Streamline your workflow with ChatGPT and Google Sheets
Table of Contents
- Introduction
- The Solution Overview
- The Context Problem
- The Solution Demonstration
- Pipeline Phases Explained
- Capture Phase
- Transformation Phase
- Materialization Phase
- The Slack Connector
- Transformation Process
- GPT Prompting Technique
- Materialization into Google Sheets
- Other Use Cases
- Conclusion
Article
Introduction
Hey everyone, in this video, I'm going to walk You through a solution that Johnny, the CTO of Estuary, has built to summarize Slack Threads in a continuous data pipeline that sends the summaries to Google Sheets. This solution is a great addition to our free and growing chat GPT playlist of videos, so be sure to check that out.
The Solution Overview
The original blog article can be found here and the code repository with the solution can be found here. In this video, we'll Show you how you can productionize a custom always-on KPT-enabled transformation pipeline with your actual data. We'll be using Estuary's Slack instance to capture Slack conversations as they're happening, derive a continuous GPT summarization of each thread's content, and materialize evolving threats and summaries into Google Sheets.
The Context Problem
Before diving into the solution, it's important to understand the context problem that AI pipelines face. GPT and all trained AI models are pure functions of their training set and Current prompt. They have great recall of their training set but start every interaction with horrible amnesia. This means that they lack the broader context needed to understand and provide Meaningful responses to human-like queries.
The Solution Demonstration
In the solution demo, we showcase a thread in Slack that we want to summarize. The GPT summary of the thread gets sent to a Google Sheet's "Threat Summaries" tab. Whenever someone adds a response to that thread, a new summary is generated near-real-time in the Google Sheet. The summary is then updated again with the new information. This demonstrates how the solution allows for continuous updates and real-time summarization of Slack threads.
Now, let's take a closer look at the solution and its different phases.
Pipeline Phases Explained
The solution can be broken down into three distinct phases: capture, transformation, and materialization.
Capture Phase
In the capture phase, we use the pre-built Slack connector to capture Incremental updates from Slack's API. This capture process allows us to Collect Slack messages and threats over a specified period. To initiate the capture, you navigate to the capture page, click on "New Capture," search for Slack, and authenticate using OAuth. Once authenticated, you can specify the number of days to look back into the past for messages and threats and provide a start date.
Transformation Phase
The transformation phase is where the magic happens. Johnny has already created the solution, which you can download from the repository shared in the description. This phase involves composing a pair of SQLite and TypeScript derivations. These derivations are collections that build themselves through the transformation of other collections.
The first SQLite derivation is responsible for indexing threat messages, along with user and Channel metadata. When a message is added or updated, the thread's denormalized content is published into the derived collection. This structured Roll-up represents the current threat state.
The Second TypeScript derivation takes these structured roll-ups and formats a text STRING for the entire thread. This formatted thread is then passed to GPT for summarization, which is referred to as a completion. The TypeScript derivations are incremental, invoking lambdas with new data as it arrives. They are transactional and pipelined, allowing for greater throughput by starting multiple API calls and waiting for them to complete before closing the transaction.
Materialization Phase
In the materialization phase, the threat summaries are materialized into a Google spreadsheet. To set up this materialization, you need to navigate to the materializations page, click on "New Materialization," select Google Sheets, and authenticate with Google. You then provide a name for the materialization, the spreadsheet URL, and select the derived collection created in the transformation phase. Once set up, the materialization will start populating the Google spreadsheet with live updates of the summaries, including threat links, timestamps, and channel names.
The Slack Connector
The pre-built Slack connector is a vital component of this solution as it allows for seamless integration with Slack's API. By configuring the capture process through the Slack connector, you can ensure that the solution captures incremental updates from Slack in real-time. This ensures that your spreadsheet stays updated with the latest thread summaries.
Transformation Process
The transformation process is the heart of the solution. It involves two main components: SQLite and TypeScript derivations. The SQLite derivation indexes threat messages and stores user and channel metadata. The TypeScript derivation takes these indexed threats and formats them into a text string for GPT summarization. The structured roll-up of threats provides the necessary context for GPT to produce sensible summaries in the output spreadsheet.
GPT Prompting Technique
To enable GPT to generate meaningful summaries, a specific prompting technique is used. Although the demo showcases a basic GPT prompting technique, it can be expanded to tackle various use cases. By leveraging the structured roll-ups and rich context obtained from the transformation process, insights from Slack threads can be summarized in a way that is easily understandable and useful for end-users.
Materialization into Google Sheets
The last phase of the solution involves materializing the threat summaries into a Google spreadsheet. This allows for easy visualization and sharing of the summarized information. Once set up, the materialization process continuously updates the spreadsheet with new threat summaries, ensuring that the information is always up-to-date.
Other Use Cases
While the demo focuses on GPT summarization, the pipeline can be extended to handle various other use cases. These include tracking workstreams discussed in a thread, materializing whole Slack threads into a different platform, such as Pinecone, for question-answering applications, and monitoring for security and regulatory compliance.
Conclusion
In conclusion, the solution created by Johnny, the CTO of Estuary, provides a seamless and efficient way to summarize Slack threads in real-time and materialize them into Google Sheets. The pipeline, consisting of the capture, transformation, and materialization phases, allows for continuous updates and provides rich context for GPT summarization.
If you want to learn more about this solution, make sure to check out Johnny's original blog article and the code repository linked in the description. Thanks for watching!
Highlights
- A solution to summarize Slack threads in real-time and materialize them into Google Sheets.
- Capture, transformation, and materialization phases are used to achieve this pipeline.
- Context problem of AI models addressed through structured roll-ups and rich context.
- Pre-built Slack connector enables seamless integration with Slack's API.
- GPT prompting technique leverages structured roll-ups for meaningful summaries.
- Materialization into Google Sheets allows for easy visualization and sharing of summaries.
- Other use cases include tracking workstreams, question-answering applications, and security monitoring.
FAQ
Q: Can this solution be used for other messaging platforms?
A: The solution is currently designed for Slack integration. However, with appropriate modifications and integrations, it can be extended to other messaging platforms.
Q: Are there any limitations to the number of threads that can be processed?
A: The scalability of the solution depends on factors such as available resources, API throughput, and infrastructure. It is recommended to optimize the pipeline design for larger workloads.
Q: Can the solution handle non-textual content in Slack threads, such as images or attachments?
A: The current implementation focuses on text-based content in Slack threads. However, additional transformations and preprocessing can be added to handle non-textual content before passing it to GPT for summarization.
Q: Is there a way to customize the summarization output to fit specific requirements?
A: Yes, the transformation phase allows for customization of the summarization output. By adapting the GPT prompting technique and adjusting the formatting of the text string, the output can be tailored to specific needs.
Q: Can this solution be used for real-time monitoring of Slack threads across multiple channels?
A: Yes, by configuring the capture phase to capture messages from multiple channels, the solution can provide real-time monitoring and summarization for threads from different channels in Slack.