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

app/ai/extractor.py

@@ -0,0 +1,60 @@
+"""
+AI-powered data extraction from raw text.
+"""
+import openai
+import json
+import logging
+from typing import Dict, Any
+
+from app.config import config
+from app.ai.prompts import EXTRACTOR_PROMPT
+from app.schema.base import ExtractedExpense
+
+# Configure the OpenAI client
+openai.api_key = config.OPENAI_API_KEY
+
+logger = logging.getLogger(__name__)
+
+def extract_expense_data(text: str) -> ExtractedExpense:
+    """
+    Uses an AI model to extract structured expense data from a raw text string.
+
+    Args:
+        text: The raw text from user input, OCR, or transcription.
+
+    Returns:
+        An ExtractedExpense object with the data found by the AI.
+    """
+    logger.info(f"Starting AI extraction for text: '{text[:100]}...'")
+    
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",  # Or another suitable model
+            messages=[
+                {"role": "system", "content": EXTRACTOR_PROMPT},
+                {"role": "user", "content": text}
+            ],
+            temperature=0.0,
+            response_format={"type": "json_object"}
+        )
+
+        # The response from OpenAI should be a JSON string in the message content
+        json_response = response.choices[0].message['content']
+        extracted_data = json.loads(json_response)
+        
+        logger.info(f"AI extraction successful. Raw JSON: {extracted_data}")
+
+        # Add the original text to the model for audit purposes
+        extracted_data['raw_text'] = text
+
+        return ExtractedExpense(**extracted_data)
+
+    except json.JSONDecodeError as e:
+        logger.error(f"Failed to decode JSON from AI response: {e}")
+        # Return a model with only the raw text for manual review
+        return ExtractedExpense(raw_text=text)
+    except Exception as e:
+        logger.error(f"An unexpected error occurred during AI extraction: {e}")
+        # Return a model with only the raw text
+        return ExtractedExpense(raw_text=text)
+