Início Rápido: Usar o Entendimento de Conteúdo do Azure na API REST das Ferramentas Foundry

Este início rápido mostra como usar a API REST de Compreensão de Conteúdo para obter dados estruturados de conteúdo multimodal em arquivos de documento, imagem, áudio e vídeo.

Pré-requisitos

Para começar, você precisa de uma assinatura ativa do Azure. Se você ainda não tiver uma conta do Azure, poderá criar uma conta gratuitamente.
Depois de ter sua assinatura do Azure, crie um Recurso da Fábrica da Microsoft no portal do Azure. Certifique-se de criá-lo em uma região com suporte.
Configure implantações de modelo padrão para o recurso de Compreensão de Conteúdo. Definir padrões cria uma conexão com os modelos de Foundry que você usa para solicitações de Compreensão de Conteúdo. Use um dos seguintes métodos:
- Portal
- REST API
1. Vá para a página de configurações de Compreensão de Conteúdo
2. Selecione o botão "+ Adicionar recurso" no canto superior esquerdo
3. Selecione o recurso Foundry que você deseja usar e clique em Avançar e, em seguida, Salvar
  - Certifique-se de deixar a opção "Habilitar implantação automática para modelos obrigatórios se não houver padrões disponíveis." marcada. Isso garante que seu recurso esteja totalmente configurado com os modelos GPT-4.1, GPT-4.1-mini e text-embedding-3-large obrigatórios. Diferentes analisadores predefinidos exigem modelos diferentes.
Seguindo estas etapas, você configura uma conexão entre os modelos de Compreensão de Conteúdo e Foundry no recurso Foundry.
1. Crie implantações do Modelo Foundry GPT-4.1, GPT-4.1-mini e text-embedding-3-large no seu recurso Foundry. Para obter detalhes sobre como implantar esses modelos, consulte Criar implantações de modelo no portal do Microsoft Foundry. Diferentes analisadores predefinidos exigem modelos diferentes, portanto, você precisa implantar os três.
2. Defina implantações de modelo padrão no nível do recurso.
  
  Antes de executar o seguinte comando cURL, faça as seguintes alterações na solicitação HTTP:
  - Substitua {endpoint} e {key} pelos valores correspondentes da instância do Foundry no portal do Azure.
  - Substitua {myGPT41Deployment}, {myGPT41MiniDeployment} e {myEmbeddingDeployment} com seus nomes reais de implantação de modelo do recurso Foundry.
```
curl -i -X PATCH "{endpoint}/contentunderstanding/defaults?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "modelDeployments": {
          "gpt-4.1": "{myGPT41Deployment}",
          "gpt-4.1-mini": "{myGPT41MiniDeployment}",
          "text-embedding-3-large": "{myEmbeddingDeployment}"
        }
      }'
```
Seguindo estas etapas, você configura uma conexão entre os modelos de Compreensão de Conteúdo e Foundry no recurso Foundry.
Neste guia, usamos a ferramenta de linha de comando cURL. Se ele não estiver instalado, você poderá baixar a versão apropriada para seu ambiente de desenvolvimento.

Comece com um analisador predefinido

Os analisadores definem como o conteúdo é processado e os insights extraídos. Oferecemos analisadores predefinidos para casos de uso comuns. Você pode personalizar analisadores predefinidos para atender melhor às suas necessidades específicas e casos de uso. Este guia de início rápido usa analisadores predefinidos de fatura, imagem, áudio e vídeo para ajudá-lo a começar.

Enviar um arquivo para análise

Antes de executar o seguinte comando cURL, faça as seguintes alterações na solicitação HTTP:

Substitua {endpoint} e {key} pelos valores correspondentes da instância do Foundry no portal do Azure.

Solicitação POST

Este exemplo usa o prebuilt-invoice analisador para extrair dados estruturados de um documento de fatura.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-invoice:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[{"url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/invoice.pdf"}]
      }'

Este exemplo usa o prebuilt-imageSearch analisador para gerar uma descrição da imagem.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-imageSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/pieChart.jpg"
          }          
        ]
      }'

Este exemplo usa o analisador prebuilt-audioSearch para extrair a transcrição de áudio, gerar um resumo e realizar a identificação do locutor.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-audioSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/audio.wav"
          }          
        ]
      }'

Este exemplo usa o analisador prebuilt-videoSearch para extrair keyframes, transcrições e segmentos de capítulo do vídeo.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-videoSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/FlightSimulator.mp4"
          }          
        ]
      }'

Resposta POST

O cabeçalho de resposta inclui um Operation-Location campo, que você usa para recuperar os resultados da operação de análise assíncrona.

HTTP/1.1 202 Accepted
Transfer-Encoding: chunked
Content-Type: application/json
request-id: aaa-bbb-ccc-ddd
x-ms-request-id: aaa-bbb-ccc-ddd
Operation-Location: {endpoint}/contentunderstanding/analyzerResults/{request-id}?api-version=2025-11-01
api-supported-versions: 2024-12-01-preview,2025-05-01-preview,2025-11-01
x-envoy-upstream-service-time: 800
apim-request-id: {request-id}
Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
x-content-type-options: nosniff
x-ms-region: West US
Date: Fri, 31 Oct 2025 05:30:17 GMT
Connection: close

Importante

Copie a URL de Localização de Operação do cabeçalho de resposta. Você usará essa URL na próxima etapa para recuperar os resultados da análise.

Obter o resultado da análise

Use o Operation-Location da POST resposta e recupere o resultado da análise.

Solicitação GET

curl -i -X GET "{endpoint}/contentunderstanding/analyzerResults/{request-id}?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}"

Resposta GET

A resposta JSON 200 (OK) inclui um campo status indicando o status da operação. Se a operação não estiver concluída, o valor de status será Running ou NotStarted. Nesses casos, você deve enviar a solicitação GET novamente, manualmente ou por meio de um script. Espere um intervalo de um segundo ou mais entre as chamadas.

{
  "id": "ce05fb5a-579e-4f0b-afb5-3532bcddeaee",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-invoice",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T20:04:55Z",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "CONTOSO LTD.\n\n\n# INVOICE\n\nContoso Headquarters\n123 456th St\nNew York, NY, 10001\n\nINVOICE: INV-100\n\nINVOICE DATE: 11/15/2019\n\nDUE DATE: 12/15/2019\n\nCUSTOMER NAME: MICROSOFT CORPORATION...",
        "fields": {
          "AmountDue": {
            "type": "object",
            "valueObject": {
              "Amount": {
                "type": "number",
                "valueNumber": 610,
                "spans": [
                  {
                    "offset": 1522,
                    "length": 7
                  }
                ],
                "confidence": 0.773,
                "source": "D(1,7.3628,8.0459,7.9272,8.0459,7.9272,8.2070,7.3628,8.2070)"
              },
              "CurrencyCode": {
                "type": "string",
                "valueString": "USD"
              }
            }
          },
          "BalanceForward": {
            "type": "object",
            "valueObject": {
              "Amount": {
                "type": "number",
                "valueNumber": 500,
                "spans": [
                  {
                    "offset": 1474,
                    "length": 7
                  }
                ],
                "confidence": 0.901,
                "source": "D(1,7.3628,7.7445,7.9278,7.7467,7.9272,7.9092,7.3622,7.9070)"
              },
              "CurrencyCode": {
                "type": "string",
                "valueString": "USD"
              }
            }
          },
          "BillingAddress": {
            "type": "string",
            "valueString": "123 Bill St, Redmond WA, 98052",
            "spans": [
              {
                "offset": 325,
                "length": 12
              },
              "..."
            ],
            "confidence": 0.712,
            "source": "D(1,0.5805,3.9471,1.2858,3.9478,1.2856,4.1115,0.5803,4.1108);..."
          },
          "BillingAddressRecipient": {
            "type": "string",
            "valueString": "Microsoft Finance",
            "spans": [
              {
                "offset": 307,
                "length": 17
              }
            ],
            "confidence": 0.815,
            "source": "D(1,0.5734,3.7392,1.8060,3.7521,1.8043,3.9201,0.5717,3.9072)"
          },
          "CountryRegion": {
            "type": "string",
            "valueString": "USA"
          },
          "CustomerAddress": {
            "type": "string",
            "valueString": "123 Other St, Redmond WA, 98052",
            "spans": [
              "..."
            ],
            "confidence": 0.744,
            "source": "..."
          },
          "CustomerAddressRecipient": {
            "type": "string",
            "valueString": "Microsoft Corp",
            "spans": [
              "..."
            ],
            "confidence": 0.437,
            "source": "..."
          },
          "CustomerId": {
            "type": "string",
            "valueString": "CID-12345",
            "spans": [
              "..."
            ],
            "confidence": 0.936,
            "source": "..."
          },
          "CustomerName": {
            "type": "string",
            "valueString": "MICROSOFT CORPORATION",
            "spans": [
              "..."
            ],
            "confidence": 0.46,
            "source": "..."
          },
          "CustomerTaxId": {
            "type": "string",
            "confidence": 0.912
          },
          "DueDate": {
            "type": "date",
            "valueDate": "2019-12-15",
            "spans": [
              "..."
            ],
            "confidence": 0.97,
            "source": "..."
          },
          "InvoiceDate": {
            "type": "date",
            "valueDate": "2019-11-15",
            "spans": [
              "..."
            ],
            "confidence": 0.939,
            "source": "..."
          },
          "InvoiceId": {
            "type": "string",
            "valueString": "INV-100",
            "spans": [
              "..."
            ],
            "confidence": 0.733,
            "source": "..."
          },
          "LineItems": {
            "type": "array",
            "valueArray": [
              {
                "type": "object",
                "valueObject": {
                  "Date": {
                    "type": "date",
                    "valueDate": "2021-03-04",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.894,
                    "source": "..."
                  },
                  "Description": {
                    "type": "string",
                    "valueString": "Consulting Services",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.589,
                    "source": "..."
                  },
                  "ProductCode": {
                    "type": "string",
                    "valueString": "A123",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.879,
                    "source": "..."
                  },
                  "Quantity": {
                    "type": "number",
                    "valueNumber": 2,
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.939,
                    "source": "..."
                  },
                  "QuantityUnit": {
                    "type": "string",
                    "valueString": "hours",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.85,
                    "source": "..."
                  },
                  "TaxAmount": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 6,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.522,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  },
                  "TaxRate": {
                    "type": "number",
                    "confidence": 0.915
                  },
                  "TotalAmount": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 60,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.972,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  },
                  "UnitPrice": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 30,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.97,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  }
                }
              },
              "... (2 additional line items)"
            ]
          }
          /*additional fields omitted*/
        },
        "kind": "document",
        "startPageNumber": 1,
        "endPageNumber": 1,
        "unit": "inch",
        "pages": [
          {
            "pageNumber": 1,
            "angle": 0,
            "width": 8.5,
            "height": 11,
            "words": [
              "... (words omitted for brevity)"
            ],
            "selectionMarks": [],
            "lines": [
              "... (lines omitted for brevity)"
            ],
            "barcodes": [],
            "formulas": []
          }
        ],
        "tables": [
          "... (tables omitted for brevity)"
        ],
        "keyValuePairs": [
          "... (key-value pairs omitted for brevity)"
        ],
        "analyzerId": "prebuilt-invoice",
        "mimeType": "application/pdf"
      }
    ]
  },
  "usage": {
    "documentStandardPages": 1,
    "contextualizationTokens": 2345,
    "tokens": {
      "gpt-4.1-mini-input": 1234,
      "gpt-4.1-mini-output": 567
    }
  }
}

{
  "id": "fe6bb69d-1d6b-4698-a50c-ce798bacdd95",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-imageSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T20:11:50Z",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "![image](pages/1)\n",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The image is a 3D pie chart illustrating the distribution of hours spent in four different ranges. The largest segment, colored purple, represents 60+ hours at 37.8%. The second largest segment, in teal, represents 50-60 hours at 36.6%. The red segment shows 40-50 hours at 18.9%, and the smallest orange segment represents 1-39 hours at 6.7%. Each segment is labeled with its corresponding range and percentage."
          }
        },
        "kind": "document",
        "startPageNumber": 1,
        "endPageNumber": 1,
        "unit": "pixel",
        "pages": [
          {
            "pageNumber": 1,
            "spans": []
          }
        ],
        "analyzerId": "prebuilt-imageSearch",
        "mimeType": "image/jpeg"
      }
    ]
  },
  "usage": {
    "contextualizationTokens": 1000,
    "tokens": {
      "gpt-4.1-mini-input": 199,
      "gpt-4.1-mini-output": 106
    }
  }
}

{
  "id": "<request-id>",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-audioSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "YYYY-MM-DDTHH:MM:SSZ",
    "stringEncoding": "utf8",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "# Audio: 00:00.000 => 01:54.670\n\nTranscript\n```\nWEBVTT\n\n00:00.080 --> 00:02.160\n<v Speaker 1>Thank you for calling Woodgrove Travel...",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "John Smith contacted Woodgrove Travel to report a negative experience with his flight from New York City to Los Angeles..."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 0,
        "endTimeMs": 114670,
        "analyzerId": "prebuilt-audioSearch",
        "mimeType": "audio/wav"
      }
    ]
  },
  "usage": {
		"audioHours": 0.032,
    "contextualization": 3194.445
    "tokens": {
      "gpt-4.1-input": 1234, 
      "gpt-4.1-output": 2345,
      "text-embedding-3-large": 3456 
    }
 
	}
}

{
  "id": "2689a699-fa3a-4ddf-9a27-c34ceaa6c597",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-videoSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T16:11:17Z",
    "warnings": [],
    "contents": [
      {
        "markdown": "# Video: 00:00.733 => 00:15.467\nWidth: 1080\nHeight: 608\n\nTranscript\n```\nWEBVTT\n\n00:01.360 --> 00:06.640\n<Speaker 1>When it comes to the neural TTS, in order to get a good voice, it's better to have good data.\n\n00:07.120 --> 00:13.320\n<Speaker 2>To achieve that, we build a universal TTS model based on 3,000 hours of data.\n\n00:13.440 --> 00:23.680\n<Speaker 1>We actually accumulated tons of the data so that this universal model is able to capture the nuance of the audio and generate a more natural voice for the algorithm.\n```\n\nKey Frames\n- 00:00.733 ![](keyFrame.733.jpg)\n- 00:02.067 ![](keyFrame.2067.jpg)\n...\n- 00:14.833 ![](keyFrame.14833.jpg)\n- 00:15.467 ![](keyFrame.15467.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The video opens with a Flight Simulator logo alongside Microsoft Azure AI branding, followed by an interview with a man discussing the importance of good data for neural text-to-speech (TTS) technology. He explains the creation of a universal TTS model trained on 3,000 hours of data to capture audio nuances and produce natural-sounding voices. Visuals include audio waveform displays and shots of data centers and server rooms, emphasizing the technological infrastructure behind the TTS model."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 733,
        "endTimeMs": 15467,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          733,
          2067
          /*... (14 additional keyframes)*/
        ],
        "transcriptPhrases": [
          {
            "speaker": "Speaker 1",
            "startTimeMs": 1360,
            "endTimeMs": 6640,
            "text": "When it comes to the neural TTS, in order to get a good voice, it's better to have good data.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 1360,
                "endTimeMs": 1600,
                "text": "When"
              },
              {
                "startTimeMs": 1600,
                "endTimeMs": 1760,
                "text": "it"
              }
              /*... (18 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 2",
            "startTimeMs": 7120,
            "endTimeMs": 13320,
            "text": "To achieve that, we build a universal TTS model based on 3,000 hours of data.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 7120,
                "endTimeMs": 7360,
                "text": "To"
              },
              {
                "startTimeMs": 7560,
                "endTimeMs": 7880,
                "text": "achieve"
              }
              /*... (13 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 1",
            "startTimeMs": 13440,
            "endTimeMs": 23680,
            "text": "We actually accumulated tons of the data so that this universal model is able to capture the nuance of the audio and generate a more natural voice for the algorithm.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 13440,
                "endTimeMs": 13600,
                "text": "We"
              },
              {
                "startTimeMs": 13600,
                "endTimeMs": 14000,
                "text": "actually"
              }
              /*... (28 additional words)*/
            ],
            "locale": "en-US"
          }
        ],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ],
        "mimeType": "video/x-m4v"
      },
      {
        "markdown": "# Video: 00:15.467 => 00:23.100\nWidth: 1080\nHeight: 608\n\n\n\nKey Frames\n- 00:15.467 ![](keyFrame.15467.jpg)\n- 00:16.933 ![](keyFrame.16933.jpg)\n...\n- 00:22.367 ![](keyFrame.22367.jpg)\n- 00:23.100 ![](keyFrame.23100.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The video transitions to scenic aerial views from the Flight Simulator, showcasing detailed landscapes including coastlines, mountains, and castles. This segment highlights the realistic graphics and immersive experience of the Flight Simulator, demonstrating the integration of advanced AI technologies to enhance the simulation."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 15467,
        "endTimeMs": 23100,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          15467,
          16933
          /*... (7 additional keyframes)*/
        ],
        "transcriptPhrases": [],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ]
      },
      {
        "markdown": "# Video: 00:23.100 => 00:43.233\nWidth: 1080\nHeight: 608\n\nTranscript\n```\nWEBVTT\n\n00:24.040 --> 00:29.120\n<Speaker 3>What we liked about cognitive services offerings were that they had a much higher fidelity.\n\n00:29.600 --> 00:32.880\n<Speaker 3>And they sounded a lot more like an actual human voice.\n\n00:33.680 --> 00:37.200\n<Speaker 4>Orlando ground 9555 requesting the end of pushback.\n\n00:38.680 --> 00:41.280\n<Speaker 4>9555 request to end pushback received.\n```\n\nKey Frames\n- 00:23.100 ![](keyFrame.23100.jpg)\n- 00:24.833 ![](keyFrame.24833.jpg)\n...\n- 00:42.633 ![](keyFrame.42633.jpg)\n- 00:43.233 ![](keyFrame.43233.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The focus shifts back to an interview with another man discussing the high fidelity and human-like quality of voices produced by cognitive services offerings. The segment concludes with visuals of an airplane on the tarmac, ground crew directing the plane, and the plane preparing for pushback, accompanied by realistic audio communications between ground control and the aircraft."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 23100,
        "endTimeMs": 43233,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          23100,
          24833
          /*... (19 additional keyframes)*/
        ],
        "transcriptPhrases": [
          {
            "speaker": "Speaker 3",
            "startTimeMs": 24040,
            "endTimeMs": 29120,
            "text": "What we liked about cognitive services offerings were that they had a much higher fidelity.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 24040,
                "endTimeMs": 24240,
                "text": "What"
              },
              {
                "startTimeMs": 24240,
                "endTimeMs": 24320,
                "text": "we"
              }
              /*... (13 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 3",
            "startTimeMs": 29600,
            "endTimeMs": 32880,
            "text": "And they sounded a lot more like an actual human voice.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 29600,
                "endTimeMs": 30080,
                "text": "And"
              },
              {
                "startTimeMs": 30080,
                "endTimeMs": 30160,
                "text": "they"
              }
              /*... (9 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 4",
            "startTimeMs": 33680,
            "endTimeMs": 37200,
            "text": "Orlando ground 9555 requesting the end of pushback.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 33680,
                "endTimeMs": 34160,
                "text": "Orlando"
              },
              {
                "startTimeMs": 34160,
                "endTimeMs": 34600,
                "text": "ground"
              }
              /*... (6 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 4",
            "startTimeMs": 38680,
            "endTimeMs": 41280,
            "text": "9555 request to end pushback received.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 38680,
                "endTimeMs": 39600,
                "text": "9555"
              },
              {
                "startTimeMs": 39600,
                "endTimeMs": 40080,
                "text": "request"
              }
              /*... (4 additional words)*/
            ],
            "locale": "en-US"
          }
        ],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ]
      }
    ]
  },
  "usage": {
    "videoHours": 0.013,
    "contextualizationTokens": 12222,
    "tokens": {
      "gpt-4.1-mini-input": 8976,
      "gpt-4.1-mini-output": 439
    }
  }
}

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Last updated on 2025-12-19