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| import gradio as gr | |
| import logging, os, sys, threading | |
| from custom_utils import connect_to_database, rag_ingestion, rag_retrieval, rag_inference | |
| lock = threading.Lock() | |
| RAG_INGESTION = False | |
| RAG_OFF = "Off" | |
| RAG_NAIVE = "Naive RAG" | |
| RAG_ADVANCED = "Advanced RAG" | |
| logging.basicConfig(stream = sys.stdout, level = logging.INFO) | |
| logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout)) | |
| def invoke(openai_api_key, prompt, rag_option): | |
| if not openai_api_key: | |
| raise gr.Error("OpenAI API Key is required.") | |
| if not prompt: | |
| raise gr.Error("Prompt is required.") | |
| if not rag_option: | |
| raise gr.Error("Retrieval-Augmented Generation is required.") | |
| with lock: | |
| db, collection = connect_to_database() | |
| if (RAG_INGESTION): | |
| return rag_ingestion(collection) | |
| else: | |
| ### Pre-retrieval processing: index filter | |
| ### Post-retrieval processing: result filter | |
| #match_stage = { | |
| # "$match": { | |
| # "accommodates": { "$eq": 2}, | |
| # "bedrooms": { "$eq": 1} | |
| # } | |
| #} | |
| #additional_stages = [match_stage] | |
| ### | |
| """ | |
| projection_stage = { | |
| "$project": { | |
| "_id": 0, | |
| "name": 1, | |
| "accommodates": 1, | |
| "address.street": 1, | |
| "address.government_area": 1, | |
| "address.market": 1, | |
| "address.country": 1, | |
| "address.country_code": 1, | |
| "address.location.type": 1, | |
| "address.location.coordinates": 1, | |
| "address.location.is_location_exact": 1, | |
| "summary": 1, | |
| "space": 1, | |
| "neighborhood_overview": 1, | |
| "notes": 1, | |
| "score": {"$meta": "vectorSearchScore"} | |
| } | |
| } | |
| additional_stages = [projection_stage] | |
| """ | |
| ### | |
| review_average_stage = { | |
| "$addFields": { | |
| "averageReviewScore": { | |
| "$divide": [ | |
| { | |
| "$add": [ | |
| "$review_scores.review_scores_accuracy", | |
| "$review_scores.review_scores_cleanliness", | |
| "$review_scores.review_scores_checkin", | |
| "$review_scores.review_scores_communication", | |
| "$review_scores.review_scores_location", | |
| "$review_scores.review_scores_value", | |
| ] | |
| }, | |
| 6 # Divide by the number of review score types to get the average | |
| ] | |
| }, | |
| # Calculate a score boost factor based on the number of reviews | |
| "reviewCountBoost": "$number_of_reviews" | |
| } | |
| } | |
| weighting_stage = { | |
| "$addFields": { | |
| "combinedScore": { | |
| # Example formula that combines average review score and review count boost | |
| "$add": [ | |
| {"$multiply": ["$averageReviewScore", 0.9]}, # Weighted average review score | |
| {"$multiply": ["$reviewCountBoost", 0.1]} # Weighted review count boost | |
| ] | |
| } | |
| } | |
| } | |
| # Apply the combinedScore for sorting | |
| sorting_stage_sort = { | |
| "$sort": {"combinedScore": -1} # Descending order to boost higher combined scores | |
| } | |
| additional_stages = [review_average_stage, weighting_stage, sorting_stage_sort] | |
| ### | |
| #additional_stages = [] | |
| ### | |
| search_results = rag_retrieval(openai_api_key, prompt, db, collection, additional_stages) | |
| return rag_inference(openai_api_key, prompt, search_results) | |
| gr.close_all() | |
| PROMPT = "Recommend a place that's modern, spacious, and within walking distance from restaurants." | |
| demo = gr.Interface( | |
| fn = invoke, | |
| inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1), | |
| gr.Textbox(label = "Prompt", value = PROMPT, lines = 1), | |
| gr.Radio([RAG_OFF, RAG_NAIVE, RAG_ADVANCED], label = "Retrieval-Augmented Generation", value = RAG_ADVANCED)], | |
| outputs = [gr.Markdown(label = "Completion")], | |
| title = "Context-Aware Reasoning Application", | |
| description = os.environ["DESCRIPTION"] | |
| ) | |
| demo.launch() |