A blog about building and using an OpenAI Assistants compatible service
Understanding cosine similarity, dot product, and Euclidean distance can be much easier with real-world analogies. These measures each capture “similarity” or “distance” in different ways — direction vs. magnitude vs. straight-line distance . We explore two narrative-style scenarios for each measure, showing when to use them and why.
Choosing the right similarity measure depends on your use case. If you’re dealing with text embeddings and want to isolate direction over magnitude, go with Cosine Similarity. When overall vector size or weighting matters, Dot Product shines. And if your model benefits from real geometric distances like clustering points in space — Euclidean Distance is the way to go.
Use multiple vector stores in an OpenAI Assistants API service to enable parallel querying across each store that each handle 5M vectors—500x OpenAI's current limit.
The OpenAI Assistant was a major leap forward for developers, but pitfalls can sink your application and team. The Ragwalla Assistant addresses the challenges related to scale, transparency, support and cost.