notdiamond
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The official TypeScript library for the Notdiamond API
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JavaScript
;
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
Object.defineProperty(exports, "__esModule", { value: true });
exports.CustomRouter = void 0;
const resource_1 = require("../core/resource.js");
const uploads_1 = require("../internal/uploads.js");
class CustomRouter extends resource_1.APIResource {
/**
* Train a custom router on your evaluation data to optimize routing for your
* specific use case.
*
* This endpoint allows you to train a domain-specific router that learns which
* models perform best for different types of queries in your application. The
* router analyzes your evaluation dataset, clusters similar queries, and learns
* model performance patterns.
*
* **Training Process:**
*
* 1. Upload a CSV file with your evaluation data
* 2. Specify which models to route between
* 3. Define the evaluation metric (score column)
* 4. The system trains asynchronously and returns a preference_id
* 5. Use the preference_id in model_select() calls once training completes
*
* **Dataset Requirements:**
*
* - Format: CSV file
* - Minimum samples: 25 (more is better for accuracy)
* - Required columns:
* - Prompt column (specified in prompt_column parameter)
* - For each model: `{provider}/{model}/score` and `{provider}/{model}/response`
*
* **Example CSV structure:**
*
* ```
* prompt,openai/gpt-4o/score,openai/gpt-4o/response,anthropic/claude-sonnet-4-5-20250929/score,anthropic/claude-sonnet-4-5-20250929/response
* "Explain quantum computing",0.95,"Quantum computing uses...",0.87,"Quantum computers leverage..."
* "Write a Python function",0.82,"def my_function()...",0.91,"Here's a Python function..."
* ```
*
* **Model Selection:**
*
* - Specify standard models: `{"provider": "openai", "model": "gpt-4o"}`
* - Or custom models with pricing:
* `{"provider": "custom", "model": "my-model", "is_custom": true, "input_price": 10.0, "output_price": 30.0, "context_length": 8192, "latency": 1.5}`
*
* **Training Time:**
*
* - Training is asynchronous and typically takes 5-15 minutes
* - Larger datasets or more models take longer
* - You'll receive a preference_id immediately
* - Check training status by attempting to use the preference_id in model_select()
*
* **Best Practices:**
*
* 1. Use diverse, representative examples from your production workload
* 2. Include at least 50-100 samples for best results
* 3. Ensure consistent evaluation metrics across all models
* 4. Use the same models you plan to route between in production
*
* **Related Documentation:** See
* https://docs.notdiamond.ai/docs/adapting-prompts-to-new-models for detailed
* guide.
*
* @example
* ```ts
* const response =
* await client.customRouter.trainCustomRouter({
* dataset_file: fs.createReadStream('path/to/file'),
* language: 'english',
* llm_providers:
* '[{"provider": "openai", "model": "gpt-4o"}, {"provider": "anthropic", "model": "claude-sonnet-4-5-20250929"}]',
* maximize: true,
* prompt_column: 'prompt',
* });
* ```
*/
trainCustomRouter(body, options) {
return this._client.post('/v2/pzn/trainCustomRouter', (0, uploads_1.multipartFormRequestOptions)({ body, ...options }, this._client));
}
}
exports.CustomRouter = CustomRouter;
//# sourceMappingURL=custom-router.js.map