Automating Knowledge Management with Amazon BDA

Introduction to Amazon Bedrock Data Automation

Use Case: Transforming Meeting Transcripts into Enterprise Knowledge Database

Scenario:

Overview of the Architecture

Key Components:

1. Microsoft Teams Recording: Meetings are recorded in Teams, capturing both audio and video content.
2. AWS Lambda: A Lambda function retrieves meeting recordings and initiates the Bedrock Data Automation workflow.
3. Amazon Bedrock Data Automation (BDA): Processes the recordings, extracting insights from conversations, video content, and images.
4. Amazon S3: Stores processed outputs from BDA for further use.
5. Amazon Bedrock Knowledge Base: Pushes insights into a vector database for efficient storage and retrieval.
6. Retrieval-Augmented Generation (RAG): Enables contextual queries by retrieving relevant information from the knowledge base using Amazon Bedrock models.

Workflow:

1. Recording Retrieval: The process begins with an AWS Lambda function retrieving the meeting recording from Microsoft Teams.
2. Data Processing with BDA: Amazon Bedrock Data Automation processes the recording:
   • Extracts text from audio (transcript generation).
   • Analyzes video content for additional context (e.g., shared slides or visual cues).
   • Identifies key insights such as deadlines, action items, or important discussions.

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3. Storage in Knowledge Base: Insights are pushed into a vector database within the Amazon Bedrock Knowledge Base for efficient storage and retrieval.
4. Contextual Queries with RAG: Users can query the knowledge base using natural language prompts like:
   • "What was the last deadline we mentioned in the last meeting?"
   • "What were the key action items discussed last week?"
     The system retrieves relevant context using RAG and provides accurate responses powered by Amazon Bedrock models.

Implementation Steps

Step 1: Clone the GitHub Repository

Important Note:

• Bedrock Data Automation is currently available only in the us-east-1 (N. Virginia) region.
• You must have a valid S3 bucket (bda_bucket_name) ready for input and output artifacts (You can create it however you want, either through the console or using Boto3.)
• Before using this script, create a BDA project at least once through the AWS Console. This ensures that the required service roles (such as data_automation_profile) are created and available.

Usage Instructions

1. Ensure you have Python 3.7+ installed.
2. Install required dependencies: pip install boto3 awswrangler pandas
3. Provide valid AWS credentials and region in your environment (e.g., using AWS CLI, environment variables, credentials file, etc.).
4. Update the main() function parameters with your own:
   1. Project name and description
   2. Input S3 URI (e.g., an image or video file)
   3. Output S3 URI
   4. Data automation profile ARN
5. Run the script: python bda_manager.py
6. Once run, the script will:
   1. Create or retrieve the specified BDA project
   2. Trigger the data automation pipeline
   3. Periodically check and print status updates
   4. Upon completion, retrieve S3 output, process results that are stored into a parquet file or CSV if needed, and store them back in an S3 location for future reference