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level2.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
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationSummaryMemory
+
+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)
+
+
+def api_call(text):
+  url = "https://api-ares.traversaal.ai/live/predict"
+
+  payload = { "query": [text]}
+  headers = {
+    "x-api-key": "ares_a0866ad7d71d2e895c5e05dce656704a9e29ad37860912ad6a45a4e3e6c399b5",
+    "content-type": "application/json"
+  }
+
+  response = requests.post(url, json=payload, headers=headers)
+
+  # here we will use the llm to summarize the response received from the ares api
+  response_data = response.json()
+  #print(response_data)
+  try:
+    response_text = response_data['data']['response_text']
+    web_urls = response_data['data']['web_url']
+    # Continue processing the data...
+  except KeyError:
+    print("Error: Unexpected response from the API. Please try again or contact the api owner.")
+    # Optionally, you can log the error or perform other error handling actions.
+  
+
+  if len(response_text) > 10000:
+    response_text = response_text[:8000]
+    prompt = f"Summarize the following text in 500-100 0 words and jsut summarize what you see and do not add anythhing else: {response_text}"
+    summary = llm.invoke(prompt)
+    print(summary)
+  else:
+    summary = response_text
+
+  result = "{} My list is: {}".format(response_text, web_urls)
+
+# Convert the result to a string
+  result_str = str(result)
+
+  return result_str
+
+
+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,
+)
+
+
+
+prompt = """Please write the response to the user query: using the final_response and api_resource and make sure you are
+The system should take into account various factors such as location, amenities, user reviews, and other relevant criteria to 
+generate informative and personalized explanations. Do not add any information that is not mentioned in the context.
+and make sure the answer is up to the point and not too long.
+
+question: when did sachin hit his 100th century?
+final_response: I can you assist you with hotel's or travels or food but cannot help other than that..
+
+"""
+
+
+def main():
+    st.title("Travel Assistant Chatbot JR")
+    st.write("Welcome to the Travel Assistant Chatbot!")
+    user_input = st.text_input("User Input:")
+    
+    if st.button("Submit"):
+        response = chain.run(user_input)
+        api_response = api_call(user_input)
+        response = llm.invoke(prompt+user_input+response + api_response)
+        st.text_area("Chatbot Response:", value=response.content)
+    
+    if st.button("Exit"):
+        st.stop()
+
+if __name__ == "__main__":
+    main()