> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini

Embed text and generate completions using Google's Gemini models.
See the API reference for [Gemini UDFs](https://lancedb.github.io/geneva/api/gemini/) and
[Embedding UDFs](https://lancedb.github.io/geneva/api/embeddings/) for all parameters.

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
pip install 'geneva[udf-text-gemini]'
```

<Warning>
  Gemini UDFs make API calls that incur **per-token costs**. Each row processed results in one
  or more API requests billed to your account. Review
  [Gemini pricing](https://ai.google.dev/gemini-api/docs/pricing) before running on large tables.
</Warning>

<Note>
  Set the `GEMINI_API_KEY` environment variable before calling any factory function below.
  The key is read **at UDF creation time** and serialized with the UDF — no cluster-level
  `env_vars` configuration is needed.
</Note>

## Embeddings

Embed text with optional task-type hints for retrieval, classification, and clustering scenarios.
See the [API reference](https://lancedb.github.io/geneva/api/embeddings/#geneva.udfs.text.embeddings.gemini_embedding_udf) for all parameters.

**Multiple embeddings tuned for different retrieval tasks:**

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from geneva.udfs import gemini_embedding_udf

table.add_columns({
    # Full-dimension embedding for document retrieval
    "embedding_doc": gemini_embedding_udf(
        column="body",
        model="gemini-embedding-001",
        task_type="RETRIEVAL_DOCUMENT",
    ),
    # Compact embedding for semantic similarity
    "embedding_sim_256": gemini_embedding_udf(
        column="body",
        model="gemini-embedding-001",
        task_type="SEMANTIC_SIMILARITY",
        output_dimensionality=256,
    ),
})
```

## Generation

Generate text from Gemini models. Supports text, image, audio, video, and document inputs.
See the [API reference](https://lancedb.github.io/geneva/api/gemini/#geneva.udfs.text.gemini.gemini_udf) for all parameters.

**Enrich a table with sentiment, captions, and transcriptions at once:**

```python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from geneva.udfs import gemini_udf

table.add_columns({
    # Classify review sentiment with a fast model
    "sentiment": gemini_udf(
        column="review",
        prompt="Classify the sentiment as positive, negative, or neutral. Return only the label.",
        model="gemini-2.5-flash",
    ),
    # Caption product images with a more capable model
    "caption": gemini_udf(
        column="image",
        prompt="Describe the main subject of this image in one sentence",
        model="gemini-2.5-pro",
        mime_type="image/jpeg",
    ),
    # Transcribe audio clips
    "transcript": gemini_udf(
        column="audio",
        prompt="Transcribe this audio clip",
        model="gemini-2.5-flash",
        mime_type="audio/mp3",
    ),
})
```

## API Reference

* [Gemini](https://lancedb.github.io/geneva/api/gemini/) — `gemini_udf()` parameters: `column`, `prompt`, `model`, `mime_type`, and more
* [Embeddings](https://lancedb.github.io/geneva/api/embeddings/) — `gemini_embedding_udf()` parameters: `column`, `model`, `task_type`, `dimensionality`, `normalize`