AWSで最安値のRAGをワンクリックで構築できるようにしてみた

はじめに

昨今話題となっているRAGですが、RAGを構築するのに欠かせない存在がベクトルデータベースです。
このベクトルデータベースが高価なために、RAGのPoCが進まないだったり、気軽に使えないという方も多いのではないでしょうか。

AWSでベクトルデータベースとなると選択肢は以下でした。

• Kendra
• OpenSearch
• Aurora Postgres

Kendraはもちろん、OpenSearchも気軽に立てられるほど安価ではありません。
AuroraでACUの最小値を0にして使用するのが、PoC時最も安価に利用できる方法でした。

今回、AWSが発表したAmazon S3 Vectorsの登場により、RAGアプリケーションの構築がより気軽に行えるようになりました。
コストやパフォーマンスは概ね以下のようなイメージです。

これらをユースケースによって、使い分ける必要があります。
私は現状以下のようなユースケースを想定しています。

ベクトルデータベース	ユースケース
S3 Vectors	PoC 小規模なRAGアプリケーション とにかくコスト優先したい場合
Aurora	小規模から中規模なRAGアプリケーション VPC内にデータを保管したい場合
OpenSearch	中規模なRAGアプリケーション
Kendra	大規模なRAGアプリケーション

本記事では非常に安価なRAGアプリケーションをServiceCatalogからワンクリックで実装できるようにしたいと思います！

ワンクリックRAGアプリ

ユーザーはRAGのソースにしたいファイルをアップロードするだけで、新しいRAGチャットを作成することができます。

アプリケーション作成時に名前と説明、ソースとなるファイルをアップロードします。

アプリケーションを作成すると、Service Catalogの製品が起動し、新たなナレッジベースとS3 Vectors Bucketを作成します。
（構築時点でS3 Vectors Bucketはプレビューのため、バージニア北部で構築、また、CloudFormation未対応であるため、カスタムリソースで作成しています。）

Service Catalogの製品テンプレート

AWSTemplateFormatVersion: '2010-09-09'
Description: 'Minimum Custom RAG with S3 vectors'

Parameters:

  ApplicationName:
    Type: String
    Description: 'Name of the app'

  Boto3LayerArn:
    Type: String
    Default: 'arn:aws:lambda:us-east-1:123456789123:layer:boto3-139:1'

Resources:
  
  # S3 Vector and Knowledge Base Creation Role
  S3VectorKnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: S3VectorKnowledgeBasePolicy
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - s3vectors:*
                  - bedrock:*
                  - bedrock-agent:*
                  - iam:PassRole
                  - s3:*
                Resource: '*'

  # S3 Vector and Knowledge Base Creation Function
  S3VectorKnowledgeBaseFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: !Sub '${ApplicationName}-s3vector-kb-creator'
      Runtime: python3.13
      Handler: index.handler
      Role: !GetAtt S3VectorKnowledgeBaseRole.Arn
      Timeout: 600
      Layers:
        - !Ref Boto3LayerArn
      Code:
        ZipFile: !Sub |
          import boto3
          import cfnresponse
          import json
          import time

          class VectorBucket:
              def __init__(self, bucket_name):
                  self.bucket_name = bucket_name
                  self.client = boto3.client('s3vectors')

              def _create_vector_bucket(self):
                  try:
                      response = self.client.create_vector_bucket(
                          vectorBucketName=self.bucket_name,
                          encryptionConfiguration={
                              'sseType': 'AES256'
                          }
                      )
                  except Exception as e:
                      print(f"Error creating vector bucket: {e}")
                      return None
                  try:
                      response = self.client.list_vector_buckets(
                          prefix=self.bucket_name
                      )
                      return response['vectorBuckets'][0]
                  except Exception as e:
                      print(f"Error listing vector buckets: {e}")
                      return None

              def _create_vector_index(self):
                  try:
                      response = self.client.create_index(
                          vectorBucketName=self.bucket_name,
                          indexName=f"{self.bucket_name}-index",
                          dataType='float32',
                          dimension=1024,
                          distanceMetric='cosine',
                          metadataConfiguration={
                            'nonFilterableMetadataKeys': [
                                'AMAZON_BEDROCK_TEXT',
                            ]
                          }
                      )
                  except Exception as e:
                      if 'ConflictException' in str(e):
                          print("Index already exists, proceeding to list indexes.")
                          pass
                      else:
                          print(f"Error creating vector index: {e}")
                          return None
                  try:
                      response = self.client.list_indexes(
                          vectorBucketName=self.bucket_name,
                          prefix=f"{self.bucket_name}-index"
                      )
                      return response['indexes'][0]
                  except Exception as e:
                      print(f"Error creating vector index: {e}")
                      return None
                  
              def _delete_vector_bucket(self):
                  try:
                      response = self.client.delete_vector_bucket(
                          vectorBucketName=self.bucket_name
                      )
                      print(f"Deleted vector bucket: {self.bucket_name}")
                  except Exception as e:
                      print(f"Error deleting vector bucket: {e}")
                      return None
                  return response
              
              def _delete_vector_index(self):
                  try:
                      response = self.client.delete_index(
                          vectorBucketName=self.bucket_name,
                          indexName=f"{self.bucket_name}-index"
                      )
                      print(f"Deleted vector index: {self.bucket_name}-index")
                  except Exception as e:
                      print(f"Error deleting vector index: {e}")
                      return None
                  return response

          def handler(event, context):
              try:
                  print(f"Event: {json.dumps(event)}")
                  
                  if event['RequestType'] == 'Create':
                      # Step 1: Create S3 Vector bucket
                      vector_bucket_name = event['ResourceProperties']['VectorBucketName']
                      vector_bucket = VectorBucket(vector_bucket_name)
                      
                      print(f"Creating S3 Vector bucket: {vector_bucket_name}")
                      create_bucket_response = vector_bucket._create_vector_bucket()
                      
                      vector_bucket_arn = create_bucket_response.get('vectorBucketArn', '')
                      print(f"S3 Vector bucket created: {vector_bucket_arn}")
                      
                      # Wait for bucket to be ready
                      time.sleep(3)
                      
                      # Step 2: Create Vector Index
                      index_name = event['ResourceProperties']['IndexName']
                      
                      print(f"Creating Vector Index: {index_name}")
                      index_response = vector_bucket._create_vector_index()
                      
                      index_arn = index_response.get('indexArn', '')
                      print(f"Vector Index created: {index_arn}")
                      
                      # Wait for index to be ready
                      time.sleep(3)
                      
                      # Step 3: Create Knowledge Base
                      bedrock_client = boto3.client('bedrock-agent')
                      kb_name = event['ResourceProperties']['KnowledgeBaseName']
                      kb_role_arn = event['ResourceProperties']['KnowledgeBaseRoleArn']
                      embedding_model_arn = event['ResourceProperties']['EmbeddingModelArn']
                      
                      print(f"Creating Knowledge Base: {kb_name}")
                      kb_response = bedrock_client.create_knowledge_base(
                          name=kb_name,
                          description='RAG Knowledge Base with S3 Vector storage',
                          roleArn=kb_role_arn,
                          knowledgeBaseConfiguration={
                              'type': 'VECTOR',
                              'vectorKnowledgeBaseConfiguration': {
                                  'embeddingModelArn': embedding_model_arn,
                                  'embeddingModelConfiguration': {
                                      'bedrockEmbeddingModelConfiguration': {
                                        'dimensions': 1024,
                                        'embeddingDataType': 'FLOAT32'
                                      }
                                  }
                              }
                          },
                          storageConfiguration={
                              'type': 'S3_VECTORS',
                              's3VectorsConfiguration': {
                                  'vectorBucketArn': vector_bucket_arn,
                                  'indexArn': index_arn
                              }
                          },
                          tags={
                            'StackName': '${AWS::StackName}'
                          }
                      )
                      print(f"Knowledge Base creation response: {kb_response}")
                      
                      knowledge_base_id = kb_response['knowledgeBase']['knowledgeBaseId']
                      knowledge_base_arn = kb_response['knowledgeBase']['knowledgeBaseArn']
                      
                      print(f"Knowledge Base created: {knowledge_base_id}")
                      
                      # Step 4: Create Data Source
                      document_bucket_arn = event['ResourceProperties']['DocumentBucketArn']
                      
                      print(f"Creating Data Source for bucket: {document_bucket_arn}")
                      ds_response = bedrock_client.create_data_source(
                          knowledgeBaseId=knowledge_base_id,
                          name=f"{kb_name}-data-source",
                          description='S3 data source for documents',
                          dataSourceConfiguration={
                              'type': 'S3',
                              's3Configuration': {
                                  'bucketArn': document_bucket_arn,
                                  'inclusionPrefixes': ['documents/']  # Optional: specify folder
                              }
                          }
                      )
                      
                      data_source_id = ds_response['dataSource']['dataSourceId']
                      print(f"Data Source created: {data_source_id}")
                      
                      # Physical Resource ID for tracking
                      physical_id = f"{vector_bucket_name}|{index_name}|{knowledge_base_id}|{data_source_id}"
                      
                      # Return data for CloudFormation
                      response_data = {
                          'VectorBucketName': vector_bucket_name,
                          'VectorBucketArn': vector_bucket_arn,
                          'IndexName': index_name,
                          'IndexArn': index_arn,
                          'KnowledgeBaseId': knowledge_base_id,
                          'KnowledgeBaseArn': knowledge_base_arn,
                          'DataSourceId': data_source_id,
                          'Message': 'S3 Vector bucket, index, and Knowledge Base created successfully'
                      }
                      
                      cfnresponse.send(event, context, cfnresponse.SUCCESS, response_data, physical_id)
                      
                  elif event['RequestType'] == 'Delete':
                      # Parse physical resource ID
                      physical_id = event.get('PhysicalResourceId', '')
                      print(f"Physical Resource ID: {physical_id}")
                      
                      if '|' in physical_id:
                          parts = physical_id.split('|')
                          vector_bucket_name = parts[0] if len(parts) > 0 else event['ResourceProperties']['VectorBucketName']
                          index_name = parts[1] if len(parts) > 1 else event['ResourceProperties']['IndexName']
                          knowledge_base_id = parts[2] if len(parts) > 2 else None
                          data_source_id = parts[3] if len(parts) > 3 else None
                      else:
                          vector_bucket_name = event['ResourceProperties']['VectorBucketName']
                          index_name = event['ResourceProperties']['IndexName']
                          knowledge_base_id = None
                          data_source_id = None
                      
                      # Cleanup resources in reverse order
                      try:
                          bedrock_client = boto3.client('bedrock-agent')
                          vector_bucket = VectorBucket(vector_bucket_name)
                          
                          # Delete Data Source first
                          if knowledge_base_id and data_source_id:
                              try:
                                  bedrock_client.delete_data_source(
                                      knowledgeBaseId=knowledge_base_id,
                                      dataSourceId=data_source_id
                                  )
                                  print(f"Deleted data source: {data_source_id}")
                                  time.sleep(5)
                              except Exception as e:
                                  print(f"Error deleting data source: {e}")
                          
                          # Delete Knowledge Base
                          if knowledge_base_id:
                              try:
                                  bedrock_client.delete_knowledge_base(knowledgeBaseId=knowledge_base_id)
                                  print(f"Deleted knowledge base: {knowledge_base_id}")
                                  time.sleep(10)
                              except Exception as e:
                                  print(f"Error deleting knowledge base: {e}")
                          
                          # Delete Vector Index
                          try:
                              vector_bucket._delete_vector_index()
                              print(f"Deleted vector index: {index_name}")
                              time.sleep(5)
                          except Exception as e:
                              print(f"Error deleting vector index: {e}")
                          
                          # Delete S3 Vector bucket
                          try:
                              vector_bucket._delete_vector_bucket()
                              print(f"Deleted S3 Vector bucket: {vector_bucket_name}")
                          except Exception as e:
                              print(f"Error deleting S3 Vector bucket: {e}")
                              
                      except Exception as delete_error:
                          print(f"Delete error (ignored): {str(delete_error)}")
                          pass
                      
                      cfnresponse.send(event, context, cfnresponse.SUCCESS, {})
                      
                  elif event['RequestType'] == 'Update':
                      # For updates, return existing values
                      physical_id = event.get('PhysicalResourceId', '')
                      if '|' in physical_id:
                          parts = physical_id.split('|')
                          response_data = {
                              'VectorBucketName': parts[0] if len(parts) > 0 else '',
                              'IndexName': parts[1] if len(parts) > 1 else '',
                              'KnowledgeBaseId': parts[2] if len(parts) > 2 else '',
                              'DataSourceId': parts[3] if len(parts) > 3 else '',
                              'Message': 'Update completed'
                          }
                      else:
                          response_data = {'Message': 'Update completed'}
                      
                      cfnresponse.send(event, context, cfnresponse.SUCCESS, response_data, physical_id)
                  else:
                      cfnresponse.send(event, context, cfnresponse.SUCCESS, {})
                      
              except Exception as e:
                  print(f"Error: {str(e)}")
                  import traceback
                  traceback.print_exc()
                  cfnresponse.send(event, context, cfnresponse.FAILED, {
                      'Error': str(e)
                  })

  # S3 Vector and Knowledge Base Custom Resource
  S3VectorKnowledgeBase:
    Type: AWS::CloudFormation::CustomResource
    Properties:
      ServiceToken: !GetAtt S3VectorKnowledgeBaseFunction.Arn
      VectorBucketName: !Sub '${ApplicationName}-vector-bucket-${AWS::AccountId}'
      IndexName: !Sub '${ApplicationName}-vector-index'
      KnowledgeBaseName: !Sub '${AWS::StackName}-knowledge-base'
      KnowledgeBaseRoleArn: !GetAtt BedrockKnowledgeBaseRole.Arn
      EmbeddingModelArn: !Sub 'arn:aws:bedrock:${AWS::Region}::foundation-model/amazon.titan-embed-text-v2:0'
      DocumentBucketArn: !GetAtt DocumentBucket.Arn

  # Document Storage Bucket
  DocumentBucket:
    Type: AWS::S3::Bucket
    Properties:
      BucketName: !Sub '${ApplicationName}-documents-${AWS::AccountId}'
      PublicAccessBlockConfiguration:
        BlockPublicAcls: true
        BlockPublicPolicy: true
        IgnorePublicAcls: true
        RestrictPublicBuckets: true

  # Lambda Execution Role
  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: BedrockAndS3Access
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - bedrock:*
                  - bedrock-agent:*
                  - bedrock-agent-runtime:*
                  - s3:GetObject
                  - s3:PutObject
                  - s3:ListBucket
                Resource: '*'

  # Document Processor Lambda Function
  DocumentProcessorFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: !Sub '${ApplicationName}-document-processor'
      Runtime: python3.13
      Handler: index.handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Timeout: 300
      Code:
        ZipFile: |
          import json
          import boto3
          import os
          
          def handler(event, context):
              print(f"Received event: {json.dumps(event)}")
              
              try:
                  bedrock_agent = boto3.client('bedrock-agent')
                  
                  # S3イベントまたは直接呼び出しを処理
                  if 'Records' in event:
                      # S3イベントからの呼び出し
                      for record in event['Records']:
                          bucket = record['s3']['bucket']['name']
                          key = record['s3']['object']['key']
                          print(f"Processing file: {bucket}/{key}")
                  
                  # Knowledge Base sync job start
                  knowledge_base_id = os.environ.get('KNOWLEDGE_BASE_ID')
                  data_source_id = os.environ.get('DATA_SOURCE_ID')
                  
                  if knowledge_base_id and data_source_id:
                      response = bedrock_agent.start_ingestion_job(
                          knowledgeBaseId=knowledge_base_id,
                          dataSourceId=data_source_id
                      )
                      print(f"Started ingestion job: {response.get('ingestionJob', {}).get('ingestionJobId')}")
                  
                  return {
                      'statusCode': 200,
                      'body': json.dumps('Document processing initiated')
                  }
              except Exception as e:
                  print(f"Error: {str(e)}")
                  return {
                      'statusCode': 500,
                      'body': json.dumps(f'Error: {str(e)}')
                  }

  # Bedrock Knowledge Base Role
  BedrockKnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: BedrockKnowledgeBasePolicy
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - s3vectors:*
                  - s3:GetObject
                  - s3:ListBucket
                Resource: '*'
              - Effect: Allow
                Action:
                  - bedrock:InvokeModel
                Resource: '*'

  # Lambda Environment Variables Update (Custom Resource)
  LambdaEnvironmentUpdater:
    Type: AWS::CloudFormation::CustomResource
    DependsOn: 
      - DocumentProcessorFunction
      - S3VectorKnowledgeBase
    Properties:
      ServiceToken: !GetAtt LambdaEnvironmentUpdateFunction.Arn
      FunctionName: !Ref DocumentProcessorFunction
      KnowledgeBaseId: !GetAtt S3VectorKnowledgeBase.KnowledgeBaseId
      DataSourceId: !GetAtt S3VectorKnowledgeBase.DataSourceId

  LambdaEnvironmentUpdateFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: !Sub '${ApplicationName}-env-updater'
      Runtime: python3.13
      Handler: index.handler
      Role: !GetAtt LambdaEnvironmentUpdateRole.Arn
      Code:
        ZipFile: |
          import boto3
          import cfnresponse
          
          def handler(event, context):
              try:
                  if event['RequestType'] in ['Create', 'Update']:
                      lambda_client = boto3.client('lambda')
                      
                      function_name = event['ResourceProperties']['FunctionName']
                      knowledge_base_id = event['ResourceProperties']['KnowledgeBaseId']
                      data_source_id = event['ResourceProperties']['DataSourceId']
                      
                      # Update Lambda environment variables
                      lambda_client.update_function_configuration(
                          FunctionName=function_name,
                          Environment={
                              'Variables': {
                                  'KNOWLEDGE_BASE_ID': knowledge_base_id,
                                  'DATA_SOURCE_ID': data_source_id
                              }
                          }
                      )
                  
                  cfnresponse.send(event, context, cfnresponse.SUCCESS, {})
              except Exception as e:
                  print(f"Error: {str(e)}")
                  cfnresponse.send(event, context, cfnresponse.FAILED, {})

  LambdaEnvironmentUpdateRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: LambdaUpdatePolicy
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - lambda:UpdateFunctionConfiguration
                  - lambda:GetFunction
                Resource: '*'

  # S3 Bucket Notification (Custom Resource to avoid circular dependency)
  S3NotificationConfiguration:
    Type: AWS::CloudFormation::CustomResource
    DependsOn: 
      - DocumentBucket
      - DocumentProcessorFunction
      - LambdaEnvironmentUpdater
    Properties:
      ServiceToken: !GetAtt S3NotificationFunction.Arn
      BucketName: !Ref DocumentBucket
      LambdaFunctionArn: !GetAtt DocumentProcessorFunction.Arn

  S3NotificationFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: !Sub '${ApplicationName}-s3-notification'
      Runtime: python3.13
      Handler: index.handler
      Timeout: 30
      Role: !GetAtt S3NotificationRole.Arn
      Code:
        ZipFile: |
          import boto3
          import cfnresponse
          import json
          
          def handler(event, context):
              try:
                  s3_client = boto3.client('s3')
                  lambda_client = boto3.client('lambda')
                  
                  bucket_name = event['ResourceProperties']['BucketName']
                  lambda_arn = event['ResourceProperties']['LambdaFunctionArn']
                  
                  if event['RequestType'] == 'Create':
                      # Add Lambda permission for S3
                      try:
                          lambda_client.add_permission(
                              FunctionName=lambda_arn,
                              StatementId='s3-trigger-permission',
                              Action='lambda:InvokeFunction',
                              Principal='s3.amazonaws.com',
                              SourceArn=f'arn:aws:s3:::{bucket_name}'
                          )
                      except lambda_client.exceptions.ResourceConflictException:
                          pass  # Permission already exists
                      
                      # Configure S3 notification
                      s3_client.put_bucket_notification_configuration(
                          Bucket=bucket_name,
                          NotificationConfiguration={
                              'LambdaFunctionConfigurations': [
                                  {
                                      'Id': 'DocumentProcessorTrigger',
                                      'LambdaFunctionArn': lambda_arn,
                                      'Events': ['s3:ObjectCreated:*']
                                  }
                              ]
                          }
                      )
                  
                  elif event['RequestType'] == 'Delete':
                      # Remove S3 notification
                      try:
                          s3_client.put_bucket_notification_configuration(
                              Bucket=bucket_name,
                              NotificationConfiguration={}
                          )
                      except:
                          pass
                      
                      # Remove Lambda permission
                      try:
                          lambda_client.remove_permission(
                              FunctionName=lambda_arn,
                              StatementId='s3-trigger-permission'
                          )
                      except:
                          pass
                  
                  cfnresponse.send(event, context, cfnresponse.SUCCESS, {})
              except Exception as e:
                  print(f"Error: {str(e)}")
                  cfnresponse.send(event, context, cfnresponse.FAILED, {})

  S3NotificationRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Policies:
        - PolicyName: S3NotificationPolicy
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - s3:PutBucketNotification
                  - s3:GetBucketNotification
                  - lambda:AddPermission
                  - lambda:RemovePermission
                Resource: '*'

Outputs:
  StackName:
    Description: Stack Name
    Value: !Ref AWS::StackName
  VectorBucketName:
    Description: Vector Bucket Name
    Value: !Ref DocumentBucket
  KnowledgeBaseId:
    Description: Knowledge Base Id
    Value: !GetAtt S3VectorKnowledgeBase.KnowledgeBaseId
  DataSourceId:
    Description: Data Source Id
    Value: !GetAtt S3VectorKnowledgeBase.DataSourceId

リソースが作成されるとアプリケーションが選択可能になり、チャットを行うことができます。

構成図

かなり省略していますが、大まかに以下のような構成です。

DynamoDBにはユーザーごとに選択できるナレッジベースIDを格納することで、一つのアプリケーションから複数のナレッジベースにつなぐようにしています。

さいごに

RAGチャット便利だけど、コストが高い、あるいは作るのが大変そうという方も多いのではないかと思います。
そんな方でも簡単に安く利用できるサービスだと思いますので、よかったら参考にしてみてください。

この後、他のログインユーザーへの共有機能などを実装予定です。

弊社では一緒に働く仲間を募集中です！

現在、様々な職種を募集しております。
カジュアル面談も可能ですので、ご連絡お待ちしております！

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