Merge pull request #2451 from MicrosoftDocs/main

1/23/2025 11:00 AM IST Publish

Author: PhilKang0704

articles/ai-services/document-intelligence/containers/disconnected.md

@@ -204,14 +204,14 @@ services:
     apikey: ${FORM_RECOGNIZER_KEY}
     billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
     Logging:Console:LogLevel:Default: Information
-    SharedRootFolder: /shared
-    Mounts:Shared: /shared
+    SharedRootFolder: /share
+    Mounts:Shared: /share
     Mounts:Output: /logs
     Mounts:License: /license
   volumes:
     - type: bind
       source: ${SHARED_MOUNT_PATH}
-      target: /shared
+      target: /share
     - type: bind
       source: ${OUTPUT_MOUNT_PATH}
       target: /logs
@@ -233,14 +233,14 @@ services:
     apikey: ${FORM_RECOGNIZER_KEY}
     billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
     Logging:Console:LogLevel:Default: Information
-    SharedRootFolder: /shared
-    Mounts:Shared: /shared
+    SharedRootFolder: /share
+    Mounts:Shared: /share
     Mounts:Output: /logs
     Mounts:License: /license
   volumes:
     - type: bind
       source: ${SHARED_MOUNT_PATH}
-      target: /shared
+      target: /share
     - type: bind
       source: ${OUTPUT_MOUNT_PATH}
       target: /logs

articles/ai-services/document-intelligence/containers/install-run.md

@@ -1,12 +1,12 @@
 ---
-title: Install and run Docker containers for Document Intelligence 
+title: Install and run Docker containers for Document Intelligence
 titleSuffix: Azure AI services
 description: Use the Docker containers for Document Intelligence on-premises to identify and extract key-value pairs, selection marks, tables, and structure from forms and documents.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: how-to
-ms.date: 11/19/2024
+ms.date: 01/22/2025
 ms.author: lajanuar
 ---
 
@@ -272,13 +272,13 @@ services:
         - AzureCognitiveServiceReadHost=http://azure-cognitive-service-read:5000
     ports:
           - "5000:5050"
-    azure-cognitive-service-read:
-      container_name: azure-cognitive-service-read
-      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
-      environment:
-          - EULA=accept
-          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
-          - apiKey={FORM_RECOGNIZER_KEY}
+  azure-cognitive-service-read:
+    container_name: azure-cognitive-service-read
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
+    environment:
+      - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
 ```
 
 Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
@@ -345,6 +345,7 @@ services:
         - apiKey={FORM_RECOGNIZER_KEY}
 ```
 
+
 ### [Custom](#tab/custom)
 
 In addition to the [prerequisites](#prerequisites), you need to do the following to process a custom document:
@@ -385,23 +386,25 @@ In addition to the [prerequisites](#prerequisites), you need to do the following
 
   1. Declare the following environment variables:
 
-  ```text
+```bash
+
+
 SHARED_MOUNT_PATH="./share"
 OUTPUT_MOUNT_PATH="./output"
 FILE_MOUNT_PATH="./files"
 DB_MOUNT_PATH="./db"
 FORM_RECOGNIZER_ENDPOINT_URI="YourFormRecognizerEndpoint"
 FORM_RECOGNIZER_KEY="YourFormRecognizerKey"
 NGINX_CONF_FILE="./nginx.conf"
-  ```
+```
 
 #### Create an **nginx** file
 
   1. Name this file **nginx.conf**.
 
   1. Enter the following configuration:
 
-```text
+```bash
 worker_processes 1;
 
 events { worker_connections 1024; }
@@ -443,6 +446,10 @@ http {
             proxy_pass http://docker-custom/swagger;
         }
 
+        location /api-docs {
+            proxy_pass http://docker-custom/api-docs;
+        }
+
         location /formrecognizer/documentModels/prebuilt-layout {
             proxy_set_header Host $host:$server_port;
             proxy_set_header Referer $scheme://$host:$server_port;
@@ -491,6 +498,9 @@ http {
 }
 
 ```
+::: moniker-end
+
+:::moniker range="<=doc-intel-3.0.0"
 
 #### Create a **docker compose** file
 
@@ -506,7 +516,94 @@ services:
     container_name: reverseproxy
     depends_on:
       - layout
-      - custom-template  
+      - custom-template
+    volumes:
+      - ${NGINX_CONF_FILE}:/etc/nginx/nginx.conf
+    ports:
+      - "5000:5000"
+  layout:
+    container_name: azure-cognitive-service-layout
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0:latest
+    environment:
+      eula: accept
+      apikey: ${FORM_RECOGNIZER_KEY}
+      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
+      Logging:Console:LogLevel:Default: Information
+      SharedRootFolder: /share
+      Mounts:Shared: /share
+      Mounts:Output: /logs
+    volumes:
+      - type: bind
+        source: ${SHARED_MOUNT_PATH}
+        target: /share
+      - type: bind
+        source: ${OUTPUT_MOUNT_PATH}
+        target: /logs
+    expose:
+      - "5000"
+
+  custom-template:
+    container_name: azure-cognitive-service-custom-template
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/custom-template-3.0:latest
+    restart: always
+    depends_on:
+      - layout
+    environment:
+      AzureCognitiveServiceLayoutHost: http://azure-cognitive-service-layout:5000
+      eula: accept
+      apikey: ${FORM_RECOGNIZER_KEY}
+      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
+      Logging:Console:LogLevel:Default: Information
+      SharedRootFolder: /share
+      Mounts:Shared: /share
+      Mounts:Output: /logs
+    volumes:
+      - type: bind
+        source: ${SHARED_MOUNT_PATH}
+        target: /share
+      - type: bind
+        source: ${OUTPUT_MOUNT_PATH}
+        target: /logs
+    expose:
+      - "5000"
+
+  studio:
+    container_name: form-recognizer-studio
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.0
+    environment:
+      ONPREM_LOCALFILE_BASEPATH: /onprem_folder
+      STORAGE_DATABASE_CONNECTION_STRING: /onprem_db/Application.db
+    volumes:
+      - type: bind
+        source: ${FILE_MOUNT_PATH} # path to your local folder
+        target: /onprem_folder
+      - type: bind
+        source: ${DB_MOUNT_PATH} # path to your local folder
+        target: /onprem_db
+    ports:
+      - "5001:5001"
+    user: "1000:1000" # echo $(id -u):$(id -g)
+
+ ```
+::: moniker-end
+
+:::moniker range=">=doc-intel-3.1.0"
+
+#### Create a **docker compose** file
+
+1. Name this file **docker-compose.yml**
+
+2. The following code sample is a self-contained `docker compose` example to run Document Intelligence Layout, Studio, and Custom template containers together. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration.
+
+```yml
+version: '3.3'
+services:
+  nginx:
+    image: nginx:alpine
+    container_name: reverseproxy
+    depends_on:
+      - layout
+      - custom-template
     volumes:
       - ${NGINX_CONF_FILE}:/etc/nginx/nginx.conf
     ports:
@@ -559,7 +656,7 @@ services:
 
   studio:
     container_name: form-recognizer-studio
-    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.0
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.1
     environment:
       ONPREM_LOCALFILE_BASEPATH: /onprem_folder
       STORAGE_DATABASE_CONNECTION_STRING: /onprem_db/Application.db
@@ -575,6 +672,7 @@ services:
     user: "1000:1000" # echo $(id -u):$(id -g)
 
  ```
+::: moniker-end
 
 The custom template container and Layout container can use Azure Storage queues or in memory queues. The `Storage:ObjectStore:AzureBlob:ConnectionString` and `queue:azure:connectionstring` environment variables only need to be set if you're using Azure Storage queues. When running locally, delete these variables.
 
@@ -635,20 +733,21 @@ $b64String = [System.Convert]::ToBase64String($bytes, [System.Base64FormattingOp
 Use the build model API to post the request.
 
 ```http
-POST http://localhost:5000/formrecognizer/documentModels:build?api-version=2023-07-31
-
-{
-    "modelId": "mymodel",
-    "description": "test model",
-    "buildMode": "template",
-
-    "base64Source": "<Your base64 encoded string>",
-    "tags": {
-       "additionalProp1": "string",
-       "additionalProp2": "string",
-       "additionalProp3": "string"
-     }
-}
+
+  POST http://localhost:5000/formrecognizer/documentModels:build?api-version=2023-07-31
+
+  {
+      "modelId": "mymodel",
+      "description": "test model",
+      "buildMode": "template",
+
+      "base64Source": "<Your base64 encoded string>",
+      "tags": {
+         "additionalProp1": "string",
+         "additionalProp2": "string",
+         "additionalProp3": "string"
+       }
+  }
 ```
 
 ---
@@ -720,4 +819,4 @@ That's it! In this article, you learned concepts and workflows for downloading,
 * [Document Intelligence container configuration settings](configuration.md)
 
 * [Azure container instance recipe](../../../ai-services/containers/azure-container-instance-recipe.md)
-::: moniker-end
+

articles/ai-services/document-intelligence/train/custom-model.md

@@ -95,7 +95,7 @@ If the language of your documents and extraction scenarios supports custom neura
 
 * For custom model training, the maximum number of pages for training data is 500 for the custom template model and 50,000 for the custom neural model.
 
-* For custom extraction model training, the total size of training data is 50 MB for template model and 1G-MB for the neural model.
+* For custom extraction model training, the total size of training data is 50 MB for template model and 1GB for the neural model.
 
 * For custom classification model training, the total size of training data is `1GB`  with a maximum of 10,000 pages.
 

articles/ai-services/openai/how-to/gpt-with-vision.md

@@ -13,17 +13,13 @@ manager: nitinme
 # Use vision-enabled chat models
 
 
-Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are GPT-4 Turbo with Vision, GPT-4o, and GPT-4o-mini.
+Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are [o1](./reasoning.md), GPT-4o, and GPT-4o-mini, GPT-4 Turbo with Vision.
 
 The vision-enabled models answer general questions about what's present in the images you upload.
 
 > [!TIP]
 > To use vision-enabled models, you call the Chat Completion API on a supported model that you have deployed. If you're not familiar with the Chat Completion API, see the [Vision-enabled chat how-to guide](/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions).
 
-## GPT-4 Turbo model upgrade
-
-[!INCLUDE [GPT-4 Turbo](../includes/gpt-4-turbo.md)]
-
 ## Call the Chat Completion APIs
 
 The following command shows the most basic way to use the GPT-4 Turbo with Vision model with code. If this is your first time using these models programmatically, we recommend starting with our [GPT-4 Turbo with Vision quickstart](../gpt-v-quickstart.md). 
@@ -39,8 +35,6 @@ Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployme
 - `Content-Type`: application/json 
 - `api-key`: {API_KEY} 
 
-
-
 **Body**: 
 The following is a sample request body. The format is the same as the chat completions API for GPT-4, except that the message content can be an array containing text and images (either a valid HTTP or HTTPS URL to an image, or a base-64-encoded image). 
 
@@ -368,6 +362,11 @@ Every response includes a `"finish_reason"` field. It has the following possible
     ```
 -->
 
+## GPT-4 Turbo model upgrade
+
+[!INCLUDE [GPT-4 Turbo](../includes/gpt-4-turbo.md)]
+
+
 ## Next steps
 
 * [Learn more about Azure OpenAI](../overview.md).

articles/ai-services/openai/how-to/reasoning.md

@@ -47,9 +47,10 @@ Once access has been granted, you'll need to create a deployment for each model.
 | **[Structured Outputs](./structured-outputs.md)** | ✅ | - | - |
 | **[Context Window](../concepts/models.md#o1-and-o1-mini-models-limited-access)** | Input: 200,000 <br> Output: 100,000 | Input: 128,000  <br> Output: 32,768 | Input: 128,000  <br> Output: 65,536 |
 | **[Reasoning effort](#reasoning-effort)** | ✅ | - | - |
-| System Messages | - | - | - |
+| **[Vision Support](./gpt-with-vision.md)** |✅ | - | - |
 | Functions/Tools | ✅  | -  |  - |
 | `max_completion_tokens` |✅ |✅ |✅ |
+| System Messages | - | - | - |
 
 **o1 series** models will only work with the `max_completion_tokens` parameter.