> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini Embedder

The `GeminiEmbedder` class is used to embed text data into vectors using Google's Gemini API. You can use it through [Google AI Studio](https://ai.google.dev/aistudio) or [Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/overview).

## Usage

For Google AI Studio:

```python theme={null}
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.google import GeminiEmbedder

# Embed sentence in database
embeddings = GeminiEmbedder().get_embedding(
    "The quick brown fox jumps over the lazy dog."
)

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Use an embedder in a knowledge base
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="gemini_embeddings",
        embedder=GeminiEmbedder(),
    ),
    num_documents=2,
)
```

For Vertex AI:

```python theme={null}
from agno.agent import AgentKnowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.google import GeminiEmbedder

embedder = GeminiEmbedder(
    vertexai=True,
    project_id="your-gcloud-project-id",  # Optional
    location="us-central1",  # Optional
)

# Use in a knowledge base
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="gemini_embeddings_vertex",
        embedder=embedder,
    ),
    num_documents=2,
)
```

## Params

| Parameter        | Type                       | Default                      | Description                                                 |
| ---------------- | -------------------------- | ---------------------------- | ----------------------------------------------------------- |
| `id`             | `str`                      | `gemini-embedding-exp-03-07` | The Gemini embedding model ID to use                        |
| `task_type`      | `str`                      | `RETRIEVAL_QUERY`            | The type of task for which embeddings are being generated   |
| `title`          | `Optional[str]`            | `None`                       | Optional title for the embedding task                       |
| `dimensions`     | `Optional[int]`            | `1536`                       | The dimensionality of the generated embeddings              |
| `api_key`        | `Optional[str]`            | `None`                       | The API key used for authenticating requests                |
| `request_params` | `Optional[Dict[str, Any]]` | `None`                       | Optional dictionary of parameters for the embedding request |
| `client_params`  | `Optional[Dict[str, Any]]` | `None`                       | Optional dictionary of parameters for the Gemini client     |
| `gemini_client`  | `Optional[GeminiClient]`   | `None`                       | Optional pre-configured Gemini client instance              |
| `vertexai`       | `bool`                     | `False`                      | Whether to use Vertex AI instead of Google AI Studio        |
| `project_id`     | `Optional[str]`            | `None`                       | Google Cloud project ID for Vertex AI                       |
| `location`       | `Optional[str]`            | `None`                       | Google Cloud region for Vertex AI                           |

## Developer Resources

* View [Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/embedders/gemini_embedder.py)