| Name | llm-embed-jina JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | |
| Summary | Embedding models from Jina AI |
| upload_time | 2023-10-26 03:09:09 |
| maintainer | |
| docs_url | None |
| author | Simon Willison |
| requires_python | |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# llm-embed-jina
[](https://pypi.org/project/llm-embed-jina/)
[](https://github.com/simonw/llm-embed-jina/releases)
[](https://github.com/simonw/llm-embed-jina/actions?query=workflow%3ATest)
[](https://github.com/simonw/llm-embed-jina/blob/main/LICENSE)
Embedding models from Jina AI
## Background
[Jina AI Launches World's First Open-Source 8K Text Embedding, Rivaling OpenAI](https://jina.ai/news/jina-ai-launches-worlds-first-open-source-8k-text-embedding-rivaling-openai/) introduces these models.
See also [Embeddings: What they are and why they matter](https://simonwillison.net/2023/Oct/23/embeddings/) for background on embeddings and an explanation of the LLM embeddings tool.
## Installation
Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
llm install llm-embed-jina
## Usage
This plugin adds support for three new embedding models:
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-large-en`](https://huggingface.co/jinaai/jina-embeddings-v2-large-en): 435 million parameters - not yet released, but it will work once it has been released.
The models will be downloaded the first time you try to use them.
See [the LLM documentation](https://llm.datasette.io/en/stable/embeddings/index.html) for everything you can do.
To get started embedding a single string, run the following:
```bash
llm embed -m jina-embeddings-v2-small-en -c 'Hello world'
```
This will output a JSON array of 512 floating point numbers to your terminal.
## Development
To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-embed-jina
python3 -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
llm install -e '.[test]'
```
To run the tests:
```bash
pytest
```
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