feat: Implement core application structure, AI extraction, persistence, and Telegram bot modules with updated configuration and dependencies.

app/ingestion/audio.py

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+"""
+Handles processing of audio inputs (e.g., voice memos).
+"""
+import logging
+
+logger = logging.getLogger(__name__)
+
+def process_audio_input(audio_data: bytes) -> str:
+    """
+    Placeholder for audio input processing.
+    This will eventually involve Speech-to-Text (STT) transcription.
+
+    Args:
+        audio_data: The raw bytes of the audio file.
+
+    Returns:
+        The transcribed text, or an empty string if failed.
+    """
+    logger.info("Processing audio input (stub).")
+    # In a real implementation, you would use a library like Whisper or a cloud service.
+    # For example:
+    # try:
+    #     result = openai.Audio.transcribe("whisper-1", io.BytesIO(audio_data))
+    #     return result['text']
+    # except Exception as e:
+    #     logger.error(f"Audio transcription failed: {e}")
+    #     return ""
+
+    return "Sample transcription from voice memo."

app/ingestion/document.py

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+"""
+Handles processing of document inputs (e.g., PDFs, Word docs).
+"""
+import logging
+
+logger = logging.getLogger(__name__)
+
+def process_document_input(doc_data: bytes) -> str:
+    """
+    Placeholder for document input processing.
+    This will eventually involve text extraction from files like PDFs.
+
+    Args:
+        doc_data: The raw bytes of the document file.
+
+    Returns:
+        The extracted text, or an empty string if failed.
+    """
+    logger.info("Processing document input (stub).")
+    # In a real implementation, you would use a library like PyMuPDF for PDFs.
+    # For example:
+    # try:
+    #     import fitz  # PyMuPDF
+    #     with fitz.open(stream=doc_data, filetype="pdf") as doc:
+    #         text = "".join(page.get_text() for page in doc)
+    #     return text
+    # except Exception as e:
+    #     logger.error(f"PDF processing failed: {e}")
+    #     return ""
+
+    return "Sample text extracted from PDF document."

app/ingestion/image.py

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+"""
+Handles processing of image inputs (e.g., receipts).
+"""
+import logging
+
+logger = logging.getLogger(__name__)
+
+def process_image_input(image_data: bytes) -> str:
+    """
+    Placeholder for image input processing.
+    This will eventually involve OCR (Optical Character Recognition).
+
+    Args:
+        image_data: The raw bytes of the image file.
+
+    Returns:
+        The extracted text from the image, or an empty string if failed.
+    """
+    logger.info("Processing image input (stub).")
+    # In a real implementation, you would use a library like Tesseract or a cloud service.
+    # For example:
+    # try:
+    #     text = pytesseract.image_to_string(Image.open(io.BytesIO(image_data)))
+    #     return text
+    # except Exception as e:
+    #     logger.error(f"OCR processing failed: {e}")
+    #     return ""
+    
+    return "Sample text extracted from receipt image."

app/ingestion/text.py

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+"""
+Handles processing of raw text inputs.
+"""
+import logging
+
+logger = logging.getLogger(__name__)
+
+def process_text_input(text: str) -> str:
+    """
+    Takes raw text, normalizes it, and prepares it for AI extraction.
+
+    In the future, this could include more complex preprocessing like
+    language detection or PII removal.
+
+    Args:
+        text: The raw input text.
+
+    Returns:
+        The processed text.
+    """
+    logger.info("Processing text input.")
+    # For now, normalization is simple. It will be moved to the preprocessing module.
+    normalized_text = text.lower().strip()
+    return normalized_text