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from dotenv import load_dotenv |
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load_dotenv() |
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import os |
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from llama_index.core import VectorStoreIndex, Settings |
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding |
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from llama_index.llms.sambanovasystems import SambaNovaCloud |
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from llama_index.llms.cohere import Cohere |
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from llama_index.vector_stores.astra_db import AstraDBVectorStore |
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import streamlit as st |
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st.set_page_config( |
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page_title="Work-Permit Chatbot", |
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page_icon=":robot_face:" |
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) |
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with st.expander("Disclaimer", icon="ℹ️"): |
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st.markdown(''' |
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The responses provided by this 🤖 chatbot are for informational purposes only and are based on the content scraped from the IRCC website. While the chatbot strives to provide accurate and helpful answers, it may occasionally provide incorrect or outdated information. 🌐 For the most reliable and up-to-date details regarding work permits and immigration policies, please refer directly to the official 🔗 [IRCC website](https://www.canada.ca/en/immigration-refugees-citizenship/services/work-canada/permit.html). ❗ The use of this chatbot does not replace professional or official advice from authorized sources. |
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''') |
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st.title("Work-Permit Chabot") |
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@st.cache_resource(show_spinner=False) |
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def init(): |
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embed_model_name = "BAAI/bge-large-en-v1.5" |
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embed_model = HuggingFaceEmbedding(model_name=embed_model_name) |
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Settings.embed_model = embed_model |
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llm = SambaNovaCloud( |
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model="Meta-Llama-3.1-70B-Instruct", |
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context_window=100000, |
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max_tokens=1024, |
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temperature=0.3, |
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) |
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Settings.llm = llm |
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astra_db_store = AstraDBVectorStore( |
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token=os.getenv('ASTRA_DB_APPLICATION_TOKEN'), |
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api_endpoint=os.getenv('ASTRA_DB_API_ENDPOINT'), |
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collection_name="ircc_wp_vector_emb", |
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embedding_dimension=1024 |
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) |
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index = VectorStoreIndex.from_vector_store(vector_store=astra_db_store) |
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system_template = ( |
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"You are an AI assistant for a government agency." |
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"\nYour role is to provide clear, concise, empathetic, and accurate responses to user queries, adapting your language and tone to the context and nature of the query." |
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"\nFormat your response as a direct and tailored answer, addressing the user's query and providing relevant context and essential information only." |
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"\nInclude RELEVANT sources in the following format: '[Your response here, demonstrating understanding and sensitivity to the user's context and situation] Sources: 1. [URL](URL) 2. [URL](URL) Remember to respect confidentiality and keep your response well-structured, easy to understand, and empathetic.'" |
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"\nMake the response concise." |
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) |
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chat_engine = index.as_chat_engine( |
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similarity_top_k=5, |
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chat_mode="condense_plus_context", |
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system_prompt=system_template, |
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verbose=True, |
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streaming=True |
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) |
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return chat_engine |
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if "chat_engine" not in st.session_state: |
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st.session_state.chat_engine = init() |
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if "messages" not in st.session_state: |
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st.session_state.messages = [{ |
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"role": "Assistant", |
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"content": "How can I help you today?" |
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}] |
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if prompt := st.chat_input("Write your query here"): |
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st.session_state.messages.append({ |
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"role": "user", |
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"content": prompt |
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}) |
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for message in st.session_state.messages: |
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with st.chat_message(message['role']): |
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st.write(message['content']) |
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if st.session_state.messages[-1]['role'] == 'user' and prompt != None: |
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print("Prompt: ", prompt) |
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with st.chat_message("assistant"): |
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query = st.session_state.messages[-1]['content'] |
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response = st.session_state.chat_engine.stream_chat(query) |
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st.write_stream(response.response_gen) |
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st.session_state.messages.append({ |
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"role": "assistant", |
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"content": response.response |
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}) |
