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42 lines
1.6 KiB
Markdown
42 lines
1.6 KiB
Markdown
# Embeddings Playground
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[`embeddings_playground.py`](embeddings_playground.py) is a single-page streamlit app for experimenting with OpenAI embeddings.
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## Installation
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Before running, install required dependencies with:
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`pip install -r apps/embeddings-playground/requirements.txt`
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(You may need to change the path to match your local path.)
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Verify installation of streamlit with `streamlit hello`.
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## Usage
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Run the script with:
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`streamlit run apps/embeddings-playground/embeddings_playground.py`
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(Again, you may need to change the path to match your local path.)
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In the app, first select your choice of:
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- distance metric (we recommend cosine)
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- embedding model (we recommend `text-embedding-ada-002` for most use cases, as of May 2023)
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Then, enter a variable number of strings to compare. Click `rank` to see:
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- the ranked list of strings, sorted by distance from the first string
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- a heatmap showing the distance between each pair of strings
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## Example
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Here's an example distance matrix for 8 example strings related to `The sky is blue`:
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From these distance pairs, you can see:
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- embeddings measure topical similarity more than logical similarity (e.g., `The sky is blue` is very close to `The sky is not blue`)
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- punctuation affects embeddings (e.g., `"THE. SKY. IS. BLUE!"` is only third closest to `The sky is blue`)
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- within-language pairs are stronger than across-language pairs (e.g., `El cielo as azul` is closer to `El cielo es rojo` than to `The sky is blue`)
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Experiment with your own strings to see what you can learn. |