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ca2bd00
1
Parent(s):
60572ef
Bug fix
Browse files- app.py +150 -208
- src/core/recommender.py +138 -78
- src/database/mongodb.py +14 -9
app.py
CHANGED
@@ -1,174 +1,57 @@
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# import gradio as gr
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# from src.main import app as fastapi_app
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# from src.core.recommender import recommender
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# import logging
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# from src.database.mongodb import mongodb
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# logger = logging.getLogger(__name__)
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# # Configure basic logging if not already set up elsewhere for Gradio context
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# logging.basicConfig(level=logging.INFO)
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-
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# # Manually initialize and attach recommender if not already present
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# if not hasattr(fastapi_app.state, "recommender"):
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# recommender.load_components()
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# fastapi_app.state.recommender = recommender
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# def get_recommendations_gradio(query: str, k: int = 5):
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# try:
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# response = fastapi_app.state.recommender.get_recommendations(query, k)
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# return response
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# except Exception as e:
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# return {"error": str(e)}
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-
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# def get_recommendations_by_id_gradio(msid: str, k: int = 5):
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# try:
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# # Corrected method name here
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# response = fastapi_app.state.recommender.get_recommendations_by_id(msid, k)
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# return response
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# except Exception as e:
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# return {"error": str(e)}
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# def get_recommendations_user_feedback_gradio(user_id: str, msid: str, clicked_msid: str, k: int = 5):
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# """
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# Handles user feedback via Gradio:
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# 1. (Optionally) Computes recommendations based on the feedback (mimicking FastAPI endpoint action).
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# 2. Saves the feedback to MongoDB.
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# 3. Returns a success message.
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# """
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# try:
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# # Step 1: (Optional) Compute recommendations based on feedback, similar to FastAPI endpoint.
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# # The result of this call is not the final output for this Gradio UI, per the request.
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# # This ensures the Gradio function performs similar actions to the API endpoint.
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# _ = fastapi_app.state.recommender.get_recommendations_user_feedback(user_id, msid, clicked_msid, k)
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# logger.info(f"Gradio: (Computed recommendations for user '{user_id}' based on click, not shown in this UI)")
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# # Step 2: Save feedback to MongoDB
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# actual_clicked_msids = [s.strip() for s in clicked_msid.split(',') if s.strip()]
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# if not actual_clicked_msids:
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# logger.warning(f"Gradio: Invalid clicked_msid: {clicked_msid} for user {user_id}")
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# return {"error": "clicked_msid parameter is invalid or does not contain valid MSIDs."}
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# logger.info(
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# f"Gradio: Saving feedback for user '{user_id}', context msid: '{msid}', clicked msids: {actual_clicked_msids}"
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# )
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# feedback_collection_name = "user_feedback_tracking"
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# # Assuming mongodb.db is the PyMongo Database object, consistent with recommender.py
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# feedback_collection = mongodb.db[feedback_collection_name]
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# user_doc = feedback_collection.find_one({"user_id": user_id})
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# if user_doc:
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# # This update logic mirrors the one in routes.py
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# feedback_collection.update_one(
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# {"user_id": user_id},
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# {"$addToSet": {"Articles": {"msid": msid, "Read": {"$each": actual_clicked_msids}}}}
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# )
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# else:
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# feedback_collection.insert_one({
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# "user_id": user_id,
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# "Articles": [{"msid": msid, "Read": actual_clicked_msids}]
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# })
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# logger.info(f"Gradio: Successfully saved feedback for user {user_id}")
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# return {"message": "Response saved successfully"}
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# except Exception as e:
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# logger.error(f"Gradio: Error in get_recommendations_user_feedback_gradio for user {user_id}: {e}", exc_info=True)
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# return {"error": f"An error occurred: {str(e)}"}
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# def get_recommendations_summary_gradio(msid: str, k: int = 5, summary: bool = True, smart_tip: bool = True):
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# try:
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# response = fastapi_app.state.recommender.get_recommendations_summary(msid, k, summary, smart_tip)
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# return response
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# except Exception as e:
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# return {"error": str(e)}
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# iface1 = gr.Interface(
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# fn=get_recommendations_gradio,
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# inputs=[
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# gr.Textbox(label="Query", placeholder="Enter your search query..."),
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# gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Number of recommendations")
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# ],
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# outputs=gr.JSON(),
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# title="Recommendation System",
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# description="Enter a query to get personalized recommendations."
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# )
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# iface2 = gr.Interface(
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# fn=get_recommendations_by_id_gradio,
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# inputs=[
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# gr.Textbox(label="MSID", placeholder="Enter the MSID..."),
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# gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Number of recommendations")
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# ],
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# outputs=gr.JSON(),
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# title="Recommendations by MSID",
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# description="Enter an MSID to get recommendations based on it."
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# )
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# iface3 = gr.Interface(
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# fn=get_recommendations_user_feedback_gradio,
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# inputs=[
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# gr.Textbox(label="User ID", placeholder="Enter your user ID..."),
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# gr.Textbox(label="MSID", placeholder="Enter the MSID..."),
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# gr.Textbox(label="Clicked MSID", placeholder="Enter the clicked MSID..."),
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# gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Number of recommendations")
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# ],
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# outputs=gr.JSON(),
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# title="User Feedback Recommendations",
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# description="Enter your user ID, MSID, and clicked MSID to get recommendations based on user feedback."
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# )
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# iface4 = gr.Interface(
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# fn=get_recommendations_summary_gradio,
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# inputs=[
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# gr.Textbox(label="MSID", placeholder="Enter the MSID..."),
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# gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Number of recommendations"),
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# gr.Checkbox(label="Summary", value=True),
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# gr.Checkbox(label="Smart Tip", value=True)
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# ],
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# outputs=gr.JSON(),
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# title="Recommendations Summary",
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# description="Enter an MSID to get a summary of recommendations."
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# )
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# demo = gr.TabbedInterface([iface1, iface2, iface3, iface4], ["Query Recommendations", "MSID Recommendations", "User Feedback Recommendations", "Recommendations Summary"])
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# if __name__ == "__main__":
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# demo.launch()
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# app = fastapi_app
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import logging
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import uvicorn
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from fastapi import HTTPException
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from pydantic import BaseModel, Field
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from typing import List
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from src.main import app as fastapi_app # Existing FastAPI app instance
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from src.core.recommender import recommender
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from src.database.mongodb import mongodb
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logger = logging.getLogger(__name__)
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# Configure basic logging if not already set up elsewhere
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if not logger.hasHandlers():
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logging.basicConfig(level=logging.INFO)
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#
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# Pydantic models for request/response bodies
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class FeedbackPayload(BaseModel):
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message: str
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# API Endpoints
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@
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async def get_recommendations_api(query: str, k: int = 5):
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"""
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Get recommendations based on a textual query.
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"""
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try:
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if not hasattr(
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logger.error("Recommender is not available.")
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raise HTTPException(status_code=503, detail="Recommender service not available")
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response =
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return response
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except Exception as e:
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logger.error(f"API Error in get_recommendations_api for query '{query}': {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
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@
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async def get_recommendations_by_id_api(msid: str, k: int = 5):
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"""
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Get recommendations based on a given MSID.
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raise HTTPException(status_code=400, detail="k must be an integer between 1 and 10")
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# Check if recommender service is available
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if not hasattr(
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logger.error("Recommender is not available.")
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raise HTTPException(status_code=503, detail="Recommender service not available")
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# Get recommendations with error handling
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try:
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response =
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if not response:
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raise HTTPException(status_code=404, detail=f"No recommendations found for MSID: {msid}")
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return response
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logger.error(f"Unexpected error in get_recommendations_by_id_api for msid '{msid}': {e}", exc_info=True)
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raise HTTPException(status_code=500, detail="An unexpected error occurred")
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@
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async def submit_user_feedback_api(payload: FeedbackPayload):
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"""
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Submit user feedback (e.g., clicked articles) and save it.
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though the primary response here is the status of feedback submission.
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"""
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try:
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if not hasattr(
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logger.error("Recommender is not available.")
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raise HTTPException(status_code=503, detail="Recommender service not available")
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# (Optional) Compute recommendations based on feedback, similar to Gradio function.
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# The result of this call is not the primary output of this API endpoint.
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if not actual_clicked_msids:
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logger.warning(f"API: Invalid clicked_msid: '{payload.clicked_msid}' for user '{payload.user_id}'")
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raise HTTPException(status_code=400, detail="clicked_msid parameter is invalid or does not contain valid MSIDs.")
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if mongodb.db is None:
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logger.error("MongoDB database connection is not available.")
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raise HTTPException(status_code=503, detail="Database service not available")
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feedback_collection = mongodb.db[feedback_collection_name]
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user_doc = feedback_collection.find_one({"user_id": payload.user_id})
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{"user_id": payload.user_id},
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{"$addToSet": {"Articles": {"msid": payload.msid, "Read": actual_clicked_msids}}}
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)
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except HTTPException:
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raise # Re-raise HTTPException directly
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@@ -294,18 +240,18 @@ async def submit_user_feedback_api(payload: FeedbackPayload):
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logger.error(f"API Error in submit_user_feedback_api for user '{payload.user_id}': {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
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@
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async def get_recommendations_summary_api(msid: str, k: int = 5, summary: bool = True, smart_tip: bool = True):
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"""
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Get recommendations with optional summary and smart tip for a given MSID.
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"""
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try:
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if not hasattr(
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logger.error("Recommender is not available.")
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raise HTTPException(status_code=503, detail="Recommender service not available")
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try:
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response =
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except RuntimeError as e:
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# Catch the meta tensor error and return a fallback
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if "meta tensor" in str(e):
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logger.error(f"API Error in get_recommendations_summary_api for msid '{msid}': {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
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# This makes fastapi_app (imported from src.main and extended here) available as 'app'
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# for ASGI servers like Uvicorn.
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app = fastapi_app
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if __name__ == "__main__":
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uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
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import logging
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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from typing import List, Optional
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from datetime import datetime
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from src.core.recommender import recommender
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from src.database.mongodb import mongodb
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from src.config.settings import API_TITLE, API_DESCRIPTION, API_VERSION
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logger = logging.getLogger(__name__)
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# Configure basic logging if not already set up elsewhere
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if not logger.hasHandlers():
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logging.basicConfig(level=logging.INFO)
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# Create a new FastAPI app instance instead of importing from src.main
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app = FastAPI(
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title=API_TITLE,
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description=API_DESCRIPTION,
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version=API_VERSION
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
|
32 |
+
)
|
33 |
+
|
34 |
+
# Initialize recommender on startup
|
35 |
+
@app.on_event("startup")
|
36 |
+
async def startup_event():
|
37 |
+
"""Initialize recommender system on startup."""
|
38 |
+
try:
|
39 |
+
logger.info("Initializing recommender system...")
|
40 |
+
recommender.load_components()
|
41 |
+
app.state.recommender = recommender
|
42 |
+
logger.info("Recommender system initialized successfully")
|
43 |
+
except Exception as e:
|
44 |
+
logger.error(f"Failed to initialize recommender system: {e}", exc_info=True)
|
45 |
+
# Don't raise here to allow the app to start even if recommender fails
|
46 |
+
|
47 |
+
@app.on_event("shutdown")
|
48 |
+
async def shutdown_event():
|
49 |
+
"""Cleanup on shutdown."""
|
50 |
+
try:
|
51 |
+
mongodb.close()
|
52 |
+
logger.info("MongoDB connection closed")
|
53 |
+
except Exception as e:
|
54 |
+
logger.error(f"Error during shutdown: {e}", exc_info=True)
|
55 |
|
56 |
# Pydantic models for request/response bodies
|
57 |
class FeedbackPayload(BaseModel):
|
|
|
64 |
message: str
|
65 |
|
66 |
# API Endpoints
|
67 |
+
@app.get("/health")
|
68 |
+
async def health_check():
|
69 |
+
"""
|
70 |
+
Health check endpoint to diagnose system status.
|
71 |
+
"""
|
72 |
+
health_status = {
|
73 |
+
"status": "healthy",
|
74 |
+
"timestamp": datetime.now().isoformat(),
|
75 |
+
"components": {}
|
76 |
+
}
|
77 |
+
|
78 |
+
# Check recommender system
|
79 |
+
if hasattr(app.state, "recommender") and app.state.recommender is not None:
|
80 |
+
health_status["components"]["recommender"] = {
|
81 |
+
"status": "available",
|
82 |
+
"models_loaded": {
|
83 |
+
"embed_model": app.state.recommender.embed_model is not None,
|
84 |
+
"reranker": app.state.recommender.reranker is not None,
|
85 |
+
"generator": app.state.recommender.generator is not None
|
86 |
+
},
|
87 |
+
"data_available": app.state.recommender.df is not None and not app.state.recommender.df.empty,
|
88 |
+
"faiss_index_available": app.state.recommender.index is not None,
|
89 |
+
"faiss_vectors": app.state.recommender.index.ntotal if app.state.recommender.index else 0
|
90 |
+
}
|
91 |
+
else:
|
92 |
+
health_status["components"]["recommender"] = {"status": "not_available"}
|
93 |
+
health_status["status"] = "degraded"
|
94 |
+
|
95 |
+
# Check MongoDB connection
|
96 |
+
try:
|
97 |
+
if mongodb.db is not None:
|
98 |
+
# Try a simple operation to test connection
|
99 |
+
mongodb.db.command("ping")
|
100 |
+
health_status["components"]["mongodb"] = {"status": "connected"}
|
101 |
+
else:
|
102 |
+
health_status["components"]["mongodb"] = {"status": "not_connected"}
|
103 |
+
health_status["status"] = "degraded"
|
104 |
+
except Exception as e:
|
105 |
+
health_status["components"]["mongodb"] = {
|
106 |
+
"status": "error",
|
107 |
+
"error": str(e)
|
108 |
+
}
|
109 |
+
health_status["status"] = "degraded"
|
110 |
+
|
111 |
+
return health_status
|
112 |
+
|
113 |
+
@app.get("/recommendations/")
|
114 |
async def get_recommendations_api(query: str, k: int = 5):
|
115 |
"""
|
116 |
Get recommendations based on a textual query.
|
117 |
"""
|
118 |
try:
|
119 |
+
if not hasattr(app.state, "recommender") or app.state.recommender is None:
|
120 |
logger.error("Recommender is not available.")
|
121 |
raise HTTPException(status_code=503, detail="Recommender service not available")
|
122 |
|
123 |
+
response = app.state.recommender.get_recommendations(query, k)
|
124 |
return response
|
125 |
except Exception as e:
|
126 |
logger.error(f"API Error in get_recommendations_api for query '{query}': {e}", exc_info=True)
|
127 |
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
|
128 |
|
129 |
+
@app.get("/recommendations/msid/")
|
130 |
async def get_recommendations_by_id_api(msid: str, k: int = 5):
|
131 |
"""
|
132 |
Get recommendations based on a given MSID.
|
|
|
140 |
raise HTTPException(status_code=400, detail="k must be an integer between 1 and 10")
|
141 |
|
142 |
# Check if recommender service is available
|
143 |
+
if not hasattr(app.state, "recommender") or app.state.recommender is None:
|
144 |
logger.error("Recommender is not available.")
|
145 |
raise HTTPException(status_code=503, detail="Recommender service not available")
|
146 |
|
147 |
+
# Check if recommender has the necessary data
|
148 |
+
if app.state.recommender.df is None or app.state.recommender.df.empty:
|
149 |
+
logger.error("Recommender data not available (MongoDB connection issue).")
|
150 |
+
raise HTTPException(
|
151 |
+
status_code=503,
|
152 |
+
detail="Recommender data not available. The service is currently unable to access the required data."
|
153 |
+
)
|
154 |
+
|
155 |
# Get recommendations with error handling
|
156 |
try:
|
157 |
+
response = app.state.recommender.get_recommendations_by_id(msid, k)
|
158 |
if not response:
|
159 |
raise HTTPException(status_code=404, detail=f"No recommendations found for MSID: {msid}")
|
160 |
return response
|
|
|
173 |
logger.error(f"Unexpected error in get_recommendations_by_id_api for msid '{msid}': {e}", exc_info=True)
|
174 |
raise HTTPException(status_code=500, detail="An unexpected error occurred")
|
175 |
|
176 |
+
@app.post("/recommendations/feedback/user/", response_model=FeedbackResponse)
|
177 |
async def submit_user_feedback_api(payload: FeedbackPayload):
|
178 |
"""
|
179 |
Submit user feedback (e.g., clicked articles) and save it.
|
|
|
181 |
though the primary response here is the status of feedback submission.
|
182 |
"""
|
183 |
try:
|
184 |
+
if not hasattr(app.state, "recommender") or app.state.recommender is None:
|
185 |
logger.error("Recommender is not available.")
|
186 |
raise HTTPException(status_code=503, detail="Recommender service not available")
|
187 |
|
188 |
# (Optional) Compute recommendations based on feedback, similar to Gradio function.
|
189 |
# The result of this call is not the primary output of this API endpoint.
|
190 |
+
try:
|
191 |
+
_ = app.state.recommender.get_recommendations_user_feedback(
|
192 |
+
payload.user_id, payload.msid, payload.clicked_msid, payload.k
|
193 |
+
)
|
194 |
+
logger.info(f"API: (Computed recommendations for user '{payload.user_id}' based on click, not part of this response)")
|
195 |
+
except Exception as e:
|
196 |
+
logger.warning(f"Could not compute recommendations based on feedback: {e}")
|
|
|
|
|
|
|
197 |
|
198 |
+
# Save feedback to MongoDB (optional - only if MongoDB is available)
|
199 |
+
try:
|
200 |
+
actual_clicked_msids = [s.strip() for s in payload.clicked_msid.split(',') if s.strip()]
|
201 |
+
if not actual_clicked_msids:
|
202 |
+
logger.warning(f"API: Invalid clicked_msid: '{payload.clicked_msid}' for user '{payload.user_id}'")
|
203 |
+
raise HTTPException(status_code=400, detail="clicked_msid parameter is invalid or does not contain valid MSIDs.")
|
|
|
|
|
|
|
|
|
|
|
|
|
204 |
|
205 |
+
logger.info(
|
206 |
+
f"API: Saving feedback for user '{payload.user_id}', context msid: '{payload.msid}', clicked msids: {actual_clicked_msids}"
|
|
|
|
|
207 |
)
|
208 |
+
|
209 |
+
feedback_collection_name = "user_feedback_tracking"
|
210 |
+
# Check if MongoDB is available
|
211 |
+
if mongodb.db is None:
|
212 |
+
logger.warning("MongoDB database connection is not available. Skipping feedback storage.")
|
213 |
+
return FeedbackResponse(message="Response processed successfully (feedback storage unavailable)")
|
214 |
+
|
215 |
+
feedback_collection = mongodb.db[feedback_collection_name]
|
216 |
+
user_doc = feedback_collection.find_one({"user_id": payload.user_id})
|
217 |
+
|
218 |
+
if user_doc:
|
219 |
+
feedback_collection.update_one(
|
220 |
+
{"user_id": payload.user_id},
|
221 |
+
{"$addToSet": {"Articles": {"msid": payload.msid, "Read": actual_clicked_msids}}}
|
222 |
+
)
|
223 |
+
else:
|
224 |
+
feedback_collection.insert_one({
|
225 |
+
"user_id": payload.user_id,
|
226 |
+
"Articles": [{"msid": payload.msid, "Read": actual_clicked_msids}]
|
227 |
+
})
|
228 |
+
|
229 |
+
logger.info(f"API: Successfully saved feedback for user '{payload.user_id}'")
|
230 |
+
return FeedbackResponse(message="Response saved successfully")
|
231 |
+
|
232 |
+
except Exception as e:
|
233 |
+
logger.error(f"Error saving feedback to MongoDB: {e}", exc_info=True)
|
234 |
+
# Don't fail the entire request if MongoDB is unavailable
|
235 |
+
return FeedbackResponse(message="Response processed successfully (feedback storage failed)")
|
236 |
|
237 |
except HTTPException:
|
238 |
raise # Re-raise HTTPException directly
|
|
|
240 |
logger.error(f"API Error in submit_user_feedback_api for user '{payload.user_id}': {e}", exc_info=True)
|
241 |
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
|
242 |
|
243 |
+
@app.get("/recommendations/summary/")
|
244 |
async def get_recommendations_summary_api(msid: str, k: int = 5, summary: bool = True, smart_tip: bool = True):
|
245 |
"""
|
246 |
Get recommendations with optional summary and smart tip for a given MSID.
|
247 |
"""
|
248 |
try:
|
249 |
+
if not hasattr(app.state, "recommender") or app.state.recommender is None:
|
250 |
logger.error("Recommender is not available.")
|
251 |
raise HTTPException(status_code=503, detail="Recommender service not available")
|
252 |
|
253 |
try:
|
254 |
+
response = app.state.recommender.get_recommendations_summary(msid, k, summary, smart_tip)
|
255 |
except RuntimeError as e:
|
256 |
# Catch the meta tensor error and return a fallback
|
257 |
if "meta tensor" in str(e):
|
|
|
271 |
logger.error(f"API Error in get_recommendations_summary_api for msid '{msid}': {e}", exc_info=True)
|
272 |
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
|
273 |
|
|
|
|
|
|
|
|
|
274 |
if __name__ == "__main__":
|
275 |
uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
|
src/core/recommender.py
CHANGED
@@ -98,7 +98,13 @@ class RecoRecommender:
|
|
98 |
logger.error(f"Error during Intel XPU check: {e}. Using CPU.")
|
99 |
logger.info(f"Selected device: {self.device}")
|
100 |
|
101 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
102 |
# Initialize model trainer
|
103 |
self.model_trainer = ModelTrainer(RERANKER_MODEL_NAME, device=self.device) # Pass the determined device
|
104 |
self._setup_indic_nlp()
|
@@ -179,6 +185,13 @@ class RecoRecommender:
|
|
179 |
def _get_model_metadata(self) -> Dict:
|
180 |
"""Retrieve current model metadata from MongoDB."""
|
181 |
try:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
182 |
metadata = self.faiss_meta_collection.find_one({"_id": self.MODEL_METADATA_DOC_ID})
|
183 |
if metadata:
|
184 |
return metadata
|
@@ -194,11 +207,15 @@ class RecoRecommender:
|
|
194 |
except Exception as e:
|
195 |
logger.error(f"Error retrieving model metadata: {e}")
|
196 |
# Return a minimal fallback to ensure core functionalities can proceed if possible
|
197 |
-
return {"_id": self.MODEL_METADATA_DOC_ID, "embedding_model_name": EMBED_MODEL_NAME}
|
198 |
|
199 |
def _update_model_metadata(self, updates: Dict) -> bool:
|
200 |
"""Update model metadata in MongoDB."""
|
201 |
try:
|
|
|
|
|
|
|
|
|
202 |
result = self.faiss_meta_collection.update_one(
|
203 |
{"_id": self.MODEL_METADATA_DOC_ID},
|
204 |
{"$set": {**updates, "metadata_last_updated": datetime.now()}}, # Key changed for clarity
|
@@ -220,6 +237,10 @@ class RecoRecommender:
|
|
220 |
def _needs_reembedding_batch(self, doc_ids: List[str], current_checksum: str) -> List[str]:
|
221 |
"""Check which documents from a batch need reembedding."""
|
222 |
try:
|
|
|
|
|
|
|
|
|
223 |
# Query for all documents in one go
|
224 |
metadata_docs = self.faiss_meta_collection.find(
|
225 |
{"_id": {"$in": doc_ids}}
|
@@ -688,30 +709,35 @@ class RecoRecommender:
|
|
688 |
self.index = None # Ensure index is None on failure
|
689 |
raise # Re-raise the exception
|
690 |
|
691 |
-
|
692 |
-
|
693 |
-
|
694 |
-
|
695 |
-
|
696 |
-
|
697 |
-
|
698 |
-
|
699 |
-
|
700 |
-
|
701 |
-
|
702 |
-
|
703 |
-
|
704 |
-
|
705 |
-
|
706 |
-
|
707 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
708 |
|
709 |
-
except Exception as e:
|
710 |
-
logger.error(f"Error loading FAISS index IDs from MongoDB: {e}", exc_info=True)
|
711 |
-
self.indexed_ids = [] # Ensure IDs list is empty on failure
|
712 |
-
# We don't re-raise here, as load_components will decide if a rebuild is needed
|
713 |
-
# based on whether self.indexed_ids is populated.
|
714 |
-
|
715 |
def build_indexes_and_save(self, data_already_loaded: bool = False):
|
716 |
"""
|
717 |
Load data (if not already loaded), build FAISS index from current self.df, and save.
|
@@ -744,15 +770,19 @@ class RecoRecommender:
|
|
744 |
# Save indexed_ids to MongoDB
|
745 |
logger.info(f"Saving FAISS index IDs to MongoDB collection '{MONGO_FAISS_META_COLLECTION_NAME}', document_id '{self.FAISS_IDS_DOC_ID}'")
|
746 |
try:
|
747 |
-
self.faiss_meta_collection
|
748 |
-
|
749 |
-
|
750 |
-
|
751 |
-
|
752 |
-
|
|
|
|
|
|
|
753 |
except Exception as e:
|
754 |
logger.error(f"Error saving FAISS index IDs to MongoDB: {e}", exc_info=True)
|
755 |
-
|
|
|
756 |
logger.info("Index building and saving complete") # Log successful completion
|
757 |
except Exception as e: # Catch any exception during the process
|
758 |
logger.error(f"Error during index building: {e}", exc_info=True) # Log the error with traceback
|
@@ -763,64 +793,94 @@ class RecoRecommender:
|
|
763 |
logger.info("Loading components...") # Log the start of component loading
|
764 |
try: # Start a try-except block for error handling
|
765 |
self.load_models() # Load all machine learning models
|
766 |
-
|
767 |
-
|
768 |
-
|
769 |
-
|
770 |
-
|
771 |
-
|
772 |
-
|
773 |
-
logger.info("
|
774 |
-
|
775 |
-
|
|
|
|
|
776 |
try:
|
777 |
self._load_faiss_index_and_ids() # Tries to load .bin and IDs from Mongo.
|
778 |
# Sets self.index and self.indexed_ids.
|
779 |
# self.indexed_ids will be [] if Mongo data for IDs is missing.
|
780 |
# Raises FileNotFoundError if .bin (INDEX_PATH) is missing.
|
781 |
|
782 |
-
|
783 |
-
|
784 |
-
|
785 |
-
|
786 |
-
|
787 |
-
|
788 |
-
|
789 |
-
|
790 |
-
|
791 |
-
|
792 |
-
|
793 |
-
|
794 |
-
|
795 |
-
|
796 |
-
|
797 |
-
|
798 |
-
|
799 |
-
|
800 |
-
|
801 |
-
|
802 |
-
|
803 |
-
|
804 |
-
|
805 |
-
|
806 |
-
|
807 |
-
|
808 |
-
|
809 |
-
|
810 |
-
|
811 |
-
|
812 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
813 |
else:
|
814 |
-
|
|
|
|
|
|
|
815 |
else:
|
816 |
# This case handles if self.index is None (FileNotFoundError caught below)
|
817 |
# or if index was loaded but empty and no IDs from Mongo.
|
818 |
-
|
819 |
-
|
|
|
|
|
|
|
820 |
|
821 |
except FileNotFoundError: # This means INDEX_PATH (.bin file) was not found.
|
822 |
-
logger.warning(f"FAISS index file ({INDEX_PATH}) not found.
|
823 |
-
self.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
824 |
logger.info("Components loaded successfully") # Log successful loading of all components
|
825 |
except Exception as e: # Catch any exception during component loading
|
826 |
logger.error(f"Error loading components: {e}", exc_info=True) # Log the error with traceback
|
|
|
98 |
logger.error(f"Error during Intel XPU check: {e}. Using CPU.")
|
99 |
logger.info(f"Selected device: {self.device}")
|
100 |
|
101 |
+
# Initialize MongoDB collection only if connection is available
|
102 |
+
if mongodb.db is not None:
|
103 |
+
self.faiss_meta_collection = mongodb.db[MONGO_FAISS_META_COLLECTION_NAME]
|
104 |
+
else:
|
105 |
+
self.faiss_meta_collection = None
|
106 |
+
logger.warning("MongoDB not available. Some features may be limited.")
|
107 |
+
|
108 |
# Initialize model trainer
|
109 |
self.model_trainer = ModelTrainer(RERANKER_MODEL_NAME, device=self.device) # Pass the determined device
|
110 |
self._setup_indic_nlp()
|
|
|
185 |
def _get_model_metadata(self) -> Dict:
|
186 |
"""Retrieve current model metadata from MongoDB."""
|
187 |
try:
|
188 |
+
if self.faiss_meta_collection is None:
|
189 |
+
logger.warning("MongoDB not available. Returning default metadata.")
|
190 |
+
return {
|
191 |
+
"_id": self.MODEL_METADATA_DOC_ID,
|
192 |
+
"embedding_model_name": EMBED_MODEL_NAME,
|
193 |
+
}
|
194 |
+
|
195 |
metadata = self.faiss_meta_collection.find_one({"_id": self.MODEL_METADATA_DOC_ID})
|
196 |
if metadata:
|
197 |
return metadata
|
|
|
207 |
except Exception as e:
|
208 |
logger.error(f"Error retrieving model metadata: {e}")
|
209 |
# Return a minimal fallback to ensure core functionalities can proceed if possible
|
210 |
+
return {"_id": self.MODEL_METADATA_DOC_ID, "embedding_model_name": EMBED_MODEL_NAME}
|
211 |
|
212 |
def _update_model_metadata(self, updates: Dict) -> bool:
|
213 |
"""Update model metadata in MongoDB."""
|
214 |
try:
|
215 |
+
if self.faiss_meta_collection is None:
|
216 |
+
logger.warning("MongoDB not available. Cannot update model metadata.")
|
217 |
+
return False
|
218 |
+
|
219 |
result = self.faiss_meta_collection.update_one(
|
220 |
{"_id": self.MODEL_METADATA_DOC_ID},
|
221 |
{"$set": {**updates, "metadata_last_updated": datetime.now()}}, # Key changed for clarity
|
|
|
237 |
def _needs_reembedding_batch(self, doc_ids: List[str], current_checksum: str) -> List[str]:
|
238 |
"""Check which documents from a batch need reembedding."""
|
239 |
try:
|
240 |
+
if self.faiss_meta_collection is None:
|
241 |
+
logger.warning("MongoDB not available. Assuming all documents need reembedding.")
|
242 |
+
return doc_ids
|
243 |
+
|
244 |
# Query for all documents in one go
|
245 |
metadata_docs = self.faiss_meta_collection.find(
|
246 |
{"_id": {"$in": doc_ids}}
|
|
|
709 |
self.index = None # Ensure index is None on failure
|
710 |
raise # Re-raise the exception
|
711 |
|
712 |
+
# Try to load IDs from MongoDB if available
|
713 |
+
if self.faiss_meta_collection is not None:
|
714 |
+
logger.info(f"Loading FAISS index IDs from MongoDB collection '{MONGO_FAISS_META_COLLECTION_NAME}', document_id '{self.FAISS_IDS_DOC_ID}'")
|
715 |
+
try:
|
716 |
+
ids_document = self.faiss_meta_collection.find_one({"_id": self.FAISS_IDS_DOC_ID})
|
717 |
+
if ids_document and "ids" in ids_document:
|
718 |
+
self.indexed_ids = ids_document["ids"]
|
719 |
+
logger.info(f"Loaded {len(self.indexed_ids)} indexed IDs from MongoDB.")
|
720 |
+
|
721 |
+
# Basic consistency check
|
722 |
+
if self.index and self.index.ntotal != len(self.indexed_ids):
|
723 |
+
logger.warning(
|
724 |
+
f"FAISS index vector count ({self.index.ntotal}) "
|
725 |
+
f"does not match loaded ID count from MongoDB ({len(self.indexed_ids)}). "
|
726 |
+
"Index might be inconsistent. Consider rebuilding."
|
727 |
+
)
|
728 |
+
else:
|
729 |
+
logger.warning(f"FAISS index IDs document not found in MongoDB or 'ids' field missing. Will attempt to build if necessary.")
|
730 |
+
self.indexed_ids = [] # Initialize as empty if not found
|
731 |
+
|
732 |
+
except Exception as e:
|
733 |
+
logger.error(f"Error loading FAISS index IDs from MongoDB: {e}", exc_info=True)
|
734 |
+
self.indexed_ids = [] # Ensure IDs list is empty on failure
|
735 |
+
# We don't re-raise here, as load_components will decide if a rebuild is needed
|
736 |
+
# based on whether self.indexed_ids is populated.
|
737 |
+
else:
|
738 |
+
logger.warning("MongoDB not available. Cannot load indexed IDs. Operating with empty ID list.")
|
739 |
+
self.indexed_ids = []
|
740 |
|
|
|
|
|
|
|
|
|
|
|
|
|
741 |
def build_indexes_and_save(self, data_already_loaded: bool = False):
|
742 |
"""
|
743 |
Load data (if not already loaded), build FAISS index from current self.df, and save.
|
|
|
770 |
# Save indexed_ids to MongoDB
|
771 |
logger.info(f"Saving FAISS index IDs to MongoDB collection '{MONGO_FAISS_META_COLLECTION_NAME}', document_id '{self.FAISS_IDS_DOC_ID}'")
|
772 |
try:
|
773 |
+
if self.faiss_meta_collection is not None:
|
774 |
+
self.faiss_meta_collection.update_one(
|
775 |
+
{"_id": self.FAISS_IDS_DOC_ID},
|
776 |
+
{"$set": {"ids": self.indexed_ids, "last_updated": datetime.now()}},
|
777 |
+
upsert=True
|
778 |
+
)
|
779 |
+
logger.info(f"Saved {len(self.indexed_ids)} indexed IDs to MongoDB.")
|
780 |
+
else:
|
781 |
+
logger.warning("MongoDB not available. Skipping indexed IDs save.")
|
782 |
except Exception as e:
|
783 |
logger.error(f"Error saving FAISS index IDs to MongoDB: {e}", exc_info=True)
|
784 |
+
# Don't raise here as the FAISS index was saved successfully
|
785 |
+
# The IDs can be regenerated if needed
|
786 |
logger.info("Index building and saving complete") # Log successful completion
|
787 |
except Exception as e: # Catch any exception during the process
|
788 |
logger.error(f"Error during index building: {e}", exc_info=True) # Log the error with traceback
|
|
|
793 |
logger.info("Loading components...") # Log the start of component loading
|
794 |
try: # Start a try-except block for error handling
|
795 |
self.load_models() # Load all machine learning models
|
796 |
+
|
797 |
+
# Try to load data from MongoDB, but handle failures gracefully
|
798 |
+
try:
|
799 |
+
self._load_data_from_mongo() # Load data from MongoDB and preprocess it
|
800 |
+
logger.info("Successfully loaded data from MongoDB")
|
801 |
+
except Exception as mongo_error:
|
802 |
+
logger.warning(f"Failed to load data from MongoDB: {mongo_error}")
|
803 |
+
logger.info("Attempting to work with existing FAISS index without MongoDB data...")
|
804 |
+
# Set df to None to indicate no MongoDB data is available
|
805 |
+
self.df = None
|
806 |
+
|
807 |
+
# Try to load FAISS index and IDs
|
808 |
try:
|
809 |
self._load_faiss_index_and_ids() # Tries to load .bin and IDs from Mongo.
|
810 |
# Sets self.index and self.indexed_ids.
|
811 |
# self.indexed_ids will be [] if Mongo data for IDs is missing.
|
812 |
# Raises FileNotFoundError if .bin (INDEX_PATH) is missing.
|
813 |
|
814 |
+
if self.index and self.index.ntotal > 0:
|
815 |
+
logger.info(f"FAISS index loaded successfully with {self.index.ntotal} vectors")
|
816 |
+
|
817 |
+
# If we have MongoDB data, proceed with normal logic
|
818 |
+
if self.df is not None and not self.df.empty:
|
819 |
+
# Consistency check and incremental update logic
|
820 |
+
if not self.indexed_ids:
|
821 |
+
logger.warning("FAISS index file loaded, but no corresponding IDs found in MongoDB. Rebuilding for consistency.")
|
822 |
+
self.build_indexes_and_save(data_already_loaded=True)
|
823 |
+
else:
|
824 |
+
logger.info("Existing FAISS index and IDs loaded from storage.")
|
825 |
+
# Proceed with incremental update logic
|
826 |
+
current_df_ids = set(self.df[self.id_col].tolist())
|
827 |
+
indexed_ids_set = set(self.indexed_ids)
|
828 |
+
new_ids_to_add = list(current_df_ids - indexed_ids_set)
|
829 |
+
|
830 |
+
if new_ids_to_add:
|
831 |
+
logger.info(f"Found {len(new_ids_to_add)} new documents to add to the index.")
|
832 |
+
new_docs_df = self.df[self.df[self.id_col].isin(new_ids_to_add)].copy()
|
833 |
+
|
834 |
+
new_embeddings, new_doc_ids_added = self._generate_embeddings(new_docs_df)
|
835 |
+
|
836 |
+
if new_embeddings.size > 0:
|
837 |
+
self.index.add(new_embeddings.astype(np.float32))
|
838 |
+
self.indexed_ids.extend(new_doc_ids_added)
|
839 |
+
logger.info(f"Added {len(new_doc_ids_added)} new vectors to FAISS index. Total vectors: {self.index.ntotal}")
|
840 |
+
|
841 |
+
# Save the updated FAISS index
|
842 |
+
faiss.write_index(self.index, INDEX_PATH)
|
843 |
+
# Try to save the updated IDs to MongoDB, but don't fail if it doesn't work
|
844 |
+
try:
|
845 |
+
self.faiss_meta_collection.update_one(
|
846 |
+
{"_id": self.FAISS_IDS_DOC_ID},
|
847 |
+
{"$set": {"ids": self.indexed_ids, "last_updated": datetime.now()}},
|
848 |
+
upsert=True
|
849 |
+
)
|
850 |
+
logger.info("Updated FAISS index and IDs saved to MongoDB.")
|
851 |
+
except Exception as e:
|
852 |
+
logger.warning(f"Could not save IDs to MongoDB: {e}")
|
853 |
+
else:
|
854 |
+
logger.info("No new documents found to add to the index. Index is up-to-date.")
|
855 |
else:
|
856 |
+
# No MongoDB data available, but we have a FAISS index
|
857 |
+
logger.info("FAISS index available but no MongoDB data. Operating in limited mode.")
|
858 |
+
if not self.indexed_ids:
|
859 |
+
logger.warning("No indexed IDs available. Some functionality may be limited.")
|
860 |
else:
|
861 |
# This case handles if self.index is None (FileNotFoundError caught below)
|
862 |
# or if index was loaded but empty and no IDs from Mongo.
|
863 |
+
if self.df is not None and not self.df.empty:
|
864 |
+
logger.info("FAISS index and/or IDs not found or empty. Building new index.")
|
865 |
+
self.build_indexes_and_save(data_already_loaded=True)
|
866 |
+
else:
|
867 |
+
logger.warning("No data available (neither MongoDB nor FAISS index). Cannot build index.")
|
868 |
|
869 |
except FileNotFoundError: # This means INDEX_PATH (.bin file) was not found.
|
870 |
+
logger.warning(f"FAISS index file ({INDEX_PATH}) not found.")
|
871 |
+
if self.df is not None and not self.df.empty:
|
872 |
+
logger.info("Building index from scratch.")
|
873 |
+
self.build_indexes_and_save(data_already_loaded=True)
|
874 |
+
else:
|
875 |
+
logger.error("Cannot build index: no data available.")
|
876 |
+
except Exception as e:
|
877 |
+
logger.error(f"Error loading FAISS index: {e}", exc_info=True)
|
878 |
+
if self.df is not None and not self.df.empty:
|
879 |
+
logger.info("Attempting to rebuild index due to loading error.")
|
880 |
+
self.build_indexes_and_save(data_already_loaded=True)
|
881 |
+
else:
|
882 |
+
logger.error("Cannot rebuild index: no data available.")
|
883 |
+
|
884 |
logger.info("Components loaded successfully") # Log successful loading of all components
|
885 |
except Exception as e: # Catch any exception during component loading
|
886 |
logger.error(f"Error loading components: {e}", exc_info=True) # Log the error with traceback
|
src/database/mongodb.py
CHANGED
@@ -44,7 +44,7 @@ class MongoDB:
|
|
44 |
"""
|
45 |
Establishes connection to MongoDB Atlas.
|
46 |
Handles connection errors and sets up the database instance.
|
47 |
-
|
48 |
"""
|
49 |
try:
|
50 |
logger.info(f"Connecting to MongoDB Atlas: DB='{MONGO_DB_NAME}'")
|
@@ -56,26 +56,31 @@ class MongoDB:
|
|
56 |
self._db = self._client[MONGO_DB_NAME]
|
57 |
logger.info("Successfully connected to MongoDB.")
|
58 |
except (ConnectionFailure, ConfigurationError) as e:
|
59 |
-
logger.
|
60 |
self._client = None
|
61 |
self._db = None
|
62 |
-
raise
|
63 |
except Exception as e:
|
64 |
-
logger.
|
65 |
self._client = None
|
66 |
self._db = None
|
67 |
-
raise
|
68 |
|
69 |
@property
|
70 |
-
def db(self) -> Database:
|
71 |
"""
|
72 |
Property to get the database instance.
|
73 |
-
|
74 |
Returns:
|
75 |
-
Database: MongoDB database instance
|
76 |
"""
|
77 |
if self._db is None:
|
78 |
-
|
|
|
|
|
|
|
|
|
|
|
79 |
return self._db
|
80 |
|
81 |
@property
|
|
|
44 |
"""
|
45 |
Establishes connection to MongoDB Atlas.
|
46 |
Handles connection errors and sets up the database instance.
|
47 |
+
Does not raise exceptions if connection fails - allows graceful degradation.
|
48 |
"""
|
49 |
try:
|
50 |
logger.info(f"Connecting to MongoDB Atlas: DB='{MONGO_DB_NAME}'")
|
|
|
56 |
self._db = self._client[MONGO_DB_NAME]
|
57 |
logger.info("Successfully connected to MongoDB.")
|
58 |
except (ConnectionFailure, ConfigurationError) as e:
|
59 |
+
logger.warning(f"MongoDB connection failed: {e}")
|
60 |
self._client = None
|
61 |
self._db = None
|
62 |
+
# Don't raise the exception - allow the application to continue without MongoDB
|
63 |
except Exception as e:
|
64 |
+
logger.warning(f"An unexpected error occurred during MongoDB connection: {e}")
|
65 |
self._client = None
|
66 |
self._db = None
|
67 |
+
# Don't raise the exception - allow the application to continue without MongoDB
|
68 |
|
69 |
@property
|
70 |
+
def db(self) -> Optional[Database]:
|
71 |
"""
|
72 |
Property to get the database instance.
|
73 |
+
Returns None if the connection is not available.
|
74 |
Returns:
|
75 |
+
Database: MongoDB database instance or None if not connected
|
76 |
"""
|
77 |
if self._db is None:
|
78 |
+
# Try to connect once, but don't keep retrying
|
79 |
+
try:
|
80 |
+
self._connect()
|
81 |
+
except Exception as e:
|
82 |
+
logger.warning(f"Failed to establish MongoDB connection: {e}")
|
83 |
+
return None
|
84 |
return self._db
|
85 |
|
86 |
@property
|