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feat!: breaking change - OpenRouter models are now read from conf/openrouter_models.json while Custom / Self-hosted models are read from conf/custom_models.json

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feat: Azure OpenAI / Azure AI Foundry support. Models should be defined in conf/azure_models.json (or a custom path). See .env.example for environment variables or see readme. https://github.com/BeehiveInnovations/zen-mcp-server/issues/265

feat: OpenRouter / Custom Models / Azure can separately also use custom config paths now (see .env.example )

refactor: Model registry class made abstract, OpenRouter / Custom Provider / Azure OpenAI now subclass these

refactor: breaking change: `is_custom` property has been removed from model_capabilities.py (and thus custom_models.json) given each models are now read from separate configuration files
This commit is contained in:
Fahad
2025-10-04 21:10:56 +04:00
parent e91ed2a924
commit ff9a07a37a
40 changed files with 1651 additions and 852 deletions
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21
docs/adding_providers.md
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@@ -9,6 +9,7 @@ Each provider:
- Defines supported models using `ModelCapabilities` objects
- Implements the minimal abstract hooks (`get_provider_type()` and `generate_content()`)
- Gets wired into `configure_providers()` so environment variables control activation
- Can leverage helper subclasses (e.g., `AzureOpenAIProvider`) when only client wiring differs
### Intelligence score cheatsheet
@@ -31,6 +32,13 @@ features ([details here](model_ranking.md)).
⚠️ **Important**: If you implement a custom `generate_content()`, call `_resolve_model_name()` before invoking the SDK so aliases (e.g. `"gpt"` → `"gpt-4"`) resolve correctly. The shared implementations already do this for you.
**Option C: Azure OpenAI (`AzureOpenAIProvider`)**
- For Azure-hosted deployments of OpenAI models
- Reuses the OpenAI-compatible pipeline but swaps in the `AzureOpenAI` client and a deployment mapping (canonical model → deployment ID)
- Define deployments in [`conf/azure_models.json`](../conf/azure_models.json) (or the file referenced by `AZURE_MODELS_CONFIG_PATH`).
- Entries follow the [`ModelCapabilities`](../providers/shared/model_capabilities.py) schema and must include a `deployment` identifier.
See [Azure OpenAI Configuration](azure_openai.md) for a step-by-step walkthrough.
## Step-by-Step Guide
### 1. Add Provider Type
@@ -227,6 +235,19 @@ DISABLED_TOOLS=debug,tracer
EXAMPLE_ALLOWED_MODELS=example-model-large,example-model-small
```
For Azure OpenAI deployments:
```bash
AZURE_OPENAI_API_KEY=your_azure_openai_key_here
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
# Models are defined in conf/azure_models.json (or AZURE_MODELS_CONFIG_PATH)
# AZURE_OPENAI_API_VERSION=2024-02-15-preview
# AZURE_OPENAI_ALLOWED_MODELS=gpt-4o,gpt-4o-mini
# AZURE_MODELS_CONFIG_PATH=/absolute/path/to/custom_azure_models.json
```
You can also define Azure models in [`conf/azure_models.json`](../conf/azure_models.json) (the bundled file is empty so you can copy it safely). Each entry mirrors the `ModelCapabilities` schema and must include a `deployment` field. Set `AZURE_MODELS_CONFIG_PATH` if you maintain a custom copy outside the repository.
**Note**: The `description` field in `ModelCapabilities` helps Claude choose the best model in auto mode.
### 5. Test Your Provider