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from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import VectorDBQA, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader, PyPDFLoader
from langchain import PromptTemplate
from PyPDF2 import PdfFileMerger
import gradio as gr
from dotenv import load_dotenv
import openai
import glob
import os
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.environ['OPENAI_API_KEY']
merge_file = 'src/retrieval_qa/pdf/merge.pdf'
if not os.path.isfile(merge_file):
pdf_file_merger = PdfFileMerger()
for file_name in glob.glob('src/retrieval_qa/pdf/*.pdf'):
pdf_file_merger.append(file_name)
pdf_file_merger.write(merge_file)
pdf_file_merger.close()
loader = PyPDFLoader(merge_file)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectordb = Chroma.from_documents(texts, embeddings)
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(
model_name="gpt-3.5-turbo"), chain_type="stuff", retriever=vectordb.as_retriever())
# プロンプトの定義
template = """
あなたは再生医療・美容医学について学習したAIアシスタントです。下記の質問に具体的で医学的な回答をしてください。
質問：{question}
回答：
"""
prompt = PromptTemplate(
input_variables=["question"],
template=template,
)
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
query = history[-1][0]
query = prompt.format(question=query)
answer = qa.run(query)
source = qa._get_docs(query)[0]
source_sentence = source.page_content
answer_source = source_sentence + "\n"+"source:" + \
source.metadata["source"] + ", page:" + str(source.metadata["page"])
history[-1][1] = answer # + "\n\n情報ソースは以下です：\n" + answer_source
return history
with gr.Blocks() as demo:
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=400)
with gr.Row():
with gr.Column(scale=0.6):
txt = gr.Textbox(
show_label=False,
placeholder="Enter text and press enter",
).style(container=False)
txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
bot, chatbot, chatbot
)
demo.launch()
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