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Neural Search Frameworks: A Head-to-Head Comparison
While working on this blog post I had a privilege of interacting with all neural search frameworks key developers / leadership: Malte Piestch (Haystack), Amin Ahmad (Vectara), Florian Hönicke (Jina AI), David Mezzetti (txtai), Daniel Vassilev (Relevance AI), George Sivulka and Swetha Revanur (Hebbia), Aarne Talman and myself (Muves). The blog title has been generated by ChatGPT. Hacker News thread: https://news.ycombinator.com/item?id=34107410
New: podcast with Connor Shorten, Research at Weaviate:
As the neural search journey develops further, we discover new frontiers of semantic, multimodal user experience. Neural search frameworks give us tools and building blocks to support creation of these experiences, and at the same time show us the way to put things into production.
This blog post will be structured similarly to the blog comparing vector databases and give you pointers for choosing a neural search framework for your project.
As the recent trend we can see various integration points between neural search frameworks and vector databases, these will also be marked on each framework, if present. Where available, you will find a Vector Podcast episode with the creators of these neural search frameworks — so this blog is a living document for you to come back and find something new.
What is a neural search framework? After studying a few of them, I would attempt to define it as:
Neural search framework is an end-to-end software layer, that allows you to create a neural search experience, including data processing, model serving and scaling capabilities in a production setting.
It may sound like an MLOPs (Machine Learning Operations) pipeline at first. However, in MLOPs the goal is to create a set of components for a generic ML project, with the vision of model training, deployment, serving, monitoring and fine-tuning. Hence, we observe a lot more MLOPs systems and frameworks, than neural search frameworks, because they…
