| Name | llm-gemini JSON |
| Version |
0.4.2
JSON |
| download |
| home_page | None |
| Summary | LLM plugin to access Google's Gemini family of models |
| upload_time | 2024-11-22 00:44:28 |
| maintainer | None |
| docs_url | None |
| author | Simon Willison |
| requires_python | None |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| coveralls test coverage |
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|
# 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://aistudio.google.com/app/apikey):
```bash
llm keys set gemini
```
```
<paste key here>
```
You can also set the API key by assigning it to the environment variable `LLM_GEMINI_KEY`.
Now run the model using `-m gemini-1.5-pro-latest`, for example:
```bash
llm -m gemini-1.5-pro-latest "A joke about a pelican and a walrus"
```
> A pelican walks into a seafood restaurant with a huge fish hanging out of its beak. The walrus, sitting at the bar, eyes it enviously.
>
> "Hey," the walrus says, "That looks delicious! What kind of fish is that?"
>
> The pelican taps its beak thoughtfully. "I believe," it says, "it's a billfish."
Other models are:
- `gemini-1.5-flash-latest`
- `gemini-1.5-flash-8b-latest` - the least expensive
- `gemini-exp-1114` - recent experimental #1
- `gemini-exp-1121` - recent experimental #2
### Images, audio and video
Gemini models are multi-modal. You can provide images, audio or video files as input like this:
```bash
llm -m gemini-1.5-flash-latest 'extract text' -a image.jpg
```
Or with a URL:
```bash
llm -m gemini-1.5-flash-8b-latest 'describe image' \
-a https://static.simonwillison.net/static/2024/pelicans.jpg
```
Audio works too:
```bash
llm -m gemini-1.5-pro-latest 'transcribe audio' -a audio.mp3
```
And video:
```bash
llm -m gemini-1.5-pro-latest 'describe what happens' -a video.mp4
```
The Gemini prompting guide includes [extensive advice](https://ai.google.dev/gemini-api/docs/file-prompting-strategies) on multi-modal prompting.
### JSON output
Use `-o json_object 1` to force the output to be JSON:
```bash
llm -m gemini-1.5-flash-latest -o json_object 1 \
'3 largest cities in California, list of {"name": "..."}'
```
Outputs:
```json
{"cities": [{"name": "Los Angeles"}, {"name": "San Diego"}, {"name": "San Jose"}]}
```
### Code execution
Gemini models can [write and execute code](https://ai.google.dev/gemini-api/docs/code-execution) - they can decide to write Python code, execute it in a secure sandbox and use the result as part of their response.
To enable this feature, use `-o code_execution 1`:
```bash
llm -m gemini-1.5-pro-latest -o code_execution 1 \
'use python to calculate (factorial of 13) * 3'
```
### Chat
To chat interactively with the model, run `llm chat`:
```bash
llm chat -m gemini-1.5-pro-latest
```
## 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
```
This project uses [pytest-recording](https://github.com/kiwicom/pytest-recording) to record Gemini API responses for the tests.
If you add a new test that calls the API you can capture the API response like this:
```bash
PYTEST_GEMINI_API_KEY="$(llm keys get gemini)" pytest --record-mode once
```
You will need to have stored a valid Gemini API key using this command first:
```bash
llm keys set gemini
# Paste key here
```
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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://aistudio.google.com/app/apikey):\n```bash\nllm keys set gemini\n```\n```\n<paste key here>\n```\nYou can also set the API key by assigning it to the environment variable `LLM_GEMINI_KEY`.\n\nNow run the model using `-m gemini-1.5-pro-latest`, for example:\n\n```bash\nllm -m gemini-1.5-pro-latest \"A joke about a pelican and a walrus\"\n```\n\n> A pelican walks into a seafood restaurant with a huge fish hanging out of its beak. The walrus, sitting at the bar, eyes it enviously.\n>\n> \"Hey,\" the walrus says, \"That looks delicious! What kind of fish is that?\"\n>\n> The pelican taps its beak thoughtfully. \"I believe,\" it says, \"it's a billfish.\"\n\nOther models are:\n\n- `gemini-1.5-flash-latest`\n- `gemini-1.5-flash-8b-latest` - the least expensive\n- `gemini-exp-1114` - recent experimental #1\n- `gemini-exp-1121` - recent experimental #2\n\n### Images, audio and video\n\nGemini models are multi-modal. You can provide images, audio or video files as input like this:\n\n```bash\nllm -m gemini-1.5-flash-latest 'extract text' -a image.jpg\n```\nOr with a URL:\n```bash\nllm -m gemini-1.5-flash-8b-latest 'describe image' \\\n -a https://static.simonwillison.net/static/2024/pelicans.jpg\n```\nAudio works too:\n\n```bash\nllm -m gemini-1.5-pro-latest 'transcribe audio' -a audio.mp3\n```\n\nAnd video:\n\n```bash\nllm -m gemini-1.5-pro-latest 'describe what happens' -a video.mp4\n```\nThe Gemini prompting guide includes [extensive advice](https://ai.google.dev/gemini-api/docs/file-prompting-strategies) on multi-modal prompting.\n\n### JSON output\n\nUse `-o json_object 1` to force the output to be JSON:\n\n```bash\nllm -m gemini-1.5-flash-latest -o json_object 1 \\\n '3 largest cities in California, list of {\"name\": \"...\"}'\n```\nOutputs:\n```json\n{\"cities\": [{\"name\": \"Los Angeles\"}, {\"name\": \"San Diego\"}, {\"name\": \"San Jose\"}]}\n```\n\n### Code execution\n\nGemini models can [write and execute code](https://ai.google.dev/gemini-api/docs/code-execution) - they can decide to write Python code, execute it in a secure sandbox and use the result as part of their response.\n\nTo enable this feature, use `-o code_execution 1`:\n\n```bash\nllm -m gemini-1.5-pro-latest -o code_execution 1 \\\n'use python to calculate (factorial of 13) * 3'\n```\n\n### Chat\n\nTo chat interactively with the model, run `llm chat`:\n\n```bash\nllm chat -m gemini-1.5-pro-latest\n```\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\nThis project uses [pytest-recording](https://github.com/kiwicom/pytest-recording) to record Gemini API responses for the tests.\n\nIf you add a new test that calls the API you can capture the API response like this:\n```bash\nPYTEST_GEMINI_API_KEY=\"$(llm keys get gemini)\" pytest --record-mode once\n```\nYou will need to have stored a valid Gemini API key using this command first:\n```bash\nllm keys set gemini\n# Paste key here\n```\n\n",
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