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How can I add ChatGPT to my web site?
ChatGPT is a powerful chatbot platform powered by machine learning and AI. Whether you’re looking to monitor user conversations or automate customer service, ChatGPT can be embedded on your website so that visitors can have real-time interactions with an intelligent chatbot. Integrating ChatGPT is easy and efficient, allowing your website to become interfaced with cutting edge AI technology within minutes. ChatGPT is the perfect way for businesses to drive engagement and collect valuable data from customer conversations in order to advance their product roadmap and streamline services.
Different ways you can add ChatGPT to your website
There are a few different ways you can add ChatGPT to your website, depending on your specific requirements and the tools and frameworks you are using. Here are a few options:
Use an API: OpenAI has an API that you can use to access ChatGPT. To use the API, you will need to sign up for an API key and then use it to make API calls from your website. You’ll need to write some code to send and receive the API calls, but you can find many examples and libraries in different languages that can help.
Use a pre-built library or SDK: Some developers have created libraries or software development kits (SDKs) that make it easier to use ChatGPT in your website. For example, Hugging Face provides a JavaScript library that you can use to integrate ChatGPT with your website.
Embed a pre-built chatbot: There are a few pre-built chatbots available that are built using ChatGPT and that you can embed in your website. For example, Botfront.io allows you to create a chatbot using the GPT-3 language model.
Requirements
Please note, to use ChatGPT or GPT-3 model, the OpenAI’s API requires a commercial or research agreement to be in place. As well some of the services may require paid subscription, so it’s recommended to check the pricing and terms of use in advance.
It’s also important to note that building a chatbot with GPT-3 or other language models can require some level of skill, mainly related to data science and natural language processing. If you have little or no experience with it, it may be better to seek professional help.
Integration
ChatGPT makes it easy to integrate artificial intelligence (AI) into your web site with just a few clicks. It employs machine learning technology to allow users to easily embed a natural language processing (NLP) chatbot into their website. ChatGPT learns from conversations, providing customers with an engaging and useful experience when visiting your site. ChatGPT will make your website stand out and provide visitors with an enjoyable experience that they won’t soon forget.
8. Save all your Prompts?: The `ChatGPT History` extension has you covered! Link: http://bit.ly/3ijtDP8
9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.
Create code to call to the OpenAI API using a natural language instruction.
Settings
Engine: code-davinci-002
Max tokens: 64
Temperature: 0
Top: p1.0
Frequency penalty: 0.0
Presence penalty: 0.0
Stop sequence: “””
Prompt
“”” Util exposes the following: util.openai() -> authenticates & returns the openai module, which has the following functions: openai.Completion.create( prompt=”<my prompt>”, # The prompt to start completing from max_tokens=123, # The max number of tokens to generate temperature=1.0 # A measure of randomness echo=True, # Whether to return the prompt in addition to the generated completion ) “”” import util “”” Create an OpenAI completion starting from the prompt “Once upon an AI”, no more than 5 tokens. Does not include the prompt.
“””
Sample response
completion = util.openai().Completion.create( prompt=”Once upon an AI”, max_tokens=5, temperature=1.0, echo=False, ) print(completion) “””
With Python
import os import openai
openai.api_key = os.getenv(“OPENAI_API_KEY”)
response = openai.Completion.create( model=”code-davinci-002″, prompt=”\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, temperature=0, max_tokens=64, top_p=1.0, frequency_penalty=0.0, presence_penalty=0.0, stop=[“\”\”\””] )
const configuration = new Configuration({ apiKey: process.env.OPENAI_API_KEY, }); const openai = new OpenAIApi(configuration);
const response = await openai.createCompletion({ model: “code-davinci-002”, prompt: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, temperature: 0, max_tokens: 64, top_p: 1.0, frequency_penalty: 0.0, presence_penalty: 0.0, stop: [“\”\”\””], });
With curl:
curl https://api.openai.com/v1/completions \ -H “Content-Type: application/json” \ -H “Authorization: Bearer $OPENAI_API_KEY” \ -d ‘{ “model”: “code-davinci-002”, “prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, “temperature”: 0, “max_tokens”: 64, “top_p”: 1.0, “frequency_penalty”: 0.0, “presence_penalty”: 0.0, “stop”: [“\”\”\””] }’
With Json:
{ “model”: “code-davinci-002”, “prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”, “temperature”: 0, “max_tokens”: 64, “top_p”: 1.0, “frequency_penalty”: 0.0, “presence_penalty”: 0.0, “stop”: [“\”\”\””] }
By Jérôme Cukier, Staff Software Engineer at Google (2020-present)
When Google took off, its key characteristic was that it was very very fast compared to its competition. The quality of the results was also impressive, and, as could be expected, it was very reliable and highly available.
That in itself didn’t make it a better product than Yahoo, which for years dominated the search engine market and which was the de facto home page to the internet, even after Google became a household name. However, this was enough to start the narrative that there was something special about Google that others just couldn’t do quite as well.
ChatGPT is not fast, is often wrong, and as a service is very unreliable. It’s down approximately 50% of the times I’m trying to use it. The technology behind it is not rocket science, that said they have a few things going for them. First, they trained a very large language model (LLM). The cost of this operation in terms of machine is massive. Google search can crawl the web and update their index all the time but the resources needed to train a LLM as big as GPT-3 are phenomenal. Second, they have a product. Microsoft, Meta, Google all could have released something similar and sooner, but didn’t. As a result, OpenAI just like Google ~23 years before it has a narrative going for them.
People’s perception of Google search
People’s perception of Google search is that it’s a service that will return 10 blue links to a query which is a list of keywords, that’s a bit unfair because for years this is neither what search results or search queries are, but then again Google has not been able to correct that impression. On the other hand, journalists know that there is a demand for stories that present ChatGPT as an all-powerful oracle that can do many things and whose output cannot be distinguished from actual people and these stories have kept coming – again, just like stories about Google in the early 2000s then about Facebook in the mid aughts.
The most common queries are about the weather, opening hours of businesses, shopping and lottery results. Those things however trite are completely out of bound for ChatGPT which doesn’t have a live connection with the real world. But then there are many things that a LLM-backed chatbot can do (or even better, that specific products supported by LLMs can do) which Google or other big tech companies just don’t offer.
ChatGPT is just one of many services that are threatening the role of Google not just as a search engine but as a central platform. It’s also very preliminary, after GPT3 will come GPT4, after ChatGPT will come waves of products with GPT APIs. So the landscape is going to change significantly over the next couple of years.
A step-by-step guide to building a chatbot based on your own documents with GPT
Chatting with ChatGPT is fun and informative — I’ve been chit-chatting with it for past time and exploring some new ideas to learn. But these are more casual use cases and the novelty can quickly wean off, especially when you realize that it can generate hallucinations.
GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
GPT-4’s improvements are evident in the system’s performance on a number of tests and benchmarks, including the Uniform Bar Exam, LSAT, SAT Math, and SAT Evidence-Based Reading & Writing exams. In the exams mentioned, GPT-4 scored in the 88th percentile and above, and a full list of exams and the system’s scores can be seen GPT-4
Image is a multimodal chatbot like ChatGPT4.
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In the early days of the internet, web sites were essentially made of static HTML files. Web servers were little more than file servers, when a user would come to a url, the web server would simply fetch it, and send it to the user via their browser, along with all kind of assets, like fonts and images.
The functionality of this kind of web pages are very limited, so eventually the web became more dynamic. When people would visit a page or interact with a form, instead of just fetching data, the server could perform an operation and prepare some content on demand. That content would still be sent to the user’s browser. There could also a little bit of code running on the browser, to animate pages, handle form and what not, but not very much.
So up until around 2010, that was the dominant model. Code could be involved to generate content but the browser wouldn’t do much, most of the logic would happen on servers which would just send prepared content to the browser.
However, in the early 2010s, this paradigm started to shift. With HTML5/CSS3, the browser became much more capable, and so people started to move the logic that would generate content from the server to the browser. Instead of sending a whole styled HTML page, a web server could just send the data needed to create it. Then, code could run on the browser to actually turn that data into HTML. That browser code could also update what the user would see, making just the required data calls.
So, in the early to mid 2010s, front-end code would typically:
render complex web pages from data retrieved from back-end,
simulate “navigation” between different views: when the user would do some actions, the entire page would change, the url would update etc. but without actually loading a new page from the server.
maintain the state of an application: the application could track certain things about the user and the session, and won’t have to reload that information from the server all the time.
dynamically update both contents and style of a web page.
Now, all of this is possible to do in “vanilla javascript”. But it’s really cumbersome to implement it, and especially tricky to do it in a performant way. There are millions and millions of “web apps” that are replacing the static “web sites” of old, and which all need to dynamically render content. Should developers reimplement that from scratch each time?
Enter the web frameworks such as React. These frameworks are abstractions that let the developers focus on the logic of their web app (where the data comes from, how content is organized) without being tied to the nitty gritty. Web frameworks make developers organize their code in building blocks called modules or components. Somebody could write a header component and someone else building a page could reuse that header component. And a third developer could change the header component, and that change would be reflected everywhere the component is used. Folks could also build 3rd party libraries compatible with the web framework ecosystem, that would address common problems that many developers face. For instance, someone could create a date picker component (a notably tricky interface) that anyone can reuse and customize. Or create a solution to deal with very long pages by only rendering what is in the browser viewport, and creating/deleting elements as a user would scroll.
To have the support of this ecosystem is a huge productivity boost. There are millions of developers who work with React, and the most popular React libraries are very elegant solutions to hard problems(the same could be said of Angular, Vue etc. though their communities are a bit smaller).
React and web frameworks aren’t exactly needed, in fact there is a reverse trend in the last couple of years to go back to server generated content in some cases or to only use vanilla javascript, but it’s a very solid foundation to build a web app.
Comments:
1- The specific rationale for React is state management and efficient page updates, it’s underlying power comes not just from the structure and tooling provided by it being a framework, but also the shadow-DOM and component lifecycle that along with state management empower greater interactivity without very slow inefficient page updates.
2- React isn’t needed, but it is a great framework that can reduce the amount of work you do in making a website/webapp.
React is great for widgets and implementing patterns. You can keep data/text separate from structure and behavior. React, angular and vue are all popular frameworks. Before that we used stuff like dust, handlebars, jQuery and UI libraries like dojo and jQuery UI.
Developers are always looking for ways to be more efficient and more maintainable. React is a current iteration tool for being more efficient.
3- It is needed as a pattern for the devs to create packages that will works (The React packages). In NPM there are many packages, but all them are following its own logic, docs or no docs, they are based on another packages, etc. With things like React, you are somehow limited to follow its rules and you are entering its ecosystem which is good. This is true for all frameworks/libraries.
React also has some configurations which follows the best practices (create-react-app, NextJS, etc), but this is the same and for others.
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