Update app.py

app.py

@@ -9,24 +9,24 @@ from langchain.vectorstores import Chroma
 import gradio as gr
 import tempfile
 
-
+#定义查询函数qa
 def qa(file, openaikey, query, chain_type, k):
     os.environ["OPENAI_API_KEY"] = openaikey
 
-    # load document
+    # load document 加载PDF文件
     loader = PyPDFLoader(file.name)
     documents = loader.load()
-    # split the documents into chunks
+    # split the documents into chunks 将PDF文件分割成小块
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
     texts = text_splitter.split_documents(documents)
-    # select which embeddings we want to use
+    # select which embeddings we want to use 使用 OpenAI 的embeddings模型为每个文本块创建一个向量嵌入
     embeddings = OpenAIEmbeddings()
-    # create the vectorestore to use as the index
+    # create the vectorestore to use as the index 创建一个向量存储VectorStore，用于后续的搜索。
     db = Chroma.from_documents(texts, embeddings)
-    # expose this index in a retriever interface
+    # expose this index in a retriever interface 使用这个向量存储VectorStore创建一个检索器retriever
     retriever = db.as_retriever(
         search_type="similarity", search_kwargs={"k": k})
-    # create a chain to answer questions
+    # create a chain to answer questions 然后使用这个检索器和 OpenAI 的模型创建一个问答链来回答问题。
     qa = RetrievalQA.from_chain_type(
         llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
     result = qa({"query": query})
@@ -37,15 +37,16 @@ def qa(file, openaikey, query, chain_type, k):
 iface = gr.Interface(
     fn=qa, 
     inputs=[
-        gr.inputs.File(label="Upload PDF"),
+        gr.inputs.File(label="上传PDF"),
         gr.inputs.Textbox(label="OpenAI API Key"),
-        gr.inputs.Textbox(label="Your question"),
+        gr.inputs.Textbox(label="你的问题"),
+        #longchain的文档documents分析功能的不同类型，具体见https://python.langchain.com.cn/docs/modules/chains/document/的解释
         gr.inputs.Dropdown(choices=['stuff', 'map_reduce', "refine", "map_rerank"], label="Chain type"),
         gr.inputs.Slider(minimum=1, maximum=5, default=2, label="Number of relevant chunks"),
     ], 
     outputs="text",
-    title="Question Answering with your PDF file",
-    description="Upload a PDF file, enter OpenAI API key, type a question and get your answer."
+    title="你可以问我关于你上传的PDF文件的任何信息！",
+    description="1) 上传一个PDF文件. 2)输入你的OpenAI API key.这将产生费用  3) 输入问题然后点击运行."
 )
 
 iface.launch()