hi

README.md

@@ -1,12 +1,9 @@
 ---
-title: SimRAG
-emoji: 🚀
-colorFrom: green
-colorTo: pink
+title: Simple RAG System
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.2.0
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,169 @@
+import os
+from pathlib import Path
+from typing import List, Dict, Any
+
+import gradio as gr
+from langchain_community.document_loaders import TextLoader, PyPDFLoader, Docx2txtLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+
+# LLM 設定（強烈建議使用環境變數）
+llm = ChatOpenAI(
+    base_url=os.getenv("LITELLM_BASE_URL"),
+    api_key=os.getenv("OPENAI_API_KEY"),
+    model=os.getenv("LLM_MODEL", "azure-gpt-4.1"),  # 改用更常見的預設模型
+    temperature=0.3,
+)
+
+# Embedding 模型（中文效果很好的小模型）
+embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")
+
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+
+prompt = PromptTemplate.from_template(
+    """你是一個有幫助且誠實的助手，請根據以下提供的上下文來回答問題。
+如果上下文不足以回答，請直接說「根據提供的文件，我無法回答這個問題。」，不要編造答案。
+
+上下文：
+{context}
+
+問題：{question}
+回答："""
+)
+
+
+# 上傳並建立向量庫
+def upload_and_build_db(files: List[Any], vectorstore_state: FAISS | None):
+    if not files:
+        return "請上傳至少一個文件。", None, vectorstore_state
+
+    docs = []
+    for file in files:
+        # Gradio 上傳的 file 是 tempfile.NamedTemporaryFile 物件，有 .name 屬性
+        file_path = file.name
+
+        try:
+            if file_path.lower().endswith(".pdf"):
+                loader = PyPDFLoader(file_path)
+            elif file_path.lower().endswith(".docx"):
+                loader = Docx2txtLoader(file_path)
+            else:
+                loader = TextLoader(file_path, encoding="utf-8")
+            
+            loaded_docs = loader.load()
+            docs.extend(loaded_docs)
+        except Exception as e:
+            return f"載入檔案失敗：{os.path.basename(file_path)}，錯誤：{str(e)}", None, vectorstore_state
+
+    if not docs:
+        return "沒有成功載入任何文件內容。", None, vectorstore_state
+
+    # 分塊
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    splits = text_splitter.split_documents(docs)
+
+    # 建立新向量庫
+    new_vectorstore = FAISS.from_documents(splits, embeddings)
+
+    success_msg = f"成功載入 {len(docs)} 個文件，共 {len(splits)} 個區塊，已建立專屬知識庫！現在可以開始提問。"
+    return success_msg, None, new_vectorstore
+
+
+# RAG 回答（使用新版 Gradio messages 格式）
+def rag_answer(question: str, history: List[Dict], vectorstore_state: FAISS | None):
+    if vectorstore_state is None:
+        new_message = {"role": "assistant", "content": "請先上傳文件並點擊「建立知識庫」。"}
+        return "", history + [new_message]
+
+    retriever = vectorstore_state.as_retriever(search_kwargs={"k": 4})
+
+    chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    try:
+        response = chain.invoke(question)
+    except Exception as e:
+        response = f"回答時發生錯誤：{str(e)}"
+
+    # 新格式：加入 user 和 assistant 兩條訊息
+    new_history = history + [
+        {"role": "user", "content": question},
+        {"role": "assistant", "content": response}
+    ]
+
+    return "", new_history
+
+
+# 清除聊天記錄
+def clear_chat():
+    return "", []
+
+
+# Gradio 介面
+with gr.Blocks(title="個人 RAG 問答系統", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 📚 個人 RAG 問答系統\n上傳你的 TXT、PDF、DOCX 文件，建立專屬知識庫，然後向它提問！")
+
+    vectorstore_state = gr.State(None)
+
+    with gr.Row():
+        file_input = gr.File(
+            label="上傳文件（支援 .txt、.pdf、.docx，可多檔）",
+            file_count="multiple",
+            type="filepath"
+        )
+
+    with gr.Row():
+        build_btn = gr.Button("建立知識庫", variant="primary", scale=1)
+        clear_btn = gr.Button("清除對話", variant="secondary", scale=1)
+
+    status = gr.Textbox(label="狀態訊息", interactive=False)
+
+    # 使用新版 messages 格式，明確初始化為空列表
+    chatbot = gr.Chatbot(
+        height=500,
+        value=[],
+        label="對話紀錄",
+        avatar_images=("https://em-content.zobj.net/source/apple/391/man-technologist_1f468-200d-1f4bb.png",
+                        "https://em-content.zobj.net/source/apple/391/robot_1f916.png")
+    )
+
+    msg = gr.Textbox(
+        label="你的問題",
+        placeholder="在這裡輸入問題，按 Enter 送出...",
+        scale=7
+    )
+
+    # 事件綁定
+    build_btn.click(
+        fn=upload_and_build_db,
+        inputs=[file_input, vectorstore_state],
+        outputs=[status, file_input, vectorstore_state]  # 清空檔案列表避免重複上傳
+    )
+
+    msg.submit(
+        fn=rag_answer,
+        inputs=[msg, chatbot, vectorstore_state],
+        outputs=[msg, chatbot]
+    )
+
+    clear_btn.click(
+        fn=clear_chat,
+        inputs=None,
+        outputs=[msg, chatbot]
+    )
+
+# Hugging Face Spaces 建議加上 share=True 產生公開連結（本地測試可關閉）
+demo.launch()

requirements.txt

@@ -0,0 +1,13 @@
+gradio
+langchain
+langchain-huggingface
+langchain_community
+langchain-openai
+langchain-core
+langchain-text-splitters   # 新增這行
+faiss-cpu
+pypdf
+python-docx
+sentence-transformers
+huggingface-hub
+openai