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suchintan's feedback + changelog (#4947)
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Co-authored-by: Ritik Sahni <ritiksahni0203@gmail.com>
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docs/self-hosted/llm-configuration.mdx
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@ -108,7 +108,21 @@ The `AZURE_DEPLOYMENT` is the name you chose when deploying the model, not the m
## Google Gemini
Gemini models through [Vertex AI](https://cloud.google.com/vertex-ai). Requires a GCP project with Vertex AI enabled.
Skyvern supports Gemini through two paths: the **Gemini API** (simpler, uses an API key) and **Vertex AI** (enterprise, uses a GCP service account).
### Gemini API
The quickest way to use Gemini. Get an API key from [Google AI Studio](https://aistudio.google.com/).
```bash .env
ENABLE_GEMINI=true
GEMINI_API_KEY=your-gemini-api-key
LLM_KEY=VERTEX_GEMINI_2.5_FLASH
```
### Vertex AI
For enterprise deployments through [Vertex AI](https://cloud.google.com/vertex-ai). Requires a GCP project with Vertex AI enabled.
```bash .env
ENABLE_VERTEX_AI=true
@ -118,7 +132,7 @@ GCP_PROJECT_ID=your-gcp-project-id
GCP_REGION=us-central1
```
### Setup steps
**Vertex AI setup steps:**
1. Create a [GCP project](https://console.cloud.google.com/) with billing enabled
2. Enable the **Vertex AI API** in your project