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POST/api/v1/datasets/synthesize

Generate synthetic evaluation cases from a system prompt or document

T2.5b — wraps the @evalguard/core Synthesizer behind a server-side endpoint. Server-side LLM (OpenAI gpt-4o-mini by default) generates 1-200 test cases using 1-7 evolution strategies (reasoning, multi-context, comparative, hypothetical, edge-case, adversarial, paraphrase). Returns structured SynthesizedTestCase[] for the caller to review + save via /api/v1/datasets.

Authentication

Send Authorization: Bearer YOUR_API_KEY on every request. Generate API keys at /dashboard/api-keys.

Request body required

Example

{
  "projectId": "00000000-0000-0000-0000-000000000000",
  "systemPrompt": "<Source system prompt to generate cases f>",
  "documentText": "<Source document text to generate cases f>",
  "count": 20,
  "strategies": [
    "reasoning",
    "edge-case",
    "adversarial"
  ]
}
Schema
{
  "application/json": {
    "schema": {
      "type": "object",
      "properties": {
        "projectId": {
          "type": "string",
          "format": "uuid",
          "pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$",
          "description": "Org's project to scope the generation to."
        },
        "systemPrompt": {
          "type": "string",
          "minLength": 10,
          "maxLength": 8000,
          "description": "Source system prompt to generate cases for. Mutually exclusive with documentText."
        },
        "documentText": {
          "type": "string",
          "minLength": 10,
          "maxLength": 50000,
          "description": "Source document text to generate cases from. Mutually exclusive with systemPrompt."
        },
        "count": {
          "default": 20,
          "type": "integer",
          "minimum": 1,
          "maximum": 200
        },
        "strategies": {
          "default": [
            "reasoning",
            "edge-case",
            "adversarial"
          ],
          "minItems": 1,
          "maxItems": 7,
          "type": "array",
          "items": {
            "type": "string",
            "enum": [
              "reasoning",
              "multi-context",
              "comparative",
              "hypothetical",
              "edge-case",
              "adversarial",
              "paraphrase",
              "concretizing",
              "constrained",
              "in-breadth"
            ]
          }
        }
      },
      "required": [
        "projectId",
        "count",
        "strategies"
      ],
      "additionalProperties": false
    }
  }
}

Response

200 example

{
  "success": false,
  "data": {
    "cases": [
      {
        "input": "string",
        "expectedOutput": "string",
        "metadata": {
          "strategy": "string",
          "sourceType": "system-prompt",
          "chunkIndex": 0,
          "qualityScore": 0
        }
      }
    ],
    "stats": {
      "requestedCount": 0,
      "actualCount": 0,
      "totalGenerated": 0,
      "totalFiltered": 0,
      "strategiesUsed": [
        "string"
      ],
      "durationMs": 0
    }
  }
}

All status codes

200Generated cases + run stats
400Validation error (mutually-exclusive fields, count out of range, etc.)
401Unauthorized
403Insufficient role (editor required)
503LLM unavailable — server-side LLM key not configured

Code samples

cURL

curl -X POST \
  https://evalguard.ai/api/v1/datasets/synthesize \
  -H "Authorization: Bearer $EVALGUARD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "projectId": "00000000-0000-0000-0000-000000000000", "systemPrompt": "<Source system prompt to generate cases f>", "documentText": "<Source document text to generate cases f>", "count": 20, "strategies": [ "reasoning", "edge-case", "adversarial" ] }'

TypeScript

import { EvalGuard } from "@evalguard/sdk";

const client = new EvalGuard({ apiKey: process.env.EVALGUARD_API_KEY });

const response = await client.request({
  method: "POST",
  path: "/api/v1/datasets/synthesize",
  body: {
    "projectId": "00000000-0000-0000-0000-000000000000",
    "systemPrompt": "<Source system prompt to generate cases f>",
    "documentText": "<Source document text to generate cases f>",
    "count": 20,
    "strategies": [
      "reasoning",
      "edge-case",
      "adversarial"
    ]
  },
});
console.log(response);

Python

from evalguard import EvalGuard
import os

client = EvalGuard(api_key=os.environ["EVALGUARD_API_KEY"])

response = client.request(
    method="POST",
    path="/api/v1/datasets/synthesize",
    body={
    "projectId": "00000000-0000-0000-0000-000000000000",
    "systemPrompt": "<Source system prompt to generate cases f>",
    "documentText": "<Source document text to generate cases f>",
    "count": 20,
    "strategies": [
        "reasoning",
        "edge-case",
        "adversarial"
    ]
},
)
print(response)

Go

package main

import (
	"context"
	"fmt"
	"net/http"
	"os"
	"strings"
)

func main() {
	body := strings.NewReader(`{"projectId":"00000000-0000-0000-0000-000000000000","systemPrompt":"<Source system prompt to generate cases f>","documentText":"<Source document text to generate cases f>","count":20,"strategies":["reasoning","edge-case","adversarial"]}`)
	req, _ := http.NewRequestWithContext(context.Background(), "POST", "https://evalguard.ai/api/v1/datasets/synthesize", body)
	req.Header.Set("Authorization", "Bearer "+os.Getenv("EVALGUARD_API_KEY"))
	req.Header.Set("Content-Type", "application/json")
	resp, err := http.DefaultClient.Do(req)
	if err != nil { panic(err) }
	defer resp.Body.Close()
	fmt.Println(resp.Status)
}

Errors

400401403503

Other Datasets endpoints