Tyler Mulligan

Tyler Mulligan

Guest Senior DevOps Engineer

Uploading documents to your AI Slackbot in real-time using S3, SQS and Pulumi on AWS

Uploading documents to your AI Slackbot in real-time using S3, SQS and Pulumi on AWS

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.

arti-architecture.png

To follow along, clone the project, git clone https://github.com/catmeme/arti.git or view it on GitHub.

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Adding data to Pinecone using S3, Embedchain and Pulumi on AWS for an AI Slack bot

Adding data to Pinecone using S3, Embedchain and Pulumi on AWS for an AI Slack bot

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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Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS

Create an AI Slack Bot to Chat with Your Data Using Embedchain, Pulumi on AWS

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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