# Hyperparameters

#### Embeddings:

In the context of Large Language Models, embeddings are mathematical representations of words in a high-dimensional space. They capture the semantic relationships between words and serve as a foundation for understanding and generating text. Currently, only "text-embeddings-ada-002" is supported by Texti, but many domain-specific open-source models will soon be available.

#### Similarity Measure:

The similarity measure defines how the model gauges the closeness or similarity between different pieces of text. Currently, Texti only supports "Cosine" as a similarity measure.

#### Chunk Size (Tokens):

This refers to the number of tokens (words or subwords) processed together as a single block or 'chunk' when documents are converted into embeddings.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.texti.ai/in-context-learning/hyperparameters.md?ask=<question>
```

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.