From 9ada754100f68bb07184902d29f3ef4b3fe09827 Mon Sep 17 00:00:00 2001 From: Ted Sanders Date: Fri, 16 Jun 2023 15:47:18 -0700 Subject: [PATCH] changes web URL to relative link --- ...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 dbe153f..b75af15 100644 --- a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb +++ b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb @@ -12,7 +12,7 @@ "There are two prominent approaches to using language models for information retrieval:\n", "\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", + "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](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", "\n",