# ******* THIS FILE CONTAINS ALL THE PROMPTS & CHAINS USED IN Functions.py *********** from Templates import * from langchain import PromptTemplate from langchain.chains import LLMChain from langchain.llms import OpenAI from dotenv import load_dotenv import os import streamlit as st class PromptTemplates: def __init__(self): self.legal_adviser_bot_prompt = PromptTemplate( input_variables=["chat_history","input",], template=legal_adviser_template ) self.case_summary_prompt = PromptTemplate( input_variables=["case_name", "case_info"], template=case_summary_template ) self.legal_case_bot_prompt = PromptTemplate( input_variables=["case_summary", "context","input"], template=legal_case_bot_template ) self.lawyer_recommendations_prompt = PromptTemplate( input_variables=["user_inputs", "matching_lawyers", "additional_info"], template=lawyer_recommendation_template ) class LLMChains: def __init__(self): load_dotenv() openai_api_key = os.getenv("OPENAI_API_KEY") obj = PromptTemplates() model_name = st.session_state["selected_model"] # generate summary chain self.legal_adviser_bot_chain = LLMChain( llm=OpenAI(model_name='gpt-3.5-turbo-16k', temperature=0.7), prompt=obj.legal_adviser_bot_prompt, verbose="true", ) # genrate bot conversastion self.case_summary_chain = LLMChain( llm=OpenAI(model_name=model_name, temperature=0.7), prompt=obj.case_summary_prompt, verbose="true", ) # genrate bot conversastion self.legal_case_bot_chain = LLMChain( llm=OpenAI(model_name=model_name, temperature=0.7), prompt=obj.legal_case_bot_prompt, verbose="true", ) self.lawyer_recommendations_chain = LLMChain( llm=OpenAI(model_name="gpt-3.5-turbo-16k", temperature=0.7), prompt=obj.lawyer_recommendations_prompt, verbose="true", )