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I'm sorry, but the provided extracts do not contain specific information or example code about implementing StreamlitChatPack to build a chatbot using Streamlit and LlamaIndex. The extracts do mention the use of Streamlit and LlamaIndex in building chatbots, but they do not provide detailed instructions or code examples for using a specific tool called StreamlitChatPack.
However, the extracts do provide some general insights into building chatbots with LlamaIndex and Streamlit. For instance, LlamaIndex serves as a bridge between your data and Large Language Models (LLMs), providing a toolkit that enables you to establish a query interface around your data for a variety of tasks, such as question-answering and summarization (
source ()).
In terms of using Streamlit, one extract provides a simple example of a Streamlit interface for a term extractor, which could potentially be adapted for a chatbot (
source ()):
import streamlit as st
st.title("🦙 Llama Index Term Extractor 🦙")
document_text = st.text_area("Or enter raw text")
if st.button("Extract Terms and Definitions") and document_text:
with st.spinner("Extracting..."):
extracted_terms = document_text # this is a placeholder!
st.write(extracted_terms)For more detailed instructions and code examples, I would recommend referring to the official documentation or tutorials for StreamlitChatPack, Streamlit, and LlamaIndex.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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