From 55b5c25573b20fa3d3c7c470e3b011c3c1e8e9a7 Mon Sep 17 00:00:00 2001 From: Ted Sanders Date: Fri, 16 Jun 2023 15:46:50 -0700 Subject: [PATCH] the model -> GPT --- ...augmented_by_query_generation_and_embeddings_reranking.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb index 1b59144c..dbe153fc 100644 --- a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb +++ b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb @@ -11,7 +11,7 @@ "\n", "There are two prominent approaches to using language models for information retrieval:\n", "\n", - "1. **Mimicking Human Browsing:** The [model triggers a search](https://openai.com/blog/chatgpt-plugins#browsing), evaluates the results, and modifies the search query if necessary. It can also follow up on specific search results to form a chain of thought, much like a human user would do.\n", + "1. **Mimicking Human Browsing:** [GPT triggers a search](https://openai.com/blog/chatgpt-plugins#browsing), evaluates the results, and modifies the search query if necessary. It can also follow up on specific search results to form a chain of thought, much like a human user would do.\n", "2. **Retrieval with Embeddings:** Calculating [embeddings](https://platform.openai.com/docs/guides/embeddings) for your content, and then using a metric like cosine distance between the user query and the embedded data to sort and [retrieve information](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb). This technique is [used heavily](https://blog.google/products/search/search-language-understanding-bert/) by search engines like Google.\n", "\n", "These approaches are both promising, but each has their shortcomings: the first one can be slow due to its iterative nature and the second one requires embedding your entire knowledge base in advance, continuously embedding new content and maintaining a vector database.\n",