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import os
import tempfile
import time
import streamlit as st
from dotenv import load_dotenv
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema import StrOutputParser
from langchain.vectorstores import Vectara
# Load environment variables from .env file
load_dotenv()
# Sidebar for PDF upload and API keys
with st.sidebar:
st.header("Configuration")
uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])
customer_id = st.text_input("Vectara Customer ID", value=os.getenv("CUSTOMER_ID", ""))
api_key = st.text_input("Vectara API Key", value=os.getenv("API_KEY", ""))
corpus_id = st.text_input("Vectara Corpus ID", value=str(os.getenv("CORPUS_ID", "")))
openai_api_key = st.text_input("OpenAI API Key", value=os.getenv("OPENAI_API_KEY", ""))
submit_button = st.button("Submit")
keys_provided = all([customer_id, api_key, corpus_id, openai_api_key])
if keys_provided:
CUSTOMER_ID = customer_id
API_KEY = api_key
CORPUS_ID = int(corpus_id)
OPENAI_API_KEY = openai_api_key
vectara_client = Vectara(
vectara_customer_id=CUSTOMER_ID,
vectara_corpus_id=CORPUS_ID,
vectara_api_key=API_KEY
)
# Function to get knowledge content from Vectara
def get_knowledge_content(vectara, query, threshold=0.5):
found_docs = vectara.similarity_search_with_score(
query,
score_threshold=threshold,
)
knowledge_content = ""
for number, (score, doc) in enumerate(found_docs):
knowledge_content += f"Document {number}: {found_docs[number][0].page_content}\n"
return knowledge_content
# Prompt and response setup
prompt = PromptTemplate.from_template(
"""You are a professional and friendly Legal Consultant and you are helping a client with a legal issue. The client is asking you for advice on a legal issue. Just explain him in detail the answer and nothing else. This is the issue: {issue}
To assist him with his issue, you need to know the following information: {knowledge}
"""
)
runnable = prompt | ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], openai_api_key=OPENAI_API_KEY) | StrOutputParser()
# Main Streamlit App
st.title("Legal Consultation Chat")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input and run the main chat interaction
if user_input := st.chat_input("Enter your issue:"):
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
knowledge_content = get_knowledge_content(vectara_client, user_input)
response = runnable.invoke({"knowledge": knowledge_content, "issue": user_input})
response_words = response.split()
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
for word in response_words:
full_response += word + " "
time.sleep(0.05)
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
# Run when the submit button is pressed
if submit_button and uploaded_file:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile:
tmpfile.write(uploaded_file.getvalue())
tmp_filename = tmpfile.name
try:
vectara_client.add_files([tmp_filename])
st.sidebar.success("PDF file successfully uploaded to Vectara!")
except Exception as e:
st.sidebar.error(f"An error occurred: {str(e)}")
finally:
os.remove(tmp_filename) # Clean up temporary file
else:
# Not all keys are provided, instruct the user to input them
st.warning("Please input all required API keys in the sidebar to proceed.") |