Hi folks! I’m working on a project that combines AI with a big Airtable database. It’s supposed to answer questions about real estate stuff. But I’m running into some problems.
My Airtable has over 10,000 records. I’m using n8n and OpenAI to handle questions. But it’s not smooth sailing:
Sometimes the AI messes up field names or hits token limits
I tried using Supabase as a middle step, but it was tricky to keep everything in sync
The AI is smart, but it struggles when I give it too much info at once
I’m wondering:
How do you handle AI queries with big Airtable databases?
Has anyone made a system to remember or store common queries?
Any tricks for only getting the data you need, instead of loading everything?
Also, how do you deal with searches when users don’t type exact matches?
If Airtable didn’t work out for you, what did you switch to? Thanks for any help!
hey, i’ve had a similar experience. using elasticsearch sped up fuzzy searches a lot, and chunking the data before ai inputs really worked. caching common queries also saved loads of time. hope that helps!
I’ve encountered similar challenges with large Airtable databases and AI integration. One effective approach we implemented was data partitioning. We divided our database into logical segments based on certain criteria, which allowed for more targeted querying and reduced the load on both Airtable and the AI model.
For improving search accuracy without exact matches, we incorporated a combination of text vectorization and cosine similarity. This method proved quite effective in handling semantic searches and misspellings.
To address token limit issues, we developed a dynamic data fetching system. It prioritizes the most relevant fields based on the query context, reducing unnecessary data transfer. This approach significantly improved response times and AI performance.
Lastly, we found that periodic data synchronization between Airtable and a more AI-friendly database like PostgreSQL offered the best of both worlds - Airtable’s user-friendly interface and robust query capabilities for AI operations.
I’ve been in a similar boat with a large Airtable database for a client’s inventory management system. One strategy that worked wonders for us was implementing a caching layer using Redis. We stored frequently accessed data and common query results in Redis, which significantly reduced the load on Airtable and sped up response times.
For AI integration, we found that breaking down the data into smaller, more digestible chunks before feeding it to the AI model helped immensely. We used a combination of semantic chunking and field-based segmentation to create more manageable data sets.
As for search functionality, we implemented fuzzy matching using the fuzzywuzzy library in Python. This allowed for more forgiving searches when users didn’t input exact matches.
Lastly, we created a custom middleware that acted as a query optimizer. It analyzed incoming requests and determined the most efficient way to fetch data, often combining cached results with fresh Airtable queries when necessary. This approach dramatically improved performance and reduced API calls to Airtable.