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Integrate Google Ads Keyword Planner into a custom GPTs
I'm seeking a developer to integrate the Google Ads Keyword Planner into my custom GPT. This project will involve: - Integration of the Google Ads Keyword Planner API into my custom GPT. - Ensuring the GPT is able to effectively utilize the keyword data provided by the Google Ads Keyword Planner. - Testing the integrated system to guarantee its functionality and efficiency. The ideal candidate should have experience in: - Developing or working with GPTs and AI systems. - Proficiency in API integration, particularly with Google Ads Keyword Planner. - Strong understanding of keyword analysis and its application in content creation and ad targeting. Please provide examples of similar projects you've completed. To add a custom GPT to your ChatGPT account and integrate it with the Google Ads Keyword Planner, follow these detailed steps: Step 1: Set Up OpenAI Account and API Access Create an OpenAI Account: Go to OpenAI and click on "Sign Up". Fill out the registration form with your email, password, and other required information. Verify your email by clicking the link sent to your email address. Obtain API Key: Log in to your OpenAI account. Navigate to the API section of the OpenAI dashboard by clicking on your profile icon and selecting "API" or "API Keys". Click on "Create New API Key". Copy the API key that is generated. You will need this key to authenticate your API requests. Step 2: Set Up Google Ads API Create Google Ads Account: Go to Google Ads and sign up for an account if you don't have one. Follow the on-screen instructions to set up your account. Enable Google Ads API: a. Create a Google Cloud Project: Go to the Google Cloud Console. Click on the Select a project dropdown at the top of the page. Click on New Project. Enter a name for your project and click Create. b. Enable the Google Ads API: Once the project is created, select it from the dropdown menu. Navigate to API & Services > Library. Search for Google Ads API and click on it. Click Enable. Create OAuth2 Credentials: a. Configure OAuth Consent Screen: In the Google Cloud Console, navigate to API & Services > Credentials. Click on Create Credentials and select OAuth 2.0 Client ID. If prompted, configure the consent screen: Select External. Fill out the required fields and save. b. Create OAuth2 Client ID: After configuring the consent screen, you will be redirected to create OAuth 2.0 credentials. Select Application type as Web application. Name your OAuth 2.0 client (e.g., "My Google Ads App"). In the Authorized redirect URIs field, add http://localhost. Click Create. Copy the Client ID and Client Secret. Obtain Refresh Token: a. Use OAuth2 Playground: Go to the OAuth2 Playground. Click on the gear icon (settings) in the top right corner. Check the box for Use your own OAuth credentials. Enter your Client ID and Client Secret obtained from the previous step. Click Close. b. Authorize APIs: In the left pane, scroll down and find Google Ads API v10. Check the box for [login to view URL] and click Authorize APIs. Click Exchange authorization code for tokens. Copy the Refresh Token. Create Configuration File: a. Create a YAML File: Open a text editor (e.g., Notepad, VS Code). Create a new file and name it google-ads.yaml. Add the following content to the file, replacing placeholders with your actual values: yaml Copy code developer_token: 'INSERT_YOUR_DEVELOPER_TOKEN_HERE' client_id: 'INSERT_YOUR_CLIENT_ID_HERE' client_secret: 'INSERT_YOUR_CLIENT_SECRET_HERE' refresh_token: 'INSERT_YOUR_REFRESH_TOKEN_HERE' Save the file in your working directory. Step 3: Create a Custom GPT with OpenAI API Install Required Libraries: Open your command prompt or terminal and type the following commands to install the necessary Python libraries: sh Copy code pip install openai google-ads Write the Script: Create a new Python file named [login to view URL] in your working directory and add the following code: python Copy code import openai import [login to view URL] # Set up OpenAI API openai.api_key = 'YOUR_OPENAI_API_KEY' # Initialize Google Ads API client client = google.ads.google_ads.client.GoogleAdsClient.load_from_storage('[login to view URL]') def get_keyword_ideas(client, customer_id, keyword_text): ga_service = client.get_service("GoogleAdsService") query = f""" SELECT [login to view URL], metrics.average_monthly_searches, [login to view URL] FROM keyword_plan_campaign_criterion WHERE [login to view URL] = '{keyword_text}' """ response = [login to view URL](customer_id=customer_id, query=query) return format_keyword_ideas(response) def format_keyword_ideas(response): keyword_ideas = [] for row in response: keyword = [login to view URL] avg_searches = row.metrics.average_monthly_searches competition = [login to view URL] [login to view URL]({ 'keyword': keyword, 'average_monthly_searches': avg_searches, 'competition': competition }) return keyword_ideas def generate_gpt_response(prompt): response = [login to view URL]( engine="davinci", prompt=prompt, max_tokens=150 ) return [login to view URL][0].[login to view URL]() # Example usage customer_id = 'YOUR_GOOGLE_ADS_CUSTOMER_ID' keyword_text = 'example keyword' keyword_ideas = get_keyword_ideas(client, customer_id, keyword_text) prompt = f"Keyword ideas for '{keyword_text}': {keyword_ideas}" gpt_response = generate_gpt_response(prompt) print(gpt_response) Step 4: Adding a Custom GPT to ChatGPT Log in to ChatGPT: Go to ChatGPT and log in to your account. Navigate to the GPTs Section: Click on your profile icon in the top-right corner. Select "Custom GPTs" from the dropdown menu. Create a New Custom GPT: Click on the "Create Custom GPT" button. Follow the prompts to configure your custom GPT: Name: Give your GPT a descriptive name (e.g., "Google Ads Keyword Planner"). Description: Describe what your GPT does (e.g., "Fetches keyword ideas from Google Ads and provides insights"). API Integration: Provide the API endpoint where your script is hosted (you may need to deploy your script on a platform like Heroku, AWS, or any other hosting service). Configure the GPT: Define the input fields that users will interact with (e.g., a text field for entering keywords). Specify how the GPT should handle the input and call your API. Customize the response format to display the keyword ideas fetched from Google Ads. Deploy Your Custom GPT: Save and deploy your custom GPT. Test it by entering a keyword and verifying that it fetches and displays keyword ideas as expected. By following these detailed steps, you can create and add a custom GPT to your ChatGPT account, integrating it with the Google Ads Keyword Planner to fetch and provide keyword data. If you encounter any issues or need further clarification, feel free to ask!
Freelancer
Tue, 2 Jul, 4:09 PM UTC
SearchGPT Is a Web Search Engine Powered by ChatGPT
OpenAI, the company behind ChatGPT and the GPT language model, is getting ready to compete directly with Google. The company just launched SearchGPT, an AI-powered search engine. SearchGPT is a "temporary prototype of new AI search features" from OpenAI. ChatGPT can already pull information from the web, but usually only for context or summarizing several different sources at once, which isn't always accurate. The initial version of SearchGPT is much more like a traditional web search from Google, Bing, or DuckDuckGo, with web results displayed as list of links next to AI-generated summaries. The main selling point for SearchGPT is that you can ask hyper-specific queries, such as "music festivals in Boone, North Carolina in August," or follow up with additional questions based on earlier searches. Google has tried to offer similar functionality with its AI Overviews, which were scaled back after they told people to eat glue and rocks. Microsoft is also starting to roll out more AI upgrades for Bing -- there are no reports yet of Bing telling people to eat weird items, but that could very well happen. OpenAI said in a blog post, "Getting answers on the web can take a lot of effort, often requiring multiple attempts to get relevant results. We believe that by enhancing the conversational capabilities of our models with real-time information from the web, finding what you're looking for can be faster and easier." OpenAI also says it is "committed to a thriving ecosystem of publishers and creators," which is a bizarre statement to make after it scraped the content of countless websites without permission or compensation to train its AI and make billions in profit. Many sites and publishers started blocking ChatGPT as soon as OpenAI created an opt-out mechanism, and it's not clear how many of them will be on board with OpenAI's attempt at a search engine. OpenAI now has different user agents (which can act as opt-out mechanisms) for AI training, SearchGPT, and user searches in ChatGPT. For example, some sites might allow the search engine but continue to block AI training. We'll have to see how that works out in the months and years ahead. Some publishers and sites have escalated to IP address blocks and other prevention methods against AI companies, especially after Perplexity (another AI search engine) was caught indexing sites that explicitly opted out of indexing. SearchGPT is currently a private beta, and you can sign up for the waitlist on the ChatGPT website. Source: OpenAI
The How-To Geek
Thu, 25 Jul, 8:00 PM UTC
How to build powerful AI Agents from LLMs with LAgent
LAgent is an open-source AI framework designed to transform large language models into versatile agents capable of executing various tasks. This lightweight framework supports multiple functionalities, including code execution, data analysis, and predictive modeling. It consists of three main components: agents, large language models, and actions, which work together to create intricate AI agents. The framework is compatible with both open-source and closed-source models and includes tools for easy installation and customization, making it a powerful tool for developers and data scientists alike. LAgent is a lightweight, open-source AI framework that uses large language models to create versatile agents. These agents can perform a variety of tasks, making the framework highly adaptable for different applications. By transforming language models into actionable agents, LAgent provides a robust platform for executing complex tasks efficiently. The modular structure of the framework, with its core components of agents, large language models, and actions, ensures flexibility and scalability to meet the needs of a wide range of projects. LAgent offers several core functionalities that make it a comprehensive solution for developers and data scientists: These functionalities work together to provide a powerful toolset for building sophisticated AI applications. Whether you need to process and analyze large amounts of data, automate repetitive tasks, or build predictive models, LAgent has the capabilities to support your project. Here are a selection of other articles from our extensive library of content you may find of interest on the subject of building and automating workloads using AI agents : Getting started with LAgent is a straightforward process. The framework has a few key installation requirements, including Git, Python, Visual Studio Code, and Pip. These tools are essential for cloning the repository, managing packages, and setting up the development environment. Streamlit is also required for the user interface, providing a seamless way to interact with the framework. The installation process involves the following steps: Detailed instructions for each step are provided in the LAgent documentation, making it easy to get the framework up and running on your system. Once installed, you can start exploring the demo applications and building your own AI agents. One of the key strengths of LAgent is its high level of customization. The framework provides templates for defining agents, as well as tools for function calling and React prompts. This allows you to tailor the framework to your specific needs and build AI agents that are optimized for your particular use case. Some common use cases for LAgent include: The versatility of LAgent makes it a valuable tool for a wide range of industries and applications. Whether you're a developer looking to automate repetitive coding tasks, a data scientist seeking to build advanced predictive models, or a business analyst aiming to streamline operations, LAgent provides the functionality and flexibility you need. With its robust core functionalities, modular components, and easy installation process, LAgent is a powerful open-source AI framework that can help you transform large language models into versatile agents. By leveraging the capabilities of LAgent, you can build sophisticated AI applications that drive innovation and efficiency in your projects. To learn more about LAgent and start building your own AI agents, visit the GitHub repository and explore the comprehensive documentation.
Geeky Gadgets
Mon, 5 Aug, 8:00 AM UTC
Automate anything with Google Gemini Agents
Automating workflows has become increasingly achievable thanks to rapid advancements in artificial intelligence technology. One standout tool for this purpose is the Google Gemini 1.5 Pro model, a innovative chatbot that excels at handling complex workflows. This guide by Prompt Engineering takes a deep dive into the capabilities of the Gemini 1.5 Pro, with a specific focus on how it can automate workflows through its powerful agentic functionalities. You'll learn about the setup process, the necessary tools and packages, and step-by-step instructions for executing an agentic workflow from start to finish. What sets the Google Gemini 1.5 Pro model apart is its advanced natural language understanding and generation abilities. This makes it one of the top choices in the rapidly evolving field of AI chatbots. Where the Gemini 1.5 Pro really shines is in agentic workflows - scenarios where the chatbot needs to autonomously plan out steps, access external tools, and retain memory of past interactions. Some key capabilities of the Gemini 1.5 Pro include: To grasp how the Gemini 1.5 Pro automates workflows, it's important to understand what we mean by an "agent" in this context. An agent is essentially a software entity that is capable of performing tasks and making decisions autonomously to achieve specific goals. The core components that enable agent functionality are: A key aspect of agentic workflows with the Gemini 1.5 Pro is the use of LangTrace, which tracks and records all the steps taken by the agent. This ensures full transparency into what actions the agent took, what information it accessed, and how it arrived at its outputs. Here are a selection of other articles from our extensive library of content you may find of interest on the subject of AI Agents : To harness the power of the Gemini 1.5 Pro for your own agentic workflows, you'll need to install and configure a few key packages: You'll also need to set up API keys for Tavily, Google, and LangTrace to enable your agent to access their services. Detailed instructions for installation and configuration can be found in the official documentation. Before your agent can assist with a workflow, it needs access to relevant data and information. This is where document processing comes in. The key steps are: This processed data is then ready for the next crucial steps - embedding and retrieval. To enable the agent to quickly find and access relevant information, the processed documents need to be embedded and stored in a searchable format. This is accomplished using: A document retriever is then created which can efficiently search through the vector store to find the most relevant pieces of information for a given query. This is a key component in allowing the agent to access the right data at the right time to inform its planning and decision making. Another vital aspect of an autonomous agent is its ability to interact with external tools and APIs. In the setup process, you'll define and describe in detail the specific tools your agent will have access to, such as: Providing clear descriptions of what each tool does is crucial for the agent's ability to reason about when and how to use them effectively. With all the pieces in place, it's time to create your agent and bring it to life. This is done using the React agent class from the Tavily framework, which handles the planning and memory retention aspects. You'll provide the agent with a set of prompt instructions that guide its high-level behavior and goals. Once instantiated, your agent is ready to handle a wide variety of queries and tasks. Some examples of what it can do include: The agent will autonomously plan out the steps needed to answer the query, access relevant tools and information, and generate a suitable response - all while keeping a record of its actions via LangTrace. Agentic workflows can be complex, with a lot happening behind the scenes. This is where observability becomes critical, especially in production environments. By using LangTrace to monitor and record all the steps taken by your agent, you gain valuable insights into its decision making process and performance. This information can help you identify bottlenecks, optimize retrieval and embedding, fine-tune prompts, and ensure your agent is operating efficiently and effectively. Detailed tracing also provides transparency and accountability, which is crucial for building trust in AI systems. The Google Gemini 1.5 Pro model and the agentic workflows it enables represent a significant leap forward in the field of AI-assisted automation. As the technology continues to evolve and mature, the potential applications are vast and exciting. From streamlining complex business processes to enhancing personal productivity, agentic AI has the power to transform the way we work and live. By understanding the capabilities and building blocks of tools like the Gemini 1.5 Pro, you can position yourself at the forefront of this revolution.
Geeky Gadgets
Sun, 11 Aug, 2:02 PM UTC
Build an app from a single prompt in less than 60 seconds using Replit AI
Ever had a fantastic app idea but felt overwhelmed by the thought of coding it yourself? What if you could turn that idea into a working app in less than a minute, without writing a single line of code? Meet Replit Agents, the AI tool that makes this possible. By converting simple text prompts into fully functional web applications, Replit Agents is making app development for everyone, from entrepreneurs to content creators. Replit Agents is a groundbreaking AI-powered tool that is transforming the landscape of web application development. With Replit AI Agents, creating a fully functional web application from a simple text prompt is no longer a distant dream but a reality that can be achieved in less than a minute. This innovative tool automates the coding and deployment process, making app creation accessible to everyone, regardless of their coding experience. At the heart of Replit Agents lies advanced AI technology, specifically the Claude 3.5 model. This sophisticated AI model is capable of interpreting text prompts and generating the necessary code to bring your app ideas to life. To harness the power of Replit Agents, you need a Replit Core subscription, which grants you access to this innovative tool. The process of using Replit Agents is remarkably simple and intuitive. All you need to do is provide a text prompt describing the app you want to create. For instance, if you envision a "link in bio" app, simply describe its features and functionality in a text prompt. Replit Agents will then take care of the rest, generating all the required files and code automatically. The tool even includes features like profile picture customization, allowing you to create a personalized app effortlessly. Check out the demonstration kindly created by Skill Leap AI below to see just how powerful Replit is already. Here are a selection of other articles from our extensive library of content you may find of interest on the subject of AI coding : Replit Agents goes beyond just generating code; it also offers a user-friendly chatbot interface that enables you to customize and refine your app. Through simple interactions with the chatbot, you can make adjustments to your app's functionality and appearance. This level of customization empowers you to create an app that truly reflects your vision and meets your specific requirements. Once your app is ready, Replit Agents makes deployment a breeze. The tool provides options to deploy your app to a custom domain, allowing you to seamlessly integrate it with your personal domain and align it with your brand. Additionally, Replit Agents offers analytics and resource management features, allowing you to monitor your app's performance and optimize its resource utilization. Replit Agents is a fantastic option for entrepreneurs, marketers, and content creators who have app ideas but may lack the technical expertise to bring them to fruition. By simplifying the app creation process and eliminating the need for coding skills, Replit Agents empowers individuals to focus on their core business activities while still being able to create powerful web applications. Replit Agents is transforming the way web applications are created, making it possible for anyone to transform a simple text prompt into a fully functional app in record time. By using the power of AI and automating the coding and deployment process, Replit Agents democratizes app development and opens up new possibilities for entrepreneurs, marketers, and content creators alike. With its user-friendly interface, customization options, and seamless deployment capabilities, Replit Agents is set to reshape the future of app development, empowering individuals to bring their app ideas to life with unprecedented ease and efficiency.
Geeky Gadgets
Mon, 16 Sept, 10:03 AM UTC
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