mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-08-26 03:49:10 +00:00
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding:utf-8 -*-
|
|
|
|
import gradio as gr
|
|
from langchain.agents import (
|
|
load_tools,
|
|
initialize_agent,
|
|
AgentType
|
|
)
|
|
from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
|
|
from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext
|
|
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
|
from llama_index import Document, GPTSimpleVectorIndex
|
|
|
|
def agent_demo():
|
|
llm = VicunaRequestLLM()
|
|
|
|
tools = load_tools(['python_repl'], llm=llm)
|
|
agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
|
|
agent.run(
|
|
"Write a SQL script that Query 'select count(1)!'"
|
|
)
|
|
|
|
def knowledged_qa_demo(text_list):
|
|
llm_predictor = LLMPredictor(llm=VicunaRequestLLM())
|
|
hfemb = VicunaEmbeddingLLM()
|
|
embed_model = LangchainEmbedding(hfemb)
|
|
documents = [Document(t) for t in text_list]
|
|
|
|
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
|
|
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
|
|
return index
|
|
|
|
|
|
def get_answer(q):
|
|
base_knowledge = """ 这是一段测试文字 """
|
|
text_list = [base_knowledge]
|
|
index = knowledged_qa_demo(text_list)
|
|
response = index.query(q)
|
|
return response.response
|
|
|
|
if __name__ == "__main__":
|
|
# agent_demo()
|
|
|
|
with gr.Blocks() as demo:
|
|
gr.Markdown("数据库智能助手")
|
|
with gr.Tab("知识问答"):
|
|
text_input = gr.TextArea()
|
|
text_output = gr.TextArea()
|
|
text_button = gr.Button()
|
|
|
|
text_button.click(get_answer, inputs=text_input, outputs=text_output)
|
|
|
|
demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
|
|
|