Hello,
I am developping simple chatbot to analyze .csv file, using langchain and I want to deploy it by streamlit.
But I cannot access to huggingface’s pretrained model using token because there is a firewall of my organization.
So I tried to download directly from Hugging Face’s repo here (all miniLM L6) :
[sentence-transformers/all-MiniLM-L6-v2 at main]
for embedding.
And I download the Llama2 model of The bloke.
Here is the source code :
import streamlit as st
from streamlit_chat import message
import tempfile
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import ConversationalRetrievalChain
DB_FAISS_PATH = 'vectorstore/db_faiss'
#Loading the model
def load_llm():
# Load the locally downloaded model here
llm = CTransformers(
model = "llama-2-7b-chat.ggmlv3.q8_0.bin",
model_type="llama",
max_new_tokens = 512,
temperature = 0.5
)
return llm
st.title("Chatbot using llama2 🦙🦜")
#st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)
uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
if uploaded_file :
#use tempfile because CSVLoader only accepts a file_path
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.getvalue())
tmp_file_path = tmp_file.name
loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={
'delimiter': ','})
data = loader.load()
#st.json(data)
embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2',
model_kwargs={'device': 'cpu'})
db = FAISS.from_documents(data, embeddings)
db.save_local(DB_FAISS_PATH)
llm = load_llm()
chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
def conversational_chat(query):
result = chain({"question": query, "chat_history": st.session_state['history']})
st.session_state['history'].append((query, result["answer"]))
return result["answer"]
if 'history' not in st.session_state:
st.session_state['history'] = []
if 'generated' not in st.session_state:
st.session_state['generated'] = ["Hello ! Ask me anything about " + uploaded_file.name + " 🤗"]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hey ! 👋"]
#container for the chat history
response_container = st.container()
#container for the user's text input
container = st.container()
with container:
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_input("Query:", placeholder="Talk to your csv data here (:", key='input')
submit_button = st.form_submit_button(label='Send')
if submit_button and user_input:
output = conversational_chat(user_input)
st.session_state['past'].append(user_input)
st.session_state['generated'].append(output)
if st.session_state['generated']:
with response_container:
for i in range(len(st.session_state['generated'])):
message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
But I do not know how can I connect my downloaded model & embedding model in offline environment.
Is there any particular parameter that I have to manipulate ?