From 37746ff3ca19675a77b8f3c4954c0ec707addce7 Mon Sep 17 00:00:00 2001 From: Ted Sanders Date: Fri, 16 Jun 2023 16:02:01 -0700 Subject: [PATCH] rewrites intro points to be more consistent with one another --- ...gmented_by_query_generation_and_embeddings_reranking.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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 48e3470..431c85e 100644 --- a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb +++ b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb @@ -9,10 +9,10 @@ "\n", "Searching for relevant information can sometimes feel like looking for a needle in a haystack, but don’t despair, GPTs can actually do a lot of this work for us. In this guide we explore a way to augment existing search systems with various AI techniques, helping us sift through the noise.\n", "\n", - "There are two prominent approaches to using language models for information retrieval:\n", + "Two ways of retrieving information for GPT are:\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](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:** Calculate [embeddings](https://platform.openai.com/docs/guides/embeddings) for your content and a user query, and then [retrieve the content](Question_answering_using_embeddings.ipynb) most related as measured by cosine similarity. 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",