File size: 3,190 Bytes
da1ce01 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import streamlit as st
import random
from app_config import SYSTEM_PROMPT, NLP_MODEL_NAME, NUMBER_OF_VECTORS_FOR_RAG, NLP_MODEL_TEMPERATURE, NLP_MODEL_MAX_TOKENS, VECTOR_MAX_TOKENS, my_vector_store, chat, tiktoken_len
from langchain.memory import ConversationSummaryBufferMemory
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from pathlib import Path
import os
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)
# Initialize vector store and LLM outside session state
retriever = my_vector_store.as_retriever(k=NUMBER_OF_VECTORS_FOR_RAG)
llm = ChatGroq(temperature=NLP_MODEL_TEMPERATURE, groq_api_key=str(os.getenv('GROQ_API_KEY')), model_name=NLP_MODEL_NAME)
def response_generator(prompt: str) -> str:
try:
docs = retriever.invoke(prompt)
my_context = [doc.page_content for doc in docs]
my_context = '\n\n'.join(my_context)
system_message = SystemMessage(content=SYSTEM_PROMPT.format(context=my_context, previous_message_summary=st.session_state.rag_memory.moving_summary_buffer))
print(system_message)
chat_messages = (system_message + st.session_state.rag_memory.chat_memory.messages + HumanMessage(content=prompt)).messages
print("total tokens: ", tiktoken_len(str(chat_messages)))
response = llm.invoke(chat_messages)
return response.content
except Exception as error:
print(error, "ERROR")
return "Oops! something went wrong, please try again."
st.markdown(
"""
<style>
.st-emotion-cache-janbn0 {
flex-direction: row-reverse;
text-align: right;
}
</style>
""",
unsafe_allow_html=True,
)
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "system", "content": SYSTEM_PROMPT}]
if "rag_memory" not in st.session_state:
st.session_state.rag_memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=5000)
if "retriever" not in st.session_state:
st.session_state.retriever = retriever
st.title("Call on Doc prescription Recommendation")
container = st.container(height=600)
for message in st.session_state.messages:
if message["role"] != "system":
with container.chat_message(message["role"]):
st.write(message["content"])
if prompt := st.chat_input("Enter your query here... "):
with container.chat_message("user"):
st.write(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
with container.chat_message("assistant"):
response = response_generator(prompt=prompt)
print("******************************************************** Response ********************************************************")
print("MY RESPONSE IS:", response)
st.write(response)
print("Response is:", response)
st.session_state.rag_memory.save_context({'input': prompt}, {'output': response})
st.session_state.messages.append({"role": "assistant", "content": response}) |