Posts Tagged ai

The cloud promised to revolutionize your business. Faster innovation. Lower costs. Unlimited scalability. But for many companies, that promise remains frustratingly out of reach. Instead of accelerating product development, infrastructure has become a bottleneck. You and your team (DevOps, platform, or infrastructure engineering teams) are bogged down by: Clunky tools and manual processes Provisioning a simple test environment takes days Rolling out updates across regions takes weeks The combinations of modern cloud architectures seems infinite You know there has to be a better way.

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AI is transforming how users interact with every category of technology today, and cloud infrastructure is no exception. Last year we launched Pulumi AI to combine generative AI with Pulumi’s knowledge of cloud infrastructure, helping users solve complex cloud development problems using Infrastructure-as-Code. Pulumi AI has seen rapid adoption and engagement, with tens of thousands of users leveraging Pulumi AI over the last year.

Today, we’re excited to introduce Pulumi Copilot, a huge leap forward in applying AI to cloud infrastructure management. Pulumi Copilot is a new conversational chat interface integrated throughout Pulumi Cloud, enabling Pulumi Cloud users to quickly accomplish a variety of cloud infrastructure management tasks by leveraging the power of large language models plus the rich capabilities of Pulumi Cloud.

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In the introductory blog post, we learned to Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS, and continued with Adding data to Pinecone using S3, Embedchain, and Pulumi on AWS for an AI Slack bot. For reference, here’s what our architecture looked like at the end of the second blog post. To follow along, clone the project, git clone https://github.com/catmeme/arti.git or view it on GitHub.

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In the introductory blog post, we learned to Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS. However, we made a few concessions in the application logic to illustrate the broader picture of what we were able to achieve combining these three technologies. Now that we have a solid foundation for deploying our Slack bot and querying our data, lets begin moving from proof-of-concept to production-ready, iteratively.

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The integration of artificial intelligence (AI) to improve user experiences is gaining popularity in today’s world. One fascinating application of AI is the creation of chatbots, which can engage users in conversation and provide helpful information or services. In this blog post, we’ll explore the process of building an AI-powered Slack bot using Embedchain, a Retrieval-Augmented Generation (RAG) framework powered by LangChain. Additionally, we’ll deploy our bot on AWS using Pulumi, a modern infrastructure as code (IaC) platform.

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In a previous blog post from me, we discussed how easy it is to build your 🦜️🔗 LangChain LLM application and use 🦜️🏓 LangServe and Pulumi to deploy it on an AWS Fargate cluster. We even went a step further and deployed a Pinecone index, all in a few lines of Pulumi code, to provide a vector store for our LLM application. Let me walk you this time a different path on creating a LLM applications.

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AI is impacting almost every industry today, and for good reason - we are seeing fundamentally new experiences being made possible across a wide variety of products, and a set of new AI capabilities that promise even more incredible change in the near future.

Software development is among the earliest and most prominent fields to realize the benefits of AI, evidenced by the rapid adoption of tools like Github Copilot which is now one of the most heavily adopted developer tools of all time. Developers are benefiting from an incredible increase in their productivity with better scale and faster time to market.

We’re seeing the impacts of AI in the cloud Infrastructure development space in two impactful and complimentary directions:

• 🤖➜☁️: AI is transforming how we author, build and manage cloud infrastructure
• ☁️➜🤖: Cloud infrastructure tooling is changing how we build and deliver AI-based applications

At Pulumi, we’ve already seen profound impacts from AI in both of these directions.

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We all know how easy it is to create, deploy, and manage any cloud infrastructure with Pulumi using your favorite programming language. With the rise of artificial intelligence (AI) more and more developers are working on LLM-powered applications and services. And with this, the need to have the same ease of use for creating, deploying, and managing the infrastructure for these applications is growing. In this blog post, we will show you how to this can be achieved with combining Pulumi and LangServe.

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Hello, Pulumi Pinecone Provider! 👋 The Pinecone integration with Pulumi offers a native way to manage Pinecone indexes, including the newly-announced serverless indexes. Utilize any of Pulumi’s supported languages to effortlessly create, update, and remove your Pinecone indexes. This integration facilitates the application of Infrastructure as Code principles, helping you to work even more efficiently. Furthermore, this gives you the benefit of tapping into Pulumi’s wide range of providers, offering you a diverse and powerful set of tools to enhance your development work.

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Earlier this year we launched Pulumi AI, a purpose-built AI assistant that can create Infrastructure as Code (IaC) from natural language prompts using Pulumi. Since launch, we’ve seen incredible adoption of Pulumi AI, with over 200,000 questions asked so far and growing fast. Pulumi AI is popular with users new to Pulumi and/or new to the Cloud, but also heavily used by many of the most advanced IaC users and organizations to quickly discover solutions to new problems they need to solve. Over the last few months, we’ve driven major improvements to Pulumi AI through the recently launched Pulumi AI Answers pages with thousands of AI generated answers to common questions, improvements to code generation correctness and performance, and expansion of the languages supported by Pulumi AI.

Today, we are taking the next big step, introducing support for deploying cloud infrastructure directly from Pulumi AI. Going from idea to running cloud infrastructure is just a natural language prompt away!

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