AI VibeFlow overview

Core concepts

• DataPipeline: the core management object in AI VibeFlow. A DataPipeline corresponds to a single vectorization rule and runs automatically after it is created.

• Source data: defines the source of the raw data to be vectorized, including the source bucket, object prefix, file types (text, image, and video), and processing mode (existing data, incremental data, or both).

• Vectorization settings: define the vector model source, Model Studio API key, vector model, video frame capture frequency (available only for multimodal models), and whether to cascade the deletion of the corresponding vectors when the source object is deleted.

• Destination data: defines the destination vector bucket, vector index, and vector key naming rule to which the vector data is written, and whether to write ObjectTag and UserMeta as scalar metadata.

• Exception handling: defines the processing policy when a single vectorization task fails (skip and continue, skip and log, or terminate immediately), and lets you specify the bucket and prefix that receive error messages.

Benefits

• End-to-end workflow: massive volumes of raw unstructured data, the vectorization process, and vector data storage are all handled within OSS in a closed loop, which simplifies the production pipeline.

• Automatic processing: supports both batch vectorization of existing data and automatic vectorization of newly uploaded data, without manual intervention.

• Synchronized data updates: when raw data in the object bucket is modified, overwritten, or deleted, you can choose to synchronously update and delete the corresponding vector data in the vector bucket, which reduces manual maintenance costs.

• Flexible vector models: directly calls the text and multimodal vector models provided by Alibaba Cloud Model Studio (such as text-embedding-v4 and tongyi-embedding-vision-plus), so you can select the appropriate model and dimensions based on your business needs.

• Transparent costs: vectorization uses your own Model Studio API key, and embedding fees are charged directly by Model Studio. On the OSS side, you are charged only for API requests and vector storage, with no additional service fees.

• Traceable failures: for each failed request, information such as the ErrorCode, ErrorMessage, and Model Studio RequestId can be saved as an object to a specified bucket, which makes it easier to locate issues and retry.

Scenarios

AI VibeFlow is suitable for all scenarios in which you need to vectorize massive volumes of unstructured data in OSS. Typical use cases include:

• Semantic retrieval and RAG: vectorize documents, images, and videos in an OSS object bucket and write the vectors to a vector bucket, and then combine them with large language models to build Retrieval-Augmented Generation (RAG) applications.

• E-commerce image and text search: perform multimodal vectorization of product images and descriptions and write the vectors to a vector bucket to support image-to-image and text-to-image search.

• Advertising and marketing asset management: automatically vectorize marketing images and videos to enable semantic recall of assets and recommendation of similar content.

• Enterprise knowledge base construction: vectorize internal knowledge documents and question bank images and write the vectors to a vector bucket to enable unified semantic retrieval across business lines.

• Multimedia content governance: combine vector similarity with scalar metadata filtering to enable fine-grained management and distribution of image and video assets.

Billing

Using AI VibeFlow incurs the following fees, which are charged separately by OSS and Alibaba Cloud Model Studio:

Limits

• A single account can create up to 1,000 DataPipelines in a single region.

• A DataPipeline corresponds to one vector model and one vector index.

• The source object bucket, the destination vector bucket, and the bucket that receives error messages must be located in the same region.

• The maximum capacity of a single vector index is 2 billion rows. After the index is full, the pipeline automatically enters the Paused state.

• When ObjectTag and UserMeta are extracted as vector metadata, you can specify up to 10 keys for each.

Permissions

When you create a DataPipeline, you must specify a RAM role that OSS uses to run vectorization tasks in the background. AI VibeFlow uses a dedicated trust policy (datapipeline-oss.aliyuncs.com) and cannot reuse other service roles such as AliyunOSSRole.

{
  "Statement": [
    {
      "Action": "ram:PassRole",
      "Effect": "Allow",
      "Resource": "acs:ram::<account-id>:role/oss-embedding-*",
      "Condition": {
        "StringEquals": {
          "acs:Service": "datapipeline-oss.aliyuncs.com"
        }
      }
    }
  ],
  "Version": "1"
}

{
  "Statement": [
    {
      "Action": "sts:AssumeRole",
      "Effect": "Allow",
      "Resource": "acs:ram::<account-id>:role/<RoleName>"
    }
  ],
  "Version": "1"
}

{
  "Version": "1",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "ram:CreateRole",
        "ram:CreatePolicy",
        "ram:AttachPolicyToRole"
      ],
      "Resource": "*"
    }
  ]
}

{
  "Version": "1",
  "Statement": [
    {
      "Action": [
        "oss:GetObject",
        "oss:GetObjectTagging",
        "oss:GetBucketStat",
        "oss:ListObjects"
      ],
      "Resource": [
        "acs:oss:*:*:<source-bucket-name>",
        "acs:oss:*:*:<source-bucket-name>/*"
      ],
      "Effect": "Allow"
    },
    {
      "Action": [
        "oss:GetVectorIndex",
        "oss:PutVectors",
        "oss:DeleteVectors"
      ],
      "Resource": [
        "acs:ossvector:*:*:<vector-bucket-name>",
        "acs:ossvector:*:*:<vector-bucket-name>/<vector-index-name>"
      ],
      "Effect": "Allow"
    },
    {
      "Action": [
        "oss:PutObject",
        "oss:GetBucketStat"
      ],
      "Resource": [
        "acs:oss:*:*:<error-output-bucket-name>",
        "acs:oss:*:*:<error-output-bucket-name>/*"
      ],
      "Effect": "Allow"
    }
  ]
}

{
  "Statement": [
    {
      "Action": "sts:AssumeRole",
      "Effect": "Allow",
      "Principal": {
        "Service": [
          "datapipeline-oss.aliyuncs.com"
        ]
      }
    }
  ],
  "Version": "1"
}

References

• AI VibeFlow getting started

• Use AI VibeFlow to produce vector data

• OSS Vectors overview

• Text and multimodal vectorization with Model Studio