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application.py
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application.py
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import streamlit as st
from streamlit_chat import message
from langchain.retrievers import AzureCognitiveSearchRetriever
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
memory = ConversationBufferMemory(
memory_key="chat_history", return_messages=True, output_key="answer"
)
def load_chain():
prompt_template = """You are a helpful assistant for questions about the fictive animal huninchen.
{context}
Question: {question}
Answer here:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
retriever = AzureCognitiveSearchRetriever(content_key="content", top_k=10)
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(),
memory=memory,
retriever=retriever,
combine_docs_chain_kwargs={"prompt": PROMPT},
)
return chain
chain = load_chain()
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("LangChain Demo")
if "generated" not in st.session_state:
st.session_state["generated"] = []
if "past" not in st.session_state:
st.session_state["past"] = []
def get_text():
input_text = st.text_input("You: ", "", key="input")
return input_text
user_input = get_text()
if user_input:
output = chain.run(question=user_input)
st.session_state.past.append(user_input)
st.session_state.generated.append(output)
if st.session_state["generated"]:
for i in range(len(st.session_state["generated"]) - 1, -1, -1):
message(st.session_state["generated"][i], key=str(i))
message(st.session_state["past"][i], is_user=True, key=str(i) + "_user")