We leveraged cutting-edge tools to create an efficient and effective study aid for LeetCode users. Here's an overview of our technical stack and how each component contributes to the guide:
- llama-index
- Function: Enhances the filling of LeetCode questions through integration with OpenAI.
- Usage:
- Utilizes a low-cost embedding model to process large amounts of data.
- Generates embedding vectors for each LeetCode question, providing a unique representation based on its content.
- NetworkX
- Function: Optimizes the study path for learners.
- Usage:
- Constructs a low-cost spanning tree to understand the relationships between different problems.
- Implements a Depth-First Search (DFS) algorithm to plan an effective study order, helping users to progress logically through related topics.
- SciKit-Learn
- Function: Clusters LeetCode problems for tailored learning experiences.
- Usage:
- Applies machine learning algorithms to cluster knowledge based on the similarity of the embeddings.
- Groups similar problems together, allowing users to focus on specific areas of study or difficulty levels.
- Pydantic
- Function: Transforms unstructured data into a structured format.
- Usage:
- Converts unstructured LeetCode problem data into structured JSON format.
- Ensures data integrity and facilitates easier manipulation and retrieval of problem information.