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import base64
from pathlib import Path
import streamlit as st
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import VoyageEmbeddings
from langchain.vectorstores.supabase import SupabaseVectorStore
from langchain.llms.together import Together
from st_supabase_connection import SupabaseConnection
msgs = StreamlitChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="history", chat_memory=msgs, return_messages=True
)
supabase_client = st.connection(
name="orbgpt",
type=SupabaseConnection,
ttl=None,
)
@st.cache_resource
def load_retriever():
# load embeddings using VoyageAI and Supabase
embeddings = VoyageEmbeddings(model="voyage-01")
vector_store = SupabaseVectorStore(
embedding=embeddings,
client=supabase_client.client,
table_name="documents",
query_name="match_documents",
)
return vector_store.as_retriever()
llm = Together(
model="togethercomputer/StripedHyena-Nous-7B",
temperature=0.5,
max_tokens=200,
top_k=1,
together_api_key=st.secrets.together_api_key,
)
retriever = load_retriever()
chat = ConversationalRetrievalChain.from_llm(llm, retriever)
st.markdown(
"<div style='display: flex;justify-content: center;'><img width='150' src='data:image/png;base64,{}' class='img-fluid'></div>".format(
base64.b64encode(Path("orbgptlogo.png").read_bytes()).decode()
),
unsafe_allow_html=True,
)
if st.button("Clear Chat", type="primary"):
msgs.clear()
if len(msgs.messages) == 0:
msgs.add_ai_message("Ask me anything about orb community projects!")
for msg in msgs.messages:
st.chat_message(msg.type).write(msg.content)
if prompt := st.chat_input("Ask something"):
st.chat_message("human").write(prompt)
msgs.add_user_message(prompt)
with st.chat_message("ai"):
with st.spinner("Processing your question..."):
response = chat({"question": prompt, "chat_history": memory.buffer})
msgs.add_ai_message(response["answer"])
st.write(response["answer"])
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