llm-gemini

API access to Google's Gemini models

Installation

Install this plugin in the same environment as LLM.

llm install llm-gemini

Usage

Configure the model by setting a key called "gemini" to your API key:

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-2.0-flash, for example:

llm -m gemini-2.0-flash "A short joke about a pelican and a walrus"

A pelican and a walrus are sitting at a bar. The pelican orders a fishbowl cocktail, and the walrus orders a plate of clams. The bartender asks, "So, what brings you two together?"

The walrus sighs and says, "It's a long story. Let's just say we met through a mutual friend... of the fin."

You can set the default model to avoid the extra -m option:

llm models default gemini-2.0-flash
llm "A joke about a pelican and a walrus"

Other models are:

• gemini-2.5-flash-preview-04-17 - Gemini 2.5 Flash preview
• gemini-2.5-pro-exp-03-25 - free experimental release of Gemini 2.5 Pro
• gemini-2.5-pro-preview-03-25 - paid preview of Gemini 2.5 Pro
• gemma-3-27b-it - Gemma 3 27B
• gemini-2.0-pro-exp-02-05 - experimental release of Gemini 2.0 Pro
• gemini-2.0-flash-lite - Gemini 2.0 Flash-Lite
• gemini-2.0-flash - Gemini 2.0 Flash
• gemini-2.0-flash-thinking-exp-01-21 - experimental "thinking" model from January 2025
• gemini-2.0-flash-thinking-exp-1219 - experimental "thinking" model from December 2024
• learnlm-1.5-pro-experimental - "an experimental task-specific model that has been trained to align with learning science principles" - more details here.
• gemini-2.0-flash-exp - Gemini 2.0 Flash
• gemini-exp-1206 - recent experimental #3
• gemini-exp-1121 - recent experimental #2
• gemini-exp-1114 - recent experimental #1
• gemini-1.5-flash-8b-latest - the least expensive
• gemini-1.5-flash-latest

Images, audio and video

Gemini models are multi-modal. You can provide images, audio or video files as input like this:

llm -m gemini-2.0-flash 'extract text' -a image.jpg

Or with a URL:

llm -m gemini-2.0-flash-lite 'describe image' \
  -a https://static.simonwillison.net/static/2024/pelicans.jpg

Audio works too:

llm -m gemini-2.0-flash 'transcribe audio' -a audio.mp3

And video:

llm -m gemini-2.0-flash 'describe what happens' -a video.mp4

The Gemini prompting guide includes extensive advice on multi-modal prompting.

JSON output

Use -o json_object 1 to force the output to be JSON:

llm -m gemini-2.0-flash -o json_object 1 \
  '3 largest cities in California, list of {"name": "..."}'

Outputs:

{"cities": [{"name": "Los Angeles"}, {"name": "San Diego"}, {"name": "San Jose"}]}

Code execution

Gemini models can write and execute code - 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:

llm -m gemini-2.0-flash -o code_execution 1 \
'use python to calculate (factorial of 13) * 3'

Google search

Some Gemini models support Grounding with Google Search, where the model can run a Google search and use the results as part of answering a prompt.

Using this feature may incur additional requirements in terms of how you use the results. Consult Google's documentation for more details.

To run a prompt with Google search enabled, use -o google_search 1:

llm -m gemini-2.0-flash -o google_search 1 \
  'What happened in Ireland today?'

Use llm logs -c --json after running a prompt to see the full JSON response, which includes additional information about grounded results.

Chat

To chat interactively with the model, run llm chat:

llm chat -m gemini-2.0-flash

Embeddings

The plugin also adds support for the gemini-embedding-exp-03-07 and text-embedding-004 embedding models.

Run that against a single string like this:

llm embed -m text-embedding-004 -c 'hello world'

This returns a JSON array of 768 numbers.

The gemini-embedding-exp-03-07 model is larger, returning 3072 numbers. You can also use variants of it that are truncated down to smaller sizes:

• gemini-embedding-exp-03-07 - 3072 numbers
• gemini-embedding-exp-03-07-2048 - 2048 numbers
• gemini-embedding-exp-03-07-1024 - 1024 numbers
• gemini-embedding-exp-03-07-512 - 512 numbers
• gemini-embedding-exp-03-07-256 - 256 numbers
• gemini-embedding-exp-03-07-128 - 128 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:

llm embed-multi readmes -d embed.db -m gemini-embedding-exp-03-07-128 \
  --files . '*/README.md'

You can then run similarity searches against that collection like this:

llm similar readmes -c 'upload csvs to stuff' -d embed.db

See the LLM embeddings documentation for further details.

Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-gemini
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

llm install -e '.[test]'

To run the tests:

pytest

This project uses 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:

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:

llm keys set gemini
# Paste key here