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import streamlit as st
from knowledge_gpt.components.sidebar import sidebar
from knowledge_gpt.ui import (
wrap_doc_in_html,
is_query_valid,
is_file_valid,
is_open_ai_key_valid,
display_file_read_error,
)
from knowledge_gpt.core.caching import bootstrap_caching
from knowledge_gpt.core.parsing import read_file
from knowledge_gpt.core.chunking import chunk_file
from knowledge_gpt.core.embedding import embed_files
from knowledge_gpt.core.qa import query_folder
from knowledge_gpt.core.utils import get_llm
EMBEDDING = "openai"
VECTOR_STORE = "faiss"
MODEL_LIST = ["gpt-3.5-turbo", "gpt-4"]
# Uncomment to enable debug mode
# MODEL_LIST.insert(0, "debug")
st.set_page_config(page_title="KnowledgeGPT", page_icon="📖", layout="wide")
st.header("📖KnowledgeGPT")
# Enable caching for expensive functions
bootstrap_caching()
sidebar()
openai_api_key = st.session_state.get("OPENAI_API_KEY")
if not openai_api_key:
st.warning(
"Enter your OpenAI API key in the sidebar. You can get a key at"
" https://platform.openai.com/account/api-keys."
)
uploaded_file = st.file_uploader(
"Upload a pdf, docx, or txt file",
type=["pdf", "docx", "txt"],
help="Scanned documents are not supported yet!",
)
model: str = st.selectbox("Model", options=MODEL_LIST) # type: ignore
with st.expander("Advanced Options"):
return_all_chunks = st.checkbox("Show all chunks retrieved from vector search")
show_full_doc = st.checkbox("Show parsed contents of the document")
if not uploaded_file:
st.stop()
try:
file = read_file(uploaded_file)
except Exception as e:
display_file_read_error(e, file_name=uploaded_file.name)
chunked_file = chunk_file(file, chunk_size=300, chunk_overlap=0)
if not is_file_valid(file):
st.stop()
if not is_open_ai_key_valid(openai_api_key, model):
st.stop()
with st.spinner("Indexing document... This may take a while⏳"):
folder_index = embed_files(
files=[chunked_file],
embedding=EMBEDDING if model != "debug" else "debug",
vector_store=VECTOR_STORE if model != "debug" else "debug",
openai_api_key=openai_api_key,
)
with st.form(key="qa_form"):
query = st.text_area("Ask a question about the document")
submit = st.form_submit_button("Submit")
if show_full_doc:
with st.expander("Document"):
# Hack to get around st.markdown rendering LaTeX
st.markdown(f"<p>{wrap_doc_in_html(file.docs)}</p>", unsafe_allow_html=True)
if submit:
if not is_query_valid(query):
st.stop()
# Output Columns
answer_col, sources_col = st.columns(2)
llm = get_llm(model=model, openai_api_key=openai_api_key, temperature=0)
result = query_folder(
folder_index=folder_index,
query=query,
return_all=return_all_chunks,
llm=llm,
)
with answer_col:
st.markdown("#### Answer")
st.markdown(result.answer)
with sources_col:
st.markdown("#### Sources")
for source in result.sources:
st.markdown(source.page_content)
st.markdown(source.metadata["source"])
st.markdown("---")
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