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pdf-rag-bot/.env

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+OPENAI_API_KEY = "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"

pdf-rag-bot/app.py

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+import streamlit as st
+import os
+import tempfile
+from dotenv import load_dotenv
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+load_dotenv()
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+st.set_page_config(page_title="PDF Destek Botu", page_icon="📄")
+st.title("📄 PDF Destek Botu (RAG + Memory)")
+st.write("Bir PDF yükleyin, içeriğine dair sorular sorun. Türkçe desteklidir.")
+
+uploaded_file = st.file_uploader("📎 PDF Dosyanızı yükleyin", type="pdf", key="pdf_uploader")
+
+if uploaded_file is not None:
+    if "last_uploaded_name" not in st.session_state or uploaded_file.name != st.session_state.last_uploaded_name:
+        with st.spinner("📚 PDF işleniyor..."):
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+                tmp.write(uploaded_file.read())
+                tmp_path = tmp.name
+
+            loader = PyPDFLoader(tmp_path)
+            documents = loader.load()
+
+            splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+            docs = splitter.split_documents(documents)
+
+            embedding = OpenAIEmbeddings(model="text-embedding-3-large")
+            vectordb = FAISS.from_documents(docs, embedding)
+
+            memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+            llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+
+            qa_chain = ConversationalRetrievalChain.from_llm(
+                llm=llm,
+                retriever=vectordb.as_retriever(search_kwargs={"k": 3}),
+                memory=memory
+            )
+
+            st.session_state.qa_chain = qa_chain
+            st.session_state.chat_history = []
+            st.session_state.last_uploaded_name = uploaded_file.name
+
+            st.success("✅ PDF başarıyla işlendi!")
+
+if "qa_chain" in st.session_state:
+    user_input = st.text_input("🧠 Sorunuzu yazın:")
+
+    if user_input:
+        with st.spinner("✍️ Yanıt hazırlanıyor..."):
+            response = st.session_state.qa_chain.run(user_input)
+            st.session_state.chat_history.append(("🧑‍💼 Siz", user_input))
+            st.session_state.chat_history.append(("🤖 Bot", response))
+
+    if st.session_state.chat_history:
+        st.markdown("---")
+        st.subheader("💬 Sohbet Geçmişi")
+        for sender, message in st.session_state.chat_history:
+            st.markdown(f"**{sender}:** {message}")

pdf-rag-bot/requirements.txt

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+streamlit
+langchain
+langchain-community
+openai
+faiss-cpu
+pypdf
+python-dotenv