Real-time embeddings for LLM apps ⚡🤖

LLMs are as good as the data you embed in your prompts. And for many real-world problems, this means the data needs to be both

• good, and

• fresh

   

Example 💁
Imagine you build a great LLM-based financial advisor… but you only feed it with outdated data.
No matter how good your model is, the predictions it will generate will be rubbish 🫣

So, the question is

How do you ensure your LLM has access to fresh data ❓

The solution 🧠

You need to build a real-time text embedding pipeline, that

• Continuously ingests raw text from your data source, in real-time

• Transforms this raw text into vector embeddings, and

• Stores these embeddings in a VectorDB, so your LLM can fetch and use them for Retrieval Augmented Generation (RAG) at inference time.

 

Example with full source code 👨🏻‍💻

In Lesson 3 of the Hands-on LLM course, you will find a full source code implementation of a real-time text embedding pipeline in Python 🐍, for a financial advisor robot.

In this case, we use

• Alpaca News API as our real-time data source,

• Bytewax to transform raw text into vector embeddings, and

• Qdrant as a Serverless Vector DB, to store and retrieve embeddings at inference time.

Video lecture 🎬 

Click below to watch the lecture ↓↓↓

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See you on Youtube.

Enjoy the weekend,

Pau