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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
GitHub - BuilderIO/micro-agent: An AI agent that writes (actually useful) code for you
Just run , give it a prompt, and it'll generate a test and then iterate on code until all test case passes. LLMs are great at giving you broken code, and it can take repeat iteration to get that code to work as expected. So why do this manually when AI can handle not just the generation but also the iteration and fixing? AI agents are cool, but general-purpose coding agents rarely work as hoped or promised. They tend to go haywire with compounding errors. Think of your Roomba getting stuck under a table, x1000. This project is not trying to be an end-to-end developer. AI agents are not capable enough to reliably try to be that yet (or probably very soon). This project won't install modules, read and write multiple files, or do anything else that is highly likely to cause havoc when it inevitably fails. It's a micro agent. It's small, focused, and does one thing as well as possible: write a test, then produce code that passes that test. Micro Agent requires Node.js v14 or later. The best way to get started is to run Micro Agent in interactive mode, where it will ask you questions about the code it generates and use your feedback to improve the code it generates. Look at that, you're now a test-driven developer. You're welcome. Micro Agent works with Claude, OpenAI, Ollama, or any OpenAI compatible provider such as Groq. You need to add your API key to the CLI: Or, for Claude: To use a custom OpenAI API endpoint, such as for use with Ollama or Groq, you can set the endpoint with: To run the Micro Agent on a file in unit test matching mode, you need to provide a test script that will run after each code generation attempt. For instance: Micro Agent can also help you match a design. To do this, you need to provide a design and a local URL to your rendered code. For instance: Micro agent will then generate code until the rendered output of your code matches more closely matches a screenshot file that you place next to the code you are editing (in this case, it would be ). The above assumes the following file structure: OpenAI is simply just not good at visual matching. We recommend using Anthropic for visual matching. To use Anthropic, you need to add your API key to the CLI: Visual matching uses a multi-agent approach where Anthropic Claude Opus will do the visual matching and feedback, and then OpenAI will generate the code to match the design and address the feedback. Micro Agent can also integrate with Visual Copilot to connect directly with Figma to ensure the highest fidelity possible design to code, including fully reusing the exact components and design tokens from your codebase. Visual Copilot connects directly to Figma to assist with pixel perfect conversion, exact design token mapping, and precise reusage of your components in the generated output. Then, Micro Agent can take the output of Visual Copilot and make final adjustments to the code to ensure it passes TSC, lint, tests, and fully matches your design including final tweaks. By default, Micro Agent will do 10 runs. If tests don't pass in 10 runs, it will stop. You can change this with the flag, like . To use a more visual interface to view and set config options you can type: To get an interactive UI like below: All config options can be overridden as environment variables, for instance: We would love your contributions to make this project better, and gladly accept PRs. Please see ./CONTRIBUTING.md for how to contribute.
GitHub
Mon, 8 Jul, 2:12 PM UTC
GFT EnterpriseGPT: secure AI platform for financial service providers in just six days
GFT EnterpriseGPT: secure AI platform for financial service providers in just six days New solution is cloud- and model-agnostic, fulfils the highest data protection and regulatory requirements Stuttgart, 15 July 2024 - According to recent studies, companies can use generative AI to speed up their processes by up to 42 percent and increase their performance accordingly. With GFT EnterpriseGPT, these advantages can be utilised securely. The platform developed by GFT Germany fulfils regulatory requirements and guarantees security for processes and data. GFT EnterpriseGPT runs on all common cloud platforms and can utilise all common language models. The standardised plug-and-play solution can be implemented quickly and without high initial costs. Return on investment is typically realised after a very short time. This is possible because the solution is highly standardised. Thanks to excellent interoperability, it can be used in day-to-day business just 6 days after the start of the project. The solution is already being used successfully throughout a leading German bank. "With GFT EnterpriseGPT, large amounts of data can be utilised efficiently after a very short start-up time. You simply ask the AI for the information you need, and tedious search times are reduced to just a few seconds. Content generation is also accelerated," explains Maximilian Baritz, Managing Director at GFT Germany. "If we start the project today, employees will be able to use the system as early as next week." EnterpriseGPT: more efficient work processes, strong data protection and confidentiality Many companies shy away from using publicly accessible GPT platforms. These often lack compliance with data protection regulation, or the confidentiality of the information entered cannot be guaranteed. In addition, they do not have access to the company's own data, which limits the benefits for the company. Thanks to the performance of the leading language models, users of GFT EnterpriseGPT get the maximum benefit from their data - securely and cost-effectively. In addition, GFT EnterpriseGPT can not only process internal company data, but is also connected to the Internet. This allows users to create new content based on their own data and web searches, formulate responses to customer enquiries and much more. EnterpriseGPT is part of the GFT AI.DA Marketplace, a platform that combines predictive and generative AI technologies and data analytics. The GFT AI.DA Marketplace supports the development and introduction of AI applications by providing a comprehensive collection of use cases, methodologies, reference architectures and preconfigured solutions. This helps to significantly accelerate the digital transformation. EnterpriseGPT users benefit from No proprietary hardware or infrastructure required GFT handles operation, maintenance and support of the Software-as-a-Service solution. This means that users do not need their own hardware or infrastructure. Individual data sources can be connected to optimise integration into existing company processes. User management can be mapped via existing identity providers and single sign-on solutions. It is also possible to operate EnterpriseGPT locally (on-premises) so that all data remains within the company. To support specialist departments in using GPT models particularly effectively in their business, GFT also offers customised training and learning materials. https://www.bcg.com/publications/2023/assessing-the-impact-of-generative-ai-on-workforce-productivity About GFT - Shaping the future of digital business GFT is a digital transformation pioneer. By leveraging next-generation technologies, we enable clients to boost their productivity with intelligent software solutions. We focus on Digital Finance, Enterprise AI & Data Solutions, and Platform Modernisation. GFT's strengths include deep technological excellence, a strong ecosystem of partners, and industry expertise. We are agile@scale and boost digital transformation for clients from the finance and insurance sectors, as well as the manufacturing industry. GFT talents create, implement, and manage software applications to enable innovative businesses while complying with regulations. With locations in 20 markets around the globe, GFT ensures proximity to its clients. We draw on over 35 years of experience and a global team of over 12,000 determined talents. GFT provides them with career opportunities in the most innovative areas of software engineering. The GFT Technologies SE share is listed in the SDAX index of the German Stock Exchange (ticker: GFT-XE).
Market Screener
Mon, 15 Jul, 10:06 AM 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
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How to Use ChatGPT: A Step-by-Step Guide for New Users
Mastering ChatGPT: A Comprehensive Guide to Unlocking Its Full Potential In this digital era, AI is transforming itself while getting integrated into various tools. The tool which is gaining more popularity and has set the trend is ChatGPT. Powered by OpenAI it gives comprehensive results within seconds by generating human like text while only following prompts. Whether someone is a writer, develop, or educator, or someone who is curious about AI, ChatGPT is useful for everyone as it unveils accurate and personalized information. However, every tool has its pros and cons, this article delves into a detailed structure of ChatGPT including ways of its proper usage, and where and how it's been utilized.
Analytics Insight
Thu, 8 Aug, 2:06 PM UTC
