创建托管式知识库

以下是使用带有 AWS CLI 或支持的 SDK（例如 Python）的 API 创建托管知识库和配置数据源的示例。致电后 CreateKnowledgeBase，您致电CreateDataSource以创建包含连接信息的数据源dataSourceConfiguration。

要了解可以通过添加可选的 vectorIngestionConfiguration 字段来应用于摄取的定制设置，请参阅自定义数据来源的摄取。

AWS Command Line Interface

第 1 步：创建知识库

使用托管嵌入模型（默认）：

aws bedrock-agent create-knowledge-base \
 --name "my-managed-kb" \
 --role-arn "arn:aws:iam::123456789012:role/BedrockKBRole" \
 --description "My managed knowledge base" \
 --knowledge-base-configuration file://kb-config.json

kb-config.json
{
    "type": "MANAGED",
    "managedKnowledgeBaseConfiguration": {
        "embeddingModelType": "MANAGED"
    }
}

使用自定义嵌入模型（客户提供的基岩模型）：

aws bedrock-agent create-knowledge-base \
 --name "my-custom-embed-kb" \
 --role-arn "arn:aws:iam::123456789012:role/BedrockKBRole" \
 --description "My managed knowledge base with custom embedding" \
 --knowledge-base-configuration file://kb-config.json

kb-config.json
{
    "type": "MANAGED",
    "managedKnowledgeBaseConfiguration": {
        "embeddingModelType": "CUSTOM",
        "embeddingModelArn": "arn:aws:bedrock:us-west-2::foundation-model/amazon.titan-embed-text-v2:0",
        "embeddingModelConfiguration": {
            "bedrockEmbeddingModelConfiguration": {
                "dimensions": 1024
            }
        }
    }
}

使用自定义的多模态嵌入模型 (TwelveLabs Marengo Embed 3.0)：

多模态嵌入模型与文本嵌入模型CUSTOMembeddingModelType相同。区别在于，bedrockEmbeddingModelConfiguration接受一个附加modelConfiguration字段，在其中传递特定模型的设置，并且必须supplementalDataStorageConfiguration为多模式存储目的地指定。

aws bedrock-agent create-knowledge-base \
 --name "my-multimodal-embed-kb" \
 --role-arn "arn:aws:iam::123456789012:role/BedrockKBRole" \
 --description "My managed knowledge base with multimodal embedding" \
 --knowledge-base-configuration file://kb-config.json

kb-config.json
{
    "type": "MANAGED",
    "managedKnowledgeBaseConfiguration": {
        "embeddingModelType": "CUSTOM",
        "embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/twelvelabs.marengo-embed-3-0-v1:0",
        "embeddingModelConfiguration": {
            "bedrockEmbeddingModelConfiguration": {
                "embeddingDataType": "FLOAT",
                "modelConfiguration": {
                    "version": "1",
                    "audio": {
                        "segmentation": {
                            "method": "dynamic",
                            "dynamic": {
                                "minDurationSec": 4
                            }
                        }
                    },
                    "video": {
                        "segmentation": {
                            "method": "fixed",
                            "fixed": {
                                "durationSec": 6
                            }
                        }
                    }
                }
            }
        },
        "supplementalDataStorageConfiguration": {
            "storageLocations": [
                {
                    "s3Location": {
                        "uri": "s3://amzn-s3-demo-bucket"
                    },
                    "type": "S3"
                }
            ]
        }
    }
}

该modelConfiguration字段是一个 JSON 对象，其内容特定于您选择的嵌入模型。该version字段为必填字段，必须设置为1。对于每种audio和video模式，您可以设置一个 segmentationmethod（取一个minDurationSec值）或fixed（取一个durationSec值）。dynamic有关TwelveLabs Marengo Embed 3.0接受的设置，请参阅TwelveLabs Marengo 嵌入 3.0。

步骤 2：创建数据源

aws bedrock-agent create-data-source \
 --name "S3-connector" \
 --description "S3 data source connector for Amazon Bedrock to use content in S3" \
 --knowledge-base-id "your-knowledge-base-id" \
 --data-source-configuration file://bedrock-s3-managed-connector-configuration.json \
 --data-deletion-policy "DELETE" \
 --vector-ingestion-configuration '{"parsingConfiguration":{"parsingStrategy":"SMART_PARSING"}}'

bedrock-s3-managed-connector-configuration.json
{
    "type": "MANAGED_KNOWLEDGE_BASE_CONNECTOR",
    "managedKnowledgeBaseConnectorConfiguration": {
        "mediaExtractionConfiguration": {
            "imageExtractionConfiguration": {
                "imageExtractionStatus": "ENABLED"
            }
        },
        "connectorParameters": {
            "type": "S3",
            "version": "1",
            "connectionConfiguration": {
                "bucketName": "your-test-s3-bucket",
                "bucketOwnerAccountId": "123456789012"
            },
            "deletionProtectionConfiguration": {
                "enableDeletionProtection": false
            }
        }
    }
}