Google Gemini Embeddings

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

uv pip install google-genai

For Vertex AI support:

uv pip install google-auth

Examples

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

​Overview

​Dependencies

​Examples

​Parameters

Overview

Dependencies

Examples

Parameters