Update app.py

app.py

@@ -5,14 +5,36 @@ from langchain_community.vectorstores import FAISS
 import os
 from langchain.prompts import PromptTemplate
 from langchain_together import Together
-import os
+from langchain.chat_models import ChatOpenAI
+from htmlTemplates import css, bot_template, user_template
+from langchain.embeddings import openai
 from langchain.memory import ConversationBufferWindowMemory
 from langchain.chains import ConversationalRetrievalChain
 import streamlit as st
 import time
 
+from openai import OpenAI
+api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=api_key)
+
+
+# creating custom template to guide llm model
+custom_template ="""<s>[INST]You will start the conversation by greeting the user and introducing yourself as qanoon-bot,\
+stating your availability for legal assistance. Your next step will depend on the user's response.\
+If the user expresses a need for legal assistance in Pakistan, you will ask them to describe their case or problem.\
+After receiving the case or problem details from the user, you will provide the solutions and procedures according to the knowledge base and also give related penal codes and procedures. \
+However, if the user does not require legal assistance in Pakistan, you will immediately thank them and\
+say goodbye, ending the conversation. Remember to base your responses on the user's needs, providing accurate and\
+concise information regarding the Pakistan legal law and rights where applicable. Your interactions should be professional and\
+focused, ensuring the user's queries are addressed efficiently without deviating from the set flows.\
+CHAT HISTORY: {chat_history}
+QUESTION: {question}
+ANSWER:
+</s>[INST]
+"""
 
-embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
+embeddings=OpenAIEmbeddings()
+#embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
 #vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory="./data")
 #db_retriever =vectordb.as_retriever(search_type="similarity",search_kwargs={'k':4})
 
@@ -62,29 +84,17 @@ if "messages" not in st.session_state:
 if "memory" not in st.session_state:
     st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history",return_messages=True) 
 
-embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
+#embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
 #db=FAISS.load_local("/content/ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
 
-prompt_template = """<s>[INST]This is a chat template and As a legal chat bot specializing in pakistan Penal Code queries and , your primary objective is to provide accurate and concise information based on the user's questions. Do not generate your own questions and answers. You will adhere strictly to the instructions provided, offering relevant context from the knowledge base while avoiding unnecessary details. Your responses will be brief, to the point, and in compliance with the established format. If a question falls outside the given context, you will refrain from utilizing the chat history and instead rely on your own knowledge base to generate an appropriate response. You will prioritize the user's query and refrain from posing additional questions. The aim is to deliver professional, precise, and contextually relevant information pertaining to the Indian Penal Code.
-CONTEXT: {context}
-CHAT HISTORY: {chat_history}
-QUESTION: {question}
-ANSWER:
-</s>[INST]
-"""
 
-prompt = PromptTemplate(template=prompt_template,
+prompt = PromptTemplate(template=custom_template,
                         input_variables=['context', 'question', 'chat_history'])
 
 # You can also use other LLMs options from https://python.langchain.com/docs/integrations/llms. Here I have used TogetherAI API
 
 from config import together_api
-llm = Together(
-    model="mistralai/Mistral-7B-Instruct-v0.2",
-    temperature=0.5,
-    max_tokens=1024,
-   together_api_key=together_api
-)
+llm=ChatOpenAI(temperature=0.2,model_name='gpt-3.5-turbo-0125')
 qa = ConversationalRetrievalChain.from_llm(
     llm=llm,
     memory=st.session_state.memory,