Hey everyone,
I’m trying to make an AI helper in n8n that can answer questions about data in my Airtable. My setup has a bunch of connected tables, kind of like a regular database. I tried using text search stuff, but it’s not great for my data and the math I need to do.
Here’s what I’m thinking about doing:
- Make the AI write Airtable formulas
- Move everything to a SQL database and have the AI write SQL
- Use OpenAI Assistant with two tools:
- One for text searches
- Another for doing math and organizing data
I like the third option best. I think it could use both tools together, maybe looking at CSV files for calculations and using text search for other info.
Has anyone done something like this in n8n? I really want to know how to make this AI helper that can handle my data questions and still work with Airtable. Any tips or examples would be awesome!
Thanks!
yo bob, i’ve messed around with similar stuff. have u thought about using zapier instead? it’s got some cool AI features that might work better with airtable. i hooked it up to chatGPT and it can do some pretty sweet data analysis without having to mess with SQL or complex formulas. just a thought if u wanna keep it simple
I’ve actually implemented something similar to what you’re describing, Bob. In my experience, your third option with OpenAI Assistant is the most flexible and powerful approach.
Here’s what worked well for me:
I set up the OpenAI Assistant with custom functions for Airtable API calls and data processing. This allowed the AI to fetch and manipulate data directly from Airtable without needing to switch to SQL.
For text searches, I used Airtable’s built-in search functionality through the API. For more complex calculations, I created a custom tool that could perform operations on the retrieved data.
The key was to design clear prompts for the AI, specifying exactly what data to fetch and how to process it. This way, the Assistant could handle both simple queries and complex data analysis tasks.
In n8n, I set up a workflow that acted as an intermediary between user input and the OpenAI Assistant. This allowed for additional pre-processing and post-processing of queries and responses.
It took some trial and error to get the prompts and functions right, but once set up, it’s been incredibly useful for querying our Airtable data efficiently.
I’ve been working with a similar setup, and I can share some insights from my experience. The OpenAI Assistant approach you’re considering is indeed robust and versatile. However, I found that integrating it directly with Airtable via the API can be more efficient than relying on CSV exports.
For my implementation, I created custom functions that interface with Airtable’s API to fetch data on demand. This eliminates the need for frequent CSV updates and ensures real-time access to your data. I also developed a set of data processing tools that the Assistant can utilize for complex calculations and analysis.
One crucial aspect was fine-tuning the prompts to guide the AI in constructing appropriate Airtable API calls and applying the right data processing tools. This required some iteration, but it significantly improved the accuracy and relevance of the responses.
In n8n, I set up a workflow that manages the interaction between user queries, the OpenAI Assistant, and Airtable. This allows for seamless integration and provides a flexible framework for expanding functionality as needed.
While it requires more initial setup, this approach has proven to be highly effective and scalable for handling a wide range of data queries within the Airtable ecosystem.