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Add explanation of embeddings #500

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Aug 16, 2024
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Add explanation of embeddings #500

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Add explanation of embeddings
InAnYan Aug 14, 2024
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Fix from code review
InAnYan Aug 14, 2024
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koppor Aug 15, 2024
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InAnYan Aug 16, 2024
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11 changes: 10 additions & 1 deletion en/ai/README.md
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Expand Up @@ -27,7 +27,16 @@ In this window you can see the following elements:

## How does the AI functionality work?

In the background, JabRef analyses the linked PDF files of library entries. The information used after the indexing is then supplied to the AI, which, to be precise, in our case is a Large Language Model (LLM). The LLM is currently not stored on your computer. Instead, we have many integrations with AI providers (OpenAI, Mistral AI, Hugging Face), so you can choose the one you like the most. These AI providers are available only remotely via the internet. In short: we send chunks of text to AI service and then receive processed responses. In order to use it you need to configure JabRef to use your API key.
In the background, JabRef analyzes the linked PDF files of library entries. The information used after the indexing is then supplied to the AI,
which, to be precise, in our case is a Large Language Model (LLM). The LLM is currently not stored on your computer. Instead, we have many
integrations with AI providers (OpenAI, Mistral AI, Hugging Face), so you can choose the one you like the most. These AI providers are available
only remotely via the internet. In short: we send chunks of text to AI service and then receive processed responses. In order to use it you need
to configure JabRef to use your API key.

JabRef processes linked files this way: the file is split into parts of fixed-length (also called *chunks*), and then an *embedding* is generated. Embedding is a
representation of a part of text. It's a vector that represents the meaning of the text. This vector has a crucial property: texts with
similar meaning have vectors that are close to (this is called *vector similarity*)! So, whenever you ask AI a question, JabRef tries to find relevant pieces
of text from the indexed files using vector similarity.

## More information

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