| Name | llm-gemini JSON |
| Version |
0.1a3
JSON |
| download |
| home_page | None |
| Summary | LLM plugin to access Google's Gemini family of models |
| upload_time | 2024-04-10 21:38:12 |
| maintainer | None |
| docs_url | None |
| author | Simon Willison |
| requires_python | None |
| license | Apache-2.0 |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# llm-gemini
[](https://pypi.org/project/llm-gemini/)
[](https://github.com/simonw/llm-gemini/releases)
[](https://github.com/simonw/llm-gemini/actions?query=workflow%3ATest)
[](https://github.com/simonw/llm-gemini/blob/main/LICENSE)
API access to Google's Gemini models
## Installation
Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
```bash
llm install llm-gemini
```
## Usage
Configure the model by setting a key called "gemini" to your [API key](https://ai.google.dev/):
```bash
llm keys set gemini
```
```
<paste key here>
```
Now run the model using `-m gemini-pro`, for example:
```bash
llm -m gemini-pro "A joke about a pelican and a walrus"
```
> Why did the pelican get mad at the walrus?
>
> Because he called him a hippo-crit.
To chat interactively with the model, run `llm chat`:
```bash
llm chat -m gemini-pro
```
If you have access to the Gemini 1.5 Pro preview you can use `-m gemini-1.5-pro-latest` to work with that model.
### Embeddings
The plugin also adds support for the `text-embedding-004` embedding model.
Run that against a single string like this:
```bash
llm embed -m text-embedding-004 -c 'hello world'
```
This returns a JSON array of 768 numbers.
This command will embed every `README.md` file in child directories of the current directory and store the results in a SQLite database called `embed.db` in a collection called `readmes`:
```bash
llm embed-multi readmes --files . '*/README.md' -d embed.db -m text-embedding-004
```
You can then run similarity searches against that collection like this:
```bash
llm similar readmes -c 'upload csvs to stuff' -d embed.db
```
See the [LLM embeddings documentation](https://llm.datasette.io/en/stable/embeddings/cli.html) for further details.
## Development
To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-gemini
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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"description": "# llm-gemini\n\n[](https://pypi.org/project/llm-gemini/)\n[](https://github.com/simonw/llm-gemini/releases)\n[](https://github.com/simonw/llm-gemini/actions?query=workflow%3ATest)\n[](https://github.com/simonw/llm-gemini/blob/main/LICENSE)\n\nAPI access to Google's Gemini models\n\n## Installation\n\nInstall this plugin in the same environment as [LLM](https://llm.datasette.io/).\n```bash\nllm install llm-gemini\n```\n## Usage\n\nConfigure the model by setting a key called \"gemini\" to your [API key](https://ai.google.dev/):\n\n```bash\nllm keys set gemini\n```\n```\n<paste key here>\n```\n\nNow run the model using `-m gemini-pro`, for example:\n\n```bash\nllm -m gemini-pro \"A joke about a pelican and a walrus\"\n```\n\n> Why did the pelican get mad at the walrus?\n>\n> Because he called him a hippo-crit.\n\nTo chat interactively with the model, run `llm chat`:\n\n```bash\nllm chat -m gemini-pro\n```\n\nIf you have access to the Gemini 1.5 Pro preview you can use `-m gemini-1.5-pro-latest` to work with that model.\n\n### Embeddings\n\nThe plugin also adds support for the `text-embedding-004` embedding model.\n\nRun that against a single string like this:\n```bash\nllm embed -m text-embedding-004 -c 'hello world'\n```\nThis returns a JSON array of 768 numbers.\n\nThis command will embed every `README.md` file in child directories of the current directory and store the results in a SQLite database called `embed.db` in a collection called `readmes`:\n\n```bash\nllm embed-multi readmes --files . '*/README.md' -d embed.db -m text-embedding-004\n```\nYou can then run similarity searches against that collection like this:\n```bash\nllm similar readmes -c 'upload csvs to stuff' -d embed.db\n```\n\nSee the [LLM embeddings documentation](https://llm.datasette.io/en/stable/embeddings/cli.html) for further details.\n\n## Development\n\nTo set up this plugin locally, first checkout the code. Then create a new virtual environment:\n```bash\ncd llm-gemini\npython3 -m venv venv\nsource venv/bin/activate\n```\nNow install the dependencies and test dependencies:\n```bash\nllm install -e '.[test]'\n```\nTo run the tests:\n```bash\npytest\n```\n",
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