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LangChain is an innovative framework for creating language-powered applications. LangChain, with its configurable approach and extensive integration features, gives developers a new level of control and flexibility when it comes to exploiting language models.

LangChain

LangChain is a framework for creating language-powered apps. The most powerful and distinct apps will not only use an API to access a language model, but will also:

Be data-aware: Connect a language model to additional data sources.

Be agentic: Permit a language model to interact with its surroundings.

The LangChain framework is designed with the above principles in mind. This is the Python specific portion of the documentation.

Prompts:

At a high level, prompts are organized by use case inside the prompts directory. To load a prompt in LangChain, you should use the following code snippet:

from langchain.prompts import load_prompt prompt = load_prompt('lc://prompts/path/to/file.json')

Chains

Chains extend beyond a single LLM call to include sequences of calls (to an LLM or another utility). LangChain offers a standard chain interface, numerous connections with other tools, and end-to-end chains for typical applications.

Chains are organized by use case inside the chains directory at a high level. Use the following code snippet to load a chain in LangChain:

from langchain.chains import load_chain chain = load_chain('lc://chains/path/to/file.json')

Agents

Gents involve an LLM making judgements on which Actions to do, performing that Action, observing an Observation, and repeating this process until completed. LangChain provides a standard agent interface, a collection of agents, and examples of end-to-end agents.

Agents are organized by use case inside the agents directory at a high level. Use the following code snippet to load an agent in LangChain:

from langchain.agents import initialize_agent llm = ... tools = ... agent = initialize_agent(tools, llm, agent="lc://agents/self-ask-with-search/agent.json") Installation

To get started, install LangChain with the following command:

pip install langchain # or conda install langchain -c conda-forge Environment Setup

Integrations with one or more model providers, data storage, APIs, and so on are frequently required when using LangChain.

Because we will be using OpenAI’s APIs in this example, we must first install their SDK:

pip install openai

We will then need to set the environment variable in the terminal.

export OPENAI_API_KEY="..."

Alternatively, you could do this from inside the Jupyter notebook (or Python script):

import os os.environ["OPENAI_API_KEY"] = "..." Building a Language Model Application: LLMs

We can begin developing our language model application now that we have installed LangChain and configured our environment.

LangChain has a number of modules that may be used to create language model applications. Modules can be integrated to make more complicated applications or used alone to construct basic apps.

LLMs: Get predictions from a language model

The most fundamental LangChain building component is calling an LLM on some input. Let’s go over an easy example of how to achieve this. Assume we’re creating a service that generates a company name based on what the company produces.

To achieve this, we must first import the LLM wrapper.

from chúng tôi import OpenAI

We can then initialize the wrapper with any arguments. In this example, we probably want the outputs to be MORE random, so we’ll initialize it with a HIGH temperature.

llm = OpenAI(temperature=0.9)

We can now call it on some input!

text = "What would be a good company name for a company that makes colorful socks?" print(llm(text)) Feetful of Fun Prompt Templates: Manage prompts for LLMs.

Calling an LLM is a good start, but it’s only the beginning. When you utilise an LLM in an application, you usually do not pass user input directly to the LLM. Instead, you’re presumably gathering user input and creating a prompt, which you then send to the LLM.

In the last example, for example, the text we provided in was hardcoded to request a name for a firm that sold colourful socks. In this hypothetical service, we’d like to take only the user input describing what the company does and format the prompt with that information.

This is simple with LangChain!

Let’s start with the prompt template:

from langchain.prompts import PromptTemplate prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", )

Let’s now see how this works! We can call the .format method to format it.

print(prompt.format(product="colorful socks")) What is a good name for a company that makes colorful socks? Chains: Combine LLMs and prompts in multi-step workflows

Until now, we’ve only used the PromptTemplate and LLM primitives on their own. Of course, a real application is a combination of primitives rather than a single one.

In LangChain, a chain is built up of links that can be primitives like LLMs or other chains.

An LLMChain is the most basic sort of chain, consisting of a Prompt Template and an LLM.

Extending on the previous example, we can build an LLMChain that accepts user input, prepares it with a Prompt Template, and then sends the processed result to an LLM.

from langchain.prompts import PromptTemplate from chúng tôi import OpenAI llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", )

We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM:

from langchain.chains import LLMChain chain = LLMChain(llm=llm, prompt=prompt)

Now we can run that chain only specifying the product!

chain.run("colorful socks")

The first chain is an LLM Chain. Although this is one of the simpler types of chains, understanding how it works will prepare you for working with more complex chains.

Agents: Dynamically Call Chains Based on User Input

So far, the chains we’ve looked at run in a predetermined order.

An LLM is no longer used by agents to choose which actions to do and in what order. An action might be examining the output of a tool or returning to the user.

Agents may be immensely strong when utilized appropriately. In this tutorial, we will demonstrate how to utilize agents using the simplest, highest-level API.

In order to load agents, you should understand the following concepts:

Tool: A function that serves a certain purpose. This can include Google Search, database lookups, Python REPLs, and other chains. A tool’s interface is presently a function that is meant to take a string as input and return a string as output.

LLM: The language model that drives the agent.

Agent: The agent to use. This should be a string containing the name of a support agent class. This notebook only covers utilizing the standard supported agents because it focuses on the simplest, highest-level API. See the documentation on custom agents if you wish to implement one.

Agents: For a list of supported agents and their specifications, see here.

Tools: For a list of predefined tools and their specifications, see here.

For this example, you will also need to install the SerpAPI Python package.

pip install google-search-results

And set the appropriate environment variables.

import os os.environ["SERPAPI_API_KEY"] = "..."

Now we can get started!

from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from chúng tôi import OpenAI # First, let's load the language model we're going to use to control the agent. llm = OpenAI(temperature=0) # Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in. tools = load_tools(["serpapi", "llm-math"], llm=llm) # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) # Now let's test it out! agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")

I need to find the temperature first, then use the calculator to raise it to the .023 power. Action: Search Action Input: "High temperature in SF yesterday" Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ... Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power. Action: Calculator Action Input: 57^.023 Observation: Answer: 1.0974509573251117

Thought: I now know the final answer Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.

> Finished chain.

Memory: Add State to Chains and Agents

All of the chains and agents we’ve encountered so far have been stateless. However, you may want a chain or agent to have some concept of “memory” in order for it to remember information from previous interactions. When designing a chatbot, for example, you want it to remember previous messages so that it can use context from that to have a better conversation. This is a sort of “short-term memory.” On the more complex side, you could imagine a chain/agent remembering key pieces of information over time – this would be a form of “long-term memory”.

LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the ConversationChain) with two different types of memory.

By default, the ConversationChain has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let’s take a look at using this chain (setting verbose=True so we can see the prompt).

from langchain import OpenAI, ConversationChain llm = OpenAI(temperature=0) conversation = ConversationChain(llm=llm, verbose=True) output = conversation.predict(input="Hi there!") print(output)

Prompt after formatting: The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there! AI:

> Finished chain. ‘ Hello! How are you today?’

output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.") print(output)

Prompt after formatting: The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there! AI: Hello! How are you today? Human: I’m doing well! Just having a conversation with an AI. AI:

> Finished chain. ” That’s great! What would you like to talk about?”

Building a Language Model Application: Chat Models

Similarly, conversation models can be used instead of LLMs. Language models are a subset of chat models. While chat models use language models behind the scenes, the interface they expose is slightly different: instead of exposing a “text in, text out” API, they expose an interface where “chat messages” are the inputs and outputs.

Because chat model APIs are still in their early stages, they are still determining the appropriate abstractions.

Get Message Completions from a Chat Model

You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage – ChatMessage takes in an arbitrary role parameter. Most of the time, you’ll just be dealing with HumanMessage, AIMessage, and SystemMessage.

from langchain.chat_models import ChatOpenAI from langchain.schema import ( AIMessage, HumanMessage, SystemMessage ) chat = ChatOpenAI(temperature=0)

You can get completions by passing in a single message.

chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])

You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.

messages = [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love programming.") ] chat(messages)

You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter:

batch_messages = [ [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love programming.") ], [ SystemMessage(content="You are a helpful assistant that translates English to French."), HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.") ], ] result = chat.generate(batch_messages) result

You can recover things like token usage from this LLMResult:

result.llm_output['token_usage'] Chat Prompt Templates

Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.

For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:

from langchain.chat_models import ChatOpenAI from chúng tôi import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) chat = ChatOpenAI(temperature=0) template = "You are a helpful assistant that translates {input_language} to {output_language}." system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_template = "{text}" human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) # get a chat completion from the formatted messages chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages()) Chains with Chat Models

The LLMChain discussed in the above section can be used with chat models as well:

from langchain.chat_models import ChatOpenAI from langchain import LLMChain from chúng tôi import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) chat = ChatOpenAI(temperature=0) template = "You are a helpful assistant that translates {input_language} to {output_language}." system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_template = "{text}" human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) chain = LLMChain(llm=chat, prompt=chat_prompt) chain.run(input_language="English", output_language="French", text="I love programming.") Agents with Chat Models

Agents can also be used with chat models; you can initialize one using AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION as the agent type.

from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.chat_models import ChatOpenAI from chúng tôi import OpenAI # First, let's load the language model we're going to use to control the agent. chat = ChatOpenAI(temperature=0) # Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in. llm = OpenAI(temperature=0) tools = load_tools(["serpapi", "llm-math"], llm=llm) # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) # Now let's test it out! agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?") > Entering new AgentExecutor chain... Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power. Action: { "action": "Search", "action_input": "Olivia Wilde boyfriend" } Observation: Sudeikis and Wilde's relationship ended in November 2023. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2023. In January 2023, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling. Thought:I need to use a search engine to find Harry Styles' current age. Action: { "action": "Search", "action_input": "Harry Styles age" } Observation: 29 years Thought:Now I need to calculate 29 raised to the 0.23 power. Action: { "action": "Calculator", "action_input": "29^0.23" } Observation: Answer: 2.169459462491557 Thought:I now know the final answer. Final Answer: 2.169459462491557 > Finished chain. '2.169459462491557' Memory: Add State to Chains and Agents

Memory may be used with chains and agents that have been initialized using conversation models. The primary difference between this and Memory for LLMs is that instead of condensing all previous messages into a string, we may store them as their own distinct memory object.

from langchain.prompts import ( ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, HumanMessagePromptTemplate ) from langchain.chains import ConversationChain from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory

prompt = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."), MessagesPlaceholder(variable_name="history"), HumanMessagePromptTemplate.from_template("{input}") ])

llm = ChatOpenAI(temperature=0) memory = ConversationBufferMemory(return_messages=True) conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)

conversation.predict(input="Hi there!")

conversation.predict(input="I'm doing well! Just having a conversation with an AI.")

conversation.predict(input="Tell me about yourself.")

You're reading Experience The Power Of Langchain: The Next Generation Of Language Learning

Future Of Trading In Next Generation

The EU’s e-Privacy law, which is requested in 2023, is defined to be the upcoming key item of legislation designed to safeguard the privacy and safety of private details.

Obviously, people in the finance industry are not any different to people in almost any other sector.

Legislation to the Real-world

However, legislation composed even a few years ago is trying hard to deal with protecting privacy on these new platforms.

To an elderly professional it might look like the brand new e-Privacy law is 1 step ahead of trading tendencies by capturing all eCommerce information (such as metadata). However, the reality is that financial companies are in reality often a step behind in the execution and ennoblement of eCommerce and security of their eCommerce data. Its time to the monetary industry to think beforehand!

Recognizing eCommerce

There are good reasons why many dealers have turned into wider eCommerce and messaging stations.

These stations are quick to use, immediately connecting with the ideal person in just about any location by means of a private device. Contrary to waiting for an email reply by way of instance, they provide visibility of this message being received and a response is sent. At extremely time-critical surroundings this makes great sense.

Most companies have their company emails and onsite voice communications securely stored and tracked in the event of a discrepancy or analysis.

It would be quite simple to insist no additional stations are used — but the truth is that the dealers may use these channels for communicating and companies will need to align with this.

The other strategy could be equally damaging. Unregulated and uncontrolled utilization of societal and cellular communications may leave the business at severe risk of information breaches and following regulatory scrutiny/punishment.

The penalties may be eye-watering. At the biggest single circumstance, a company has been fined $2 million for failing to employ a reasonable supervisory system to reassess emails.

The dangers can be harder to control whether the organisation permits traders a degree of BYOD (bring your own device) flexibility within their own role. How do a financial company protect against privacy or data breaches on a stage and device it does not directly control?

Embracing digital transformation

Certainly, neither prohibiting eCommerce utilize or turning a blind eye on its use by dealers are sensible strategies for almost any contemporary financial company. The sensible strategy is to adopt this electronic transformation and also to take possession of it.

Many young dealers and clients wish to use the hottest eCommerce to match their tastes, so companies will need to make sure that this is enabled but additionally that apparatus and eCommerce stations are correctly monitored and controlled. Assessing all eCommerce information is properly gathered, securely kept and readily available for reports and investigation in a minute’s notice guarantees any breaches (or possible breaches) could be addressed promptly.

Meeting fresh e-Privacy Regulations

This strategy will be more significant when the new EU e-Privacy Legislation comes into power. Interestingly, however, the new laws will also pay for the solitude of the dealers themselves, in addition to customers and the company.

Fines for Indices will probably about the very same amounts as GDPR (around $20 million or around four percent of global yearly turnover, whichever is the greatest ) — significantly severe enough to induce companies financial hardship in addition to reputation damage.

Giving the Men and Women what they need

It’s critical to first comprehend the growth of eCommerce and then to use the ideal Reg Tech solutions to make sure your company stays at the very front of the shift, instead of being left behind with it.

How Jumping Genes Hijack Their Way Into The Next Generation Of Babies

As we all learned in health class, when a baby animal is created, genetic material from two biological parents combines to create a new being—one with some genes from each parent. What you may not know is that a third genetic element is involved in this process, a hitchhiker whose existence and self-propagation may be essential to life as we know it.

Transposon, or transposable element, is the scientific name for these hitchhikers lurking in our genome. These DNA sequences are able to move around within the genome and replicate themselves, sometimes with negative consequences for their hosts. Transposon-related mutations have been blamed for hemophilia and some kinds of cancer. But research over the past decade has revealed that our relationship with these elements, which make up a large percentage of the human genome, is much more complex than previously thought. The mutations caused by transposons’ presence and movements have also shaped evolution over the millennia. Until now, however, nobody had looked at the question of how transposons manage to incite this change by hitchhiking into the next generation after conception.

For the first time, new research has shown the kinds of cells that transposons target in order to “jump” into the future with embryos who will develop into new beings. Understanding this process will let us understand more about the transposons’ function and relationships. To explore this question, Zhao Zhang and his team at the Carnegie Institution for Science relied on the oft-studied fruit fly.

In theory, if transposons were allowed to run unchecked in the body, they’d result in so many genetic errors that we’d simply die. But somewhere along the way, animals developed a defensive strategy: a set of RNA molecules that limit the ability of the transposons to, well, transcribe themselves. Although transposons sometimes manage to slip past these defenses, known as piRNA, the genome is reasonably stable, with the transposons staying put and not transposing all that often.

That makes it difficult to track when they do transpose, specifically into the cells that create the next generation—a question that had never been asked before in any case, says Zhang.

“For our study what we were trying to do is reach single-cell resolution,” he says—that is, track how transposons moved through cells on an individual basis rather than find their presence in a piece of tissue that has many different kinds of cells in it. To do this, they turned off a specific kind of piRNA and watched how the jumping genes moved as the egg developed from two germ cells (one from each parent).

Jumping genes, which mobilize around the genome, use nurse cells to manufacture invading products that preferentially integrate into the genome of developing egg cells, called oocytes. Zhao Zhang

They found that some jumping genes—known as retrotransposons—rely on “nurse cells” that produce genetic supplies like proteins and RNA for the developing egg. They tag along with some of those supplies into the egg, where they transpose themselves into the egg DNA hundreds or even thousands of times.

This research offers new insights into the strange world of transposons and how they have made themselves such a lasting part of our evolution. “It reveals the complex life of transposons,” says Cornell University molecular biologist Cedric Feschotte, who was not involved with this study. There’s more work to do, of course, but the new research reveals an elegant strategy that these genetic hitchhikers use to keep on heading down the road.

The Power Of The Samsung

A great company name holds weight, and endures over time. It’s memorable, recognizable and trustworthy.

But it can’t be born overnight. That’s why when YESCO Electronics, the LED sign and display manufacturer, was acquired by Samsung, a lot of consideration went into the brand’s new name: Prismview, A Samsung Electronics Company.

Merging the new (Prismview) with the established (Samsung), the name evokes trust, something especially important in technology. Samsung’s vast resources added to existing expertise and has positioned Prismview to become the world leader in digital signage.

People trust the Samsung brand, and so they trust Prismview. But what is in a name? And what does it bestow on Prismview?

A proven track record

A well-established company has more than just years under its belt, it has years of providing quality services and producing quality products.

One issue people face when purchasing large-scale tech like LED displays is cost. In an effort to save money, they may opt for a cheaper display from a relatively new company that hasn’t been around long enough to prove itself. Keeping costs low is important, but budget products can come with long-term problems. If and when the display needs repairs down the line, the question is, will that company still be around to make the necessary fixes or provide the necessary parts?

Founded in 1938, Samsung has a long, proven track record of success building high-quality products that can stand the test of time. In 2023, the company maintained its ranking as one of the top 10 Best Global Brands (#6) in the annual list by Interbrand. Samsung is a globally established company, and customers can confidently trust it will be around for the long-haul. And so will Prismview.

A network of global support

Over the past 25 years, Prismview has installed LED displays around the world. Armed with the elite customer service capabilities of the in-house Network Operations Center (the NOC), Prismview is able to effectively support customers worldwide.

And now, with the added resources of Samsung’s global offerings, Prismview is not only able to expand the reach of the NOC, but also provide customers with additional support options. Samsung’s global network of operations and support resources have been able to amplify Prismview’s worldwide reach, giving customers a higher tier of service.

A legacy of excellence

There’s a lot of work that goes into ensuring a product is ready to go to market. Each iteration of a new product has to go through ample rounds of testing and strict checks to meet compliance standards. To do this effectively takes substantial resources and a commitment to excellence that only an established company like Samsung can have.

As part of the Samsung company, these quality assurance checks inform Prismview’s processes as well. In order for the Prismview displays to fall under the Samsung name, they have to meet the highest standards.

And Prismview itself has its own legacy of rigorous quality control. With a manufacturing site in Logan, Utah, as opposed to overseas, Prismview is able to consistently ensure the highest quality product for customers. A multitiered approach to guaranteeing a high standard of craftsmanship is a hallmark of Samsung Prismview.

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A commitment to continued innovation

The best tech companies don’t sit on their laurels. They constantly evolve, adapt and innovate across their product offerings. But innovation (and not just a one-time upgrade, but constant improvements) takes substantial resources to continuously invest in research and development.

This is what Samsung is known for, and what the company has proven to do across both consumer and commercial product lines. Prismview capitalizes on this. With newfound resources and a commitment to innovation that is coming from their parent company, Prismview is able to promise cutting-edge technology to their partners for now, and well into the future.

With Prismview products, customers aren’t just getting top-of-the-line LED displays. They’re getting a promise of quality, a network of service, and a long-term, committed partner. And thanks to the Samsung name, customers have this assurance well before their new display is installed.

Explore the latest in digital signage from a time-tested brand.

How To Switch The Language Of The Page Using Javascript?

Whenever you develop a website or application for a worldwide business, you must also focus on which language your audience can understand. For example, English is an international language, but in some parts of the world, people don’t understand English as they speak German, Spanish etc.

Here, we will learn to switch the language of the web page using JavaScript.

Syntax

Users should follow the syntax below to change the language of the web page using JavaScript.

if (lang == "en") { element.innerHTML = "content"; } else if (lang == "fr") { element.innerHTML = "content"; } else if (lang == "de") { element.innerHTML = "content"; }

In the above syntax, we have written the if-else statement to change the content of the web page according to the language selected. Users need to replace the content with the content of a particular language.

Example 1

In the example below, we added some div element content. Whenever users press any button to change the web page’s language, we invoke the changeLanguage() function by passing the language as a parameter. In the changeLanguage() function, we access the div element and change its content according to the language selected.

function changeLanguage(lang) { let element = document.getElementById(“div”); if (lang == “en”) { element.innerHTML = “Hi How are you! This is written in English.”; } else if (lang == “fr”) { element.innerHTML = “Bonjour Comment allez-vous! Cela est écrit en français.”; } else if (lang == “de”) { element.innerHTML = “Hallo Wie geht es dir! Das ist auf Deutsch geschrieben.”; } }

Example 2

We have created a web page with multiple elements in the example below. Also, we have given the unique id to every element. In JavaScript, we have created the object named ‘languageContent’. In the object, we have stored the language as a key and the content as a value. In the content object, we have used the element id as a key and its content in a particular language as a value.

In the switchLang() function, we access the content of a particular language from the languageContent object and replace the content of all elements on the web page.

let languageContent = { “en”: { “text1”: “This is a sample content”, “language”: “English”, “BrandName”: “TutorialsPoint”, “Programming_lang”: “JavaScript”, }, “fr”: { “text1”: “Ceci est un contenu d’exemple”, “language”: “Français”, “BrandName”: “TutorialsPoint”, “Programming_lang”: “JavaScript”, }, “es”: { “text1”: “Este es un contenido de ejemplo”, “language”: “Español”, “BrandName”: “TutorialsPoint”, “Programming_lang”: “JavaScript”, } } function swithcLang(lang) { for (let key in languageContent[lang]) { document.getElementById(key).innerHTML = languageContent[lang][key]; } }

Users learned to switch the language of a web page using JavaScript. In the first example, we have given a demo of how we can switch between multiple languages.

We can use the second example for the real-time website. Developers need to create a JSON file to store the content rather than in the same files, as real-time apps can have lots of content. After that, they can use for loop to iterate through all elements of the JSON file and update the content of the webpage.

The Paradoxical Power Of The Tiny Tweet

The Paradoxical Power of the Tiny Tweet

Twitter followings are small. Personally, I recently hit more than 2,000 followers on Twitter after a few solid years of use. It’s nothing to crow about, not publicly at least. Most of my favorite Twitter friends have exponentially more followers than I do, and those are just the people who will meet me for a drink. Compared to celebrities, I’m followed by almost nobody. Of course it’s about quality, not quantity, and I cherish the conversations I have with any and all of my Twitter followers, but I don’t fool myself into thinking I’m influential or famous because of a measly 2,000 people.

[aquote]We can fool ourselves that the Twitter echo chamber is sparking revolutions[/aquote]

Twitter may have millions of users, but in terms of its effect on the zeitgeist, I’d stay it’s still minimal. Until my parents ask about it, it isn’t popular enough to matter. Sure, we can fool ourselves into believing the Twitter echo chamber is sparking revolutions and acting as a force for good, but mostly it’s a way to report on earthquakes in California and kvell about bad airline service.

After hurricane Sandy hit, I found myself called to northeastern New Jersey for a business meeting. All of the hotels were completely booked, filled mostly with refugees who still lacked power and the utility workers called in from around the country to try to restore it. I travelled with a team of three people, and all of us had to stay at different hotels because no single spot could accommodate us all.

When I checked into the Hampton Inn in Parsippany, I was told my room had been smoked. Of course, the hotel is non-smoking, but the previous tenants had decided the fee for smoking was worth paying to avoid battling what must have been an all-consuming religion of cigarette addiction. The room smelled of people who smoked like they were on a plane barreling towards the Atlantic with multiple engine fires.

The front desk apologized, but they could not offer a different room until the next night. I’d have to suffer. Hampton Inn has a very generous 100% guarantee, but the front desk only offered a “discount,” and then only “if I asked.”

I did not ask. I went to my room to sniff it out. It was awful, but I would survive, save the throbbing in my skull. Still, I did not ask for the refund. Not yet, at least.

Instead, I did what most of my friends would do. I went to Twitter. I did my best to brutalize Hampton Inn in the most civil way I could muster (ie. no cursing).

I heard from the corporate social networking response team within minutes. Minutes. If I had set a fire in my room and waited for the emergency squad to arrive, they would have taken longer to reach me than the Hilton International social networking team took to respond. They resolved my issue quickly, even beyond my expectations. At one point, I’ll admit I stooped so low as to provide my online bona fides and bylines, as a way of forewarning them this column would be coming. But it didn’t matter, because their response was already formidable.

For the same reason, Twitter is unusually popular with celebrities. You might think it’s a great place to drum up publicity and support, but is it really? I don’t think a Twitter following is so large that sheer numbers alone can make the difference. I don’t think the audience is paying close enough attention to catch the message, even in short bursts.

I think it is the highly personal nature of the interaction. It is direct, unmediated feedback. When I went to speak to the front desk, I liked to think I was speaking directly to Hilton International, but I was not. I was speaking to a poor young woman who was doing her best to manage a perturbed customer in a time of real crisis. It wasn’t even my crisis, I just stepped in it.

[aquote]Corporations have granted supreme power to social networking teams[/aquote]

Somehow, Twitter messages are taken more seriously. I am now tweeting directly to the corporation as a whole. Corporations, wisely I think, have granted supreme power to their social networking teams to respond to these issues. The front desk could offer me a discount, but the social networking team could do much more. They have resources, connections, and the ear of the power brokers.

I also think there is a fascinating dynamic in the follower / following relationship. If I am following you, you can send me a private message. I cannot respond, not privately, unless you are following me. For celebrities, this means that any celebrity can lean over and whisper in your ear, using a Direct Message. It is fleeting, and titillating. So much more personal than catching an eye in a crowd or reaching out a hand to shake.

Corporations have the opposite relationship. If they want to talk to you personally, they need to ask you to follow them. Now, you are the power broker. You can keep screaming into the wind, or you can make the connection and hear what they have to say; but first they have to reach out personally, directly, and ask to be heard.

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