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@@ -60,16 +60,9 @@ Training code is released in the `contrastors` [repository](https://github.com/n
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  ## Usage
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- Note `nomic-embed-text` *requires* prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`.
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- For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries.
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- For example, you are building a RAG application over the top of Wikipedia. You would embed all Wikipedia articles with the prefix `search_document`
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- and any questions you ask with `search_query`. For example:
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- ```python
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- queries = ["search_query: who is the first president of the united states?", "search_query: when was babe ruth born?"]
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- documents = ["search_document: <article about US Presidents>", "search_document: <article about Babe Ruth>"]
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- ```
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- You can
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  ### Transformers
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  ```python
 
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  ## Usage
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+ Remember `nomic-embed-text` *requires* prefixes and so, when using Nomic Embed in multimodal RAG scenarios (e.g. text to image retrieval),
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+ you should use the `search_query: ` prefix.
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  ### Transformers
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  ```python