app.py

import gradio as gr
from huggingface_hub import InferenceClient
from langchain_core.vectorstores import InMemoryVectorStore
from langchain.chains import RetrievalQA
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_groq import ChatGroq


"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

model_name = "llama-3.3-70b-versatile"
groq_api_key = "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"
groq_chat = ChatGroq(groq_api_key=groq_api_key, model_name=model_name)
embeddings = HuggingFaceEmbeddings(
    model_name = "pkshatech/GLuCoSE-base-ja"
)
vector_store = InMemoryVectorStore.load(
    "/content/sample_data/kinousei_vector_store", embeddings
)
retriever = vector_store.as_retriever(search_kwargs={"k": 4})


def fetch_response(user_input):
  chat = ChatGroq(
    api_key = groq_api_key,
    model_name = model_name
  )
  system_prompt = (
    "あなたは便利なアシスタントです。"
    "マニュアルの内容から回答してください。"
    "\n\n"
    "{context}"
    )

  prompt = ChatPromptTemplate.from_messages(
      [
          ("system", system_prompt),
           ("human", "{input}"),
          ]
      )
  # ドキュメントのリストを渡せるchainを作成
  question_answer_chain = create_stuff_documents_chain(groq_chat, prompt)
  # RetrieverとQAチェーンを組み合わせてRAGチェーンを作成
  rag_chain = create_retrieval_chain(retriever, question_answer_chain)

  response = rag_chain.invoke({"input": user_input})
  return [response["answer"], response["context"][0], response["context"][1]]


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
with gr.Blocks() as demo:
    gr.Markdown('''# SOP事業マスター \n
                     SOP作成研究に関して、公募要領やQAを参考にRAGを使って回答します。
                ''')
    with gr.Row():
        with gr.Column():
            user_input = gr.Textbox(label="User Input")
            submit = gr.Button("Submit")
            answer = gr.Textbox(label="Answer")
    with gr.Row():
        with gr.Column():
            source1 = gr.Textbox(label="回答ソース１")
        with gr.Column():
            source2 = gr.Textbox(label="回答ソース２")
    submit.click(fetch_response, inputs=user_input, outputs=[answer, source1, source2])

if __name__ == "__main__":
    demo.launch()