connect oracle db

app.py

@@ -2,9 +2,8 @@ import streamlit as st
 import os
 import getpass
 import time
-from chatbot_helper import generate, get_docs
+from chatbot_helper import oracle_db
 from llama_index.core.llms import ChatMessage
-# from groq import Groq
 from pinecone import Pinecone
 from llama_index.llms.groq import Groq
 from semantic_router.encoders import HuggingFaceEncoder
@@ -58,12 +57,11 @@ if prompt := st.chat_input("What is up?"):
         st.markdown(prompt)
     
     if not prompt.__contains__("Yes") or not prompt.__contains__("No"):
-        docs = get_docs(prompt, 3,encoder, index)
-        docs = [str(i) for i in docs]
+        docs = oracle_db(prompt, 5)
 
         # result = generate(prompt, docs, groq_client, st.session_state.messages)
 
-    docs = "\n---\n".join(docs)
+    docs = "\n---\n".join([str(i) for i in docs])
     
     system_message =f'''
         You are a real state assistant act as that and help users find best properties in Dubai that fit there requirement using the

chatbot_helper.py

@@ -36,4 +36,24 @@ def generate(query: str, docs: list[str], groq_client, messages):
     print(chat_response)
     for chunk in chat_response:
         return chunk.choices[0].delta.content
-    # return chat_response.choices[0].message.content
+    # return chat_response.choices[0].message.content
+    
+def oracle_db(query:str, top_k:int) -> list[dict]:
+    import oracledb
+    connection = oracledb.connect(user="ai", password="testtest",dsn="91.75.21.131:9522/FREEPDB1")
+    cursor = connection.cursor()
+    exist = cursor.execute("""SELECT v.id, prop.*, t.*,
+            VECTOR_DISTANCE(v.vector,TO_VECTOR(VECTOR_EMBEDDING(ALL_MINILM_L12_V2 USING :query as data)), COSINE) AS distance
+        FROM 
+            ai.my_vectors v
+            JOIN ai.dld_property prop ON prop.property_id = v.id
+            JOIN ai.dld_trans t ON t.prop_id = v.id
+        ORDER BY distance ASC
+        FETCH FIRST :top_k ROWS ONLY""", query=query, top_k=top_k)
+    columns = [col[0] for col in cursor.description]
+    cursor.rowfactory = lambda *args: dict(zip(columns, args))
+    exist = cursor.fetchall()
+    print(query)
+    print(exist)
+    connection.close()
+    return exist