Spaces:
Runtime error
Runtime error
File size: 4,161 Bytes
2392ba8 1e53020 6a57640 66b40bc 1e53020 6a57640 1e53020 6a57640 1e53020 6a57640 1e53020 2392ba8 1e53020 2392ba8 1e53020 d9be466 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 1e53020 2392ba8 bb22cc4 66b40bc 6a57640 2392ba8 66b40bc bb22cc4 66b40bc 6a57640 66b40bc 2392ba8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
import os
import streamlit as st
from langchain.chat_models import AzureChatOpenAI
from knowledge_gpt.components.sidebar import sidebar
from knowledge_gpt.core.caching import bootstrap_caching
from knowledge_gpt.core.chunking import chunk_file
from knowledge_gpt.core.embedding import embed_files
from knowledge_gpt.core.parsing import read_file
from knowledge_gpt.core.qa import query_folder
from knowledge_gpt.ui import display_file_read_error
from knowledge_gpt.ui import is_file_valid
from knowledge_gpt.ui import is_query_valid
from knowledge_gpt.ui import wrap_doc_in_html
st.set_page_config(page_title="ReferenceBot", page_icon="📖", layout="wide")
# add all secrets into environmental variables
if os.path.exists(
os.path.dirname(os.path.abspath(__file__)) + "/../.streamlit/secrets.toml"
): # to avoid redundant print by calling st.secrets
for key, value in st.secrets.items():
os.environ[key] = value
def main():
EMBEDDING = "openai"
VECTOR_STORE = "faiss"
MODEL_LIST = ["gpt-3.5-turbo", "gpt-4"]
# Uncomment to enable debug mode
# MODEL_LIST.insert(0, "debug")
st.header("📖ReferenceBot")
# Enable caching for expensive functions
bootstrap_caching()
sidebar()
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()
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",
deployment=os.environ["ENGINE_EMBEDDING"],
model=os.environ["ENGINE"],
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_base=os.environ["OPENAI_API_BASE"],
openai_api_type="azure",
chunk_size=1,
)
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)
with st.spinner("Setting up AzureChatOpenAI bot..."):
llm = AzureChatOpenAI(
openai_api_base=os.environ["OPENAI_API_BASE"],
openai_api_version=os.environ["OPENAI_API_VERSION"],
deployment_name=os.environ["ENGINE"],
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_type="azure",
temperature=0,
)
with st.spinner("Querying folder to get result..."):
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("---")
if __name__ == "__main__":
main()
|