From 7e2ecf0636f932adef07bb3bdd6d4f937e689e76 Mon Sep 17 00:00:00 2001 From: Mikko Ohtamaa Date: Sat, 18 Oct 2025 20:56:33 +0300 Subject: [PATCH] Typo --- docs/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial.md b/docs/tutorial.md index ce3beef1..dec7f51b 100644 --- a/docs/tutorial.md +++ b/docs/tutorial.md @@ -18,7 +18,7 @@ Embeddings serve as the foundational bridge between unstructured text data and c You technically can create vector embeddings yourself without relying on pre-built models like OpenAI's, but it's impractical for most users due to the complexity and resources involved in training or implementing from scratch. Generating embeddings requires a neural network architecture (e.g., transformer-based) trained on massive datasets to learn semantic relationships, which demands significant computational power (GPUs/TPUs), expertise in machine learning, and access to billions of text examples for fine-tuning. -`haiku.rag` is a Python libraru allowing you to set up your own embeddings database, pipeline to feed into it and query it with different LLM providers and related services. +`haiku.rag` is a Python library allowing you to set up your own embeddings database, pipeline to feed into it and query it with different LLM providers and related services. ## Setup