Szybki start: korzystanie z usługi Azure Content Understanding w interfejsie API REST narzędzi Foundry

Ten szybki start pokazuje, jak używać interfejsu API REST usługi Content Understanding do pozyskiwania uporządkowanych danych z wielomodalnej zawartości dokumentów, obrazów, dźwięków i plików wideo.

Wymagania wstępne

Aby rozpocząć pracę, potrzebujesz aktywnej subskrypcji platformy Azure. Jeśli nie masz konta platformy Azure, utwórz je bezpłatnie.
Po utworzeniu subskrypcji platformy Azure utwórz zasób Microsoft Foundry w witrynie Azure Portal. Pamiętaj, aby utworzyć go w obsługiwanym regionie.
Skonfiguruj domyślne wdrożenia modelu dla zasobu Content Understanding. Ustawienie wartości domyślnych powoduje utworzenie połączenia z modelami usługi Foundry używanymi dla żądań usługi Content Understanding. Wybierz jedną z poniższych metod:
- Portal
- API REST
1. Przejdź do strony Ustawień usługi Content Understanding
2. Wybierz przycisk "+ Dodaj zasób" w lewym górnym rogu
3. Wybierz zasób Foundry, którego chcesz użyć, a następnie kliknij przycisk Dalej, a następnie pozycję Zapisz
  - Pamiętaj, aby pozostawić pole wyboru "Włącz automatyczne wdrażanie dla wymaganych modeli, jeśli nie są dostępne żadne wartości domyślne". Dzięki temu zasób jest w pełni skonfigurowany z wymaganymi modelami GPT-4.1, GPT-4.1-mini oraz modelami text-embedding-3-large. Różne wstępnie utworzone analizatory wymagają różnych modeli.
Wykonując te kroki, skonfigurujesz połączenie między modelami Content Understanding i Foundry w zasobie usługi Foundry.
1. Utwórz wdrożenia Modeli Foundry GPT-4.1, GPT-4.1-mini i text-embedding-3-large w zasobach Foundry. Aby uzyskać szczegółowe informacje na temat wdrażania tych modeli, zobacz Tworzenie wdrożeń modeli w portalu Microsoft Foundry. Różne wstępnie utworzone analizatory wymagają różnych modeli, dlatego należy wdrożyć wszystkie trzy.
2. Zdefiniuj domyślne wdrożenia modelu na poziomie zasobu.
  
  Przed uruchomieniem następującego polecenia cURL wprowadź następujące zmiany w żądaniu HTTP:
  - Zastąp {endpoint} i {key} odpowiednimi wartościami z wystąpienia Foundry w portalu Azure.
  - Zastąp {myGPT41Deployment}, {myGPT41MiniDeployment} i {myEmbeddingDeployment} rzeczywistymi nazwami wdrożeń modelu z zasobu 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}"
        }
      }'
```
Wykonując te kroki, skonfigurujesz połączenie między modelami Content Understanding i Foundry w zasobie usługi Foundry.
W tym przewodniku użyjemy narzędzia wiersza polecenia cURL. Jeśli nie jest zainstalowany, możesz pobrać odpowiednią wersję dla środowiska deweloperskiego.

Rozpocznij korzystanie z wstępnie utworzonego analizatora

Analizatory definiują sposób przetwarzania zawartości oraz wyodrębniane szczegółowe informacje. Oferujemy wstępnie przygotowane analizatory dla typowych przypadków użycia. Możesz dostosować wstępnie utworzone analizatory , aby lepiej dopasować je do konkretnych potrzeb i przypadków użycia. Ten szybki start używa wstępnie skonfigurowanych analizatorów faktur, obrazów, dźwięków i wideo, aby ułatwić rozpoczęcie pracy.

Wysyłanie pliku do analizy

Przed uruchomieniem następującego polecenia cURL wprowadź następujące zmiany w żądaniu HTTP:

Zastąp {endpoint} i {key} odpowiednimi wartościami z wystąpienia Foundry w portalu Azure.

Żądanie POST

W tym przykładzie użyto analizatora prebuilt-invoice do wyodrębnienia danych strukturalnych z dokumentu faktury.

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"}]
      }'

W tym przykładzie użyto analizatora prebuilt-imageSearch do wygenerowania opisu obrazu.

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"
          }          
        ]
      }'

W tym przykładzie użyto analizatora prebuilt-audioSearch do wyodrębnienia transkrypcji audio, wygenerowania podsumowania i wykonania etykietowania osoby mówiącej.

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"
          }          
        ]
      }'

W tym przykładzie użyto analizatora prebuilt-videoSearch do wyodrębniania klatek kluczowych, transkrypcji i segmentów rozdziałów z wideo.

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"
          }          
        ]
      }'

Odpowiedź POST

Nagłówek odpowiedzi zawiera Operation-Location pole, które służy do pobierania wyników operacji analizy asynchronicznej.

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

Ważne

Skopiuj adres URL lokalizacji operacji z nagłówka odpowiedzi. Użyjesz tego adresu URL w następnym kroku, aby pobrać wyniki analizy.

Uzyskiwanie wyniku analizy

Użyj Operation-Location z POST odpowiedzi i pobierz wynik analizy.

Żądanie GET

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

Odpowiedź GET

Odpowiedź JSON 200 (OK) zawiera status pole wskazujące stan operacji. Jeśli operacja nie została ukończona, wartość to statusRunning lub NotStarted. W takich przypadkach należy ponownie wysłać GET żądanie ręcznie lub za pomocą skryptu. Poczekaj interwał co najmniej jednej sekundy między wywołaniami.

{
  "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)",
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            "text": "And they sounded a lot more like an actual human voice.",
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            "text": "Orlando ground 9555 requesting the end of pushback.",
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            "text": "9555 request to end pushback received.",
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                "text": "9555"
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                "text": "request"
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              /*... (4 additional words)*/
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}

Następny krok

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Last updated on 2025-11-20