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app.py

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+import os
+import uuid  # 고유 세션 ID 생성을 위한 라이브러리 추가
+import gradio as gr
+from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.chat_history import BaseChatMessageHistory, InMemoryChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+# 1. LLM 초기화
+llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+
+# 2. 문서 로드 및 벡터 DB 구축 (서버 구동 시 1회 실행)
+loader = PyPDFLoader("Maximizing Muscle Hypertrophy.pdf")
+pages = loader.load_and_split()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(pages)
+
+embeddings = GoogleGenerativeAIEmbeddings(model="gemini-embedding-001")
+vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
+retriever = vectorstore.as_retriever()
+
+# 3. RAG 체인 및 메모리 설정
+store = {}
+def get_session_history(session_id: str) -> BaseChatMessageHistory:
+    if session_id not in store:
+        store[session_id] = InMemoryChatMessageHistory()
+    return store[session_id]
+
+qa_prompt = ChatPromptTemplate.from_messages([
+    ("system", """논문 리뷰 전문가입니다. 제공된 문서를 바탕으로 한국어로 답변하세요.
+문서에 없는 내용은 모른다고 답하세요.
+
+{context}"""),
+    MessagesPlaceholder("chat_history"),
+    ("human", "{input}"),
+])
+
+question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+conversational_rag_chain = RunnableWithMessageHistory(
+    rag_chain,
+    get_session_history,
+    input_messages_key="input",
+    history_messages_key="chat_history",
+    output_messages_key="answer",
+)
+
+# 4. Gradio 연동 함수 (session_id를 동적으로 받도록 수정)
+def chat_response(message, history, session_id):
+    response = conversational_rag_chain.invoke(
+        {"input": message},
+        config={"configurable": {"session_id": session_id}}
+    )
+    return response["answer"]
+
+# 5. 다중 사용자 환경을 고려한 Gradio UI 실행 설정
+with gr.Blocks() as demo:
+    # 접속하는 사용자(브라우저)마다 고유한 UUID를 생성하여 상태값으로 은닉
+    session_state = gr.State(lambda: str(uuid.uuid4()))
+    
+    gr.ChatInterface(
+        fn=chat_response,
+        additional_inputs=[session_state], # 백엔드 함수에 고유 세션 ID 전달
+        title="💪 근비대 극대화 논문 Q&A 봇",
+        description="'Maximizing Muscle Hypertrophy' 논문에 대해 궁금한 점을 물어보세요!"
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+langchain-google-genai
+langchain-community
+langchain-chroma
+langchain-text-splitters
+pypdf
+chromadb