t2ag3 commited on
Commit
aafd261
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1 Parent(s): 02e3c4c

Update app.py

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Files changed (1) hide show
  1. app.py +5 -10
app.py CHANGED
@@ -20,7 +20,7 @@ def load_vector_store(embedding_model_name, vector_store_file, k=4):
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  retriever = vector_store.as_retriever(search_kwargs={"k": k})
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  return retriever
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- def fetch_response(groq_api_key, user_input, retriever1, retriever2):
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  chat = ChatGroq(
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  api_key = groq_api_key,
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  model_name = model_name
@@ -41,19 +41,17 @@ def fetch_response(groq_api_key, user_input, retriever1, retriever2):
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  # ドキュメントのリストを渡せるchainを作成
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  question_answer_chain = create_stuff_documents_chain(chat, prompt)
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  # RetrieverとQAチェーンを組み合わせてRAGチェーンを作成
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- rag_chain = create_retrieval_chain(retriever1, question_answer_chain)
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- rag_chain2 = create_retrieval_chain(retriever2, question_answer_chain)
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  response = rag_chain.invoke({"input": user_input})
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- response2 = rag_chain.invoke({"input": user_input})
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- return [response["answer"], response["context"][0], response["context"][1], response2["answer"], response2["context"][0], response2["context"][1]]
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  """
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  For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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  """
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  retriever = load_vector_store(emb_model_name, "kaihatsu_vector_store", 4)
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- retriever_rechunk = load_vector_store(emb_model_name, "kaihatsu_vector_store_rechunk", 4)
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  with gr.Blocks() as demo:
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  gr.Markdown('''# 「スマート農業技術の開発・供給に関する事業」マスター \n
@@ -66,16 +64,13 @@ with gr.Blocks() as demo:
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  user_input = gr.Textbox(label="User Input")
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  submit = gr.Button("Submit")
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  answer = gr.Textbox(label="Answer")
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- answer2 = gr.Textbox(label="Answer")
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  with gr.Row():
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  with gr.Column():
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  source1 = gr.Textbox(label="回答ソース1")
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- source2_1 = gr.Textbox(label="回答ソース1")
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  with gr.Column():
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  source2 = gr.Textbox(label="回答ソース2")
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- source2_2 = gr.Textbox(label="回答ソース2")
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- submit.click(fetch_response, inputs=[api_key, user_input, retriever, retriever_rechunk], outputs=[answer, source1, source2, answer2, source2_1, source2_2])
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  if __name__ == "__main__":
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  demo.launch()
 
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  retriever = vector_store.as_retriever(search_kwargs={"k": k})
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  return retriever
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+ def fetch_response(groq_api_key, user_input):
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  chat = ChatGroq(
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  api_key = groq_api_key,
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  model_name = model_name
 
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  # ドキュメントのリストを渡せるchainを作成
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  question_answer_chain = create_stuff_documents_chain(chat, prompt)
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  # RetrieverとQAチェーンを組み合わせてRAGチェーンを作成
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+ rag_chain = create_retrieval_chain(retriever, question_answer_chain)
 
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  response = rag_chain.invoke({"input": user_input})
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+ return [response["answer"], response["context"][0], response["context"][1]]
 
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  """
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  For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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  """
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  retriever = load_vector_store(emb_model_name, "kaihatsu_vector_store", 4)
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+ #retriever_rechunk = load_vector_store(emb_model_name, "kaihatsu_vector_store_rechunk", 4)
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  with gr.Blocks() as demo:
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  gr.Markdown('''# 「スマート農業技術の開発・供給に関する事業」マスター \n
 
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  user_input = gr.Textbox(label="User Input")
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  submit = gr.Button("Submit")
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  answer = gr.Textbox(label="Answer")
 
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  with gr.Row():
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  with gr.Column():
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  source1 = gr.Textbox(label="回答ソース1")
 
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  with gr.Column():
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  source2 = gr.Textbox(label="回答ソース2")
 
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+ submit.click(fetch_response, inputs=[api_key, user_input], outputs=[answer, source1, source2])
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  if __name__ == "__main__":
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  demo.launch()