https://github.com/snpranav/vector-embedding-playground
https://github.com/snpranav/vector-embedding-playground
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/snpranav/vector-embedding-playground
- Owner: snpranav
- Created: 2024-09-11T19:45:00.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-11T20:29:36.000Z (8 months ago)
- Last Synced: 2025-02-14T04:53:30.938Z (3 months ago)
- Language: Python
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vector Embeddings Playground
In this repo we use OpenAI's text embeddings API to understand how we can use vector similarities to identify similar text inputs.
A text embedding is just a vector representation of text data. In this repo, we use OpenAI's `text-embedding-3-large` to demonstrate properties of text embedding models.
## Setup
Install Packages
```
pip install -r requirements.txt
```
Add OpenAI API key to your environment. Get a key from [here](https://platform.openai.com/api-keys)
```
export OPENAI_API_KEY=
```
## See examples:
* [vector-embedding.py](./vector-embedding.py) - Shows you what a text embedding looks like in vector form.
* [similar-sentence.py](./similar-sentence.py) - Create text embeddings of 2 similar sentences and calculates the cosine similarities (AKA how close 2 vectors are to each other; i.e. how similar are 2 sentences). In this case the similarity value will be closer to 1 instead of 0 since the 2 input sentences have great similarity.
* [dissimilar-sentence.py](./dissimilar-sentence.py) - Create text embeddings of 2 similar sentences and calculates the cosine similarities (AKA how close 2 vectors are to each other; i.e. how similar are 2 sentences). In this case the similarity value will be closer to 0 instead of 1 since the 2 input sentences have little to no similarity.
* [king-queen.py](./king-queen.py) - Shows how elaborately text embeddings are able to capture meanings of sentences. In here we take a sentence about a king subtract it from a sentance about a man and add it to a sentance about a woman. Here we can see that the final sentance is greatly similar to a sentance about a queen in vector form.