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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2024-04-26 00:50:47 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2024-04-26 00:50:47 +0200
commitee4384cec384f10fe3118c829c6b27e4c117d08f (patch)
treed7421a7573f89157cbd4be39ac8c21602d515c97 /content
parentd3084959d3626234a508b59b314cfa32d3350fa4 (diff)
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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.