Amazon Bedrock: Revolutionising Generative AI Integration with Unmatched Speed and Flexibility

Amazon Bedrock offers a straightforward way to develop and scale generative AI applications using foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon itself. With a single API, you can integrate these models into your product within days, making the process both quick and efficient.

https://aws.amazon.com/bedrock/

Launched in September 2023, Bedrock is relatively new but has already seen rapid development. In just under a year, it has introduced several significant features:

• Knowledge Base with Built-in Retrieval Augmented Generation (RAG): This seamlessly integrates a retrieval-based approach, allowing large language models (LLMs) to generate more accurate responses without additional fine-tuning.
• Support for Vector Stores: Bedrock now includes extended support for vector stores, enhancing the flexibility of data storage and retrieval.
• Continued Pre-Training Custom Models: This feature allows models to acquire new domain knowledge, making them adaptable and up-to-date.
• Agent Functionality: This dynamic feature enables prompt-driven actions, adding flexibility to the platform.

Using AWS Console, AWS CLI, and AWS SDK, integrating generative AI into your product becomes a swift and straightforward process.

When comparing Bedrock to OpenAI, particularly ChatGPT, it’s important to note that while OpenAI has been around longer, Bedrock is making its mark with a diverse array of features. Here's a breakdown of five key highlights of Bedrock:

1. Foundation Models

Bedrock excels in offering models from six providers with 19 models available as of December 27, 2023, with more expected over time. Some notable models include:

• Amazon Titan Text G1 — Express
• Amazon Titan Embeddings G1 — Text
• Anthropic Claude V2.1
• Cohere Embed English

Starting with a model through the AWS Console is recommended for an initial exploration.

2. Provision Throughput

Like many AWS services, Bedrock defaults to an on-demand mode, ideal for experimentation. However, for more consistent performance in production environments, you can purchase Provision Throughput for both customized and foundation models.

3. Agent

Bedrock’s Agent feature allows for the creation of an autonomous agent that calls the API on behalf of users via a Lambda function. If you have an existing Lambda function for business logic, it can easily integrate with the Agent. The Agent processes prompts into actions through pre-processing, orchestration, and post-processing steps.

4. Knowledge Base

The Knowledge Base feature, built on Retrieval Augmented Generation (RAG), allows for enhanced LLM responses without needing additional training. It works by converting relevant documentation into vector values stored in a vector store. When a user asks a question, the system retrieves relevant documents, which are then incorporated into the prompt sent to the LLM. This approach is particularly useful for creating chatbots or other AI applications that require domain-specific knowledge without extensive training.

Bedrock supported file formats for the Knowledge Base include:

• Plain text (.txt)
• Markdown (.md)
• HyperText Markup Language (.html)
• Microsoft Word document (.doc/.docx)
• Comma-separated values (.csv)
• Microsoft Excel spreadsheet (.xls/.xlsx)
• Portable Document Format (.pdf)

Supported vector stores in the Knowledge Base encompass:

• Amazon OpenSearch Serverless
• Amazon Aurora (recently added)
• Pinecone
• Redis Enterprise Cloud

5. Custom Model

Bedrock offers two ways to customize models:

• Fine-Tuning: This approach involves improving model performance on specific tasks using labeled data relevant to the task.

prompt”: “<prompt text>”, “completion”: “<expected generated text>”} {“prompt”: “<prompt text>”, “completion”: “<expected generated text>”} {“prompt”: “<prompt text>”, “completion”: “<expected generated text>”}

• Continued Pre-Training: This unique feature allows models to learn new domain knowledge using unlabeled data, making it possible to incorporate private or confidential information without the need for base model training.

{“input”: “<input text>”} {“input”: “<input text>”} {“input”: “<input text>”}

Conclusion

Amazon Bedrock is designed with both businesses and developers in mind. Business users can immediately see results and experiment with the technology using the AWS Console, while developers can start integrating Bedrock into their products within hours using the AWS SDK. With its rapidly expanding list of models, vector stores, and the flexibility of the Continued Pre-Training model, Bedrock has the potential to significantly reduce the time it takes to bring AI products to market, leveraging the robust AWS ecosystem.