This repo contains the source files to build a fully functional and interactive AI chatbot and integrate it with Slack as an App. The integration is serverless both on the AWS Bedrock AI side (serverless AI mode) and on the AWS side (Lambda).
This code is discussed at length on LetsDoDevOps.com, you can find the articles here:
- Part 1: Covers how to build a slack bot in websocket mode
- Part 2: How to deploy an AWS Bedrock AI resource and connect to it to ask a request locally from your terminal with python3
- Part 3: We’ll connect our slack bot with Bedrock locally using python3 with ngrok so slack users can have conversations with AI
- Part 4: How to convert your local script to an event-driven serverless, cloud-based app in AWS Lambda
- Part 5: Building a RAG Knowledge Base of an entire Confluence wiki, and teaching our Slack Bot to Read it in Real-Time
- Part 6: Adding support for DOC/X, XLS/X, PDF, and More to Chat with your Data
- Part 7: Streaming Token Responses from AWS Bedrock to Slack using converse_stream()
- Part 8: ReRanking knowledge base responses to improve AI model response efficacy
- Part 9: Reducing Cost and Complexity with a Lambda Receiver Pattern
Requests are relayed from the Slack App to a private Lambda URL which is attached to a "receiver" lambda. This spins up quickly, and validates the message. This message is forwarded to the Vera bot, which does the actual work, and responds to Slack that the message has been received.
The Vera bot is triggered by the Receiver lambda. It fetches the necessary secrets from secrets manager (authentication is via IAM role) to enable the slack app for decoding the requests, then extracts both the request, and if applicable, the slack thread, which is all encoded into the request to the AI in Bedrock. The response is relayed and posted to the thread.
Slack User tags Bot in shared thread (bot must be invited) or DMs Bot --> Slack App sees trigger, sends webhook to Lambda URL --> Lambda reads package and extracts message and thread context, constructs AI request --> AI request to Bedrock, Bedrock creates response --> Lambda relays response back to Slack App --> Slack App posts to Slack within thread.
All conversations with the bot happen in threads. This is to help separate topics of conversation for the AI, since long or multi-topic conversations can confuse AI responses.
All authentication except for the Slack app is keyless IAM roles.
Requests to the Lambda are logged to Cloudtrail, and if the retention period remains the default, they will be kept for 90 days. This can be disabled by commented out the print() statement in the Python code that prints the request.
Responses from the AI bot back to the user are not logged.
There are no servers to maintain. We use ARM Lambdas, primarily because I am very lazy. I don't want to maintain servers and patching, and my current development mac has an ARM CPU.
This is built on Python 3.12, which is not schedule to go EOL until the end of 2028, ref: https://devguide.python.org/versions/
Lambda can spin up hundreds of concurrencies without much effort, so monitoring isn't a major concern. The logging package is installed in the python lambda, so logs could quickly be added for anything of note (and I welcome PRs to add that!)
Assuming 100 requests per week (will depend on your biz size, use) that take ~5-10 seconds total (assuming on the high end) Lambda cost: $0.03 / month
AI cost (depends on request complexity), assuming 1k tokens per request: $3.20/month
Total without a knowledge base: $3.23/month for ~100 requests of moderate complexity.
Bedrock Knowledge Bases are expensive. Though they're "serverless", they don't spin down to $0. Instead, they spin down to about ~$60/day, or about $1.8k/month. That's a lot! You can work with AWS Support to turn off "vector preload" setting on the OpenSearch serverless instances, which brings the cost down significantly - to around $35/day, or just over $1k/month. That's still a lot, but way more reasonable than $25k/yr.
Knowledge bases can contain lots of different "data sources" that they read and synchronize with, so you can scale out more data without accruing more cost.
Total with a single confluence knowledge base: ~1.1k/month.
