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import gradio as gr | |
from langchain_core.vectorstores import InMemoryVectorStore | |
from langchain.chains import RetrievalQA | |
from langchain_community.embeddings import HuggingFaceEmbeddings | |
from langchain_groq import ChatGroq | |
from langchain_core.prompts import ChatPromptTemplate | |
from langchain.chains import create_retrieval_chain | |
from langchain.chains.combine_documents import create_stuff_documents_chain | |
""" | |
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 | |
""" | |
model_name = "llama-3.3-70b-versatile" | |
emb_model_name = "pkshatech/GLuCoSE-base-ja" | |
def load_vector_store(embedding_model_name, vector_store_file, k=4): | |
embeddings = HuggingFaceEmbeddings(model_name = embedding_model_name) | |
vector_store = InMemoryVectorStore.load(vector_store_file, embeddings) | |
retriever = vector_store.as_retriever(search_kwargs={"k": k}) | |
return retriever | |
def fetch_response(groq_api_key, 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(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 | |
""" | |
retriever = load_vector_store(emb_model_name, "kaihatsu_vector_store", 4) | |
#retriever_rechunk = load_vector_store(emb_model_name, "kaihatsu_vector_store_rechunk", 4) | |
with gr.Blocks() as demo: | |
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 | |
「スマート農業技術の開発・供給に関する事業」に関して、公募要領や審査要領を参考にRAGを使って回答します。 | |
''') | |
with gr.Row(): | |
api_key = gr.Textbox(label="Groq API key") | |
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=[api_key, user_input], outputs=[answer, source1, source2]) | |
if __name__ == "__main__": | |
demo.launch() | |