How to use the LLD Agent to plan new features for the crewAIInc/crewAI codebase
Understanding the many details of a codebase’s design can be challenging, especially when working with complex codebases. While high-level documentation provides a good starting point, developers often need to understand the nitty-gritty implementation details before they can make changes. This is where Potpie’s Low-Level Design (LLD) Agent comes into play, offering a powerful tool for detailed system design analysis and implementation guidance.
Understanding Potpie’s LLD Agent Through a Real Example
Let’s examine how Potpie’s LLD Agent helps in understanding and implementing a knowledge storage system using Weaviate. The agent breaks down the existing implementation and provides structured guidance for new feature development.The Core Implementation AnalysisWhen examining the knowledge storage system, the LLD Agent first presents the existing codebase structure. It identifies two key files: base_knowledge_storage.py and knowledge_storage.py. The base file defines an abstract base class with essential methods like search(), save(), and reset(), while the implementation file extends this with Weaviate-specific functionality.The agent provides detailed insights into each component:
Abstract Base Implementation
The BaseKnowledgeStorage class defines the core interface
Search functionality includes parameters for query, limit, and filtering
Save operations handle document storage with metadata support
Reset capabilities allow for knowledge base clearing
Extended Implementation
Integration with ChromaDB for embedding handling
Custom configuration options for collection management
Enhanced search efficiency through vector operations
Design Guidance and Best PracticesThe LLD Agent doesn’t just show the code—it provides structured guidance for implementation. For the Weaviate integration, it outlines specific steps:
Schema Definition: Creating appropriate classes and properties in the knowledge graph
Client Initialization: Setting up and configuring the Weaviate client
Method Implementation: Detailed guidance for search, save, and reset operations
Embedder Configuration: Integration of embedding services for vector searches
The agent will analyze your codebase and provide detailed design guidance, just as it did for the Weaviate knowledge storage implementation.By using Potpie’s LLD Agent, developers can:
Understand existing implementations thoroughly
Design new features with confidence
Maintain consistency across the codebase
Reduce implementation time through guided development
This approach transforms the often-challenging task of low-level design into a structured, guided process that ensures both quality and consistency in your implementations.Check out the above chat at:“Referring existing knowledge storage implementation create a LLD for a knowledge storage implementation using weaviate”https://app.potpie.ai/chat/0193965d-d86f-7a36-9ee2-f047ca9ea1c0