Summarization
This is an example of using the Completion
endpoint for text summarization.
Installing SDK
Install using pip.
pip install bedrock_anthropic
Example
An example of using the Completion
endpoint to summarize text.
import os
from bedrock_anthropic import AnthropicBedrock
anthropic = AnthropicBedrock(
access_key=os.getenv("AWS_ACCESS_KEY"),
secret_key=os.getenv("AWS_SECRET_KEY"),
region=os.getenv("AWS_REGION")
)
prompt = """Please provide a summary of the following text.
<text>
AWS took all of that feedback from customers, and today we are excited to
announce Amazon Bedrock, \ a new service that makes FMs from AI21 Labs,
Anthropic, Stability AI, and Amazon accessible via an API. \ Bedrock is the
easiest way for customers to build and scale generative AI-based applications
using FMs, \ democratizing access for all builders. Bedrock will offer the
ability to access a range of powerful FMs \ for text and images—including
Amazons Titan FMs, which consist of two new LLMs we’re also announcing \
today—through a scalable, reliable, and secure AWS managed service. With
Bedrock’s serverless experience, \ customers can easily find the right model
for what they’re trying to get done, get started quickly, privately \
customize FMs with their own data, and easily integrate and deploy them into
their applications using the AWS \ tools and capabilities they are familiar
with, without having to manage any infrastructure (including integrations \
with Amazon SageMaker ML features like Experiments to test different models
and Pipelines to manage their FMs at scale).
</text>
"""
completion = anthropic.Completion.create(
model="anthropic.cluade-v2",
prompt=prompt,
max_tokens_to_sample=1000
)
print(completion["completion"])
Response
Here is a summary of the key points from the text:
- Amazon announced a new service called Amazon Bedrock that provides API access to large language models from AI companies like AI21 Labs, Anthropic, Stability AI, and Amazon.
- Bedrock aims to democratize access to generative AI models and make it easy for developers to build applications using these models.
- Bedrock offers text and image models including Amazon's own Titan models. It provides a serverless experience so developers can easily find, customize, integrate, and deploy models without managing infrastructure.
- Bedrock allows developers to leverage other AWS services like SageMaker to test models and manage them at scale.
- Overall, Bedrock simplifies access to a range of powerful AI models for developers to build generative AI applications.