Comprehensive Plan for Quranic Verse Chatbot Objectives Provide accurate and contextually relevant Quranic guidance. Include Surah name and verse number in each response. Ensure high accuracy in determining the type of query and provide combined solutions from the Quran, Hadith, and scholarly interpretations. Handle specific queries about translations, explanations, reasons for revelation, and more. 1. Data Structuring a. Comprehensive Source Data Store detailed information for each verse, including: Verse Number Surah Name Text (Arabic) Translations: Multiple translations (e.g., The Clear Quran, Saheeh International, Abdul Haleem). Reason for Revelation Main Theme Explanation Present-Day Relevance Related Hadith Scholarly Interpretations Legal Rulings Combined Solutions 2. Database Design Expand the database schema to include fields for combined solutions from multiple sources. Database Tables: Verses Table: verse_id (Primary Key) surah_name verse_number text_arabic text_embedding Translations Table: translation_id (Primary Key) verse_id (Foreign Key) translator text_translation translation_embedding Revelation Table: revelation_id (Primary Key) verse_id (Foreign Key) reason reason_embedding Themes Table: theme_id (Primary Key) verse_id (Foreign Key) main_theme theme_embedding Explanations Table: explanation_id (Primary Key) verse_id (Foreign Key) explanation_text explanation_embedding PresentDayRelevance Table: relevance_id (Primary Key) verse_id (Foreign Key) relevance_text relevance_embedding Hadith Table: hadith_id (Primary Key) verse_id (Foreign Key) hadith_text hadith_embedding ScholarlyInterpretations Table: interpretation_id (Primary Key) verse_id (Foreign Key) interpretation_text interpretation_embedding LegalRulings Table: ruling_id (Primary Key) verse_id (Foreign Key) ruling_text ruling_embedding CombinedSolutions Table: solution_id (Primary Key) query solution_text solution_embedding 3. Data Collection and Input a. Collect Data: Translations: Gather translations from authorized sources or publishers. Revelation Context: Use authenticated tafsir books (e.g., Tafsir Ibn Kathir). Explanations and Themes: Extract from reputable tafsir literature. Present-Day Relevance: Analyze the verses with the help of contemporary scholars to provide context on how each verse applies to modern-day scenarios. Related Hadith: Use authenticated Hadith collections like Sahih Bukhari, Sahih Muslim. Scholarly Interpretations and Legal Rulings: Extract from recognized Islamic jurisprudence sources. Combined Solutions: Develop solutions that integrate the Quran, Hadith, and scholarly opinions for contemporary issues. b. Generate Embeddings: Use a pretrained language model like BERT or GPT-3 to generate embeddings for each text field (e.g., verse text, translation, explanation). 4. Query Handling Logic a. Enhanced Intent Classification Use a pretrained NLP model fine-tuned to classify complex intents, including those requiring combined sources. Model Training: Train a model on a dataset of complex queries with combined sources. Inference Function: def classify_intent(query): inputs = tokenizer_intent(query, return_tensors='pt', padding=True, truncation=True) outputs = model_intent(**inputs) predicted_intent = torch.argmax(outputs.logits, dim=1).item() intent_map = {0: 'guidance', 1: 'legal_ruling', 2: 'environment', 3: 'combined_solution', 4: 'translation', 5: 'explanation', 6: 'revelation_reason', 7: 'present_day_relevance'} return intent_map[predicted_intent] b. Keyword Matching and Embedding Similarity Use keyword matching and embeddings to validate and refine the intent classification. def keyword_match(query): keywords = { 'guidance': ['what does the Quran say about', 'guidance on', 'how should I'], 'legal_ruling': ['Islamic ruling on', 'what does the Quran say about', 'is it allowed to'], 'environment': ['Quranic view on environment', 'how should we treat the environment'], 'combined_solution': ['solution from the Quran and Hadith', 'combined guidance', 'Islamic view on'], 'translation': ['translate', 'translation'], 'explanation': ['explain', 'explanation'], 'revelation_reason': ['why', 'reveal', 'reason'], 'present_day_relevance': ['relevance', 'today', 'current'] } for intent, words in keywords.items(): if any(word in query.lower() for word in words): return intent return None def get_embedding(text): inputs = tokenizer_embedding(text, return_tensors="pt") outputs = model_embedding(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy() return embeddings def get_embeddings_and_similarity(query_embedding, database_embeddings): similarity_scores = cosine_similarity(query_embedding, database_embeddings) closest_match_index = similarity_scores.argmax() return closest_match_index Combining Methods to Determine Intent: def determine_intent(query): model_intent = classify_intent(query) keyword_intent = keyword_match(query) if model_intent == keyword_intent: return model_intent else: log_discrepancy(query, model_intent, keyword_intent) return model_intent def log_discrepancy(query, model_intent, keyword_intent): with open('discrepancy_log.txt', 'a') as log_file: log_file.write(f"Query: {query}, Model Intent: {model_intent}, Keyword Intent: {keyword_intent}\n") 5. Implementing the Query Handling Logic Determine Intent and Handle Query: def handle_query(query): intent = determine_intent(query) if intent == 'guidance': return get_guidance(query) elif intent == 'legal_ruling': return get_legal_ruling(query) elif intent == 'environment': return get_environment_guidance(query) elif intent == 'combined_solution': return get_combined_solution(query) elif intent == 'translation': return get_translation(query) elif intent == 'explanation': return get_explanation(query) elif intent == 'revelation_reason': return get_revelation_reason(query) elif intent == 'present_day_relevance': return get_present_day_relevance(query) else: return {"error": "Unable to determine intent"} def get_guidance(query): verse_id = find_relevant_verse(query, 'guidance') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "guidance": data["explanation"] } def get_legal_ruling(query): verse_id = find_relevant_verse(query, 'legal_ruling') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "legal_ruling": data["ruling"] } def get_environment_guidance(query): verse_id = find_relevant_verse(query, 'environment') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "environment_guidance": data["explanation"] } def get_combined_solution(query): query_embedding = get_embedding(query) combined_solutions = fetch_all_combined_solutions_from_database() best_match_index = get_embeddings_and_similarity(query_embedding, combined_solutions['embeddings']) best_solution = combined_solutions['solutions'][best_match_index] return { "surah_name": best_solution["surah_name"], "verse_number": best_solution["verse_number"], "solution": best_solution["solution"] } def get_translation(query): verse_id = find_relevant_verse(query, 'translation') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "translation": data["translation"] } def get_explanation(query): verse_id = find_relevant_verse(query, 'explanation') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "explanation": data["explanation"] } def get_revelation_reason(query): verse_id = find_relevant_verse(query, 'revelation_reason') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "revelation_reason": data["reason"] } def get_present_day_relevance(query): verse_id = find_relevant_verse(query, 'present_day_relevance') data = fetch_data_from_database(verse_id) return { "surah_name": data["surah_name"], "verse_number": data["verse_number"], "present_day_relevance": data["relevance"] } Database Query Functions: def find_relevant_verse(query, intent): query_embedding = get_embedding(query) if intent == 'guidance': verses = fetch_all_verses_from_database() elif intent == 'legal_ruling': verses = fetch_all_rulings_from_database() elif intent == 'environment': verses = fetch_all_environment_verses_from_database() elif intent == 'translation': verses = fetch_all_translations_from_database() elif intent == 'explanation': verses = fetch_all_explanations_from_database() elif intent == 'revelation_reason': verses = fetch_all_revelation_reasons_from_database() elif intent == 'present_day_relevance': verses = fetch_all_present_day_relevances_from_database() best_match_index = get_embeddings_and_similarity(query_embedding, verses['embeddings']) return verses['verse_ids'][best_match_index] def fetch_all_verses_from_database(): # Fetch embeddings and verse IDs from the database return { 'verse_ids': [1, 2, 3], 'embeddings': [[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]] } def fetch_data_from_database(verse_id): # Fetch detailed data for the verse return { "surah_name": "Al-Baqarah", "verse_number": "2:256", "explanation": "This verse discusses...", "translation": "There shall be no compulsion in [acceptance of] the religion...", "reason": "This verse was revealed to address...", "relevance": "In today's context, this verse signifies...", "ruling": "According to Islamic jurisprudence, ..." } def fetch_all_combined_solutions_from_database(): # Fetch combined solutions and their embeddings return { 'solutions': [ { "surah_name": "Al-Baqarah", "verse_number": "2:275", "solution": "Interest is prohibited in Islam. Allah says in Surah Al-Baqarah, Verse 275: 'Those who consume interest cannot stand [on the Day of Resurrection] except as one stands who is being beaten by Satan into insanity. That is because they say, 'Trade is [just] like interest.' But Allah has permitted trade and has forbidden interest.' According to Hadith, Prophet Muhammad (peace be upon him) also said, 'Gold for gold, silver for silver, wheat for wheat, barley for barley, dates for dates, salt for salt, like for like, hand to hand. Whoever pays more or demands more has dealt in riba. The receiver and the giver are equally guilty.' (Sahih Muslim)." }, # Add more combined solutions as needed ], 'embeddings': [ # Embeddings for combined solutions [0.1, 0.2, 0.3], # More embeddings ] } 6. User Interface and Experience Design Interaction Flow: Greeting: Welcome message with guidance on how to use the chatbot. Query Prompt: Clear prompt asking users to specify their query. Response Display: Display the verse details, explanation, or solution in a user-friendly format. Example Interaction Flow: def welcome_message(): return "Welcome! How can I assist you today with guidance from the Quran?" def prompt_user(): return "Please enter your query, and I will provide you with relevant information from the Quran." def display_response(response): print(f"Surah: {response['surah_name']}, Verse: {response['verse_number']}") print(f"Response: {response['solution']}") 7. Integration and Deployment API Endpoints: from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/query', methods=['POST']) def query(): user_query = request.json.get('query') response = handle_query(user_query) return jsonify(response) if __name__ == '__main__': app.run(debug=True) Deploying the Application: Host the API: Use a cloud provider like AWS, GCP, or Heroku to host your Flask application. Ensure Scalability: Use load balancing and auto-scaling features to handle multiple user queries simultaneously. 8. Post-Deployment User Feedback: Implement a feedback system to improve the chatbot based on user input. Collect feedback on the accuracy and usefulness of responses. Continuous Improvement: Regularly update the database with new information and improve the chatbot's understanding. Monitor the logs for discrepancies between the NLP model and keyword matching to refine the model. Full Detailed Plan Summary Objectives Provide accurate and contextually relevant Quranic guidance, including combined solutions from the Quran, Hadith, and scholarly interpretations. Include Surah name and verse number in each response. Ensure high accuracy in determining the type of query. Data Structuring Store detailed information for each verse, including embeddings for contextual matching and combined solutions from multiple sources. Database Schema Create relational tables to store verses, translations, explanations, scholarly interpretations, legal rulings, and combined solutions. Query Handling Logic Use a combination of NLP intent classification, keyword matching, and embedding similarity. Implementation Train models, set up API endpoints, and handle query responses. User Interface Design user-friendly interaction flows and response displays. Integration and Deployment Deploy the application on scalable cloud infrastructure. Post-Deployment Collect user feedback and continuously improve the system. Example Workflow User Query: "What is the ruling on interest?" Intent Determination: NLP Model: Classifies as "combined_solution". Keyword Matching: Validates as "combined_solution". Embedding Matching: Finds the closest match in the combined solutions database. Fetch Data: Retrieve the relevant combined solution from the database. Response: { "surah_name": "Al-Baqarah", "verse_number": "2:275", "solution": "Interest is prohibited in Islam. Allah says in Surah Al-Baqarah, Verse 275: 'Those who consume interest cannot stand [on the Day of Resurrection] except as one stands who is being beaten by Satan into insanity. That is because they say, 'Trade is [just] like interest.' But Allah has permitted trade and has forbidden interest.' According to Hadith, Prophet Muhammad (peace be upon him) also said, 'Gold for gold, silver for silver, wheat for wheat, barley for barley, dates for dates, salt for salt, like for like, hand to hand. Whoever pays more or demands more has dealt in riba. The receiver and the giver are equally guilty.' (Sahih Muslim)." } Display to User: Surah: Al-Baqarah, Verse: 2:275 Solution: Interest is prohibited in Islam. Allah says in Surah Al-Baqarah, Verse 275: 'Those who consume interest cannot stand [on the Day of Resurrection] except as one stands who is being beaten by Satan into insanity. That is because they say, 'Trade is [just] like interest.' But Allah has permitted trade and has forbidden interest.' According to Hadith, Prophet Muhammad (peace be upon him) also said, 'Gold for gold, silver for silver, wheat for wheat, barley for barley, dates for dates, salt for salt, like for like, hand to hand. Whoever pays more or demands more has dealt in riba. The receiver and the giver are equally guilty.' (Sahih Muslim). Conclusion By following this detailed plan, you can create a comprehensive and reliable chatbot that provides accurate Quranic guidance, including combined solutions from the Quran, Hadith, and scholarly interpretations. This approach leverages modern NLP techniques, embeddings, and a well-structured database to maintain high accuracy and relevance, ensuring that users receive precise and contextually relevant responses to their queries. Detailed Database Guide Designing the database to support the Quranic Verse Chatbot requires careful planning to ensure it is scalable, efficient, and easy to query. For this purpose, a relational database management system (RDBMS) like PostgreSQL is recommended due to its robustness, support for complex queries, and advanced indexing capabilities. However, a NoSQL database like MongoDB could also be considered for flexibility with JSON-like documents. Database Design for Quranic Verse Chatbot Choice of Database Relational Database: PostgreSQL NoSQL Database: MongoDB Schema Design for PostgreSQL Tables Verses Table Translations Table Revelation Table Themes Table Explanations Table PresentDayRelevance Table Hadith Table ScholarlyInterpretations Table LegalRulings Table CombinedSolutions Table Detailed Schema 1. Verses Table CREATE TABLE Verses ( verse_id SERIAL PRIMARY KEY, surah_name VARCHAR(255) NOT NULL, verse_number VARCHAR(10) NOT NULL, text_arabic TEXT NOT NULL, text_embedding BYTEA -- Store embeddings as byte array ); 2. Translations Table CREATE TABLE Translations ( translation_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), translator VARCHAR(255) NOT NULL, text_translation TEXT NOT NULL, translation_embedding BYTEA ); 3. Revelation Table CREATE TABLE Revelation ( revelation_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), reason TEXT NOT NULL, reason_embedding BYTEA ); 4. Themes Table CREATE TABLE Themes ( theme_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), main_theme TEXT NOT NULL, theme_embedding BYTEA ); 5. Explanations Table CREATE TABLE Explanations ( explanation_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), explanation_text TEXT NOT NULL, explanation_embedding BYTEA ); 6. PresentDayRelevance Table CREATE TABLE PresentDayRelevance ( relevance_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), relevance_text TEXT NOT NULL, relevance_embedding BYTEA ); 7. Hadith Table CREATE TABLE Hadith ( hadith_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), hadith_text TEXT NOT NULL, hadith_embedding BYTEA ); 8. ScholarlyInterpretations Table CREATE TABLE ScholarlyInterpretations ( interpretation_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), interpretation_text TEXT NOT NULL, interpretation_embedding BYTEA ); 9. LegalRulings Table CREATE TABLE LegalRulings ( ruling_id SERIAL PRIMARY KEY, verse_id INT REFERENCES Verses(verse_id), ruling_text TEXT NOT NULL, ruling_embedding BYTEA ); 10. CombinedSolutions Table CREATE TABLE CombinedSolutions ( solution_id SERIAL PRIMARY KEY, query TEXT NOT NULL, solution_text TEXT NOT NULL, solution_embedding BYTEA ); Schema Design for MongoDB In MongoDB, we can use a document-based structure. Each verse and its related data can be embedded within a single document. { "verse_id": 1, "surah_name": "Al-Baqarah", "verse_number": "2:275", "text_arabic": "Some Arabic text", "text_embedding": [0.1, 0.2, 0.3], "translations": [ { "translator": "Saheeh International", "text_translation": "Translation text", "translation_embedding": [0.1, 0.2, 0.3] } ], "revelation": { "reason": "Revelation context", "reason_embedding": [0.1, 0.2, 0.3] }, "themes": [ { "main_theme": "Theme text", "theme_embedding": [0.1, 0.2, 0.3] } ], "explanations": [ { "explanation_text": "Explanation text", "explanation_embedding": [0.1, 0.2, 0.3] } ], "present_day_relevance": { "relevance_text": "Relevance text", "relevance_embedding": [0.1, 0.2, 0.3] }, "hadith": [ { "hadith_text": "Hadith text", "hadith_embedding": [0.1, 0.2, 0.3] } ], "scholarly_interpretations": [ { "interpretation_text": "Interpretation text", "interpretation_embedding": [0.1, 0.2, 0.3] } ], "legal_rulings": [ { "ruling_text": "Ruling text", "ruling_embedding": [0.1, 0.2, 0.3] } ], "combined_solutions": [ { "query": "Query text", "solution_text": "Solution text", "solution_embedding": [0.1, 0.2, 0.3] } ] } Step-by-Step Implementation Setup PostgreSQL Database: Install PostgreSQL and create a new database. Create tables as per the schema above. Setup MongoDB Database: Install MongoDB and create a new database. Insert documents as per the schema above. Data Ingestion: Collect data from various sources (translations, tafsirs, hadith, etc.). Write scripts to parse the data and insert it into the respective tables/documents. Embedding Generation: Use a pretrained model like BERT or GPT-3 to generate embeddings for text fields. Store embeddings in the database. API Development: Develop a Flask API to handle queries and fetch data from the database. Implement endpoints to query the database and return results based on the user's request. Frontend Development: Create a user-friendly interface for interacting with the chatbot. Implement features to allow users to input queries and display responses with Surah name and verse number. Example API Endpoint in Flask from flask import Flask, request, jsonify import psycopg2 app = Flask(__name__) def connect_db(): return psycopg2.connect( dbname="yourdbname", user="yourdbuser", password="yourdbpassword", host="yourdbhost", port="yourdbport" ) @app.route('/query', methods=['POST']) def query(): user_query = request.json.get('query') intent = determine_intent(user_query) response = handle_query(intent, user_query) return jsonify(response) def determine_intent(query): # Use NLP model and keyword matching to determine intent return "guidance" # Example intent def handle_query(intent, query): conn = connect_db() cur = conn.cursor() if intent == "guidance": cur.execute("SELECT * FROM Explanations WHERE verse_id = %s", (1,)) elif intent == "translation": cur.execute("SELECT * FROM Translations WHERE verse_id = %s", (1,)) # Add more conditions for different intents result = cur.fetchone() cur.close() conn.close() return result if __name__ == '__main__': app.run(debug=True) By following this comprehensive plan, you can set up a robust database system to support the Quranic Verse Chatbot, ensuring it provides accurate and contextually relevant responses to user queries.
