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2 changes: 1 addition & 1 deletion .github/workflows/spellcheck.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,6 @@ jobs:
uses: actions/checkout@v6

- name: Spell Check Repo
uses: crate-ci/typos@v1.43.5
uses: crate-ci/typos@v1.44.0
with:
files: docs/**/**/*.md docs/**/**/*.mdx
4 changes: 2 additions & 2 deletions docs/genai/04_how_to_guides/02_embeddings.mdx
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Expand Up @@ -4,15 +4,15 @@ While Decoder-only LLMs gained massive popularity via their usage in chatbots, E

```mermaid
flowchart LR;
A["natual language text: <br> *GenAI can be used for research*"]
A["natural language text: <br> *GenAI can be used for research*"]
B["encoder-only LLM"]
C["vector embedding <br> [0.052, 0.094, 0.244, ...]"]
A-- "Input" -->B;
B-- "Output" -->C;
```

:::tip
Embeddings have the ability to encode the semantic meaning of the natual language text/images!
Embeddings have the ability to encode the semantic meaning of the natural language text/images!
:::

The snippet below uses the `text-embedding-3-small` model to create 32-dimensional floating point vector embeddings for the input string:
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Expand Up @@ -17,7 +17,7 @@ flowchart TB;
C["encoder-only LLM"]
D@{shape: procs, label: "text chunk embedding"}
E[("vector database")]
F["natual language prompt"]
F["natural language prompt"]
G["query embedding"]
I["relevant chunks"]
J["original prompt with added context"]
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