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level1.py

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+from langchain_experimental.agents import create_csv_agent
+from dotenv import load_dotenv
+from langchain_openai import AzureChatOpenAI
+import os
+load_dotenv()
+import streamlit as st
+import pandas as pd
+from langchain_community.document_loaders import JSONLoader
+import requests
+from langchain_openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+
+
+llm = AzureChatOpenAI(openai_api_version=os.environ.get("AZURE_OPENAI_VERSION", "2023-07-01-preview"),
+        azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt4chat"),
+        azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT", "https://gpt-4-trails.openai.azure.com/"),
+        api_key=os.environ.get("AZURE_OPENAI_KEY"))
+
+
+def metadata_func(record: str, metadata: dict) -> dict:
+    lines = record.split('\n')
+    locality_line = lines[10]
+    price_range_line = lines[12]
+    locality = locality_line.split(': ')[1]
+    price_range = price_range_line.split(': ')[1]
+    metadata["location"] = locality
+    metadata["price_range"] = price_range
+
+    return metadata
+
+# Instantiate the JSONLoader with the metadata_func
+jq_schema = '.parser[] | to_entries | map("\(.key): \(.value)") | join("\n")'
+loader = JSONLoader(
+    jq_schema=jq_schema,
+    file_path='data.json',
+    metadata_func=metadata_func,
+)
+
+# Load the JSON file and extract metadata
+documents = loader.load()
+
+
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    # Check if the FAISS index file already exists
+    if os.path.exists("faiss_index"):
+        # Load the existing FAISS index
+        vectorstore = FAISS.load_local("faiss_index", embeddings=embeddings)
+        print("Loaded existing FAISS index.")
+    else:
+        # Create a new FAISS index
+        embeddings = OpenAIEmbeddings()
+        vectorstore = FAISS.from_documents(documents=text_chunks, embedding=embeddings)
+        # Save the new FAISS index locally
+        vectorstore.save_local("faiss_index")
+        print("Created and saved new FAISS index.")
+    return vectorstore
+
+#docs = new_db.similarity_search(query)
+
+vector = get_vectorstore(documents)
+
+
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationSummaryMemory
+
+template = """
+
+context:- I have low budget what is the best hotel in Instanbul?
+anser:- The other hotels in instanbul are costly and are not in your budget. so the best hotel in instanbul for you is hotel is xyz."
+
+Don’t give information not mentioned in the CONTEXT INFORMATION. 
+The system should take into account various factors such as location, amenities, user reviews, and other relevant criteria to 
+generate informative and personalized explanations.
+{context} 
+Question: {question}
+Answer:"""
+
+prompt = PromptTemplate(template=template, input_variables=["context","question"])
+
+chain_type_kwargs = {"prompt": prompt}
+chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=vector.as_retriever(),
+    chain_type_kwargs=chain_type_kwargs,
+)
+
+
+def main():
+    st.title("Hotel Assistant Chatbot")
+    st.write("Welcome to the Hotel Assistant Chatbot!")
+    user_input = st.text_input("User Input:")
+    
+    if st.button("Submit"):
+        response = chain.run(user_input)
+        st.text_area("Chatbot Response:", value=response)
+    
+    if st.button("Exit"):
+        st.stop()
+
+if __name__ == "__main__":
+    main()