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import os | |
import streamlit as st | |
from llama_index.core import ( | |
VectorStoreIndex, | |
SimpleDirectoryReader, | |
StorageContext, | |
load_index_from_storage, | |
) | |
from dotenv import load_dotenv | |
import openai | |
# Load environment variables | |
load_dotenv() | |
# Set OpenAI API key | |
openai.api_key = os.environ['OPENAI_API_KEY'] | |
# Define the storage directory | |
PERSIST_DIR = "./storage" | |
# Check if storage already exists and load or create the index | |
if not os.path.exists(PERSIST_DIR): | |
# Load the documents and create the index | |
documents = SimpleDirectoryReader( | |
"data", | |
exclude_hidden=False, | |
).load_data() | |
index = VectorStoreIndex.from_documents(documents) | |
# Store it for later | |
index.storage_context.persist(persist_dir=PERSIST_DIR) | |
else: | |
# Load the existing index | |
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) | |
index = load_index_from_storage(storage_context) | |
# Create a QueryEngine for Retrieval & Augmentation | |
query_engine = index.as_query_engine() | |
# Streamlit app | |
st.title("RAG-Based Homeopathic Chat Assistant") | |
def get_medical_llm_response(query): | |
# Generate response from the specialized medical LLM | |
response = openai.chat.completions.create( | |
model="gpt-3.5-turbo", # Assuming this is a more evolved model suited for medical queries | |
messages=[ | |
{"role": "system", "content": "You are an expert in Homeopathic treatment with advanced training on medicine and diagnosis."}, | |
{"role": "user", "content": query} | |
] | |
) | |
return response.choices[0].message.content.strip() | |
# Initialize session state for chat history | |
if 'messages' not in st.session_state: | |
st.session_state.messages = [] | |
# Display chat messages from history on app rerun | |
for message in st.session_state.messages: | |
with st.chat_message(message["role"]): | |
st.markdown(message["content"]) | |
# Get user input | |
user_query_prefix = "Suggest all possible diagnosis, remedies and medicines with potency & dosage for symptoms combining " | |
if user_input := st.chat_input("Enter the symptoms separated by comma"): | |
# Add user message to chat history | |
user_input = user_query_prefix + user_input | |
st.session_state.messages.append({"role": "user", "content": user_input}) | |
with st.chat_message("user"): | |
st.markdown(user_input) | |
with st.spinner('Generating response...'): | |
# Get the RAG-based response | |
rag_response = query_engine.query(user_input).response | |
# Combine RAG response with LLM response | |
combined_query = f"Based on the following information, provide a comprehensive response:\n\n{rag_response}\n\nUser's query: {user_input}" | |
llm_response = get_medical_llm_response(combined_query) | |
# Add assistant message to chat history | |
st.session_state.messages.append({"role": "assistant", "content": llm_response}) | |
with st.chat_message("assistant"): | |
st.markdown(llm_response) | |