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jblitzar
/
github-python-corpus

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import os
import gc
import tempfile
import uuid
import pdfplumber
import streamlit as st
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.core import PromptTemplate
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.readers.docling import DoclingReader
from llama_index.core.node_parser import MarkdownNodeParser
if "session_id" not in st.session_state:
st.session_state.session_id = uuid.uuid4()
st.session_state.doc_cache = {}
user_session_id = st.session_state.session_id
llm_client = None
@st.cache_resource
def initialize_llm():
initialized_llm = Ollama(model="llama3.2", request_timeout=120.0)
return initialized_llm
def clear_chat_history():
st.session_state.chat_messages = []
st.session_state.chat_context = None
gc.collect()
def show_pdf_preview(uploaded_file):
st.markdown("### PDF Preview")
with pdfplumber.open(uploaded_file) as pdf:
first_page = pdf.pages[0]
text = first_page.extract_text()
st.write(text)
with st.sidebar:
st.header("Add your documents!")
uploaded_file = st.file_uploader("Select `.pdf` file", type=["pdf"])
if uploaded_file:
try:
with tempfile.TemporaryDirectory() as temp_dir:
saved_file_path = os.path.join(temp_dir, uploaded_file.name)
with open(saved_file_path, "wb") as file:
file.write(uploaded_file.getvalue())
document_key = f"{user_session_id}-{uploaded_file.name}"
st.write("Indexing PDF document...")
if document_key not in st.session_state.get('doc_cache', {}):
if os.path.exists(temp_dir):
reader = DoclingReader()
directory_loader = SimpleDirectoryReader(
input_dir=temp_dir,
file_extractor={".pdf": reader},
)
else:
st.error('Unable to find the uploaded file, please check again...')
st.stop()
documents = directory_loader.load_data()
loaded_llm = initialize_llm()
embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5", trust_remote_code=True)
Settings.embed_model = embedding_model
markdown_parser = MarkdownNodeParser()
index = VectorStoreIndex.from_documents(
documents=documents,
transformations=[markdown_parser],
show_progress=True
)
Settings.llm = loaded_llm
query_engine = index.as_query_engine(streaming=True)
custom_qa_prompt = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information above I want you to think step by step to answer the query in a highly precise and crisp manner focused on the final answer, in case you don't know the answer say 'I don't know!'.\n"
"Query: {query_str}\n"
"Answer: "
)
prompt_template = PromptTemplate(custom_qa_prompt)
query_engine.update_prompts({"response_synthesizer:text_qa_template": prompt_template})
st.session_state.doc_cache[document_key] = query_engine
else:
query_engine = st.session_state.doc_cache[document_key]
st.success("Let's Chat Now!")