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the model -> GPT

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Ted Sanders 2023-06-16 15:46:50 -07:00
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examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb
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@ -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",