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import langchain
from langchain.embeddings.openai import OpenAIEmbeddings
# from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import VectorDBQA
from langchain.chains import RetrievalQA
from langchain.document_loaders import DirectoryLoader
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.evaluation.qa import QAGenerateChain
import magic
import os
import streamlit as st
from streamlit_chat import message
st.title("Welcome to AutoBot")
if 'responses' not in st.session_state:
st.session_state['responses'] = ["How can I assist you?"]
if 'requests' not in st.session_state:
st.session_state['requests'] = []
openai_api_key = os.getenv("OPENAI_API_KEY", "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")
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
new_db = FAISS.load_local("faiss_index_diagnostics_RCV", embeddings)
llm = OpenAI(openai_api_key=openai_api_key, temperature=0.0)
# if 'buffer_memory' not in st.session_state:
memory= ConversationBufferMemory(memory_key="chat_history", return_messages=True)
retriever = new_db.as_retriever()
chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type="stuff", memory= memory,retriever=retriever, verbose=False)
# container for chat history
response_container = st.container()
# container for text box
textcontainer = st.container()
with textcontainer:
query = st.text_input(label="Please Enter Your Prompt Here: ", placeholder="Ask me")
if query:
with st.spinner("Generating..."):
# conversation_string = get_conversation_string()
# st.code(conversation_string)
# refined_query = query_refiner(conversation_string, query)
# st.subheader("Refined Query:")
# st.write(refined_query)
# context = find_match(refined_query)
# print(context)
response = chain.run(query)
st.session_state.requests.append(query)
st.session_state.responses.append(response)
with response_container:
if st.session_state['responses']:
for i in range(len(st.session_state['responses'])):
message(st.session_state['responses'][i],key=str(i))
if i < len(st.session_state['requests']):
message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user')
# with st.expander('Message history'):
# st.info(memory.buffer)
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