In Artificial Intelligence, large language models (LLMs)<\/a> have become essential, tailored for specific tasks, rather than monolithic entities. The AI world today has project-built models that have heavy-duty performance in well-defined domains \u2013 be it coding assistants who have figured out developer workflows, or research agents navigating content across the vast information hub autonomously.\u00a0In this piece, we analyse some of the best SOTA LLMs that address fundamental problems while incorporating significant shifts in how we get information and produce original content.<\/p>\n Understanding the distinct orientations will help professionals choose the best AI-adapted tool for their particular needs while closely adhering to the frequent reminders in an increasingly AI-enhanced workstation environment.<\/p>\n Note: This is my experience with all the mentioned SOTA LLMs, and it may vary with your use cases.<\/em><\/p>\n Claude 3.7 Sonnet<\/a> has emerged as the unbeatable leader (SOTA LLMs) in coding related works and software development in the constantly changing world of AI. Now, although the model was launched on February 24, 2025, it has been equipped with such abilities that can work wonders in areas beyond. According to some, it is not an incremental improvement but, rather, a break-through leap that redefines all that can be done with AI-assisted programming.<\/p>\n Claude 3.7 Sonnet distinguishes itself through unprecedented coding intelligence:<\/p>\n Claude 3.7 Sonnet introduces a revolutionary approach to AI reasoning, offering:<\/p>\n The model knows to excel in different things:<\/p>\n Claude 3.7 Sonnet is not just some language model; it\u2019s a sophisticated AI companion capable not only of following subtle instructions but also of implementing its own corrections and providing expert oversight in various fields.<\/p>\n Also Read:<\/p>\n Google DeepMind has accomplished a technological leap with Gemini 2.0 Flash<\/a> that transcends the limits of interactivity with multimodal AI. This is not merely an update; rather, it is a paradigm shift concerning what AI could do.<\/p>\n Before running the example code, you\u2019ll need to install the Google AI Python SDK:<\/p>\n Example: Calculating the Sum of the First 50 Prime Numbers<\/strong><\/p>\n Gemini 2.0 Flash enables developers to:<\/p>\n Gemini 2.0 is not just a technological advance but also a window into the future of AI, where models can understand, reason, and act across multiple domains with unprecedented sophistication.<\/p>\n Also Read: <\/p>\n The OpenAI o3-mini-high is an exceptional approach to mathematically solving problems and has advanced reasoning capabilities. The whole model is built to solve some of the most complicated mathematical problems with a depth and precision that are unprecedented. Instead of just punching numbers into a computer, o3-mini-high provides a better approach to reasoning about mathematics that enables reasonably difficult problems to be broken into segments and answered step by step.<\/p>\n Mathematical reasoning is where this model truly shines. Its enhanced chain-of-thought architecture allows for a far more complete consideration of mathematical problems, allowing the user not only to receive answers, but also detailed explanations of how those answers were derived. This approach is huge in scientific, engineering, and research contexts in which the understanding of the problem-solving process is as important as the result.<\/p>\n The performance of the model is really amazing in all types of mathematics. It can do simple computations as well as complex scientific calculations very accurately and very deeply. Its striking feature is that it solves incredibly complicated multi-step problems that would stump even the best standard AI models. For example, many complicated math problems can be broken down into intuitive steps with this awesome AI tool. There are several benchmark tests like AIME and GPQA in which this model performs at a level comparable to some gigantic models.<\/p>\n What really sets o3-mini-high<\/a> apart from anything is its nuanced approach to mathematical reasoning. This variant then takes more time than the standard model to process and explain mathematical problems. Although that means response tends to be longer, it avails the user of better and more substantiated reasoning. This model just does not answer; it takes the user through all the reasoning and processing, which really makes it an invaluable tool for educational purposes, research, or professional applications that require full-scale mathematics.<\/p>\n In practice, o3-mini-high finds major value in scenarios where the application requires advanced mathematical reasoning. This ability to dissect difficult problems will be particularly helpful to scientific researchers, engineers, and advanced students. Whether developing intricately defined algorithms, addressing multi-step mathematical problems, or conducting thorough scientific calculations, this model literally offers a level of mathematical insight far beyond anything most people would ever expect from a traditional computational tool.<\/p>\n Dense transformer framework forms the basis for the model architecture, enabling the performance of all mathematical problems in a closely defined way. Such an advanced model deals with various constraints and reasons out verified steps making it best suited for very advanced maths where computation alone cannot represent genuine mathematical understanding.<\/p>\n If you are not already part of the OpenAI beta program, you\u2019ll need to request access by visiting OpenAI\u2019s API page. Once you sign up, you may need to wait for approval to access the o3-mini<\/a> models.<\/p>\n Once you have access, log in to the OpenAI API platform and generate an API key. This key is necessary for making API requests. To generate the key, go to API Keys and click on \u201cCreate New Secret Key\u201d. Once generated, make sure to copy the key and save it securely.<\/p>\n To interact with the OpenAI API<\/a>, you will need to install the OpenAI Python SDK. You can do this using the following command:<\/p>\n After installing the OpenAI SDK, you need to initialize the client by setting up the API key:<\/p>\n O3-mini-high is particularly well-suited for:<\/p>\n Most definitely, the OpenAI o3-mini-high entails a very considerable plus in mathematical reasoning, way beyond what one could expect of traditional computation. Combining advanced reasoning techniques with a thorough understanding of the methodology of solving mathematical problems, this model provides a real solution for anyone needing more than a mere quick answer.<\/p>\n Also Read: <\/p>\n As AI evolves at breakneck speed, ElevenLabs<\/a> stands out as a revolutionary technology that is forever changing the shape of how we work with audio tech. At its heart, the ElevenLabs API <\/a>embodies an elaborate ecosystem of voice synthesis tools that give developers and producers ease and flexibility in creating very natural-sounding speech like never before.<\/p>\n The only difference between ElevenLabs and traditional voice synthesis tools is the underpinning used for voice generation: The former applies cutting-edge machine learning algorithms to encompass all the fine-grained subtleties in human speech. This API permits developers to fine-tune the parameters that affect the voice with remarkable precision. Users can change parameters representing emotion strength, similarity of reference voice, and intensity of speaking style, thereby giving an unprecedented degree of control over audio generation.<\/p>\n Create an account at elevenlabs.io<\/a> and select an appropriate subscription plan.<\/p>\n In your ElevenLabs dashboard, navigate to the Profile section to create and copy your API key.<\/p>\n Real power behind ElevenLabs lies in very extensive customization. Developers can tweak voice settings down to minute details. The stability setting controls highlights of emotional variations, while the similarity boost settings increase voice replication accuracy. Such tools can be used to produce incredibly human-like voices with adjustable features for different use cases.<\/p>\n With such power comes the need for careful implementation considerations. API key security must be prioritized, rate limits must be respected, and error handling must have a priority in implementation. Cashing the generated audio will prove to be a performance booster, while eliminating a few API calls. A good awareness of these aspects may grant smooth integration, coupled with optimal utilization of the capabilities offered by the platform.<\/p>\n ElevenLabs have come up with a pricing system which is considered to be inclusive and flexible. The free tier supports developers to play and prototype, whereas advanced use cases use pay-as-you-go and subscription models. The token-based pricing is an advantage as it allows developers to pay only for the resources consumed according to the needs of a project, no matter the scale.<\/p>\n The platform recognizes that working with advanced AI technologies can present challenges.\u00a0<\/p>\n More than an API, ElevenLabs is a glimpse into the future of human-computer interaction. The platform is indeed taking down barriers by democratizing high-end voice synthesis technologies that could open doors to advanced communication, entertainment, and accessibility.<\/p>\n For developers and creators who want to push the edges of audio technology, ElevenLabs provides a fittingly powerful and flexible solution. Consider its features and customization options; innovators can then put them to use in creating engaging audio experiences that sound natural, and pretty much anything else that these innovators wish to accomplish.<\/p>\n In an increasingly developing arena for large language models, OpenAI\u2019s Deep Research<\/a> is a pioneering solution specifically designed for exhaustive research. Contrary to the usual LLMs, which are good in either text generation or coding, Deep Research is an absolutely new paradigm in itself concerning how an AI can autonomously navigate, synthesize, and document information from all over the web.<\/p>\n Deep Research is far more than the latest development of ChatGPT with browsing capability is, rather, an independent agent built on OpenAI\u2019s upcoming o3 reasoning model<\/a>, turning upside-down what AI research can do in essence. Where typical LLMs concern themselves only with the prompt, Deep Research engages a topic with much more thoroughness and full documentation.\u00a0<\/p>\n This tool stands apart from the rest in terms of its independent workflow for research:<\/p>\n Deep Research\u2019s capabilities aren\u2019t just marketing claims\u2014they\u2019re backed by impressive benchmark performance that demonstrates its research superiority:<\/p>\n The performance\u2019s ability to scale with the complexity of tasks is especially interesting. According to OpenAI\u2019s internal evaluations, Deep Research\u2019s accuracy increases with the number of tool calls. Thus, research paths explored parallel higher quality in the final output.<\/p>\n Follow the detailed guide in the article to build your Deep Research Agent: The article will walk you through:<\/p>\n Standard language models excel at generating text, answering questions, or writing code based on their training data. However, they fundamentally struggle with:<\/p>\n A meticulous research assistant is what actually Deep Research is, and that\u2019s how it overcomes various limitations. Instead of acting like a typical chatbot, it helps in investigating research and evaluation to compile. This fundamentally alters how knowledge workers can use such things as AI.<\/p>\n For professionals conducting serious research, Deep Research offers distinct advantages over traditional LLMs:<\/p>\n The tool particularly shines in scenarios requiring 1-3 hours of human research time\u2014tasks too complex for quick web searches but not so specialized that they require proprietary knowledge sources.<\/p>\n Deep Research is the first of a new breed of AI tools that will focus on research autonomously. Still very much in the early stages and subject to the occasional error and confusion regarding the fast-changing state of affairs, it nonetheless shows AI moving beyond simple text generation into genuine partnership in research.<\/p>\n Future improvements being planned while OpenAI continues with its development are:<\/p>\n Deep research is the sort of AI that would give knowledge workers and research professionals a sneak preview of how machines will change the gathering and synthesis of information in the future.<\/p>\n Perplexity AI<\/a> is the latest entrant in the fiercely competitive domain of AI search tools owing to its huge potential in confronting the incumbents such as Google, Bing, and ChatGPT<\/a> browsing capabilities. But it is not just the actual web-surfing capability that sets Perplexity apart; instead, it is the mechanism of delivering, showcasing, and integrating information that is reinventing search experience.<\/p>\n Contrary to conventional search engines, which usually yield results in the form of hyperlinks necessitating further exploration, here is a fundamentally different approach:<\/p>\n Thus research is transformed from a multi-step process into what is essentially an informative experience with enormous savings in terms of time and disinvestment of cognitive energy.<\/p>\n Perplexity offers two distinct search experiences:<\/p>\n Quick Search<\/strong> provides rapid, concise answers to straightforward queries\u2014ideal for fact-checking or basic information needs.<\/p>\n Pro Search<\/strong> represents a significant evolution in search technology by:<\/p>\n To implement Perplexity AI for web search, you\u2019ll need to use their API. Below is a step-by-step guide on how to install and implement Perplexity AI for web search using Python.<\/p>\n You\u2019ll need requests for making HTTP requests and optionally python-dotenv for managing API keys.<\/p>\n Here\u2019s a basic example of how to use Perplexity\u2019s API for a web search:<\/p>\n Perplexity AI offers a range of models for web search, catering to different needs and complexity levels. The default model is optimized for speed and web browsing, providing fast and accurate answers suitable for quick searches. For more advanced tasks, Perplexity Pro subscribers can access models like GPT-4 Omni, Claude 3.5 Sonnet, and others from leading AI companies. These models excel in complex reasoning, creative writing, and deeper analysis, making them ideal for tasks requiring nuanced language understanding or advanced problem-solving. Additionally, Perplexity Pro allows users to perform in-depth internet searches with access to multiple sources, enhancing the breadth and depth of search results. This variety of models empowers users to choose the best fit for their specific requirements, whether it\u2019s a simple query or a more intricate research task.<\/p>\n Perplexity extends beyond standalone search through powerful integrations:<\/p>\n Perplexity demonstrates particular excellence in several key areas:<\/p>\n When searching for current events like the Notre-Dame cathedral restoration, Perplexity delivers comprehensive summaries with key dates, critical details, and multimedia content\u2014all presented in an easily digestible format.<\/p>\n For business and professional users, Perplexity excels at:<\/p>\n Students and researchers benefit from:<\/p>\n Daily tasks become more efficient with Perplexity\u2019s approach to:<\/p>\n When contrasted with other top search and AI solutions:<\/p>\n Versus Google\/Bing:<\/strong><\/p>\n Versus ChatGPT:<\/strong><\/p>\n To maximize Perplexity\u2019s capabilities:<\/p>\n Perplexity is more than a search tool; it heralds a paradigm change in how we interact with information online. Perplexity has laid its foundation in bridging the best aspects of search with AI: while traditional search engines were designed and built as if they would remain dominant.\u00a0<\/p>\n For users looking for a more efficient, complete, and transparent means for information discovery, Perplexity is giving a glimpse into the future of search: where finding information is less about clicking on links and more about receiving contextually verified knowledge directly.<\/p>\n Also Read: <\/p>\n The age of generalist AI is fading as specialized SOTA LLMs take center stage. OpenAI\u2019s Deep Research automates complex, citation-backed inquiries, while Perplexity AI transforms web search with rich media results. These aren\u2019t mere upgrades\u2014they\u2019re a paradigm shift in how we access and apply knowledge.<\/p>\n Success won\u2019t hinge on choosing a single AI but on leveraging the right tool for the task. By integrating these specialized systems, knowledge workers can achieve unprecedented productivity, deeper insights, and smarter decision-making. The future belongs not to one dominant AI but to an ecosystem of expert-driven models.<\/p>\n <\/a>\n <\/div>\n<\/p><\/div>\n Gen AI Intern at Analytics Vidhya Feel free to connect with me at [email\u00a0protected]<\/a><\/p>\n<\/p><\/div>\n<\/p><\/div>\n 1. Claude 3.7 Sonnet<\/h2>\n
Unmatched Coding Capabilities<\/h3>\n
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Key Strengths<\/h3>\n
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Technological Innovations<\/h3>\n
Advanced Reasoning Capabilities<\/h4>\n
\n
Versatile Use Cases<\/h4>\n
\n
Hands-On Guide: Your First Claude 3.7 Sonnet Project<\/h3>\n
Prerequisites<\/h4>\n
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Step-by-Step Implementation<\/h3>\n
1. Install the Anthropic SDK<\/h4>\n
!pip install anthropic<\/code><\/pre>\n2. Set Up Your API Environment<\/h4>\n
export ANTHROPIC_API_KEY='your-api-key-here'<\/code><\/pre>\n3. Python Code Example:\u00a0<\/h4>\n
import anthropic\nclient = anthropic.Anthropic()\nmessage = client.messages.create(\n\u00a0\u00a0\u00a0\u00a0model=\"claude-3-7-sonnet-20250219\",\n\u00a0\u00a0\u00a0\u00a0max_tokens=1000,\n\u00a0\u00a0\u00a0\u00a0temperature=1,\n\u00a0\u00a0\u00a0\u00a0system=\"You are a world-class poet. Respond only with short poems.\",\n\u00a0\u00a0\u00a0\u00a0messages=[\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"role\": \"user\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"content\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"type\": \"text\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"text\": \"Why is the ocean salty?\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0]\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0}\n\u00a0\u00a0\u00a0\u00a0]\n)\nprint(message.content)<\/code><\/pre>\nOutput<\/h4>\n
[TextBlock(text=\"The ocean's salty brine,\\nA tale of time and design.\\nRocks
Best Practices<\/h3>\n
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Pricing and Availability<\/h3>\n
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2. Gemini 2.0 Flash<\/h2>\n
Understanding Gemini 2.0 Flash<\/h3>\n
Key Technological Advancements<\/h3>\n
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Hands-On Guide: Code Execution with Gemini 2.0 Flash<\/h3>\n
Prerequisites<\/h4>\n
\n
Installation and Setup<\/h3>\n
!pip install google-generativeai<\/code><\/pre>\nfrom google import genai\nfrom google.genai import types\n# Set up your API key\nclient = genai.Client(api_keyGoogle DeepMind=\"GEMINI_API_KEY\")\n# Create a prompt that requires code generation and execution\nresponse = client.models.generate_content(\n\u00a0\u00a0model=\"gemini-2.0-flash\",\n\u00a0\u00a0contents=\"What is the sum of the first 50 prime numbers? \"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'Generate and run code for the calculation, and make sure you get all 50.',\n\u00a0\u00a0config=types.GenerateContentConfig(\n\u00a0\u00a0\u00a0\u00a0tools=[types.Tool(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0code_execution=types.ToolCodeExecution\n\u00a0\u00a0\u00a0\u00a0)]\n\u00a0\u00a0)\n)\n# Print the response\nprint(response.text)<\/code><\/pre>\nOutput<\/h4>\n
![\"\"\/](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdNAQmzMqv4L1V6ijlCE9Bp69k1i6ayD0cCNOEGtoA6deVoptIcIHljhUV6cPvzjbBi0XypoMI6PdfPvqe-tsWbQ0iKcCOkJrl6ix2qXLh2wVoUkB_-7t0BsEgIRSNIyBkAgcFHZg?key=CvEpXyqeVz2xZ85Oxxj6j7A5\")
Real-World Applications<\/h3>\n
\n
Availability and Access<\/h3>\n
\n
3. OpenAI o3-mini-high<\/h2>\n
The Essence of Mathematical Reasoning<\/h3>\n
Performance Across Mathematical Domains<\/h3>\n
Unique Approach to Problem-Solving<\/h3>\n
Considerations and Limitations<\/h3>\n
\n
Practical Applications in Mathematical Problem-Solving<\/h3>\n
Technical Architecture and Mathematical Reasoning<\/h3>\n
Hands-On: Practical Guide to Using o3-mini-high for Mathematical Problem-Solving<\/h3>\n
Step 1: Sign up for API Access<\/h4>\n
Step 2: Generate an API Key<\/h4>\n
Step 3: Install the OpenAI Python SDK<\/h4>\n
!pip install openai<\/code><\/pre>\nStep 4: Initialize the OpenAI Client<\/h4>\n
import os\nimport openai\n# Set your API key as an environment variable\nos.environ[\"OPENAI_API_KEY\"] = \"your_api_key_here\"<\/code><\/pre>\nStep 5: Make Requests to the o3-mini-high Model<\/h4>\n
# Or configure the client directly\nclient = openai.OpenAI(api_key=\"your_api_key_here\")\n# Example chat completion request\nresponse = client.chat.completions.create(\n\u00a0\u00a0\u00a0\u00a0model=\"o3-mini-high\",\n\u00a0\u00a0\u00a0\u00a0messages=[\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\"role\": \"user\", \"content\": \"Write a function to calculate the Fibonacci sequence.\"}\n\u00a0\u00a0\u00a0\u00a0],\n\u00a0\u00a0\u00a0\u00a0temperature=0.7,\n\u00a0\u00a0\u00a0\u00a0max_tokens=1500\n)\n# Print the response\nprint(response.choices[0].message.content)<\/code><\/pre>\nIdeal Use Cases<\/h3>\n
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4. ElevenLabs API<\/h2>\n
Technological Capabilities<\/h3>\n
\n
Technical Architecture and Functionality<\/h3>\n
Installation and Integration<\/h3>\n
Step 1: Sign Up for ElevenLabs<\/h4>\n
Step 2: Generate an API Key<\/h4>\n
Step 3: Install the SDK<\/h4>\n
!pip install elevenlabs\u00a0<\/code><\/pre>\nStep 4: Initialize the Client<\/h4>\n
from elevenlabs import set_api_key, generate, play, save\n# Set your API key\nset_api_key(\"your_api_key_here\")<\/code><\/pre>\nStep 5: Generate Voice Audio<\/h4>\n
# Generate speech with a pre-made voice\naudio = generate(\n\u00a0\u00a0\u00a0\u00a0text=\"Hello world! This is ElevenLabs text-to-speech API.\",\n\u00a0\u00a0\u00a0\u00a0voice=\"Rachel\"\n)\n# Play the audio or save to file\nplay(audio)\nsave(audio, \"output_speech.mp3\")<\/code><\/pre>\nStep 6: Voice Customization<\/h4>\n
from elevenlabs.api import Voice, VoiceSettings\naudio = generate(\n\u00a0\u00a0\u00a0\u00a0text=\"This uses custom voice settings.\",\n\u00a0\u00a0\u00a0\u00a0voice=Voice(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0voice_id=\"21m00Tcm4TlvDq8ikWAM\",\u00a0 # Rachel's voice ID\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0settings=VoiceSettings(\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0stability=0.7,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0similarity_boost=0.5\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0)\n\u00a0\u00a0\u00a0\u00a0)\n)<\/code><\/pre>\nVoice Customization Capabilities<\/h3>\n
Practical Applications<\/h3>\n
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Best Practices and Considerations<\/h3>\n
Cost and Accessibility<\/h3>\n
Troubleshooting and Support<\/h3>\n
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Future of Voice Technology<\/h3>\n
5. OpenAI Deep Research<\/h2>\n
The Research Powerhouse<\/h3>\n
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Benchmark-Breaking Performance<\/h3>\n
\n
Implement the Research Agent<\/strong><\/h4>\n
\ud83d\udc49 Build Your Own Deep Research Agent<\/a><\/p>\n\n
When Traditional LLMs Fall Short?<\/h3>\n
\n
Real-World Application Advantage<\/h3>\n
\n
The Future of AI Research Assistants<\/h3>\n
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6. Perplexity AI<\/h2>\n
A New Paradigm in Search Technology<\/h3>\n
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Key Features That Drive Performance<\/h3>\n
Quick Search vs. Pro Search<\/h4>\n
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Installation and Integration<\/h3>\n
Step 1: Obtain an API Key<\/h3>\n
\n
Step 2: Install Required Packages<\/h3>\n
!pip install requests python-dotenv<\/code><\/pre>\nStep 3: Implement Perplexity AI Search<\/h3>\n
import requests\nimport os\nfrom dotenv import load_dotenv\n# Load API key from .env file if using\nload_dotenv()\n\n# Set API key\nPERPLEXITY_API_KEY = os.getenv('PERPLEXITY_API_KEY')\ndef perplexity_search(query):\n\u00a0\u00a0\u00a0\u00a0url = \"https:\/\/api.perplexity.ai\/chat\/completions\"\n\u00a0\u00a0\u00a0\u00a0headers = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'accept': 'application\/json',\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'content-type': 'application\/json',\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'Authorization': f'Bearer {PERPLEXITY_API_KEY}'\n\u00a0\u00a0\u00a0\u00a0}\n\n\u00a0\u00a0\u00a0\u00a0data = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"model\": \"mistral-7b-instruct\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"stream\": False,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"max_tokens\": 1024,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"frequency_penalty\": 1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"temperature\": 0.0,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"messages\": [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"role\": \"system\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"content\": \"Provide a concise answer.\"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0},\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"role\": \"user\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"content\": query\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0}\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0]\n\u00a0\u00a0\u00a0\u00a0}\n\u00a0\u00a0\u00a0\u00a0response = requests.post(url, headers=headers, json=data)\n\u00a0\u00a0\u00a0\u00a0if response.status_code == 200:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return response.json()\n\u00a0\u00a0\u00a0\u00a0else:\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return None\n# Example usage\nquery = \"How many stars are in the Milky Way?\"\nresponse = perplexity_search(query)\nif response:\n\u00a0\u00a0\u00a0\u00a0print(response)\nelse:\n\u00a0\u00a0\u00a0\u00a0print(\"Failed to retrieve response.\")<\/code><\/pre>\nIntegration Capabilities<\/h3>\n
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Real-World Application Strengths<\/h3>\n
1. Information Discovery<\/h4>\n
2. Professional Research<\/h4>\n
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3. Academic Applications<\/h4>\n
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4. Practical Planning<\/h4>\n
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How It Compares to Other Leading Tools?<\/h3>\n
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Optimization Tips for Power Users<\/h3>\n
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The Future of AI-Powered Search<\/h3>\n
Conclusion<\/h2>\n
\n ![\"Riya](\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_6IQzGzn.webp\")
<\/p>\n
Department of Computer Science, Vellore Institute of Technology, Vellore, India
I am currently working as a Gen AI Intern at Analytics Vidhya, where I contribute to innovative AI-driven solutions that empower businesses to leverage data effectively. As a final-year Computer Science student at Vellore Institute of Technology, I bring a solid foundation in software development, data analytics, and machine learning to my role.<\/p>\nLogin to continue reading and enjoy expert-curated content.<\/h4>\n