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POST
/
v1beta
/
models
/
{model}
:generateContent
Gemini generateContent
curl --request POST \
  --url https://maas.apigo.ai/v1beta/models/{model}:generateContent \
  --header 'Content-Type: application/json' \
  --header 'x-goog-api-key: <api-key>' \
  --data '
{
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "text": "请总结这段内容"
        }
      ]
    }
  ]
}
'
{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "text": "这里是总结结果。"
          }
        ]
      },
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 24,
    "candidatesTokenCount": 36,
    "totalTokenCount": 60
  }
}

Documentation Index

Fetch the complete documentation index at: https://docs.apigo.ai/llms.txt

Use this file to discover all available pages before exploring further.

Gemini’s official docs split video into two flows: video understanding through generateContent, and long-running generation through operation-style APIs.
  • Official docs allow video input through inline data, Files API uploads, Cloud Storage URLs, and even YouTube URLs
  • This path is suited for summaries, scene analysis, temporal reasoning, and subtitle-assisted understanding
  • Larger or reusable videos are better handled through Files API references inside contents.parts
  • For Veo-style generation models, use /v1beta/models/{model}:predictLongRunning
  • Video-understanding text still usually comes from candidates[].content.parts[]
  • Google’s official docs also document format support, frame sampling, and tokenization constraints for longer videos

Authorizations

​
x-goog-api-key
string
header
required

Path Parameters

​
model
string
required

Gemini model name.

Example:

"gemini-2.5-pro"

Body

application/json
​
contents
object[]
required
​
systemInstruction
object
​
generationConfig
object
​
safetySettings
object[]
​
tools
object[]

Response

Successful Gemini content response

​
candidates
object[]
​
usageMetadata
object