Upload app.py

app.py

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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/llama-2-13b-chat.Q4_K_S.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)
+