billschumacher / athena Goto Github PK

🚀 Feature Request

Feature description

Integrate prompt completions tracking with the SQLAlchemy model and cache similar prompts in Redis to avoid making additional API calls for similar requests.

Rationale and use cases

Reducing the number of API calls can save costs and improve response times for users. By storing prompt completions in the SQLAlchemy model and caching similar prompts in Redis, we can optimize the system to deliver faster responses and minimize external API calls.

Proposed implementation (if applicable)

• Implement a caching mechanism using Redis to store similar input prompts and their responses.
• When a user submits a prompt, first check Redis for cached responses with similar prompts. If found, return the cached response.
• If the prompt is not found in the Redis cache, query the database for similar prompts and their responses. If found, store the response in Redis and return it to the user.
• If the prompt is not found in the cache or database, make an API call to get the completion, store it in the database and Redis cache, and return the response to the user.

Potential drawbacks and alternatives

• Managing cache consistency and expiration could become complex as the number of prompts and completions grows.
• Depending on the similarity threshold, the system may return responses that are not an exact match for the input prompt, which may affect the user experience.
• Alternative solutions include using more advanced caching mechanisms, such as machine learning-based models to predict and prefetch prompt completions, or implementing a content-based recommendation system to identify and return similar prompts.

🚀 Feature Request

Feature description

Implement a code review feature using ChatGPT to analyze and provide feedback on the following aspects of the code: quality, duplicate lines, performance, typing, and adherence to Google style docstrings.

Rationale and use cases

Code reviews are essential in identifying and addressing potential issues in codebases. Automating code reviews using ChatGPT will help developers identify problems, maintain consistent coding standards, and improve overall code quality. This feature would cater to developers seeking an AI-assisted review process.

Proposed implementation (if applicable)

• Create a function to analyze code snippets and identify low-quality code, duplicate lines, performance issues, typing problems, and adherence to Google style docstrings.
• Use ChatGPT to generate insightful and actionable feedback on the identified issues.
• Allow users to submit code snippets for review through the API or chat interface.

Potential drawbacks and alternatives

• The accuracy of ChatGPT's code analysis may not be on par with human reviewers, potentially leading to overlooked issues or false positives.
• There might be a learning curve for developers to understand and interpret the feedback provided by ChatGPT.
• Alternative solutions could include integrating existing linters and code quality tools or exploring other AI-based code review tools.

🚀 Feature Request

Feature description

Use spacy for intent/text classification.

Rationale and use cases

rasa_nlu has been deprecated and limits the python version and operating systems supported.

I've looked into rasa extensively and they seem to do a breaking release quite often.

It seems like a bad choice to continue using their library.

Proposed implementation (if applicable)

I'm still researching the best way to implement this, if anyone can help that would be great.

I think it would be a good idea to parse the available plugins for keywords (needs to be added to plugin base) then change the can_process to check for the intent and a reasonable confidence score.

Potential drawbacks and alternatives

There's quite a few other libraries but most are unmaintained, upgrading rasa might be possible but their documentation leaves a lot to be desired and they make breaking changes frequently.

🚀 Feature Request

Feature description

Add Celery support for managing and executing asynchronous tasks in the Athena application.

Rationale and use cases

Integrating Celery would enable Athena to efficiently handle long-running tasks or tasks that can be executed in parallel. By delegating these tasks to a separate worker process, Athena can continue processing other requests without being blocked. This ensures better responsiveness and scalability. Use cases include processing large datasets, running machine learning models, or handling API requests that require significant computation time.

Proposed implementation (if applicable)

• Install Celery and the necessary broker, such as Redis or RabbitMQ, as dependencies.
• Configure Celery in the Athena application, including setting up the broker URL and other required settings.
• Define Celery tasks for specific long-running operations or parallelizable tasks.
• Update the application code to delegate these tasks to Celery workers, using .apply_async() or .delay() methods.
• Deploy Celery worker processes alongside the Athena application, ensuring they have access to the same codebase and can communicate with the broker.

Potential drawbacks and alternatives

• Adding Celery introduces additional complexity to the application and requires maintenance of the Celery worker processes.
• Depending on the specific use cases, other asynchronous task processing libraries or approaches might be more suitable, such as Python's built-in concurrent.futures library or using asynchronous programming with asyncio. However, Celery provides a more mature and feature-rich solution for managing asynchronous tasks, making it a good choice for many applications.

🛠️ Refactoring or Code Improvement

Affected Component(s)
API and Views

Describe the issue or improvement
Currently, our API works, but it's pretty basic and lacks documentation. It would be great to implement automatic documentation with Swagger and REST to make it easier to use and extend, and to improve maintainability.

Proposed changes (if applicable)
I propose that we use Flask-RESTX to implement automatic documentation and to improve the structure and readability of our code.

Expected benefits
By implementing automatic documentation with Flask-RESTX, we can improve the usability and maintainability of our API. It will also make it easier for new developers to understand and work with our code.

Additional context

🚀 Feature Request

Feature description

I suggest adding support for a relational database, such as PostgreSQL, to store and manage structured data, and a vector database, such as Faiss, Redis or Milvus, to efficiently store and search high-dimensional vectors for similarity searches.

Rationale and use cases

Relational databases are useful for organizing structured data into tables with predefined schemas, ensuring data consistency, and enabling complex queries. This feature would be beneficial for storing user-related data, conversation history, and other structured data that can be easily queried and analyzed.

Vector databases provide efficient storage and search capabilities for high-dimensional data, which is useful for tasks such as nearest neighbor search, similarity search, and clustering in the context of AI applications. This feature would enhance Athena's capabilities in natural language understanding, processing, and recommendation systems by enabling efficient vector-based operations.

Proposed implementation (if applicable)

• Integrate a relational database, such as PostgreSQL, using an ORM like SQLAlchemy for easy interaction with the database.
• Choose a vector database, such as Faiss or Milvus, and integrate it with Athena for vector storage and similarity search capabilities.
• Update the API and input processing logic to incorporate the newly integrated databases for various use cases.
• Add necessary configuration and setup instructions for the databases in the README and deployment files.

Potential drawbacks and alternatives

• Additional complexity: Integrating databases can increase the complexity of the system, requiring more maintenance and potentially affecting performance.
• Learning curve: Users may need to learn how to interact with and manage the databases effectively.
• Scalability: Depending on the chosen databases, there could be limitations in terms of scalability, requiring additional consideration for large-scale deployments.
• Alternative solutions could include using NoSQL databases, such as MongoDB or Elasticsearch, which offer flexible data models and are well-suited for handling large amounts of unstructured or semi-structured data.

🛠️ Refactoring or Code Improvement

Affected Component(s)

All Python files in the project.

Describe the issue or improvement

Improve code quality by using ChatGPT with the following prompt Check the code for the following issues: low quality, duplicate lines, performance, typing, and Google style docstrings: on all Python files currently in the project.

Proposed changes (if applicable)

• Use the specified prompt to analyze and provide feedback on the Python files.
• Apply the suggestions from ChatGPT to address low-quality code, duplicate lines, performance issues, typing problems, and adherence to Google style docstrings.

Expected benefits

Improving the code based on ChatGPT's feedback will result in a more maintainable, consistent, and readable codebase. This will enhance overall code quality and make future modifications easier for developers.

Additional context

The project was initially generated using small code snippets from ChatGPT. Therefore, it is expected that some inconsistencies or issues may be present in the code. This refactoring effort will help in addressing those issues and aligning the codebase with best practices.

🚀 Feature Request

Feature description

Add a file watch functionality to the docker-compose x-develop watch configuration, allowing for automatic container rebuilds and restarts when changes are made to specified files or directories.

Rationale and use cases

This feature is necessary for developers who want to streamline their development workflow by automating the build and restart process when they make changes to their code. The use cases for this feature include, but are not limited to:

• Developing and testing applications in a local environment
• Quickly iterating on code changes without manually rebuilding and restarting containers
• Detecting and fixing issues faster by automating the development cycle

Proposed implementation (if applicable)

The file watch functionality could be implemented using a the expiremental x-develop watch configuration, which can monitor changes to specified files and directories and trigger actions when changes are detected. This configuration can automate container rebuilds and restarts when changes are made.

Potential drawbacks and alternatives

A potential drawback of this feature is increased resource usage, as the container will be continually rebuilding and restarting. However, this can be mitigated by specifying only the necessary files and directories to monitor. An alternative solution is to manually rebuild and restart the container when changes are made, but this can be time-consuming and error-prone.