# datasette-llm-embed

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Datasette plugin adding a `llm_embed(model_id, text)` SQL function.

## Installation

```bash
datasette install datasette-llm-embed
```

## Usage

Adds a SQL function that can be called like this:
```sql
select llm_embed('sentence-transformers/all-mpnet-base-v2', 'This is some text')
```
This embeds the provided text using the specified embedding model and returns a binary blob, suitable for use with plugins such as [datasette-faiss](https://datasette.io/plugins/datasette-faiss).

The models need to be installed using [LLM](https://llm.datasette.io/) plugins such as [llm-sentence-transformers](https://github.com/simonw/llm-sentence-transformers).

Use `llm_embed_cosine(a, b)` to calculate cosine similarity between two vector blobs:

```sql
select llm_embed_cosine(
    llm_embed('sentence-transformers/all-mpnet-base-v2', 'This is some text'),
    llm_embed('sentence-transformers/all-mpnet-base-v2', 'This is some other text')
)
```
## Models that require API keys

If your embedding model needs an API key - for example the `ada-002` model from OpenAI - you can configure that key in `metadata.yml` (or JSON) like this:

```yaml
plugins:
  datasette-llm-embed:
    keys:
      ada-002:
        $env: OPENAI_API_KEY
```
The key here should be the full model ID of the model - not an alias.

You can then set the `OPENAI_API_KEY` environment variable to the key you want to use before starting Datasette:
```bash
export OPENAI_API_KEY=sk-1234567890
```
Once configured, calls like this will use the API key that has been provided:
```sql
select llm_embed('ada-002', 'This is some text')
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd datasette-llm-embed
python3 -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```
pip install -e '.[test]'
```
```
To run the tests:
```bash
pytest
```
