NeriaNeria.Meridian
Genai integration

Generate content (Gemini format)

POST/genai/v1beta/models/{model}:generateContent

Generates content using Google Gemini API format. The model is specified in the URL path.

Path Parameters

model*string

Model name with action (e.g., gemini-pro:generateContent)

Request Body

application/json

TypeScript Definitions

Use the request body type in TypeScript.

Response Body

application/json

application/json

application/json

curl -X POST "http://localhost:8080/genai/v1beta/models/string:generateContent" \  -H "Content-Type: application/json" \  -d '{}'
{
  "candidates": [
    {
      "content": {
        "role": "user",
        "parts": [
          {
            "text": "string",
            "thought": true,
            "thoughtSignature": "string",
            "inlineData": {
              "mimeType": "string",
              "data": "string",
              "displayName": "string"
            },
            "fileData": {
              "mimeType": "string",
              "fileUri": "string",
              "displayName": "string"
            },
            "functionCall": {
              "id": "string",
              "name": "string",
              "args": {}
            },
            "functionResponse": {
              "id": "string",
              "name": "string",
              "response": {},
              "willContinue": true,
              "scheduling": "string"
            },
            "executableCode": {
              "language": "string",
              "code": "string"
            },
            "codeExecutionResult": {
              "outcome": "OUTCOME_UNSPECIFIED",
              "output": "string"
            },
            "videoMetadata": {
              "fps": 0,
              "startOffset": "string",
              "endOffset": "string"
            }
          }
        ]
      },
      "finishReason": "FINISH_REASON_UNSPECIFIED",
      "finishMessage": "string",
      "tokenCount": 0,
      "safetyRatings": [
        {
          "category": "string",
          "probability": "string",
          "probabilityScore": 0,
          "severity": "string",
          "severityScore": 0,
          "blocked": true,
          "overwrittenThreshold": "string"
        }
      ],
      "citationMetadata": {},
      "index": 0,
      "groundingMetadata": {},
      "urlContextMetadata": {
        "urlMetadata": [
          {
            "retrievedUrl": "string",
            "urlRetrievalStatus": "string"
          }
        ]
      },
      "avgLogprobs": 0,
      "logprobsResult": {
        "chosenCandidates": [
          {
            "token": "string",
            "tokenId": 0,
            "logProbability": 0
          }
        ],
        "topCandidates": [
          {
            "candidates": [
              {
                "token": "string",
                "tokenId": 0,
                "logProbability": 0
              }
            ]
          }
        ]
      }
    }
  ],
  "promptFeedback": {
    "blockReason": "string",
    "blockReasonMessage": "string",
    "safetyRatings": [
      {
        "category": "string",
        "probability": "string",
        "probabilityScore": 0,
        "severity": "string",
        "severityScore": 0,
        "blocked": true,
        "overwrittenThreshold": "string"
      }
    ]
  },
  "usageMetadata": {
    "promptTokenCount": 0,
    "candidatesTokenCount": 0,
    "totalTokenCount": 0,
    "cachedContentTokenCount": 0,
    "thoughtsTokenCount": 0,
    "toolUsePromptTokenCount": 0,
    "trafficType": "string",
    "cacheTokensDetails": [
      {
        "modality": "string",
        "tokenCount": 0
      }
    ],
    "candidatesTokensDetails": [
      {
        "modality": "string",
        "tokenCount": 0
      }
    ],
    "promptTokensDetails": [
      {
        "modality": "string",
        "tokenCount": 0
      }
    ],
    "toolUsePromptTokensDetails": [
      {
        "modality": "string",
        "tokenCount": 0
      }
    ]
  },
  "modelVersion": "string",
  "responseId": "string",
  "createTime": "2019-08-24T14:15:22Z"
}
{
  "error": {
    "code": 0,
    "message": "string",
    "status": "string",
    "details": [
      {
        "@type": "string",
        "fieldViolations": [
          {
            "description": "string"
          }
        ]
      }
    ]
  }
}
{
  "error": {
    "code": 0,
    "message": "string",
    "status": "string",
    "details": [
      {
        "@type": "string",
        "fieldViolations": [
          {
            "description": "string"
          }
        ]
      }
    ]
  }
}

Embed content (Gemini format) POST

Creates embeddings using Google Gemini API format.

Generate image (Gemini format) POST

For Imagen models, use the :predict suffix (e.g., imagen-3.0-generate-001:predict). For Gemini models, use :generateContent with generationConfig.responseModalities: ["IMAGE"] in the request body.