Update app.py

app.py

@@ -11,64 +11,22 @@ from langchain.schema import AIMessage, HumanMessage
 
 
 import os
-# os.environ["OPENAI_API_KEY"] = 'sk-U'
-
 OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
 
 import pinecone
-
 # 初始化 pinecone
 pinecone.init(
   api_key=os.getenv('pinecone_api_key'),
   environment="gcp-starter"
 )
 
-index_name="pdf-index"
-
-
-pdf_files = ['./ANSYS_Fluent_Text_Command_List.pdf']
-raw_text = ''
-
-for file in pdf_files:
-    reader = PdfReader(file)
-    for i, page in enumerate(reader.pages):
-
-        text = page.extract_text()
-        if text:
-            raw_text += text
-
-
-text_splitter = CharacterTextSplitter(
-     separator="\n",
-     chunk_size=1000,
-     chunk_overlap=200,
-     length_function=len
- )
-
-# texts = text_splitter.split_text(raw_text)
-
-with open("output.txt", "w", encoding="utf-8") as file:
-    file.write(raw_text)
-
-texts = text_splitter.split_text(raw_text)
+index_name="text-index"
 
 embeddings = OpenAIEmbeddings()
-
-# 持久化数据
-#docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)
-
 # 加载数据
 docsearch = Pinecone.from_existing_index(index_name, embeddings)
-
-#embeddings = OpenAIEmbeddings()
-#embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-#docsearch = FAISS.from_texts(texts, embeddings)
 chain = load_qa_chain(OpenAI(), chain_type="stuff")
 
-
-
-#llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
-
 def predict(message, history):
     history_langchain_format = []
     for human, ai in history:
@@ -84,8 +42,6 @@ def predict(message, history):
             partial_message = partial_message + chunk['choices'][0]['delta']['content']
             yield partial_message
 
-    
-    #return response
 
 gr.ChatInterface(predict).queue().launch()