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Spotlight Base branch #108

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@shreyanshs7 shreyanshs7 commented Sep 10, 2024

Summary by CodeRabbit

  • New Features

    • Introduced a Django admin interface setup for easier management of application data.
    • Added functionality for generating responses from OpenAI's GPT-4 and AWS Bedrock services.
    • Established a framework for handling and formatting support responses with citations.
    • Created a foundational structure for routing HTTP requests in the application.
    • Implemented a new model for storing user queries and associated metadata.
    • Developed an AI assistant prompt to provide tailored suggestions for expense management.
  • Chores

    • Updated docker-compose configuration with new environment variables for AWS and OpenAI integration.
    • Added a new dependency for AWS service integration in the requirements file.
  • Tests

    • Established a test suite for future unit testing of the application.

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coderabbitai bot commented Sep 10, 2024

Walkthrough

This pull request introduces several new files and configurations for the spotlight application within a Django project. Key additions include an admin interface setup, application configuration, model structure, integration with OpenAI and AWS Bedrock services, and foundational components for handling user queries and responses. It also establishes a testing framework and URL routing structure, along with necessary environment variables for cloud service integration.

Changes

File Path Change Summary
apps/spotlight/admin.py Introduced basic setup for Django admin interface; prepares for future model registrations.
apps/spotlight/apps.py Added application configuration class SpotlightConfig for managing the spotlight app.
apps/spotlight/llm.py Implemented functions for interacting with OpenAI and AWS Bedrock APIs for generating responses.
apps/spotlight/models.py Established a Django model named Query for storing user queries and associated metadata.
apps/spotlight/prompts/support_genie.py Introduced a constant PROMPT for a question-answering agent's operational guidelines.
apps/spotlight/prompts/spotlight_prompt.py Defined a comprehensive prompt for an AI assistant to facilitate user interactions in expense management.
apps/spotlight/service.py Added HelpService class with methods for handling citations and formatting support responses.
apps/spotlight/tests.py Created a test suite placeholder for future unit tests.
apps/spotlight/urls.py Established URL configuration structure; currently without defined routes.
docker-compose.yml.template Added environment variables for AWS and OpenAI integration.
requirements.txt Added boto3 dependency for AWS service integration.
quickbooks_desktop_api/settings.py Added apps.spotlight to the list of installed applications.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant HelpService
    participant OpenAI
    participant AWS_Bedrock

    User->>HelpService: Request support response
    HelpService->>OpenAI: Get response with system prompt
    OpenAI-->>HelpService: Return generated response
    HelpService->>AWS_Bedrock: Get support response using prompt template
    AWS_Bedrock-->>HelpService: Return Bedrock response
    HelpService-->>User: Return formatted support response
Loading

🐇 In the spotlight, changes take flight,
New paths are forged, oh what a sight!
With models and prompts, we leap and bound,
In code we trust, new magic found.
So hop along, let queries flow,
With AI's help, our knowledge will grow! 🌟

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Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between efe0247 and c18581c.

Files selected for processing (1)
  • apps/spotlight/prompts/spotlight_prompt.py (1 hunks)
Additional comments not posted (1)
apps/spotlight/prompts/spotlight_prompt.py (1)

1-406: Excellent prompt definition!

The prompt is well-structured and provides clear guidelines for the AI assistant's behavior and output format. The JSON response structure is clearly defined, making it easy to parse and process the assistant's suggestions. The examples cover various scenarios and help illustrate the expected behavior of the assistant.

Great work on creating a comprehensive and well-documented prompt!


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Tests Skipped Failures Errors Time
60 0 💤 0 ❌ 0 🔥 9.044s ⏱️

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Actionable comments posted: 6

Outside diff range, codebase verification and nitpick comments (1)
apps/spotlight/llm.py (1)

29-52: LGTM, but improve the exception handling.

The function is correctly interacting with the OpenAI API.

However, please address the following suggestion from the static analysis tool:

  • Use raise ... from syntax in the except clause to distinguish the exception from errors in exception handling.

Apply this diff to improve the exception handling:

-    except (openai.OpenAIError, json.JSONDecodeError) as e:
-        raise Exception(message=str(e))
+    except (openai.OpenAIError, json.JSONDecodeError) as e:
+        raise Exception(message=str(e)) from e
Tools
Ruff

51-51: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 96fee28 and a58b8d2.

Files selected for processing (11)
  • apps/spotlight/admin.py (1 hunks)
  • apps/spotlight/apps.py (1 hunks)
  • apps/spotlight/llm.py (1 hunks)
  • apps/spotlight/models.py (1 hunks)
  • apps/spotlight/prompts/support_genie.py (1 hunks)
  • apps/spotlight/service.py (1 hunks)
  • apps/spotlight/tests.py (1 hunks)
  • apps/spotlight/urls.py (1 hunks)
  • apps/spotlight/views.py (1 hunks)
  • docker-compose.yml.template (1 hunks)
  • requirements.txt (1 hunks)
Files skipped from review due to trivial changes (1)
  • apps/spotlight/apps.py
Additional context used
Ruff
apps/spotlight/models.py

1-1: django.db.models imported but unused

Remove unused import: django.db.models

(F401)

apps/spotlight/tests.py

1-1: django.test.TestCase imported but unused

Remove unused import: django.test.TestCase

(F401)

apps/spotlight/admin.py

1-1: django.contrib.admin imported but unused

Remove unused import: django.contrib.admin

(F401)

apps/spotlight/views.py

1-1: django.shortcuts.render imported but unused

Remove unused import: django.shortcuts.render

(F401)

apps/spotlight/urls.py

15-15: django.urls.path imported but unused

Remove unused import: django.urls.path

(F401)

apps/spotlight/llm.py

51-51: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

Additional comments not posted (8)
apps/spotlight/prompts/support_genie.py (1)

1-12: LGTM!

The prompt is well-structured and provides clear instructions for the question-answering agent. Key aspects covered:

  • Sticking to facts from the search results
  • Avoiding assumptions
  • Responding in third person
  • Handling cases where an answer cannot be found

The use of placeholders allows for dynamic insertion of search results and output format instructions.

The code changes are approved.

requirements.txt (1)

1-2: LGTM!

The addition of the boto3 dependency is approved.

apps/spotlight/service.py (2)

1-33: LGTM!

The code structure and logic look good. The changes are approved.


28-31: Verify the llm.get_support_response_from_bedrock function.

The get_support_response method uses the llm.get_support_response_from_bedrock function, which is not a standard Bedrock API.

Please verify if this is a custom wrapper function and ensure that it is implemented correctly to interact with the Bedrock service.

docker-compose.yml.template (2)

28-32: LGTM!

The addition of the new environment variables is approved.


28-32: Verify the environment variable values in the deployment process.

The values for the new environment variables (AWS_REGION, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, OPENAI_API_KEY, KNOWLEDGE_BASE_ID) are empty in the template file.

Please ensure that the actual values are being set correctly in the deployment process, such as through a CI/CD pipeline or a secrets management system, to enable the intended integration with AWS services and OpenAI functionalities.

apps/spotlight/llm.py (2)

1-26: LGTM!

The code segment is correctly setting up the required dependencies and configurations for OpenAI and AWS Bedrock integration.


55-94: LGTM!

The function is correctly interacting with the AWS Bedrock API to retrieve and generate a response based on the provided input message and prompt template. The configuration for the API request looks appropriate.

@@ -0,0 +1,3 @@
from django.db import models
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Remove the unused import.

The import statement from django.db import models is unused in this file.

Remove the unused import:

-from django.db import models
Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
from django.db import models
Tools
Ruff

1-1: django.db.models imported but unused

Remove unused import: django.db.models

(F401)

@@ -0,0 +1,3 @@
from django.test import TestCase
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Remove the unused import.

The import statement from django.test import TestCase is unused in this file.

Remove the unused import:

-from django.test import TestCase
Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
from django.test import TestCase
Tools
Ruff

1-1: django.test.TestCase imported but unused

Remove unused import: django.test.TestCase

(F401)

@@ -0,0 +1,3 @@
from django.contrib import admin
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Remove the unused import.

The import statement from django.contrib import admin is unused in this file.

Remove the unused import:

-from django.contrib import admin
Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
from django.contrib import admin
Tools
Ruff

1-1: django.contrib.admin imported but unused

Remove unused import: django.contrib.admin

(F401)

@@ -0,0 +1,3 @@
from django.shortcuts import render
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Remove the unused import.

The import statement from django.shortcuts import render is unused in this file.

Remove the unused import:

-from django.shortcuts import render
Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
from django.shortcuts import render
Tools
Ruff

1-1: django.shortcuts.render imported but unused

Remove unused import: django.shortcuts.render

(F401)

1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.urls import path
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Remove the unused import for now.

The import statement from django.urls import path is unused in this file.

Remove the unused import for now:

-from django.urls import path

Note: You will likely need to add this import back once you start defining URL patterns.

Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
from django.urls import path
Tools
Ruff

15-15: django.urls.path imported but unused

Remove unused import: django.urls.path

(F401)

Comment on lines +95 to +96
except json.JSONDecodeError as e:
print(e)
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Improve the error handling.

Printing the exception is not an ideal way to handle errors. Consider the following improvements:

  • Use a logging framework to log the error for better visibility and debugging.
  • Raise the exception or return an appropriate error response to propagate the error and handle it at a higher level.

Here's an example of how you can improve the error handling:

import logging

logger = logging.getLogger(__name__)

try:
    # ...
except json.JSONDecodeError as e:
    logger.error(f"Error decoding JSON response from AWS Bedrock: {str(e)}")
    raise Exception("Failed to decode JSON response from AWS Bedrock") from e

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Tests Skipped Failures Errors Time
60 0 💤 0 ❌ 0 🔥 8.540s ⏱️

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Tests Skipped Failures Errors Time
60 0 💤 0 ❌ 0 🔥 9.385s ⏱️

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2 participants