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import streamlit as st |
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import time |
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from langchain.document_loaders import PyPDFLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain_together import TogetherEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.chat_models import ChatOpenAI |
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from langchain.chains import RetrievalQA |
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def batch_embed(texts, embeddings_model, batch_size=50): |
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all_embeddings = [] |
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for i in range(0, len(texts), batch_size): |
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batch = texts[i:i+batch_size] |
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embs = embeddings_model.embed_documents([doc.page_content for doc in batch]) |
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all_embeddings.extend(embs) |
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return all_embeddings |
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@st.cache_resource |
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def load_chunks_and_embeddings(): |
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pdf_loader = PyPDFLoader('test1.pdf') |
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pages = pdf_loader.load() |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=0) |
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docs = text_splitter.split_documents(pages) |
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embeddings = TogetherEmbeddings( |
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api_key="0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979" |
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) |
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vectorstore = FAISS.from_documents([], embedding=embeddings) |
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progress = st.progress(0, text="🔄 در حال پردازش چانکها...") |
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total = len(docs) |
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batch_size = 50 |
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for i in range(0, total, batch_size): |
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batch_docs = docs[i:i+batch_size] |
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embeddings_batch = embeddings.embed_documents([doc.page_content for doc in batch_docs]) |
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vectorstore.add_embeddings(embeddings_batch, batch_docs) |
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progress.progress(min((i+batch_size)/total, 1.0)) |
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progress.empty() |
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return vectorstore |
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with st.spinner("📚 در حال بارگذاری فایل و ساخت امبدینگها... لطفا صبور باشید"): |
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vectorstore = load_chunks_and_embeddings() |
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llm = ChatOpenAI( |
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base_url="https://api.together.xyz/v1", |
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api_key='0291f33aee03412a47fa5d8e562e515182dcc5d9aac5a7fb5eefdd1759005979', |
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model="meta-llama/Llama-3-70B-Instruct-Turbo-Free" |
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) |
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retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 10}) |
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chain = RetrievalQA.from_chain_type( |
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llm=llm, |
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chain_type='stuff', |
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retriever=retriever, |
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input_key='question' |
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) |
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if 'messages' not in st.session_state: |
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st.session_state.messages = [] |
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if 'pending_prompt' not in st.session_state: |
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st.session_state.pending_prompt = None |
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st.title("📄🤖 دستیار PDF شما") |
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for msg in st.session_state.messages: |
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with st.chat_message(msg['role']): |
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st.markdown(f"🗨️ {msg['content']}", unsafe_allow_html=True) |
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prompt = st.chat_input("سوالی از PDF داری؟") |
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if prompt: |
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st.session_state.messages.append({'role': 'user', 'content': prompt}) |
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st.session_state.pending_prompt = prompt |
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st.rerun() |
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if st.session_state.pending_prompt: |
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with st.chat_message('ai'): |
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thinking = st.empty() |
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thinking.markdown("🤖 در حال فکر کردن...") |
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response = chain.run(f'فقط به زبان فارسی جواب بده. سوال: {st.session_state.pending_prompt}') |
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answer = response.split("Helpful Answer:")[-1].strip() |
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if not answer: |
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answer = "متأسفم، اطلاعات دقیقی در این مورد ندارم." |
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thinking.empty() |
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full_response = "" |
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placeholder = st.empty() |
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for word in answer.split(): |
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full_response += word + " " |
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placeholder.markdown(full_response + "▌") |
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time.sleep(0.03) |
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placeholder.markdown(full_response) |
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st.session_state.messages.append({'role': 'ai', 'content': full_response}) |
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st.session_state.pending_prompt = None |
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