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

# Google Gemini Embeddings

> Using Google Gemini embedding models with Upsonic

## Overview

Google Gemini provides embedding models including gemini-embedding-001, text-embedding-005, and text-multilingual-embedding-002. Supports both Gemini Developer API and Vertex AI with advanced features like safety filtering, task-specific embeddings, and configurable dimensionality.

**Provider Class:** `GeminiEmbedding`

**Config Class:** `GeminiEmbeddingConfig`

## Dependencies

```bash theme={null}
uv pip install google-genai
```

For Vertex AI support:

```bash theme={null}
uv pip install google-auth
```

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import GeminiEmbedding, GeminiEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider (Developer API)
embedding = GeminiEmbedding(GeminiEmbeddingConfig(
    model_name="gemini-embedding-001",
    task_type="RETRIEVAL_DOCUMENT"
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="gemini_docs",
    vector_size=3072,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)
```

## Parameters

| Parameter                 | Type                     | Description                                                       | Default                  | Source   |
| ------------------------- | ------------------------ | ----------------------------------------------------------------- | ------------------------ | -------- |
| `model_name`              | `str`                    | Gemini embedding model name                                       | `"gemini-embedding-001"` | Specific |
| `api_key`                 | `str \| None`            | Google AI API key (uses GOOGLE\_API\_KEY env var if None)         | `None`                   | Specific |
| `task_type`               | `str`                    | Embedding task type (RETRIEVAL\_DOCUMENT, RETRIEVAL\_QUERY, etc.) | `"SEMANTIC_SIMILARITY"`  | Specific |
| `title`                   | `str \| None`            | Optional title for context                                        | `None`                   | Specific |
| `enable_safety_filtering` | `bool`                   | Enable Google's safety filtering                                  | `True`                   | Specific |
| `safety_settings`         | `Dict[str, str]`         | Safety filtering settings                                         | Default dict             | Specific |
| `use_vertex_ai`           | `bool`                   | Use Vertex AI API instead of Gemini Developer API                 | `False`                  | Specific |
| `use_google_cloud_auth`   | `bool`                   | Use Google Cloud authentication                                   | `False`                  | Specific |
| `project_id`              | `str \| None`            | Google Cloud project ID                                           | `None`                   | Specific |
| `location`                | `str`                    | Google Cloud location                                             | `"us-central1"`          | Specific |
| `api_version`             | `str`                    | API version to use (v1beta, v1, v1alpha)                          | `"v1beta"`               | Specific |
| `output_dimensionality`   | `int \| None`            | Output embedding dimension (128-3072)                             | `None`                   | Specific |
| `embedding_config`        | `Dict[str, Any] \| None` | Additional embedding configuration                                | `None`                   | Specific |
| `enable_batch_processing` | `bool`                   | Enable batch processing optimization                              | `True`                   | Specific |
| `enable_caching`          | `bool`                   | Enable response caching                                           | `False`                  | Specific |
| `cache_ttl_seconds`       | `int`                    | Cache TTL in seconds                                              | `3600`                   | Specific |
| `requests_per_minute`     | `int`                    | Requests per minute limit                                         | `60`                     | Specific |
| `batch_size`              | `int`                    | Batch size for document embedding                                 | `100`                    | Base     |
| `max_retries`             | `int`                    | Maximum number of retries on failure                              | `3`                      | Base     |
| `normalize_embeddings`    | `bool`                   | Whether to normalize embeddings to unit length                    | `True`                   | Base     |
| `show_progress`           | `bool`                   | Whether to show progress during batch operations                  | `True`                   | Base     |