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Overview

Google Gemini provides embedding models through the Gemini API with simple API key authentication.

Quick Start

from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="gemini/text-embedding-004"
)
print(f"Dimensions: {len(result.embeddings[0])}")

CLI Usage

praisonai embed "Hello world" --model gemini/text-embedding-004

Setup

export GEMINI_API_KEY="your-gemini-api-key"
# or
export GOOGLE_API_KEY="your-google-api-key"

Available Models

ModelDimensionsUse Case
gemini/text-embedding-004768Latest, recommended
gemini/embedding-001768Legacy model

Batch Embeddings

from praisonaiagents import embedding

texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
    input=texts,
    model="gemini/text-embedding-004"
)
print(f"Generated {len(result.embeddings)} embeddings")

Task Types

from praisonaiagents import embedding

# For retrieval queries
result = embedding(
    input="What is machine learning?",
    model="gemini/text-embedding-004",
    task_type="RETRIEVAL_QUERY"
)

# For documents
result = embedding(
    input="Machine learning is...",
    model="gemini/text-embedding-004",
    task_type="RETRIEVAL_DOCUMENT"
)