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import streamlit as st
from langchain.llms import LlamaCpp
from langchain.memory import ConversationBufferMemory
from langchain.chains import RetrievalQA
from langchain.embeddings import FastEmbedEmbeddings
from langchain.vectorstores import Chroma
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain import hub
def init_retriever():
"""
Initialize and return the retriever function
"""
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(model_path="./models/openhermes-2.5-neural-chat-7b-v3-1-7b.Q5_K_M.gguf",
n_ctx=4000,
max_tokens=4000,
f16_kv=True,
callback_manager=callback_manager,
verbose=True)
embeddings = FastEmbedEmbeddings(model_name="BAAI/bge-small-en-v1.5", cache_dir="./embedding_model/")
db = Chroma(persist_directory="./vectordb/", embedding_function=embeddings)
rag_prompt_llama = hub.pull("rlm/rag-prompt-llama")
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=db.as_retriever(),
chain_type_kwargs={"prompt": rag_prompt_llama},
)
qa_chain.callback_manager = callback_manager
qa_chain.memory = ConversationBufferMemory()
return qa_chain
# Check if retriever is already initialized in the session state
if "retriever" not in st.session_state:
st.session_state.retriever = init_retriever()
# Function to apply rounded edges using CSS
def add_rounded_edges(image_path="./randstad_featuredimage.png", radius=30):
st.markdown(
f'<style>.rounded-img{{border-radius: {radius}px; overflow: hidden;}}</style>',
unsafe_allow_html=True,
)
st.image(image_path, use_column_width=True, output_format='auto')
# add side bar
with st.sidebar:
# add Randstad logo
add_rounded_edges()
st.title("💬 HR Chatbot")
st.caption("🚀 A chatbot powered by Local LLM")
clear = False
# Add clear chat button
if st.button("Clear Chat History"):
clear = True
st.session_state.messages = []
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "How can I help you?"}]
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").write(prompt)
chain = st.session_state.retriever
if clear:
chain.clean()
msg = chain.run(st.session_state.messages)
st.session_state.messages.append({"role": "assistant", "content": msg})
st.chat_message("assistant").write(msg)
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