我们榜单上最后一个真正的大牌是微软的Azure Data Studio。它是一个跨平台工具，适用于在Windows、macOS和Linux上使用本地和云数据平台的数据专业人员。虽然SQL Server是Azure Data Studio的关键DBMS，你也可以使用一个特殊的扩展连接到MySQL数据库。Studio提供了一个现代的编辑器体验，包括IntelliSense补全、代码片段、源代码控制集成、集成终端、内置查询结果集图表和可定制的仪表板。
优势
干净和直观的界面，从微软Visual Studio的提示
相当强大的一套功能的免费产品
与Azure数据服务无缝集成
有扩展，给访问新的功能和额外的服务
优秀的文档、支持服务和大型社区
缺点
虽然Azure Data Studio是一款高级产品，但也有一些ide可以更深入地实现可视化数据库设计和查询构建、表设计、服务器管理和数据库管理
curl --location 'http://$EXTERNAL_IP/v1/models/model:predict' \\ --header 'Content-Type: application/json' \\ --data '{ "prompt": "Who was president of the united states of america in 1890?" }'
defread_document() -> str: whileTrue: try: # Get the file path from the user file_path = input("Enter the file path: ") # Read the file withopen(file_path, 'r') as file: data = file.read() return Document( content=data, title="Doc Title", id=str(uuid.uuid4()), uri=file_path ) except Exception as e: print(f"An error occurred: {e}") input("Press enter to continue...")
In order to make testing our new RAG model easier, we can Allow unauthenticated invocations for each of our GCP services (hosted Llama 2 model, the hosted Qdrant image, any API server you have set up).
Make sure you set up authentication after your testing is complete or you might run into some surprises on your next billing cycle. GPUs ain’t cheap!
curl --location '$API_SERVICE_URL/ask-question' \\ --header 'Content-Type: application/json' \\ --data '{ "question": "Who was president of the united states of america in 1890?" }'
Replace $API_SERVICE_URL with the URL to the service that is responsible for connecting the vector store and Llama 2.
from azure.cognitiveservices.vision.computervision import ComputerVisionClient from azure.cognitiveservices.vision.computervision.models import OperationStatusCodes from azure.cognitiveservices.vision.computervision.models import VisualFeatureTypes from msrest.authentication import CognitiveServicesCredentials
from array import array import os from PIL import Image
# Call the "GET" API and wait for it to retrieve the results whileTrue: read_result = computervision_client.get_read_result(operation_id) if read_result.status notin ['notStarted', 'running']: break time.sleep(1)
layout = [] if read_result.status == OperationStatusCodes.succeeded: for text_result in read_result.analyze_result.read_results: for line in text_result.lines: layout.append({line.text:line.bounding_box}) print(layout) print("End of Computer Vision.") return layout
[{'News Contact About':[11.0,32.0,979.0,7.0,982.0,97.0,13.0,123.0]}, {'Welcome to Yeyu's site':[216.0,483.0,2210.0,373.0,2222.0,558.0,227.0,675.0]}, {'INPUT':[262.0,847.0,658.0,824.0,664.0,938.0,264.0,958.0]}, {'Button':[1713.0,1021.0,2084.0,1011.0,2089.0,1127.0,1713.0,1140.0]}, {'It's So FunTo Have You Here!':[217.0,1463.0,2348.0,1420.0,2350.0,1585.0,222.0,1651.0]}, {'Copyright@2023. Yeyulab':[586.0,2146.0,2123.0,2069.0,2129.0,2224.0,591.0,2297.0]}]
defhtml_gen(layout): prompt = PromptTemplate( template="""This is a layout of a handwriting website design, including text and their coordinates of four outer vertices. Make an HTML modern sans-serif website that reflects these elements and decide which CSS can be used to match their relative positions, try to use proper layout tags to match their font size and relative placement based on their coordinates. Use <ul> and <li> if the elements look like as menu list. Smartly use function tags like <button> <input> if their names look like that. Your design should be prior to the coordinates, then you should also use some imagination for the layout and CSS from common web design principle. Remember, don't use absolute coordinates in your HTML source code. Generate only source code file, no description: {layout}.\n""", input_variables=["layout"] ) llm = ChatOpenAI(model="gpt-4-0613",temperature=0) chain = LLMChain(prompt=prompt, llm=llm) output = chain.run(layout=layout) print(output)
return output
最关键的部分是prompt模板，其中包含许多关于每个元素的文本位置的说明，并强调样式。
c) Streamlit
作为最后一步，我们应该使用Streamlit和它的小部件将所有这些功能包装成一个流畅的用户交互。
让我们先安装Streamlit。
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!pip install streamlit
导入Streamlit包和组件类。
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import streamlit as st import streamlit.components.v1 as components
USER: Hi, I want to cancel my subscription. LLAMA: Hello, thank you for contacting us. May I ask why you want to cancel your subscription?
USER: I don't use your service anymore. LLAMA: I see. Well, we are sorry to lose you as a customer. To cancel your subscription, you will need to log in to your account and go to the settings page. There you will find an option to cancel your subscription. Do you need any help with that?
USER: No, thank you. I can do it myself. LLAMA: OK then. Thank you for choosing us and we hope you have a great day.
