added aws bedrock test

[gyliu513]

User description

Summary by CodeRabbit

• New Features

  • Introduced a joke generation service that utilizes AWS Bedrock's text generation capabilities.
  • Added functionality to create and display jokes through a simple execution workflow.

• Documentation

  • Updated documentation to reflect the new joke generation service and its usage.

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PR Type

Enhancement, Documentation

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Description

• Introduced a new joke generation service that utilizes AWS Bedrock's text generation capabilities.
• Implemented a create_joke task that sends a prompt to the text generation model and processes the response.
• Created a joke_workflow to execute the joke creation task and print the generated joke.
• Initialized Traceloop for monitoring and tracing the joke generation service.

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Changes walkthrough 📝

	Relevant files
Enhancement	bedrock1.py Introduce joke generation service using AWS Bedrock            aws/bedrock1.py Added a new joke generation service using AWS Bedrock's text generation capabilities. Implemented create_joke task to invoke the text generation model. Created joke_workflow to execute the joke creation task. Initialized Traceloop for monitoring and tracing. +50/-0

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💡 PR-Agent usage:
Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

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Commits

Files that changed from the base of the PR and between f7342c2 and 9361ed0.

Walkthrough

The new feature integrates AWS Bedrock's text generation capabilities to create a simple joke generation service. Utilizing cloud-based AI, the application generates jokes through defined workflows, enhancing user interaction with a playful twist. The implementation allows for straightforward execution and demonstrates effective use of AWS tools in a Python environment.

Changes

Files	Change Summary
aws/bedrock1.py	Introduced a joke generation service utilizing AWS Bedrock. Added functions for creating jokes and managing workflows.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant JokeService
    participant AWS_Bedrock

    User->>JokeService: Request a joke
    JokeService->>AWS_Bedrock: Send prompt for joke generation
    AWS_Bedrock-->>JokeService: Return generated joke
    JokeService-->>User: Display joke

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Poem

🐰 In a world where laughter blooms,
Jokes now dance in digital rooms.
AWS whispers, "Here’s a jest!"
With a hop and a giggle, we’re truly blessed!
Let humor flow, both bright and light,
A rabbit's joy, our hearts ignite! 🌟

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[github-actions]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 Security concerns No specific security vulnerabilities like SQL injection or XSS are introduced in this PR. However, the lack of error handling could potentially expose sensitive information in error messages or logs.

⚡ Key issues to review Hardcoded Region The AWS region is hardcoded in the boto3 client initialization at line 13. Consider making it configurable to enhance flexibility and deployment in different environments. Error Handling There is no error handling for the API calls or JSON operations in the create_joke function. Adding try-except blocks could improve reliability and error reporting. Security Concern The function create_joke potentially exposes sensitive data if the output includes errors or tracebacks that contain AWS resource identifiers or other sensitive information.

[github-actions]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible issue	Add exception handling for the model invocation It's recommended to handle exceptions for the invoke_model method to ensure that the application can gracefully handle cases where the model invocation fails or returns an unexpected response. aws/bedrock1.py [31-35] -response = bedrock_runtime.invoke_model( - body=body, - modelId="amazon.titan-text-express-v1", - accept="application/json", - contentType="application/json" -) +try: + response = bedrock_runtime.invoke_model( + body=body, + modelId="amazon.titan-text-express-v1", + accept="application/json", + contentType="application/json" + ) +except Exception as e: + # Handle the exception (e.g., logging, retrying, or fallback mechanisms) + print(f"Failed to invoke model: {e}") + return None Suggestion importance[1-10]: 10 Why: Adding exception handling is crucial for ensuring the application can gracefully handle failures during model invocation, improving robustness and reliability.	10
Enhancement	Adjust the temperature parameter to avoid deterministic outputs The temperature parameter in the text generation configuration should not be set to 0 as it can lead to deterministic and repetitive outputs. Consider setting it to a small but non-zero value to introduce a slight randomness. aws/bedrock1.py [26] -"temperature":0, #Temperature controls randomness; higher values increase diversity, lower values boost predictability. +"temperature":0.1, #Temperature controls randomness; higher values increase diversity, lower values boost predictability. Suggestion importance[1-10]: 8 Why: Setting the temperature parameter to a small non-zero value introduces slight randomness, which can improve the diversity of generated text without making it too unpredictable.	8
	Define stop sequences for controlled text generation The stopSequences parameter is currently an empty list, which might cause the text generation to run indefinitely or until the maximum token count is reached. Define meaningful stop sequences to ensure controlled text generation. aws/bedrock1.py [25] -"stopSequences":[], #define phrases that signal the model to conclude text generation. +"stopSequences":[".", "?", "!"], #define phrases that signal the model to conclude text generation. Suggestion importance[1-10]: 7 Why: Defining stop sequences helps control the text generation process, preventing it from running indefinitely and ensuring more coherent outputs.	7
	Adjust the topP parameter to balance creativity and coherence The topP parameter is set to 0.9, which is generally high and might lead to less predictable text generation. Consider adjusting this value based on the desired level of creativity versus coherence in the generated text. aws/bedrock1.py [27] -"topP":0.9 # Top P is a text generation technique, sampling from the most probable tokens in a distribution. +"topP":0.7 # Top P is a text generation technique, sampling from the most probable tokens in a distribution. Suggestion importance[1-10]: 6 Why: Adjusting the topP parameter can help balance the trade-off between creativity and coherence in the generated text, but the optimal value may vary based on specific use cases.	6