Amazon Bedrock, Amazon Transcribe Amazon OpenSearch

• Scalability: Automatically scales to handle varying loads.
• Cost-Effective: Pay-as-you-go pricing reduces overhead.
• High Performance: Enables fast, efficient similarity searches.
• Enhanced AI Responses: Integrates with RAG for contextually accurate outputs.
• Seamless Transcription: Combines Amazon Transcribe for easy audio-to-text processing.

• Overview: This blog is part two of our series on processing and utilizing audio transcriptions. After Amazon Transcribe completes its task and generates a transcription file, we will embed the text using Amazon Bedrock through the LangChain API. The embedded data will then be stored in an Amazon OpenSearch Serverless service which can be utilized as a vector database. This index enables efficient retrieval and allows for RAG (Retrieval-Augmented Generation) searches against the audio transcription, enhancing the search experience with contextual and relevant results.
• Security: As discussed in the first article of this series, security is a critical aspect of any solution. In this example, we use AWS Lambda and Amazon OpenSearch Serverless , both deployed within a VPC. This setup includes network policies and data access policies to manage index access effectively. For more details, please refer to this article.We also utilize the LangChain API with the BedrockEmbeddings library for text embedding. It is important to emphasize the type of data being handled, especially if it is confidential or private, to meet regulatory and security requirements. Be sure to use AWS libraries, Bedrock Guardrails, and follow best security practices to ensure that security is integrated throughout AWS services and Amazon OpenSearch, safeguarding data security and integrity in your RAG application.

• Purpose: The purpose of this solution is to enable seamless integration of audio transcription files into a RAG (Retrieval-Augmented Generation) system to perform semantic searches. By embedding transcribed audio content and storing it in a vector database, users can leverage advanced search capabilities that go beyond simple keyword matching. This allows for more meaningful and context-aware retrieval of information, improving the efficiency and relevance of search results within applications that require comprehensive analysis and querying of audio-derived text data.
• Objective: To demonstrate the embedding process of transcribed audio content and its integration into a vector database for enabling semantic searches within a RAG solution.

Architecture

• Workflow Overview:

• Amazon S3: For storing the transcribed JSON files. We configure Amazon EventBridge to trigger an AWS Lambda function when an transcription file created by Amazon Transcribe job.
• AWS EventBridge: Trigger the AWS Lambda function upon an Object Create event.
• AWS Lambda: The function is deployed in VPC to allow secure and private access to Amazon OpenSearch Serverless database, developed in Python with the AWS SDK, Boto3, is designed to accept a JSON payload from Amazon EventBridge and ingest an embedding file into the Amazon OpenSearch Service vector database. This setup ensures seamless integration with event-driven architectures by automatically triggering the function when a file upload event occurs. The Python code processes the input payload and embeds the data into the Amazon OpenSearch index, supporting efficient data processing and integration for prompt semantic querying and content updates.
• Amazon OpenSearch: A Serverless vector database stores vector embeddings, which are numerical representations of data (e.g., words or images) capturing their semantics. This type of database is managed by cloud providers, removing the need for infrastructure management and scaling automatically based on use.It integrates with Retrieval-Augmented Generation (RAG) by enabling efficient similarity searches, such as calculating the cosine similarity of vectors. This allows the system to quickly identify and retrieve the most contextually relevant information, making it highly effective for generating responses based on related content. When a query is made, it’s converted into a vector, and the database finds the closest matches. This allows RAG systems to retrieve relevant context efficiently and use it to enhance their generated responses. Benefits include scalability, cost-effectiveness, and high performance.
• IAM Roles: The IAM roles for the AWS Lambda function include the necessary policies to access both Amazon OpenSearch Serverless vector database and the Amazon S3 bucket.

Conclusion

• Summary: This blog covers how to integrate Amazon Transcribe with a Amazon OpenSearch Serverless vector database for processing and storing transcribed text as vector embeddings. This setup enables efficient similarity searches and retrieval for applications like Retrieval-Augmented Generation (RAG). By converting transcribed audio into embeddings, the system can enhance responses by retrieving contextually relevant content quickly. Benefits include seamless scalability, cost-effectiveness, and high performance, making it perfect for real-time AI solutions that need transcription and contextual search capabilities.