mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-09-12 20:53:48 +00:00
chores: extra code clean
This commit is contained in:
csunny
@@ -1,74 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import gradio as gr
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from langchain.agents import AgentType, initialize_agent, load_tools
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from llama_index import (
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Document,
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GPTVectorStoreIndex,
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LangchainEmbedding,
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LLMPredictor,
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ServiceContext,
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)
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from pilot.model.llm_out.vicuna_llm import VicunaEmbeddingLLM, VicunaRequestLLM
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def agent_demo():
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llm = VicunaRequestLLM()
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tools = load_tools(["python_repl"], llm=llm)
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agent = initialize_agent(
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tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run("Write a SQL script that Query 'select count(1)!'")
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def knowledged_qa_demo(text_list):
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llm_predictor = LLMPredictor(llm=VicunaRequestLLM())
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hfemb = VicunaEmbeddingLLM()
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embed_model = LangchainEmbedding(hfemb)
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documents = [Document(t) for t in text_list]
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service_context = ServiceContext.from_defaults(
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llm_predictor=llm_predictor, embed_model=embed_model
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)
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index = GPTVectorStoreIndex.from_documents(
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documents, service_context=service_context
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)
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return index
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def get_answer(q):
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base_knowledge = """ """
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text_list = [base_knowledge]
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index = knowledged_qa_demo(text_list)
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response = index.query(q)
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return response.response
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def get_similar(q):
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from pilot.vector_store.extract_tovec import knownledge_tovec_st
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docsearch = knownledge_tovec_st("./datasets/plan.md")
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docs = docsearch.similarity_search_with_score(q, k=1)
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for doc in docs:
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dc, s = doc
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print(s)
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yield dc.page_content
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if __name__ == "__main__":
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# agent_demo()
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with gr.Blocks() as demo:
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gr.Markdown("数据库智能助手")
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with gr.Tab("知识问答"):
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text_input = gr.TextArea()
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text_output = gr.TextArea()
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text_button = gr.Button()
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text_button.click(get_similar, inputs=text_input, outputs=text_output)
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demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
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@@ -1,82 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import json
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import os
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import sys
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from urllib.parse import urljoin
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import gradio as gr
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import requests
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ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.append(ROOT_PATH)
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from langchain.prompts import PromptTemplate
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from pilot.configs.config import Config
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from pilot.conversation import conv_qa_prompt_template, conv_templates
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llmstream_stream_path = "generate_stream"
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CFG = Config()
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def generate(query):
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template_name = "conv_one_shot"
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state = conv_templates[template_name].copy()
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# pt = PromptTemplate(
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# template=conv_qa_prompt_template,
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# input_variables=["context", "question"]
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# )
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# result = pt.format(context="This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.",
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# question=query)
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# print(result)
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state.append_message(state.roles[0], query)
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state.append_message(state.roles[1], None)
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prompt = state.get_prompt()
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params = {
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"model": "chatglm-6b",
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"prompt": prompt,
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"temperature": 1.0,
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"max_new_tokens": 1024,
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"stop": "###",
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}
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response = requests.post(
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url=urljoin(CFG.MODEL_SERVER, llmstream_stream_path), data=json.dumps(params)
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)
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skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
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for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
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if chunk:
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data = json.loads(chunk.decode())
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if data["error_code"] == 0:
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if "vicuna" in CFG.LLM_MODEL:
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output = data["text"][skip_echo_len:].strip()
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else:
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output = data["text"].strip()
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state.messages[-1][-1] = output + "▌"
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yield (output)
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if __name__ == "__main__":
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print(CFG.LLM_MODEL)
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with gr.Blocks() as demo:
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gr.Markdown("数据库SQL生成助手")
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with gr.Tab("SQL生成"):
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text_input = gr.TextArea()
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text_output = gr.TextArea()
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text_button = gr.Button("提交")
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text_button.click(generate, inputs=text_input, outputs=text_output)
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demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
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@@ -1,19 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import logging
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import sys
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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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# read the document of data dir
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documents = SimpleDirectoryReader("data").load_data()
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# split the document to chunk, max token size=500, convert chunk to vector
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index = GPTVectorStoreIndex(documents)
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# save index
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index.save_to_disk("index.json")
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@@ -1,21 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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import gradio as gr
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def change_tab():
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return gr.Tabs.update(selected=1)
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with gr.Blocks() as demo:
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with gr.Tabs() as tabs:
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with gr.TabItem("Train", id=0):
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t = gr.Textbox()
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with gr.TabItem("Inference", id=1):
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i = gr.Image()
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btn = gr.Button()
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btn.click(change_tab, None, tabs)
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demo.launch()
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@@ -1,18 +0,0 @@
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from pilot.embedding_engine.csv_embedding import CSVEmbedding
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# path = "/Users/chenketing/Downloads/share_ireserve双写数据异常2.xlsx"
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path = "xx.csv"
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model_name = "your_path/all-MiniLM-L6-v2"
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vector_store_path = "your_path/"
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pdf_embedding = CSVEmbedding(
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file_path=path,
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model_name=model_name,
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vector_store_config={
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"vector_store_name": "url",
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"vector_store_path": "vector_store_path",
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},
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)
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pdf_embedding.source_embedding()
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print("success")
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@@ -1,18 +0,0 @@
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from pilot.embedding_engine.pdf_embedding import PDFEmbedding
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path = "xxx.pdf"
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path = "your_path/OceanBase-数据库-V4.1.0-应用开发.pdf"
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model_name = "your_path/all-MiniLM-L6-v2"
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vector_store_path = "your_path/"
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pdf_embedding = PDFEmbedding(
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file_path=path,
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model_name=model_name,
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vector_store_config={
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"vector_store_name": "ob-pdf",
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"vector_store_path": vector_store_path,
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},
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)
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pdf_embedding.source_embedding()
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print("success")
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@@ -1,17 +0,0 @@
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from pilot.embedding_engine.url_embedding import URLEmbedding
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path = "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023"
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model_name = "your_path/all-MiniLM-L6-v2"
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vector_store_path = "your_path"
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pdf_embedding = URLEmbedding(
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file_path=path,
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model_name=model_name,
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vector_store_config={
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"vector_store_name": "url",
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"vector_store_path": "vector_store_path",
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},
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)
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pdf_embedding.source_embedding()
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print("success")
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@@ -1,67 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import dashscope
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import requests
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import hashlib
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from http import HTTPStatus
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from dashscope import Generation
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def call_with_messages():
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messages = [
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{"role": "system", "content": "你是生活助手机器人。"},
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{"role": "user", "content": "如何做西红柿鸡蛋？"},
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]
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gen = Generation()
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response = gen.call(
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Generation.Models.qwen_turbo,
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messages=messages,
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stream=True,
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top_p=0.8,
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result_format="message", # set the result to be "message" format.
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)
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for response in response:
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# The response status_code is HTTPStatus.OK indicate success,
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# otherwise indicate request is failed, you can get error code
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# and message from code and message.
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if response.status_code == HTTPStatus.OK:
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print(response.output) # The output text
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print(response.usage) # The usage information
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else:
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print(response.code) # The error code.
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print(response.message) # The error message.
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def build_access_token(api_key: str, secret_key: str) -> str:
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"""
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Generate Access token according AK, SK
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"""
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url = "https://aip.baidubce.com/oauth/2.0/token"
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params = {
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"grant_type": "client_credentials",
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"client_id": api_key,
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"client_secret": secret_key,
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}
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res = requests.get(url=url, params=params)
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if res.status_code == 200:
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return res.json().get("access_token")
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def _calculate_md5(text: str) -> str:
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md5 = hashlib.md5()
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md5.update(text.encode("utf-8"))
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encrypted = md5.hexdigest()
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return encrypted
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def baichuan_call():
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url = "https://api.baichuan-ai.com/v1/stream/chat"
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if __name__ == "__main__":
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call_with_messages()
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@@ -1,257 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import torch
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.llms.base import LLM
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from llama_index import (
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GPTListIndex,
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GPTVectorStoreIndex,
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LangchainEmbedding,
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LLMPredictor,
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PromptHelper,
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SimpleDirectoryReader,
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)
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from transformers import pipeline
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class FlanLLM(LLM):
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model_name = "google/flan-t5-large"
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pipeline = pipeline(
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"text2text-generation",
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model=model_name,
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device=0,
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model_kwargs={"torch_dtype": torch.bfloat16},
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)
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def _call(self, prompt, stop=None):
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return self.pipeline(prompt, max_length=9999)[0]["generated_text"]
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def _identifying_params(self):
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return {"name_of_model": self.model_name}
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def _llm_type(self):
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return "custome"
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llm_predictor = LLMPredictor(llm=FlanLLM())
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hfemb = HuggingFaceEmbeddings()
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embed_model = LangchainEmbedding(hfemb)
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text1 = """
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执行计划是对一条 SQL 查询语句在数据库中执行过程的描述。用户可以通过 EXPLAIN 命令查看优化器针对指定 SQL 生成的逻辑执行计划。
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如果要分析某条 SQL 的性能问题，通常需要先查看 SQL 的执行计划，排查每一步 SQL 执行是否存在问题。所以读懂执行计划是 SQL 优化的先决条件，而了解执行计划的算子是理解 EXPLAIN 命令的关键。
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OceanBase 数据库的执行计划命令有三种模式：EXPLAIN BASIC、EXPLAIN 和 EXPLAIN EXTENDED。这三种模式对执行计划展现不同粒度的细节信息:
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EXPLAIN BASIC 命令用于最基本的计划展示。
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EXPLAIN EXTENDED 命令用于最详细的计划展示（通常在排查问题时使用这种展示模式）。
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EXPLAIN 命令所展示的信息可以帮助普通用户了解整个计划的执行方式。
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EXPLAIN 命令格式如下：
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EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt
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format_name:
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{ TRADITIONAL | JSON }
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explainable_stmt:
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{ SELECT statement
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| DELETE statement
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| INSERT statement
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| REPLACE statement
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| UPDATE statement }
|
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|
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|
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EXPLAIN 命令适用于 SELECT、DELETE、INSERT、REPLACE 和 UPDATE 语句，显示优化器所提供的有关语句执行计划的信息，包括如何处理该语句，如何联接表以及以何种顺序联接表等信息。
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一般来说，可以使用 EXPLAIN EXTENDED 命令，将表扫描的范围段展示出来。使用 EXPLAIN OUTLINE 命令可以显示 Outline 信息。
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|
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FORMAT 选项可用于选择输出格式。TRADITIONAL 表示以表格格式显示输出，这也是默认设置。JSON 表示以 JSON 格式显示信息。
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|
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使用 EXPLAIN PARTITITIONS 也可用于检查涉及分区表的查询。如果检查针对非分区表的查询，则不会产生错误，但 PARTIONS 列的值始终为 NULL。
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|
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对于复杂的执行计划，可以使用 PRETTY 或者 PRETTY_COLOR 选项将计划树中的父节点和子节点使用树线或彩色树线连接起来，使得执行计划展示更方便阅读。示例如下：
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obclient> CREATE TABLE p1table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 2;
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Query OK, 0 rows affected
|
||||
|
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obclient> CREATE TABLE p2table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 4;
|
||||
Query OK, 0 rows affected
|
||||
|
||||
obclient> EXPLAIN EXTENDED PRETTY_COLOR SELECT * FROM p1table p1 JOIN p2table p2 ON p1.c1=p2.c2\G
|
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*************************** 1. row ***************************
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Query Plan: ==========================================================
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||||
|ID|OPERATOR |NAME |EST. ROWS|COST|
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||||
----------------------------------------------------------
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||||
|0 |PX COORDINATOR | |1 |278 |
|
||||
|1 | EXCHANGE OUT DISTR |:EX10001|1 |277 |
|
||||
|2 | HASH JOIN | |1 |276 |
|
||||
|3 | ├PX PARTITION ITERATOR | |1 |92 |
|
||||
|4 | │ TABLE SCAN |P1 |1 |92 |
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||||
|5 | └EXCHANGE IN DISTR | |1 |184 |
|
||||
|6 | EXCHANGE OUT DISTR (PKEY)|:EX10000|1 |184 |
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||||
|7 | PX PARTITION ITERATOR | |1 |183 |
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||||
|8 | TABLE SCAN |P2 |1 |183 |
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||||
==========================================================
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||||
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||||
Outputs & filters:
|
||||
-------------------------------------
|
||||
0 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil)
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||||
1 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil), dop=1
|
||||
2 - output([P1.C1], [P2.C2], [P1.C2], [P2.C1]), filter(nil),
|
||||
equal_conds([P1.C1 = P2.C2]), other_conds(nil)
|
||||
3 - output([P1.C1], [P1.C2]), filter(nil)
|
||||
4 - output([P1.C1], [P1.C2]), filter(nil),
|
||||
access([P1.C1], [P1.C2]), partitions(p[0-1])
|
||||
5 - output([P2.C2], [P2.C1]), filter(nil)
|
||||
6 - (#keys=1, [P2.C2]), output([P2.C2], [P2.C1]), filter(nil), dop=1
|
||||
7 - output([P2.C1], [P2.C2]), filter(nil)
|
||||
8 - output([P2.C1], [P2.C2]), filter(nil),
|
||||
access([P2.C1], [P2.C2]), partitions(p[0-3])
|
||||
|
||||
1 row in set
|
||||
|
||||
|
||||
|
||||
|
||||
## 执行计划形状与算子信息
|
||||
|
||||
在数据库系统中，执行计划在内部通常是以树的形式来表示的，但是不同的数据库会选择不同的方式展示给用户。
|
||||
|
||||
如下示例分别为 PostgreSQL 数据库、Oracle 数据库和 OceanBase 数据库对于 TPCDS Q3 的计划展示。
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```sql
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obclient> SELECT /*TPC-DS Q3*/ *
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FROM (SELECT dt.d_year,
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item.i_brand_id brand_id,
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item.i_brand brand,
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Sum(ss_net_profit) sum_agg
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FROM date_dim dt,
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store_sales,
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item
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WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
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AND store_sales.ss_item_sk = item.i_item_sk
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AND item.i_manufact_id = 914
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AND dt.d_moy = 11
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GROUP BY dt.d_year,
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item.i_brand,
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item.i_brand_id
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ORDER BY dt.d_year,
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sum_agg DESC,
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brand_id)
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WHERE ROWNUM <= 100;
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PostgreSQL 数据库执行计划展示如下：
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Limit (cost=13986.86..13987.20 rows=27 width=91)
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Sort (cost=13986.86..13986.93 rows=27 width=65)
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Sort Key: dt.d_year, (sum(store_sales.ss_net_profit)), item.i_brand_id
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HashAggregate (cost=13985.95..13986.22 rows=27 width=65)
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Merge Join (cost=13884.21..13983.91 rows=204 width=65)
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Merge Cond: (dt.d_date_sk = store_sales.ss_sold_date_sk)
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Index Scan using date_dim_pkey on date_dim dt (cost=0.00..3494.62 rows=6080 width=8)
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Filter: (d_moy = 11)
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Sort (cost=12170.87..12177.27 rows=2560 width=65)
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Sort Key: store_sales.ss_sold_date_sk
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Nested Loop (cost=6.02..12025.94 rows=2560 width=65)
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Seq Scan on item (cost=0.00..1455.00 rows=16 width=59)
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Filter: (i_manufact_id = 914)
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Bitmap Heap Scan on store_sales (cost=6.02..658.94 rows=174 width=14)
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Recheck Cond: (ss_item_sk = item.i_item_sk)
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Bitmap Index Scan on store_sales_pkey (cost=0.00..5.97 rows=174 width=0)
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Index Cond: (ss_item_sk = item.i_item_sk)
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Oracle 数据库执行计划展示如下：
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Plan hash value: 2331821367
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--------------------------------------------------------------------------------------------------
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| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
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--------------------------------------------------------------------------------------------------
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| 0 | SELECT STATEMENT | | 100 | 9100 | 3688 (1)| 00:00:01 |
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|* 1 | COUNT STOPKEY | | | | | |
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| 2 | VIEW | | 2736 | 243K| 3688 (1)| 00:00:01 |
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|* 3 | SORT ORDER BY STOPKEY | | 2736 | 256K| 3688 (1)| 00:00:01 |
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| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 |
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|* 5 | HASH JOIN | | 2736 | 256K| 3686 (1)| 00:00:01 |
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|* 6 | TABLE ACCESS FULL | DATE_DIM | 6087 | 79131 | 376 (1)| 00:00:01 |
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| 7 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
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| 8 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
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|* 9 | TABLE ACCESS FULL | ITEM | 18 | 1188 | 375 (0)| 00:00:01 |
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|* 10 | INDEX RANGE SCAN | SYS_C0010069 | 159 | | 2 (0)| 00:00:01 |
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| 11 | TABLE ACCESS BY INDEX ROWID| STORE_SALES | 159 | 2703 | 163 (0)| 00:00:01 |
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--------------------------------------------------------------------------------------------------
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OceanBase 数据库执行计划展示如下：
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|ID|OPERATOR |NAME |EST. ROWS|COST |
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-------------------------------------------------------
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|0 |LIMIT | |100 |81141|
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|1 | TOP-N SORT | |100 |81127|
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|2 | HASH GROUP BY | |2924 |68551|
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|3 | HASH JOIN | |2924 |65004|
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|4 | SUBPLAN SCAN |VIEW1 |2953 |19070|
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|5 | HASH GROUP BY | |2953 |18662|
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|6 | NESTED-LOOP JOIN| |2953 |15080|
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|7 | TABLE SCAN |ITEM |19 |11841|
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|8 | TABLE SCAN |STORE_SALES|161 |73 |
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|9 | TABLE SCAN |DT |6088 |29401|
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=======================================================
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由示例可见，OceanBase 数据库的计划展示与 Oracle 数据库类似。
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OceanBase 数据库执行计划中的各列的含义如下：
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列名 含义
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ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。
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OPERATOR 操作算子的名称。
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NAME 对应表操作的表名（索引名）。
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EST. ROWS 估算该操作算子的输出行数。
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COST 该操作算子的执行代价（微秒）。
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OceanBase 数据库 EXPLAIN 命令输出的第一部分是执行计划的树形结构展示。其中每一个操作在树中的层次通过其在 operator 中的缩进予以展示，层次最深的优先执行，层次相同的以特定算子的执行顺序为标准来执行。
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问题: update a not exists (b…)
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我一开始以为 B是驱动表，B的数据挺多的 后来看到NLAJ，是说左边的表关联右边的表
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所以这个的驱动表是不是实际是A，用A的匹配B的，这个理解有问题吗
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回答: 没错 A 驱动 B的
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问题: 光知道最下最右的是驱动表了 所以一开始搞得有点懵 :sweat_smile:
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回答: nlj应该原理应该都是左表(驱动表)的记录探测右表(被驱动表)， 选哪张成为左表或右表就基于一些其他考量了，比如数据量， 而anti join/semi join只是对 not exist/exist的一种优化，相关的原理和资料网上可以查阅一下
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问题: 也就是nlj 就是按照之前理解的谁先执行 谁就是驱动表 也就是执行计划中的最右的表
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而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧
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回答: nlj也是左表的表是驱动表，这个要了解下计划执行方面的基本原理，取左表的一行数据，再遍历右表，一旦满足连接条件，就可以返回数据
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anti/semi只是因为not exists/exist的语义只是返回左表数据，改成anti join是一种计划优化，连接的方式比子查询更优
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"""
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from llama_index import Document
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text_list = [text1]
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documents = [Document(t) for t in text_list]
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num_output = 250
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max_input_size = 512
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max_chunk_overlap = 20
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prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
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index = GPTListIndex(
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documents,
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embed_model=embed_model,
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llm_predictor=llm_predictor,
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prompt_helper=prompt_helper,
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)
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index.save_to_disk("index.json")
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if __name__ == "__main__":
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import logging
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logging.getLogger().setLevel(logging.CRITICAL)
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for d in documents:
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print(d)
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response = index.query("数据库的执行计划命令有多少?")
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print(response)
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Reference in New Issue
Block a user