Archives/openai-cookbook
2
0
Fork 0
mirror of https://github.com/openai/openai-cookbook synced 2024-11-19 15:25:37 +00:00
Code Issues Packages Projects Releases Wiki Activity

rewrites intro points to be more consistent with one another

Browse Source
This commit is contained in:
Ted Sanders 2023-06-16 16:02:01 -07:00
parent 8a5a5e6761
commit fbeaf34deb
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb
View File

@ -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",