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The Google Gemini pipeline supports configurable aspect ratios and resolutions for image generation with **Gemini 3 image models** (e.g., `gemini-3-pro-image-preview`, `gemini-3-flash-image-preview`).
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The Google Gemini pipeline supports configurable aspect ratios and resolutions for image generation with **Gemini 3/3.1 image models** (e.g., `gemini-3.1-flash-image-preview`, `gemini-3-pro-image-preview`, `gemini-3-flash-image-preview`).
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> [!IMPORTANT]
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> **Model Compatibility**: The `aspect_ratio` and `image_size` parameters (ImageConfig) are **only supported by Gemini 3 image models**. Gemini 2.5 image models (e.g., `gemini-2.5-flash-image-preview`) support image generation but do not support these configuration parameters. When using Gemini 2.5 image models, default aspect ratio and resolution will be used automatically.
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> **Model Compatibility**: The `aspect_ratio` and `image_size` parameters (ImageConfig) are **only supported by Gemini 3/3.1 image models**. Gemini 2.5 image models (e.g., `gemini-2.5-flash-image-preview`) support image generation but do not support these configuration parameters. When using Gemini 2.5 image models, default aspect ratio and resolution will be used automatically.
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### Aspect Ratio
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@@ -334,13 +334,14 @@ for part in response.parts:
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### Model Compatibility
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| Model | ImageConfig Support (aspect_ratio, image_size) |
Not all parameters are supported by every Veo model. The pipeline automatically gates features based on the model used. Unsupported parameters are silently skipped to avoid API errors.
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| Feature | Veo 3.1 | Veo 3.1 Fast | Veo 3 | Veo 3 Fast | Veo 2|
> ¹ The Veo API supports up to 3 reference images for Veo 3.1, but the pipeline currently only forwards a single attached image via the `image` parameter.
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>
@@ -568,8 +569,9 @@ When enabled, sources and google queries from the search used by Gemini will be
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The pipeline supports **Enterprise Web Search** for grounding, which provides organization-level management of search results.
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To enable Enterprise Search:
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1. Set `GOOGLE_USE_ENTERPRISE_SEARCH=true` (or toggle the Valve in the UI).
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2. Ensure `GOOGLE_GENAI_USE_VERTEXAI=true` (Enterprise Search is a Vertex AI feature).
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1. Set `GOOGLE_USE_ENTERPRISE_SEARCH=true` (or toggle the Valve in the UI).
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2. Ensure `GOOGLE_GENAI_USE_VERTEXAI=true` (Enterprise Search is a Vertex AI feature).
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When enabled, the pipeline will use the `enterprise_web_search` tool instead of the standard `google_search` tool whenever grounding is requested.
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@@ -672,25 +674,30 @@ The Google Gemini pipeline supports advanced thinking configuration to control h
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Gemini 3 models support the `thinking_level` parameter, which controls the depth of reasoning:
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-**`"low"`**: Minimizes latency and cost, suitable for simple tasks, chat, or high-throughput APIs.
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-**`"high"`**: Maximizes reasoning depth, ideal for complex problem-solving, code analysis, and agentic workflows.
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-**Most Gemini 3 models**: support **`"low"`**and **`"high"`**.
> Gemini 3 models use `thinking_level` and do **not** use `thinking_budget`. The thinking budget setting is ignored for Gemini 3 models.
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If you configure an unsupported value for a specific model, the pipeline automatically falls back to the closest supported thinking level instead of sending an invalid API request.
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Set via environment variable:
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```bash
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# Use low thinking level for faster responses
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# Use low thinking level for most Gemini 3 models
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GOOGLE_THINKING_LEVEL="low"
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# Use high thinking level for complex reasoning
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GOOGLE_THINKING_LEVEL="high"
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# Use minimal thinking level for gemini-3.1-flash-image-preview
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GOOGLE_THINKING_LEVEL="minimal"
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```
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#### Per-Chat Override (Reasoning Effort)
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The per-chat `reasoning_effort` value can override the environment-level `GOOGLE_THINKING_LEVEL` setting. When a chat specifies a `reasoning_effort` value (e.g., "low"or "high"), it takes precedence over the global environment setting. This allows users to customize reasoning depth on a per-conversation basis.
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The per-chat `reasoning_effort` value can override the environment-level `GOOGLE_THINKING_LEVEL` setting. When a chat specifies a `reasoning_effort` value (for example, `"low"`, `"minimal"`, or `"high"`), it takes precedence over the global environment setting. This allows users to customize reasoning depth on a per-conversation basis.
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**Example API Usage:**
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@@ -784,11 +791,11 @@ The pipeline automatically extracts token usage metadata from every Gemini respo
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### What is tracked
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| Field | Description |
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| --- | --- |
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|`prompt_tokens`| Number of tokens in the input (messages + system prompt) |
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|`completion_tokens`| Number of tokens generated by the model |
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|`total_tokens`| Sum of prompt and completion tokens |
|`prompt_tokens`| Number of tokens in the input (messages + system prompt) |
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|`completion_tokens`| Number of tokens generated by the model |
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|`total_tokens`| Sum of prompt and completion tokens|
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### How it works
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@@ -800,11 +807,10 @@ No additional configuration is required. Token usage is tracked automatically fo
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> [!NOTE]
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> Thinking tokens consumed during internal reasoning are **not** included in `completion_tokens` — they are captured separately by the Gemini API in `thoughts_token_count` but are not forwarded to Open WebUI at this time.
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