Praison AI

Google Gemini Embeddings

Generate embeddings using Google Gemini API

Overview

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

Quick Start

python
from praisonaiagents import embedding

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

CLI Usage

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

Setup

bash
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

python
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

python
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"
)

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