الحل: دعم تحميل ملفات PDF في RAG

import streamlit as st
from langchain import hub
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
import fitz  # PyMuPDF

# Specify the filename of your local image
image_filename = 'Educative.png'

# Use st.image to display the image
st.image(image_filename, use_column_width=True)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

def read_pdf(file):
    pdf_document = fitz.open(stream=file.read(), filetype="pdf")
    text = ""
    for page_num in range(pdf_document.page_count):
        page = pdf_document.load_page(page_num)
        text += page.get_text()
    return text

def generate_response(uploaded_file, openai_api_key, query_text):
    # Load document if file is uploaded
    if uploaded_file is not None:
        # Check if the file is a PDF
        if uploaded_file.type == "application/pdf":
            document_text = read_pdf(uploaded_file)
        else:
            document_text = uploaded_file.read().decode()

        documents = [document_text]
        # Split documents into chunks
        text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
        texts = text_splitter.create_documents(documents)
        llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_api_key)
        # Select embeddings
        embeddings = OpenAIEmbeddings(model="text-embedding-3-small", openai_api_key=openai_api_key)
        # Create a vectorstore from documents
        database = Chroma.from_documents(texts, embeddings)
        # Create retriever interface
        retriever = database.as_retriever()
        prompt = hub.pull("rlm/rag-prompt")
        rag_chain = (
            {"context": retriever | format_docs, "question": RunnablePassthrough()}
            | prompt
            | llm
            | StrOutputParser()
        )
        # Create QA chain
        response = rag_chain.invoke(query_text)
        return response

# File upload
uploaded_file = st.file_uploader('Upload an article', type=['txt', 'pdf'])
# Query text
query_text = st.text_input('Enter your question:', placeholder='Please provide a short summary.', disabled=not uploaded_file)

# Form input and query
result = None
with st.form('myform', clear_on_submit=False, border=False):
    openai_api_key = st.text_input('OpenAI API Key', type='password', disabled=not (uploaded_file and query_text))
    submitted = st.form_submit_button('Submit', disabled=not(uploaded_file and query_text))
    if submitted and openai_api_key.startswith('sk-'):
        with st.spinner('Calculating...'):
            response = generate_response(uploaded_file, openai_api_key, query_text)
            result = response
if result:
    st.info(result)