraph
             overlap_size: Desired overlap size
 
         Returns:
-            Tuple of (new_paragraphs, new_start, new_size)
+            Tuple of (new_paragraphs, new_size)
         """
         if overlap_size > 0:
-            prev_text, _, prev_end = previous_segment
             overlap_text = logic_utils.calculate_overlap_text(
-                text=prev_text,
+                text=previous_segment,
                 overlap_size=overlap_size,
                 from_start=False,
             )
             return (
                 [overlap_text, new_paragraph],
-                prev_end - len(overlap_text),
                 len(overlap_text) + new_para_size,
             )
         else:
-            _, _, prev_end = previous_segment
-            return ([new_paragraph], prev_end, new_para_size)
+            return ([new_paragraph], new_para_size)
 
     def _chunk_by_sections(
         self,
@@ -297,6 +287,7 @@ class ParagraphChunker(IChunker):
         Chunk document by processing each section independently.
 
         This prevents chunks from spanning across section boundaries.
+        Each chunk is prefixed with the document title and section title.
 
         Args:
             document: Document with sections
@@ -308,18 +299,22 @@ class ParagraphChunker(IChunker):
         all_chunks = []
         global_sequence = 0
 
-        for section_index, section in enumerate(document.sections):
-            # Split this section's content into paragraph-based segments
-            segments = self._split_and_group_paragraphs(section.content, strategy)
+        # Get document title from metadata
+        document_title = document.metadata.display_name
+
+        for section_index, section in enumerate(document.sections):
+            # Split this section's content into paragraph-based chunks
+            chunk_texts = self._split_and_group_paragraphs(section.content, strategy)
+
+            # Create chunks for this section with title prefix
+            for text in chunk_texts:
+                # Prepend document title and section title to chunk content
+                prefixed_content = f"{document_title}\n{section.title}\n{text}"
 
-            # Create chunks for this section
-            for text, start_char, end_char in segments:
                 chunk = Chunk(
                     document_id=document.id,
-                    content=text,
+                    content=prefixed_content,
                     sequence_number=global_sequence,
-                    start_char=start_char,
-                    end_char=end_char,
                     section_title=section.title,
                     section_index=section_index,
                 )
@@ -333,16 +328,16 @@ class ParagraphChunker(IChunker):
 
     def _create_chunks(
         self,
-        segments: List[tuple[str, int, int]],
+        chunk_texts: List[str],
         document_id,
         section_title: Optional[str] = None,
         section_index: Optional[int] = None,
     ) -> List[Chunk]:
         """
-        Create Chunk entities from text segments.
+        Create Chunk entities from text strings.
 
         Args:
-            segments: List of (text, start_pos, end_pos) tuples
+            chunk_texts: List of chunk text strings
             document_id: ID of parent document
             section_title: Optional section title
             section_index: Optional section index
@@ -352,13 +347,11 @@ class ParagraphChunker(IChunker):
         """
         chunks = []
 
-        for sequence_number, (text, start_char, end_char) in enumerate(segments):
+        for sequence_number, text in enumerate(chunk_texts):
             chunk = Chunk(
                 document_id=document_id,
                 content=text,
                 sequence_number=sequence_number,
-                start_char=start_char,
-                end_char=end_char,
                 section_title=section_title,
                 section_index=section_index,
             )

src/adapters/outgoing/extractors/docx_extractor.py

@@ -1,13 +1,15 @@
 """
 DOCX Extractor - Concrete implementation for Word document extraction.
 
-This adapter implements the IExtractor port using python-docx library.
-It maps python-docx exceptions to domain exceptions.
+This adapter implements the IExtractor port using Docling library.
+It maps Docling exceptions to domain exceptions.
 """
 import logging
 from pathlib import Path
 from typing import List
 
+from docling.document_converter import DocumentConverter
+
 from ....core.domain.exceptions import (
     EmptyContentError,
     ExtractionError,
@@ -21,22 +23,23 @@ logger = logging.getLogger(__name__)
 
 class DocxExtractor(IExtractor):
     """
-    Concrete DOCX extractor using python-docx.
+    Concrete DOCX extractor using Docling.
 
     This adapter:
-    1. Extracts text from DOCX files using python-docx
-    2. Handles paragraphs and tables
-    3. Maps exceptions to domain exceptions
+    1. Extracts text from DOCX files using Docling's DocumentConverter
+    2. Converts DOCX to Markdown format
+    3. Extracts metadata from document
     """
 
     def __init__(self) -> None:
-        """Initialize DOCX extractor."""
+        """Initialize DOCX extractor with Docling converter."""
         self._supported_extensions = ['docx']
-        logger.debug("DocxExtractor initialized")
+        self._converter = DocumentConverter()
+        logger.debug("DocxExtractor initialized with Docling")
 
     def extract(self, file_path: Path) -> Document:
         """
-        Extract text and metadata from DOCX file.
+        Extract text and metadata from DOCX file using Docling.
 
         Args:
             file_path: Path to the DOCX file
@@ -54,21 +57,22 @@ class DocxExtractor(IExtractor):
             # Validate file
             self._validate_file(file_path)
 
-            # Extract text
-            text = self._extract_text_from_docx(file_path)
+            # Convert DOCX to markdown using Docling
+            result = self._converter.convert(str(file_path))
+            markdown_text = result.document.export_to_markdown()
 
             # Validate content
-            if not text or not text.strip():
+            if not markdown_text or not markdown_text.strip():
                 raise EmptyContentError(file_path=str(file_path))
 
             # Create metadata
             metadata = self._create_metadata(file_path)
 
             # Build document with raw_markdown
-            document = Document(raw_markdown=text, metadata=metadata)
+            document = Document(raw_markdown=markdown_text, metadata=metadata)
 
             logger.info(
-                f"Successfully extracted {len(text)} characters from {file_path.name}"
+                f"Successfully extracted {len(markdown_text)} characters from {file_path.name}"
             )
             return document
 
@@ -130,83 +134,6 @@ class DocxExtractor(IExtractor):
         if file_path.stat().st_size == 0:
             raise EmptyContentError(file_path=str(file_path))
 
-    def _extract_text_from_docx(self, file_path: Path) -> str:
-        """
-        Extract text from DOCX using python-docx.
-
-        Args:
-            file_path: Path to DOCX file
-
-        Returns:
-            Extracted text content
-
-        Raises:
-            ExtractionError: If DOCX extraction fails
-        """
-        try:
-            import docx
-
-            logger.debug(f"Reading DOCX: {file_path}")
-            document = docx.Document(file_path)
-
-            # Extract paragraphs
-            text_parts = self._extract_paragraphs(document)
-
-            # Extract tables
-            table_text = self._extract_tables(document)
-            if table_text:
-                text_parts.extend(table_text)
-
-            return "\n".join(text_parts)
-
-        except ImportError:
-            raise ExtractionError(
-                message="python-docx library not installed",
-                details="Install with: pip install python-docx",
-                file_path=str(file_path),
-            )
-        except Exception as e:
-            raise ExtractionError(
-                message=f"DOCX extraction failed: {str(e)}",
-                file_path=str(file_path),
-            )
-
-    def _extract_paragraphs(self, document) -> List[str]:
-        """
-        Extract text from all paragraphs.
-
-        Args:
-            document: python-docx Document object
-
-        Returns:
-            List of paragraph texts
-        """
-        paragraphs = []
-        for paragraph in document.paragraphs:
-            text = paragraph.text.strip()
-            if text:
-                paragraphs.append(text)
-        return paragraphs
-
-    def _extract_tables(self, document) -> List[str]:
-        """
-        Extract text from all tables.
-
-        Args:
-            document: python-docx Document object
-
-        Returns:
-            List of table cell texts
-        """
-        table_texts = []
-        for table in document.tables:
-            for row in table.rows:
-                for cell in row.cells:
-                    text = cell.text.strip()
-                    if text:
-                        table_texts.append(text)
-        return table_texts
-
     def _create_metadata(self, file_path: Path) -> DocumentMetadata:
         """
         Create source-neutral document metadata from file.
@@ -222,6 +149,6 @@ class DocxExtractor(IExtractor):
         return DocumentMetadata(
             source_id=str(file_path.absolute()),
             source_type=SourceType.FILE,
-            display_name=file_path.name,
+            display_name=file_path.stem,
             size_bytes=stat.st_size,
         )

src/adapters/outgoing/extractors/excel_extractor.py

@@ -0,0 +1,154 @@
+"""
+Excel Extractor - Concrete implementation for Excel file extraction.
+
+This adapter implements the IExtractor port using Docling library.
+It maps Docling exceptions to domain exceptions.
+"""
+import logging
+from pathlib import Path
+from typing import List
+
+from docling.document_converter import DocumentConverter
+
+from ....core.domain.exceptions import (
+    EmptyContentError,
+    ExtractionError,
+)
+from ....core.domain.models import Document, DocumentMetadata, SourceType
+from ....core.ports.outgoing.extractor import IExtractor
+
+
+logger = logging.getLogger(__name__)
+
+
+class ExcelExtractor(IExtractor):
+    """
+    Concrete Excel extractor using Docling.
+
+    This adapter:
+    1. Extracts text from Excel files (.xlsx, .xls) using Docling's DocumentConverter
+    2. Converts Excel to Markdown format
+    3. Extracts metadata from spreadsheet
+    """
+
+    def __init__(self) -> None:
+        """Initialize Excel extractor with Docling converter."""
+        self._supported_extensions = ['xlsx', 'xls']
+        self._converter = DocumentConverter()
+        logger.debug("ExcelExtractor initialized with Docling")
+
+    def extract(self, file_path: Path) -> Document:
+        """
+        Extract text and metadata from Excel file using Docling.
+
+        Args:
+            file_path: Path to the Excel file
+
+        Returns:
+            Document entity with extracted content and metadata
+
+        Raises:
+            ExtractionError: If extraction fails
+            EmptyContentError: If no text could be extracted
+        """
+        try:
+            logger.info(f"Extracting text from Excel: {file_path}")
+
+            # Validate file
+            self._validate_file(file_path)
+
+            # Convert Excel to markdown using Docling
+            result = self._converter.convert(str(file_path))
+            markdown_text = result.document.export_to_markdown()
+
+            # Validate content
+            if not markdown_text or not markdown_text.strip():
+                raise EmptyContentError(file_path=str(file_path))
+
+            # Create metadata
+            metadata = self._create_metadata(file_path)
+
+            # Build document with raw_markdown
+            document = Document(raw_markdown=markdown_text, metadata=metadata)
+
+            logger.info(
+                f"Successfully extracted {len(markdown_text)} characters from {file_path.name}"
+            )
+            return document
+
+        except EmptyContentError:
+            raise
+        except ExtractionError:
+            raise
+        except Exception as e:
+            logger.error(f"Excel extraction failed for {file_path}: {str(e)}")
+            raise ExtractionError(
+                message=f"Failed to extract text from {file_path.name}",
+                details=str(e),
+                file_path=str(file_path),
+            )
+
+    def supports_file_type(self, file_extension: str) -> bool:
+        """
+        Check if this extractor supports Excel files.
+
+        Args:
+            file_extension: File extension (e.g., 'xlsx', 'xls')
+
+        Returns:
+            True if Excel files are supported
+        """
+        return file_extension.lower() in self._supported_extensions
+
+    def get_supported_types(self) -> List[str]:
+        """
+        Get list of supported file extensions.
+
+        Returns:
+            List containing 'xlsx' and 'xls'
+        """
+        return self._supported_extensions.copy()
+
+    def _validate_file(self, file_path: Path) -> None:
+        """
+        Validate file exists and is readable.
+
+        Args:
+            file_path: Path to validate
+
+        Raises:
+            ExtractionError: If file is invalid
+        """
+        if not file_path.exists():
+            raise ExtractionError(
+                message=f"File not found: {file_path}",
+                file_path=str(file_path),
+            )
+
+        if not file_path.is_file():
+            raise ExtractionError(
+                message=f"Path is not a file: {file_path}",
+                file_path=str(file_path),
+            )
+
+        if file_path.stat().st_size == 0:
+            raise EmptyContentError(file_path=str(file_path))
+
+    def _create_metadata(self, file_path: Path) -> DocumentMetadata:
+        """
+        Create document metadata from Excel file.
+
+        Args:
+            file_path: Path to the Excel file
+
+        Returns:
+            DocumentMetadata entity
+        """
+        stat = file_path.stat()
+
+        return DocumentMetadata(
+            source_id=str(file_path.absolute()),
+            source_type=SourceType.FILE,
+            display_name=file_path.stem,
+            size_bytes=stat.st_size,
+        )

src/adapters/outgoing/extractors/markdown_extractor.py

@@ -181,6 +181,6 @@ class MarkdownExtractor(IExtractor):
         return DocumentMetadata(
             source_id=str(file_path.absolute()),
             source_type=SourceType.FILE,
-            display_name=file_path.name,
+            display_name=file_path.stem,
             size_bytes=stat.st_size,
         )

src/adapters/outgoing/extractors/pdf_extractor.py

@@ -1,13 +1,15 @@
 """
 PDF Extractor - Concrete implementation for PDF text extraction.
 
-This adapter implements the IExtractor port using PyPDF2 library.
-It maps PyPDF2 exceptions to domain exceptions.
+This adapter implements the IExtractor port using Docling library.
+It maps Docling exceptions to domain exceptions.
 """
 import logging
 from pathlib import Path
 from typing import List
 
+from docling.document_converter import DocumentConverter
+
 from ....core.domain.exceptions import (
     EmptyContentError,
     ExtractionError,
@@ -21,22 +23,23 @@ logger = logging.getLogger(__name__)
 
 class PDFExtractor(IExtractor):
     """
-    Concrete PDF extractor using PyPDF2.
+    Concrete PDF extractor using Docling.
 
     This adapter:
-    1. Extracts text from PDF files using PyPDF2
-    2. Maps PyPDF2 exceptions to domain exceptions
-    3. Creates Document entities with metadata
+    1. Extracts text from PDF files using Docling's DocumentConverter
+    2. Converts PDF to Markdown format
+    3. Extracts metadata including page count
     """
 
     def __init__(self) -> None:
-        """Initialize PDF extractor."""
+        """Initialize PDF extractor with Docling converter."""
         self._supported_extensions = ['pdf']
-        logger.debug("PDFExtractor initialized")
+        self._converter = DocumentConverter()
+        logger.debug("PDFExtractor initialized with Docling")
 
     def extract(self, file_path: Path) -> Document:
         """
-        Extract text and metadata from PDF file.
+        Extract text and metadata from PDF file using Docling.
 
         Args:
             file_path: Path to the PDF file
@@ -54,21 +57,22 @@ class PDFExtractor(IExtractor):
             # Validate file
             self._validate_file(file_path)
 
-            # Extract text
-            text = self._extract_text_from_pdf(file_path)
+            # Convert PDF to markdown using Docling
+            result = self._converter.convert(str(file_path))
+            markdown_text = result.document.export_to_markdown()
 
             # Validate content
-            if not text or not text.strip():
+            if not markdown_text or not markdown_text.strip():
                 raise EmptyContentError(file_path=str(file_path))
 
-            # Create metadata
-            metadata = self._create_metadata(file_path)
+            # Create metadata with page count from Docling result
+            metadata = self._create_metadata(file_path, result)
 
             # Build document with raw_markdown
-            document = Document(raw_markdown=text, metadata=metadata)
+            document = Document(raw_markdown=markdown_text, metadata=metadata)
 
             logger.info(
-                f"Successfully extracted {len(text)} characters from {file_path.name}"
+                f"Successfully extracted {len(markdown_text)} characters from {file_path.name}"
             )
             return document
 
@@ -130,89 +134,35 @@ class PDFExtractor(IExtractor):
         if file_path.stat().st_size == 0:
             raise EmptyContentError(file_path=str(file_path))
 
-    def _extract_text_from_pdf(self, file_path: Path) -> str:
+    def _create_metadata(self, file_path: Path, result) -> DocumentMetadata:
         """
-        Extract text from PDF using PyPDF2.
+        Create document metadata from PDF file and Docling result.
 
         Args:
-            file_path: Path to PDF file
-
-        Returns:
-            Extracted text content
-
-        Raises:
-            ExtractionError: If PDF extraction fails
-        """
-        try:
-            import PyPDF2
-
-            logger.debug(f"Reading PDF: {file_path}")
-            text_parts = []
-
-            with open(file_path, 'rb') as pdf_file:
-                pdf_reader = PyPDF2.PdfReader(pdf_file)
-                num_pages = len(pdf_reader.pages)
-                logger.debug(f"PDF has {num_pages} pages")
-
-                for page_num, page in enumerate(pdf_reader.pages, start=1):
-                    page_text = self._extract_page_text(page, page_num)
-                    if page_text:
-                        text_parts.append(page_text)
-
-            return "\n\n".join(text_parts)
-
-        except ImportError:
-            raise ExtractionError(
-                message="PyPDF2 library not installed",
-                details="Install with: pip install PyPDF2",
-                file_path=str(file_path),
-            )
-        except Exception as e:
-            raise ExtractionError(
-                message=f"PDF extraction failed: {str(e)}",
-                file_path=str(file_path),
-            )
-
-    def _extract_page_text(self, page, page_num: int) -> str:
-        """
-        Extract text from a single page.
-
-        Args:
-            page: PyPDF2 page object
-            page_num: Page number for logging
-
-        Returns:
-            Extracted page text
-        """
-        try:
-            import PyPDF2
-
-            text = page.extract_text()
-            logger.debug(f"Extracted page {page_num}")
-            return text
-
-        except PyPDF2.errors.PdfReadError as e:
-            logger.warning(f"Failed to extract page {page_num}: {str(e)}")
-            return ""
-        except Exception as e:
-            logger.warning(f"Error on page {page_num}: {str(e)}")
-            return ""
-
-    def _create_metadata(self, file_path: Path) -> DocumentMetadata:
-        """
-        Create source-neutral document metadata from file.
-
-        Args:
-            file_path: Path to the file
+            file_path: Path to the PDF file
+            result: Docling conversion result
 
         Returns:
             DocumentMetadata entity
         """
         stat = file_path.stat()
 
+        # Extract page count from Docling result
+        page_count = None
+        try:
+            if hasattr(result.document, 'pages'):
+                page_count = len(result.document.pages)
+        except Exception as e:
+            logger.warning(f"Could not extract page count: {str(e)}")
+
+        extra_metadata = {}
+        if page_count is not None:
+            extra_metadata['page_count'] = str(page_count)
+
         return DocumentMetadata(
             source_id=str(file_path.absolute()),
             source_type=SourceType.FILE,
-            display_name=file_path.name,
+            display_name=file_path.stem,
             size_bytes=stat.st_size,
+            extra_metadata=extra_metadata,
         )

src/adapters/outgoing/extractors/txt_extractor.py

@@ -200,6 +200,6 @@ class TxtExtractor(IExtractor):
         return DocumentMetadata(
             source_id=str(file_path.absolute()),
             source_type=SourceType.FILE,
-            display_name=file_path.name,
+            display_name=file_path.stem,
             size_bytes=stat.st_size,
         )

src/adapters/outgoing/extractors/zip_extractor.py

@@ -227,7 +227,7 @@ class ZipExtractor(IExtractor):
                 continue
 
             # Skip files with 'nohf' in their name
-            if 'nohf' in filename.lower():
+            if 'nohf' not in filename.lower():
                 logger.debug(f"Skipping 'nohf' file: {filename}")
                 continue
 
@@ -312,6 +312,6 @@ class ZipExtractor(IExtractor):
         return DocumentMetadata(
             source_id=str(file_path.absolute()),
             source_type=SourceType.FILE,
-            display_name=file_path.name,
+            display_name=file_path.stem,
             size_bytes=stat.st_size,
         )

src/bootstrap.py

@@ -15,6 +15,7 @@ from .adapters.outgoing.chunkers.context import ChunkingContext
 from .adapters.outgoing.chunkers.fixed_size_chunker import FixedSizeChunker
 from .adapters.outgoing.chunkers.paragraph_chunker import ParagraphChunker
 from .adapters.outgoing.extractors.docx_extractor import DocxExtractor
+from .adapters.outgoing.extractors.excel_extractor import ExcelExtractor
 from .adapters.outgoing.extractors.factory import ExtractorFactory
 from .adapters.outgoing.extractors.markdown_extractor import MarkdownExtractor
 from .adapters.outgoing.extractors.pdf_extractor import PDFExtractor
@@ -118,6 +119,7 @@ class ApplicationContainer:
         # Register all extractors
         factory.register_extractor(PDFExtractor())
         factory.register_extractor(DocxExtractor())
+        factory.register_extractor(ExcelExtractor())
         factory.register_extractor(TxtExtractor())
         factory.register_extractor(MarkdownExtractor())
         factory.register_extractor(ZipExtractor())

src/core/config.py

@@ -14,6 +14,13 @@ class Settings(BaseSettings):
     S3_ENDPOINT_URL: Optional[str] = "https://cdn.d.aiengines.ir"
     S3_PRESIGNED_URL_EXPIRATION: int = 3600
     S3_UPLOAD_PATH_PREFIX: str = "extractions"
+
+    API_KEY: str = "some-secret-api-key"
+    API_KEY_NAME: str = "API-Key"
+
+    DOCS_USERNAME: str = "admin"
+    DOCS_PASSWORD: str = "admin"
+
     LOG_LEVEL: str = "INFO"
 
     model_config = SettingsConfigDict(

src/core/domain/models.py

@@ -126,7 +126,7 @@ class DocumentSection(BaseModel):
         level: Header level (1-6 for h1-h6, 0 for Introduction)
         content: Section content with preserved Markdown formatting
     """
-    title: str = Field(..., min_length=1, description="Section title")
+    title: Optional[str] = Field(None, min_length=1, description="Section title")
     level: int = Field(..., ge=0, le=6, description="Header level (0=intro)")
     content: str = Field(..., description="Section content with formatting")
 
@@ -138,7 +138,9 @@ class DocumentSection(BaseModel):
     @classmethod
     def normalize_title(cls, value: str) -> str:
         """Normalize title by stripping whitespace."""
-        return value.strip()
+        if value:
+            return value.strip()
+        return value
 
     def is_introduction(self) -> bool:
         """Check if this is the introduction section."""
@@ -358,8 +360,6 @@ class Chunk(BaseModel):
         document_id: ID of the parent document
         content: Text content of the chunk
         sequence_number: Order of this chunk in the document
-        start_char: Starting character position in original document
-        end_char: Ending character position in original document
         section_title: Title of the section this chunk belongs to
         section_index: Index of the section in document.sections
         metadata: Optional metadata specific to this chunk
@@ -368,8 +368,6 @@ class Chunk(BaseModel):
     document_id: UUID = Field(..., description="Parent document ID")
     content: str = Field(..., min_length=1, description="Chunk text content")
     sequence_number: int = Field(..., ge=0, description="Chunk order in document")
-    start_char: int = Field(..., ge=0, description="Start position in document")
-    end_char: int = Field(..., gt=0, description="End position in document")
     section_title: Optional[str] = Field(None, description="Section title")
     section_index: Optional[int] = Field(None, ge=0, description="Section index")
     metadata: Dict[str, str] = Field(default_factory=dict)
@@ -378,27 +376,6 @@ class Chunk(BaseModel):
         "frozen": True,  # Chunks are immutable
     }
 
-    @model_validator(mode='after')
-    def validate_position_consistency(self) -> 'Chunk':
-        """Ensure end position is after start position."""
-        if self.end_char <= self.start_char:
-            raise ValueError(
-                f"end_char ({self.end_char}) must be greater than "
-                f"start_char ({self.start_char})"
-            )
-
-        # Validate content length matches position range
-        content_length = len(self.content)
-        position_range = self.end_char - self.start_char
-
-        if abs(content_length - position_range) > 10:  # Allow small variance
-            raise ValueError(
-                f"Content length ({content_length}) doesn't match "
-                f"position range ({position_range})"
-            )
-
-        return self
-
     def get_length(self) -> int:
         """Get the length of the chunk content."""
         return len(self.content)

src/core/domain/parsers.py

@@ -50,40 +50,25 @@ def parse_markdown(text: str) -> List[DocumentSection]:
     sections: List[DocumentSection] = []
     current_heading: str | None = None
     current_level: int = 0
-    current_content_parts: List[str] = []
-
-    def finalize_section() -> None:
-        """Helper to finalize and append the current section."""
-        if current_heading is not None or current_content_parts:
-            content = "".join(current_content_parts).strip()
-            if content:  # Only add sections with actual content
-                title = current_heading if current_heading else "Introduction"
-                sections.append(
-                    DocumentSection(
-                        title=title,
-                        level=current_level,
-                        content=content,
-                    )
-                )
 
     # Walk through all children of the document
     for child in doc.children:
         if isinstance(child, Heading):
-            # Finalize previous section before starting new one
-            finalize_section()
-
-            # Start new section
+            # Update current heading context
             current_heading = _extract_heading_text(child)
             current_level = child.level
-            current_content_parts = []
         else:
             # Render content back to markdown format instead of HTML
             rendered = md_renderer.render(child).strip()
             if rendered:
-                current_content_parts.append(rendered + "\n\n")
-
-    # Finalize the last section
-    finalize_section()
+                # Create a separate section for each paragraph/block
+                sections.append(
+                    DocumentSection(
+                        title=current_heading,
+                        level=current_level,
+                        content=rendered,
+                    )
+                )
 
     return sections