{"id":103354,"date":"2026-03-10T18:11:45","date_gmt":"2026-03-10T12:41:45","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=103354"},"modified":"2026-03-18T19:15:12","modified_gmt":"2026-03-18T13:45:12","slug":"building-an-ai-chatbot-with-rasa-and-ollama","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/building-an-ai-chatbot-with-rasa-and-ollama\/","title":{"rendered":"Building an AI Chatbot with Rasa and Ollama: Exploring the Process"},"content":{"rendered":"\n<p><strong>AI chatbots<\/strong> have become increasingly significant in contemporary applications. Chatbots are transforming online communication by enabling quicker, smarter digital interactions that respond to customer queries and help users find information faster. As the field of <strong>artificial intelligence (AI) <\/strong>continues to develop, developers can now create more sophisticated chatbots that understand natural language and provide more precise answers.<\/p>\n\n\n\n<p>The combination of<strong> Ollama<\/strong> and <strong>Rasa<\/strong> is one of the potent methods of developing such chatbots. Rasa assists with dialogue and intent management, whereas Ollama enables you to run powerful AI models on the fly. In this blog, you will learn how these two tools can collaborate, and you will have a basic step-by-step process on how to create your own AI chatbot.<\/p>\n\n\n\n<p><strong><em>Quick Answer:<\/em><\/strong><\/p>\n\n\n\n<p>You can build an <strong>AI chatbot<\/strong> with <strong>Rasa<\/strong> and <strong>Ollama<\/strong> by setting up the environment, creating a Rasa project, connecting it with Ollama\u2019s local AI model, and running the chatbot to handle user conversations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Rasa and Ollama: Powering AI Chatbots Together<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-1200x630.png\" alt=\"\" class=\"wp-image-104188\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-1200x630.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-300x158.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-768x403.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-1536x806.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-2048x1075.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/1-3-1-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Rasa<\/strong><\/h3>\n\n\n\n<p><strong>Rasa<\/strong> is a platform for creating intelligent applications. It offers features for text perception, dialogue, and working with structured data. It is an <strong>open-source tool<\/strong> used by developers to build systems that can<a href=\"https:\/\/www.guvi.in\/blog\/must-know-nlp-hacks-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\"> <strong>understand natural language<\/strong><\/a> and make decisions based on input.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Ollama<\/strong><\/h3>\n\n\n\n<p><strong>Ollama<\/strong> is a tool that lets you run<a href=\"https:\/\/www.guvi.in\/blog\/guide-to-large-language-models\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong> LLM (Large Language Models)<\/strong><\/a> on your system. It enables developers to use advanced AI without relying on cloud services, giving them control over performance, privacy, and customization.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How Rasa &amp; Ollama Are Used to Develop Your Own AI Chatbot<\/strong><\/h3>\n\n\n\n<ul>\n<li>To create a fully functional chatbot, Rasa and Ollama collaborate to combine conversation management with advanced artificial intelligence processing.<\/li>\n<\/ul>\n\n\n\n<ul>\n<li>First, Rasa receives user input, then identifies user intent, extracts valuable information (entities), and directs the conversation through its dialogue management system.<\/li>\n<\/ul>\n\n\n\n<ul>\n<li>After Rasa processes the user message, it forwards the input to Ollama, which connects a Large Language Model <strong>(LLM)<\/strong> to your system.<\/li>\n<\/ul>\n\n\n\n<ul>\n<li>The LLM in Ollama interprets the input, produces a meaningful, context-sensitive response, and then transfers it to Rasa.<\/li>\n<\/ul>\n\n\n\n<ul>\n<li>Finally, Rasa provides this answer to the user through the chat interface.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Note:<\/strong><\/h4>\n\n\n\n<p>Here, we are using the <strong>Llama3<\/strong> LLM to generate AI responses.<\/p>\n\n\n\n<p><strong><em>Also Read:<\/em><\/strong><a href=\"https:\/\/www.guvi.in\/blog\/influence-of-chatbots-on-customer-services\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em> The Influence of Chatbots on Customer Services<\/em><\/strong><\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\">\n  <strong style=\"font-size: 22px; color: #ffffff;\">\ud83d\udca1 Did You Know?<\/strong> <br \/><br \/>\n  <span>\n    The first chatbot, <strong style=\"color: #110053;\">ELIZA<\/strong>, was created in <strong style=\"color: #110053;\">1966<\/strong> by <strong style=\"color: #110053;\">Joseph Weizenbaum<\/strong> at <strong style=\"color: #110053;\">MIT<\/strong>.\n  <\/span>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to Prepare the Environment Setup<\/strong><\/h2>\n\n\n\n<p>The following steps are required to set up the AI chatbot project environment:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Create a Virtual Environment<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>python -m venv bot-env<\/code><\/pre>\n\n\n\n<ul>\n<li>Creates an isolated<a href=\"https:\/\/www.guvi.in\/blog\/how-to-create-virtual-environment-in-python\/\" target=\"_blank\" rel=\"noreferrer noopener\"> <strong>virtual Python environment<\/strong><\/a> named bot-env.<\/li>\n\n\n\n<li>Keeps project dependencies separate from your system Python.<\/li>\n\n\n\n<li>Ensures Rasa and other packages won\u2019t conflict with other projects.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Activate the Virtual Environment<\/strong><\/h3>\n\n\n\n<p><strong>Windows:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>bot-env\\Scripts\\activate<\/code><\/pre>\n\n\n\n<p><strong>macOS\/Linux:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>source bot-env\/bin\/activate<\/code><\/pre>\n\n\n\n<ul>\n<li>Switches your terminal to use the virtual environment.<\/li>\n\n\n\n<li>All Python commands and pip install will now affect only this project.<\/li>\n\n\n\n<li>The terminal prompt changes to show (bot-env) to indicate activation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Install Rasa and Rasa SDK<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install rasa rasa-sdk<\/code><\/pre>\n\n\n\n<ul>\n<li>Installs <strong>Rasa<\/strong>, the main framework for building AI chatbots.<\/li>\n\n\n\n<li>Installs <strong>the Rasa SDK<\/strong>, which lets you create <strong>custom actions<\/strong> and logic for your chatbot.<\/li>\n\n\n\n<li>Prepares your virtual environment so your chatbot project can run and interact with the AI model.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Download and Install Ollama<\/strong><\/h3>\n\n\n\n<ul>\n<li>Download from<a href=\"https:\/\/ollama.com\/download\" target=\"_blank\" rel=\"noreferrer noopener\"> <strong><em>Download Ollama<\/em><\/strong><\/a> depending on your OS (Windows, macOS, Linux).<\/li>\n\n\n\n<li>Ollama provides the <strong>LLM platform<\/strong> that runs AI models like Llama 3.<\/li>\n\n\n\n<li>This step enables your chatbot to generate intelligent responses.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Pull the Llama 3 Model<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>ollama pull llama3<\/code><\/pre>\n\n\n\n<ul>\n<li>Download the <strong>Llama 3 model<\/strong> to your computer.<\/li>\n\n\n\n<li>The model will be used to generate AI responses for your chatbot.<\/li>\n\n\n\n<li>This only needs to be done <strong>once<\/strong> per machine.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Run the Llama 3 Model<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>ollama run llama3<\/code><\/pre>\n\n\n\n<ul>\n<li>Starts the Llama 3 model in a terminal.<\/li>\n\n\n\n<li>The model listens for requests (from Rasa) to generate responses.<\/li>\n\n\n\n<li>Must be running in a <strong>separate terminal<\/strong> while you test your chatbot.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7. Initialize the Rasa Project<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>rasa init --no-prompt<\/code><\/pre>\n\n\n\n<ul>\n<li>Creates a new Rasa project with all default folders and files.<\/li>\n\n\n\n<li>Includes domain.yml, data\/, actions.py, and other essential files.<\/li>\n\n\n\n<li>&#8211;no-prompt skips interactive setup and creates the project automatically.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Note:<\/strong><\/h4>\n\n\n\n<p>After running rasa init &#8211;no-prompt, Rasa creates a project with all the necessary files and folders for your chatbot.<\/p>\n\n\n\n<ul>\n<li><strong>data\/ folder<\/strong> \u2013 this is the training data:<\/li>\n<\/ul>\n\n\n\n<ul>\n<li><strong>nlu.yml<\/strong> \u2192 trains the bot to decipher user messages<\/li>\n\n\n\n<li><strong>rules.yml <\/strong>\u2192 rules set that cannot be altered by the user<\/li>\n\n\n\n<li><strong>stories.yml <\/strong>\u2192 indicates example dialogs of the bot<\/li>\n\n\n\n<li><strong>config.yml <\/strong>&#8211; installs NLU pipeline and dialogue policies.<\/li>\n\n\n\n<li><strong>actions.py<\/strong> &#8211; create your own Python actions for the bot.<\/li>\n\n\n\n<li><strong>endpoints.yml<\/strong> &#8211; opine links to outside services.<\/li>\n\n\n\n<li><strong>credentials.yml <\/strong>&#8211; channel credentials such as Slack or Telegram.<\/li>\n\n\n\n<li><strong>tests\/ folder<\/strong> &#8211; stores test stories to test bot responses.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Step-by-Step Implementation Guide<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-1200x630.png\" alt=\"\" class=\"wp-image-104190\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-1200x630.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-300x158.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-768x403.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-1536x806.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-2048x1075.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/2-1-3-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Follow these steps carefully to quickly get your chatbot running and integrated with the Llama 3 model:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Define Intents and Responses<\/strong><\/h3>\n\n\n\n<p>Update data\/nlu.yml with the user intents and domain.yml with responses. This tells your bot <strong>what the user might say<\/strong> and <strong>how it should reply<\/strong>.<\/p>\n\n\n\n<p><strong>nlu.yml:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>version: \"3.1\"\n\nnlu:\n\n- intent: greet\n\n&nbsp;&nbsp;examples: |\n\n&nbsp;&nbsp;&nbsp;&nbsp;- hi\n\n&nbsp;&nbsp;&nbsp;&nbsp;- hello\n\n&nbsp;&nbsp;&nbsp;&nbsp;- hey<\/code><\/pre>\n\n\n\n<p><strong>domain.yml:<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>responses:\n\n&nbsp;&nbsp;utter_greet:\n\n&nbsp;&nbsp;&nbsp;&nbsp;- text: \"Hello! How can I help you today?\"<\/code><\/pre>\n\n\n\n<p>This trains the bot to recognize greetings and respond appropriately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Create a Custom Action to Call Llama 3<\/strong><\/h3>\n\n\n\n<p>Edit actions.py to make a function that <strong>sends the user message to Llama 3 and returns the response<\/strong>.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from rasa_sdk import Action, Tracker\n\nfrom rasa_sdk.executor import CollectingDispatcher\n\nimport subprocess\n\nclass ActionCallLlama3(Action):\n\n&nbsp;&nbsp;&nbsp;&nbsp;def name(self):\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return \"action_call_llama3\"\n\n&nbsp;&nbsp;&nbsp;&nbsp;def run(self, dispatcher, tracker, domain):\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;user_message = tracker.latest_message.get('text')\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# Call Llama3 via terminal command\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;result = subprocess.run(&#91;\"ollama\", \"run\", \"llama3\", user_message], capture_output=True, text=True)\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dispatcher.utter_message(text=result.stdout)\n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return &#91;]<\/code><\/pre>\n\n\n\n<p>This lets your bot <strong>generate intelligent AI replies<\/strong> using Llama 3 rather than predefined responses.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Update domain.yml for Custom Action<\/strong><\/h3>\n\n\n\n<p>Tell Rasa about your new action:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>actions:\n\n&nbsp;&nbsp;- action_call_llama3<\/code><\/pre>\n\n\n\n<p>This makes Rasa aware of the custom action so it can call it during conversations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: Train Your Rasa Model<\/strong><\/h3>\n\n\n\n<p>Run this in your terminal:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code><strong>rasa train<\/strong><\/code><\/pre>\n\n\n\n<p>Rasa reads your training data (nlu.yml, stories.yml) and builds a model that understands user messages and responds with predefined responses or your custom Llama 3 action.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 5: Run Rasa Action Server<\/strong><\/h3>\n\n\n\n<p>Start the server that handles custom actions:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code><strong>rasa run actions<\/strong><\/code><\/pre>\n\n\n\n<p>This keeps your custom Llama 3 action active and ready to use in conversations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 6: Run the Rasa Chatbot<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-1200x630.png\" alt=\"\" class=\"wp-image-104191\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-1200x630.png 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-300x158.png 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-768x403.png 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-1536x806.png 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-2048x1075.png 2048w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/3-1-2-150x79.png 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Start the bot itself:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code><strong>rasa shell<\/strong><\/code><\/pre>\n\n\n\n<p>Now you can chat with your bot. Rasa will <strong>process user input, decide on a response, and call Llama 3 if needed<\/strong>.<\/p>\n\n\n\n<p>This is how the <strong>live AI chatbot<\/strong> looks in the terminal \u2013 refer to the <strong>screenshot<\/strong> below:<\/p>\n\n\n\n<p>AI is transforming the world faster than ever, and mastering it can open doors to endless opportunities. Don\u2019t get left behind \u2013 enroll in <strong>HCL GUVI\u2019s<\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=Building+an+AI+Chatbot+with+Rasa+and+Ollama%3A+Exploring+the+Process\" target=\"_blank\" rel=\"noreferrer noopener\"><strong> AI\/ML Course<\/strong><\/a>, and ride the wave of the AI revolution today!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p><strong>Rasa<\/strong> and <strong>Ollama<\/strong> are useful for building intelligent, efficient conversational systems by enabling the creation of AI chatbots. By integrating Rasa&#8217;s conversation management with Ollama&#8217;s local AI models, developers can create responsive chatbots that understand users&#8217; needs. Once the configuration is complete and the procedures are followed properly, developing an AI chatbot will become much easier.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1773116676526\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>What is the role of Rasa in building an AI chatbot?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Rasa manages conversations and understands messages. It helps the chatbot detect intent and respond in a structured way.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1773116684535\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Why is Ollama used with Rasa?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Ollama lets developers run AI models locally. Combined with Rasa, it helps the AI chatbot generate smarter, natural responses.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1773116698102\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Do you need coding knowledge to build a AI chatbot with Rasa and Ollama?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Basic Python and chatbot knowledge helps, making setup, connecting Ollama, and managing responses easier.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>AI chatbots have become increasingly significant in contemporary applications. Chatbots are transforming online communication by enabling quicker, smarter digital interactions that respond to customer queries and help users find information faster. As the field of artificial intelligence (AI) continues to develop, developers can now create more sophisticated chatbots that understand natural language and provide more [&hellip;]<\/p>\n","protected":false},"author":64,"featured_media":104187,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933,715],"tags":[],"views":"718","authorinfo":{"name":"Abhishek Pati","url":"https:\/\/www.guvi.in\/blog\/author\/abhishek-pati\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/Feature-image-17-300x116.png","jetpack_featured_media_url":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/03\/Feature-image-17.png","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/103354"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/64"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=103354"}],"version-history":[{"count":7,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/103354\/revisions"}],"predecessor-version":[{"id":104192,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/103354\/revisions\/104192"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/104187"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=103354"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=103354"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=103354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}