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+---
+title: "Retrieval Augmented Generation"
+date: 2024-04-26 00:36
+tags:
+  [
+    "deep learning",
+    "retrieval augmented generation",
+    "vector database",
+    "ollama",
+    "llm",
+  ]
+draft: false
+---
+
+I implemented a retrieval augmented generation (RAG) 
+[program](https://github.com/aktersnurra/rag) for fun with the goal of being able to 
+search my personal library. My focus was to make this run locally with only open
+source models. This was achieved with `ollama` and `sentence-transformers` for 
+downloading and running these models locally. However, the project was expanded to
+integrate with cohere and its rerank and command-r+ models, since I was curious about
+the command-r+ performance. These models can be downloaded and run locally, but it took
+ages for my computer to generate any output, since the command-r+ is huge.
+
+
+Here is a [presentation](/rag.html) that gives a brief overview of what a RAG system
+is, and how it can be improved with reranking.