Focus on products and software basic

Leveraging No code to build AI infrastructure with Similarity Search tools

we'll walk you through how to harness the power of Pinecone and OpenAI's GPT-4 to find the needle in the haystack when it comes to text-based data. This can be used for a plethora of use-cases when combined with powerful LLMs.

We'll show you how to transform text into something computers can easily understand and compare, known as embeddings all within bubble.io.

But that's not all! We'll also guide you through storing these embeddings and their metadata in Pinecone, a vector search and storage service that makes organizing and accessing your data a breeze.

This will be the first step in the process to set you up to be able to use upload and ingest your own data so you can create custom LLMs The best part? You don't need a degree in computer science to follow along! We can leverage no code tools.

‍

Here's the workflow within bubble.io

1. Turning Text into Numbers with OpenAI Embeddings model 🧮 First up, we need to convert our text into something computers can understand - embeddings (one-dimensional vector representations). That's where GPT-4 comes in handy. Just send the text to OpenAI's API, and it'll give you the embeddings. Easy peasy!
2. Storing the Goods in Pinecone 🌲 Now we'll stash our embeddings and their metadata (the original text) in Pinecone, a super cool vector search and storage service. We'll create a unique ID string and a namespace to keep things organized. It's like a tidy little drawer for our data.
3. Asking Pinecone to Find the Closest Buddies 🔍 Time for the fun part! With our embeddings and metadata safely stored in Pinecone, we can ask it to find the most relevant matches for any question or prompt. We'll convert the user's query into embeddings using GPT-4 and then search for the closest buddy in Pinecone.
4. Showing Off the Results 🎉 Once Pinecone finds the best matching metadata, we can show it off to the user as the top response to their query. Voilà!

As mentioned this is the first step of the process in of creating an LLM powered application that can handle large documents of data (such as your company's knowledge base)

Next Step

From here would could then use the metadata that was returned from pinecone and utilize that as context with another user-generated prompt to help answer & resolve a customer related query

This paired with agents pulling external company data such as CRM, order history, live tracking data, etc. Can really make something valuable

‍

If you want a step-by-step tutorial on this check out my youtube video that will be going live here on my channel soon!

‍https://www.youtube.com/@nocodeblackbox