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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2020_emaster_keynote.pdf filter=lfs diff=lfs merge=lfs -text

2020_emaster_keynote.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8dc98802d24d9c1f51d53f5c082241e68e29a7212661c626afc32a2fa6d37ec0
+size 4046421

app.py

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+import gradio as gr
+from dotenv import load_dotenv
+load_dotenv()
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain.chains import RetrievalQA
+
+#Loader
+loader = PyPDFLoader("2020_emaster_keynote.pdf")
+pages = loader.load_and_split()
+
+#Split
+text_splitter = RecursiveCharacterTextSplitter(
+    # Set a really small chunk size, just to show.
+    chunk_size = 300,
+    chunk_overlap  = 20,
+    length_function = len,
+    is_separator_regex = False,
+)
+texts = text_splitter.split_documents(pages)
+
+#Embedding
+embeddings_model = OpenAIEmbeddings()
+
+# load it into Chroma
+db = Chroma.from_documents(texts, embeddings_model)
+
+# question = "데이터베이스의 정의?"
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.5)
+qa_chain = RetrievalQA.from_chain_type(llm,retriever=db.as_retriever())
+
+
+# 인터페이스를 생성.
+with gr.Blocks() as demo:
+    gr.Image('images/emaster.png')
+
+    gr.Text('''
+                이런 질문 어떠세요? 
+                데이터베이스의 정의
+            ''')    
+    chatbot = gr.Chatbot(label="정보처리산업기사챗봇") # 청년정책챗봇 레이블을 좌측 상단에 구성
+    msg = gr.Textbox(label="질문해주세요!")  # 하단의 채팅창의 레이블
+    clear = gr.Button("대화 초기화")  # 대화 초기화 버튼
+
+    # 챗봇의 답변을 처리하는 함수
+    def respond(message, chat_history):
+      result = qa_chain({"query": message})
+    #   result = qa_chain(message)
+      bot_message = result['result']
+
+      # 채팅 기록에 사용자의 메시지와 봇의 응답을 추가.
+      chat_history.append((message, bot_message))
+      return "", chat_history
+
+    # 사용자의 입력을 제출(submit)하면 respond 함수가 호출.
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+    # '초기화' 버튼을 클릭하면 채팅 기록을 초기화.
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+# 인터페이스 실행.
+demo.launch(debug=True, share=True)

requirements.txt

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+langchain
+python-dotenv
+pypdf
+cryptography
+openai
+chromadb
+tiktoken
+gradio