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import os
import streamlit as st
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_databricks.vectorstores import DatabricksVectorSearch
DATABRICKS_HOST = os.environ.get("DATABRICKS_HOST")
DATABRICKS_TOKEN = os.environ.get("DATABRICKS_TOKEN")
VS_ENDPOINT_NAME = os.environ.get("VS_ENDPOINT_NAME")
VS_INDEX_NAME = os.environ.get("VS_INDEX_NAME")
if DATABRICKS_HOST is None:
raise ValueError("DATABRICKS_HOST environment variable must be set")
if DATABRICKS_TOKEN is None:
raise ValueError("DATABRICKS_API_TOKEN environment variable must be set")
TITLE = "VUMC Chatbot"
DESCRIPTION="The first generation VUMC chatbot with knowledge of Vanderbilt specific terms."
EXAMPLE_PROMPTS = [
"Write a short story about a robot that has a nice day.",
"In a table, what are some of the most common misconceptions about birds?",
"Give me a recipe for vegan banana bread.",
"Code a python function that can run merge sort on a list.",
"Give me the character profile of a gumdrop obsessed knight in JSON.",
"Write a rap battle between Alan Turing and Claude Shannon.",
]
st.set_page_config(layout="wide")
st.title(TITLE)
st.markdown(DESCRIPTION)
st.markdown("\n")
# use this to format later
with open("style.css") as css:
st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
# Same embedding model we used to create embeddings of terms
# make sure we cache this so that it doesnt redownload each time, hindering Space start time if sleeping
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en", cache_folder="./langchain_cache/")
vector_store = DatabricksVectorSearch(
endpoint=VS_ENDPOINT_NAME,
index_name=VS_INDEX_NAME,
embedding=embeddings,
text_column="name",
columns=["name", "description"],
)
results = vector_store.similarity_search(query="Tell me about what a data lake is.", k=5)
st.write(results)
# DBRX mainbody minus functions
# main = st.container()
# with main:
# history = st.container(height=400)
# with history:
# for message in st.session_state["messages"]:
# avatar = None
# if message["role"] == "assistant":
# avatar = MODEL_AVATAR_URL
# with st.chat_message(message["role"],avatar=avatar):
# if message["content"] is not None:
# st.markdown(message["content"])
# if message["error"] is not None:
# st.error(message["error"],icon="🚨")
# if message["warning"] is not None:
# st.warning(message["warning"],icon="⚠️")
# if prompt := st.chat_input("Type a message!", max_chars=1000):
# handle_user_input(prompt)
# st.markdown("\n") #add some space for iphone users
# with st.sidebar:
# with st.container():
# st.title("Examples")
# for prompt in EXAMPLE_PROMPTS:
# st.button(prompt, args=(prompt,), on_click=handle_user_input) |