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ai_brain.py

@@ -1,33 +0,0 @@
-from sentence_transformers import SentenceTransformer
-from supabase import create_client, Client
-from config import settings
-
-SUPABASE_URL = settings.SUPABASE_URL
-SUPABASE_KEY = settings.SUPABASE_KEY
-
-supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
-
-def test_ai_database():
-    print("Downloading AI tool (this takes a few seconds)...")
-    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-    
-    print("Converting text into numbers...")
-    # This turns English text into numbers so the database can search it later
-    embedding_numbers = model.encode("Farmers get 6000 rupees a year").tolist()
-    
-    print("Saving to Supabase...")
-    data = {
-        "scheme_title": "Farmer Test Scheme",
-        "chunk_text": "Farmers get 6000 rupees a year",
-        "embedding": embedding_numbers,
-        "source_url": "test.com"
-    }
-    
-    try:
-        supabase.table("document_chunks").insert(data).execute()
-        print("✅ SUCCESS! The data is saved in your database!")
-    except Exception as e:
-        print(f"❌ Error: {e}")
-
-if __name__ == "__main__":
-    test_ai_database()

ask_brain.py

@@ -1,44 +0,0 @@
-from sentence_transformers import SentenceTransformer
-from supabase import create_client, Client
-from config import settings
-
-SUPABASE_URL = settings.SUPABASE_URL
-SUPABASE_KEY = settings.SUPABASE_KEY
-
-supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
-
-def ask_question():
-    print("Waking up the AI...")
-    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-    
-    # We are asking about a "kisan" (which we didn't save in the database)
-    user_question = "How can a kisan get financial help?"
-    print(f"\nQuestion: '{user_question}'")
-    
-    print("Converting question into numbers...")
-    query_numbers = model.encode(user_question).tolist()
-    
-    print("Searching Supabase...")
-    try:
-        # Ask Supabase to run the 'match_documents' SQL function
-        response = supabase.rpc("match_documents", {
-            "query_embedding": query_numbers,
-            "match_count": 1
-        }).execute()
-        
-        matches = response.data
-        
-        if matches:
-            best_match = matches[0]
-            print("\n✨ --- AI FOUND A MATCH! --- ✨")
-            print(f"Scheme: {best_match['scheme_title']}")
-            print(f"Details: {best_match['chunk_text']}")
-            print(f"Accuracy Score: {best_match['similarity']:.2f}")
-        else:
-            print("\n❌ No documents found.")
-            
-    except Exception as e:
-        print(f"❌ Error: {e}")
-
-if __name__ == "__main__":
-    ask_question()