> ## Documentation Index
> Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Google

> Integrate Google Gemini models through the Adaline Proxy for automatic telemetry and observability.

# Google

Integrate Google Gemini models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes.

## Supported Models

**Chat Models**

| Model                                   | Description                                  |
| --------------------------------------- | -------------------------------------------- |
| `gemini-3.1-pro-preview`                | Latest Gemini 3.1 Pro preview                |
| `gemini-3.1-pro-preview-customtools`    | Gemini 3.1 Pro preview with custom tools     |
| `gemini-3-pro-preview`                  | Gemini 3 Pro preview                         |
| `gemini-3-flash-preview`                | Gemini 3 Flash preview                       |
| `gemini-2.5-pro`                        | Gemini 2.5 Pro                               |
| `gemini-2.5-pro-preview-03-25`          | Gemini 2.5 Pro March 2025 preview            |
| `gemini-2.5-flash`                      | Gemini 2.5 Flash                             |
| `gemini-2.5-flash-preview-04-17`        | Gemini 2.5 Flash April 2025 preview          |
| `gemini-2.5-flash-lite`                 | Lightweight Gemini 2.5                       |
| `gemini-2.5-flash-lite-preview-09-2025` | Gemini 2.5 Flash Lite September 2025 preview |
| `gemini-2.0-flash`                      | Fast Gemini 2.0 model                        |
| `gemini-2.0-flash-lite`                 | Lightweight Gemini 2.0                       |
| `gemini-1.5-pro`                        | Gemini 1.5 Pro, 1M token context             |
| `gemini-1.5-pro-latest`                 | Gemini 1.5 Pro latest                        |
| `gemini-1.5-pro-002`                    | Gemini 1.5 Pro version 002                   |
| `gemini-1.5-pro-001`                    | Gemini 1.5 Pro version 001                   |
| `gemini-1.5-flash`                      | Gemini 1.5 Flash                             |
| `gemini-1.5-flash-latest`               | Gemini 1.5 Flash latest                      |
| `gemini-1.5-flash-002`                  | Gemini 1.5 Flash version 002                 |
| `gemini-1.5-flash-001`                  | Gemini 1.5 Flash version 001                 |

**Embedding Models**

| Model                | Description                 |
| -------------------- | --------------------------- |
| `text-embedding-004` | Latest text embedding model |
| `text-embedding-001` | Legacy embedding model      |

## Proxy Base URL

```
https://gateway.adaline.ai/v1/google
```

## Prerequisites

1. A [Google AI API key](https://aistudio.google.com/app/apikey)
2. An [Adaline API key](/docs/admin/create-api-keys), project ID, and prompt ID

## Chat Completions

### Complete Chat

<CodeGroup>
  ```python Python theme={null}
  from google import genai
  from google.genai import types

  client = genai.Client(
      http_options={
          "base_url": "https://gateway.adaline.ai/v1/google",
          "headers": {
              "adaline-api-key": "your-adaline-api-key",
              "adaline-project-id": "your-project-id",
              "adaline-prompt-id": "your-prompt-id",
          },
      },
      api_key="your-google-api-key",
  )

  response = client.models.generate_content(
      model="gemini-1.5-pro",
      contents="What is the future of artificial intelligence?",
      config=types.GenerateContentConfig(
          http_options=types.HttpOptions(
              headers={
                  "adaline-trace-name": "google-chat-completion"  # Optional
              }
          )
      )
  )

  print(response.text)
  ```
</CodeGroup>

<Note>
  Google's SDK uses `http_options` for base URL and headers, rather than `base_url` and `extra_headers` like OpenAI-compatible SDKs.
</Note>

### Stream Chat

<CodeGroup>
  ```python Python theme={null}
  from google import genai
  from google.genai import types

  client = genai.Client(
      http_options={
          "base_url": "https://gateway.adaline.ai/v1/google",
          "headers": {
              "adaline-api-key": "your-adaline-api-key",
              "adaline-project-id": "your-project-id",
              "adaline-prompt-id": "your-prompt-id",
          },
      },
      api_key="your-google-api-key",
  )

  stream = client.models.generate_content_stream(
      model="gemini-1.5-pro",
      contents="Explain the concept of machine learning in detail.",
      config=types.GenerateContentConfig(
          http_options=types.HttpOptions(
              headers={
                  "adaline-trace-name": "google-stream-chat"  # Optional
              }
          )
      )
  )

  for chunk in stream:
      if hasattr(chunk, 'text') and chunk.text:
          print(chunk.text, end="")
  ```
</CodeGroup>

## Embeddings

<CodeGroup>
  ```python Python theme={null}
  from google import genai
  from google.genai import types

  client = genai.Client(
      http_options={
          "base_url": "https://gateway.adaline.ai/v1/google",
          "headers": {
              "adaline-api-key": "your-adaline-api-key",
              "adaline-project-id": "your-project-id",
              "adaline-prompt-id": "your-prompt-id",
          },
      },
      api_key="your-google-api-key",
  )

  response = client.models.embed_content(
      model="text-embedding-004",
      contents="The quick brown fox jumps over the lazy dog",
      config=types.EmbedContentConfig(
          http_options=types.HttpOptions(
              headers={
                  "adaline-trace-name": "google-embedding"  # Optional
              }
          )
      )
  )
  ```
</CodeGroup>

## Next Steps

* [Multi-Step Workflows](/docs/integrations/examples/multi-step-workflows) — RAG pipelines, multi-step generation, and conversational agents
* [Headers Reference](/docs/reference/proxy/headers) — Complete header documentation

***

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