Anvia

Gemini ​

@anvia/gemini connects Anvia to Google's Gemini API or Gemini models on Vertex AI. One GeminiClient creates provider-neutral models for completions, embeddings, image generation, and transcription.

Choose the connection at the application boundary. Agents, extractors, pipelines, and media workflows receive an Anvia model and do not need to know which Google endpoint created it.

Choose a connection ​

Use apiKey for the Gemini API. Use vertexAi: { projectId, location } for Vertex AI. Use client to inject an existing GoogleGenAI instance owned by the application.

Start with the Gemini API for a simple API-key integration. Use Vertex AI when the application already relies on Google Cloud IAM, project-level quotas, or regional deployment controls.

Create a completion model ​

ts
import { GeminiClient } from '@anvia/gemini'

const gemini = new GeminiClient({
  apiKey: process.env.GEMINI_API_KEY!,
})

export const model = gemini.completionModel({
    modelId: 'gemini-3.6-flash'
})

Pass model to an agent, direct completion, extractor, or pipeline. Keep the client and credentials in server-only code.

Model factories ​

Use completionModel() for text, tools, schemas, and multimodal understanding. Use embeddingModel() for vectors. Use imageGenerationModel({ api: 'generateContent', ... }) for Gemini-native image models and api: 'generateImages' for Imagen. Use transcriptionModel() for audio-to-text and listModels() for normalized inventory.

These factories share a client, but they are different model contracts. For example, a completion model that understands an image cannot be passed where an image-generation model is required.

Completion capabilities ​

The completion adapter supports streaming, tools and tool choice, image input, document-like file input, output schemas, and reasoning content. The exact Gemini model and deployment must also support the requested capability.

In this section ​

Built for Anvia.