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from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
import os
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
import streamlit as st
import time
from dotenv import load_dotenv, find_dotenv
from langchain_together import Together
load_dotenv(find_dotenv())
st.set_page_config(page_title="VX1000 BetaV Model")
col1, col2, col3 = st.columns([1, 2, 1])
with col2:
st.title("VX1000 BetaV 🦾")
st.caption('⚠️ **_Note: Please Wait 3 second after Prompt_**')
st.markdown(
"""
<style>
div.stButton > button:first-child {
background-color: #ffd0d0;
}
div.stButton > button:active {
background-color: #ff6262;
}
.st-emotion-cache-6qob1r {
position: relative;
height: 100%;
width: 100%;
background-color: black;
overflow: overlay;
}
div[data-testid="stStatusWidget"] div button {
display: none;
}
.reportview-container {
margin-top: -2em;
}
#MainMenu {visibility: hidden;}
.stDeployButton {display:none;}
footer {visibility: hidden;}
#stDecoration {display:none;}
button[title="View fullscreen"]{
visibility: hidden;}
</style>
""",
unsafe_allow_html=True,
)
def reset_conversation():
st.session_state.messages = []
st.session_state.memory.clear()
if "messages" not in st.session_state:
st.session_state.messages = []
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-ablated", model_kwargs={"trust_remote_code": True})
db = FAISS.load_local("data_byte", embeddings, allow_dangerous_deserialization=True)
db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
prompt_template = """<s>[INST]This is a chat template ,Don't generate gym related every time answer,your name is VX1000 BetaV Model,this model is made by Tarun,trained by Tarun,made by Tarun ,llm model is made by Tarun,you have 1 bilion parameters, and you are the private gpt model , your primary objective is to provide accurate and concise information related to code, question solving, 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, rely on your own knowledge base to generate an appropriate response. You will prioritize the user's query and refrain from posing additional questions and do not repeat the prompt template and the things that you have said already.
QUESTION: {question}
CONTEXT: {context}
CHAT HISTORY: {chat_history}[/INST]
ASSISTANT:
</s>
"""
prompt = PromptTemplate(template=prompt_template,
input_variables=['question', 'context', 'chat_history'])
llm = Together(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
temperature=0.7,
max_tokens=1024,
top_k=1,
together_api_key=os.environ['T_API']
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
memory=st.session_state.memory,
retriever=db_retriever,
combine_docs_chain_kwargs={'prompt': prompt}
)
for message in st.session_state.messages:
with st.chat_message(message.get("role")):
st.write(message.get("content"))
input_prompt = st.chat_input("Say something")
if input_prompt:
with st.chat_message("user"):
st.write(input_prompt)
st.session_state.messages.append({"role": "user", "content": input_prompt})
with st.chat_message("assistant"):
with st.status("Lifting data, one bit at a time 💡🦾...", expanded=True):
result = qa.invoke(input=input_prompt)
message_placeholder = st.empty()
full_response = "⚠️ **_Note: Information provided may be inaccurate._** \n\n\n"
for chunk in result["answer"]:
full_response += chunk
time.sleep(0.02)
message_placeholder.markdown(full_response + " ▌")
st.button('Reset All Chat 🗑️', on_click=reset_conversation)
st.session_state.messages.append({"role": "assistant", "content": result["answer"]}) |