Get started

Install the adapter with Core:

pnpm add @anvia/core @anvia/gemini

Use an API key for the Gemini Developer API:

import { AgentBuilder } from '@anvia/core'
import { GeminiClient } from '@anvia/gemini'

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

const agent = new AgentBuilder(
  'assistant',
  gemini.completionModel('gemini-2.5-flash'),
).build()

const result = await agent.prompt('Summarize this document.').send()
console.log(result.output)

Add embeddings

const documents = gemini.embeddingModel('gemini-embedding-001', {
  taskType: 'RETRIEVAL_DOCUMENT',
  dimensions: 768,
})

const vectors = await documents.embedTexts([
  'Anvia is a provider-neutral TypeScript runtime.',
])

Create a second model configured with RETRIEVAL_QUERY for queries while keeping model and dimensions aligned with indexed documents.

Use Vertex AI

const vertex = new GeminiClient({
  vertexai: true,
  project: 'my-gcp-project',
  location: 'us-central1',
})

Vertex mode and API-key mode are mutually exclusive. See Vertex AI.

Before production

• Keep credentials server-side.
• Use explicit model IDs for each capability.
• Confirm model availability in the selected API or Vertex region.
• Test image, audio, tools, and schemas with the exact model.
• Reindex vectors when dimensions or task configuration changes.
• Treat malformed provider output as an error.