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import os |
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import streamlit as st |
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from llama_index.llms.replicate import Replicate |
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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex |
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from llama_index.core.settings import Settings |
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from langchain.embeddings.huggingface import HuggingFaceBgeEmbeddings |
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os.environ["REPLICATE_API_TOKEN"] = st.secrets.replicate_key |
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st.header("Why Can't We All Just Get Along Chatbot") |
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@st.cache_resource(show_spinner=False) |
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def load_data(): |
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with st.spinner(text="Loading and indexing Henry and Fry's chapter – hang tight! This should take 1-2 minutes."): |
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llm = Replicate(model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5") |
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Settings.llm = llm |
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Settings.embed_model = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en") |
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documents = SimpleDirectoryReader("/Users/es26698/Documents/Book_Club/book_chapter/").load_data() |
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index = VectorStoreIndex.from_documents(documents,) |
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return index |
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index = load_data() |
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query_engine = index.as_query_engine() |
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prompt = st.text_input('Enter your question here:', 'Your question...') |
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if st.button('Submit Query'): |
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with st.spinner("Generating response... this may take a few minutes..."): |
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resp = query_engine.query(prompt) |
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st.write(resp.response) |
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