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Currently, the project supports problems from sources like vjudge and AtCoder, but it does not include Leetcode problems. Adding Leetcode problems to the dataset will expand the available problem set and improve the semantic search functionality.
Proposed Changes:
Scrape or acquire Leetcode problem data: Either through scraping the website or using unofficial APIs to gather problem details.
Format problems into JSON: Each Leetcode problem should follow the same structure as the existing problems (e.g., problem ID, title, statement, tags, etc.).
Add problems to the problems/ folder: Ensure that problems are added with appropriate naming conventions (e.g., problems/leetcode_100.json).
Update the system to process Leetcode problems: Run the existing processing pipeline (build_summary, build_embedding, build_locale) for Leetcode problems.
Include Leetcode as a supported data source: Modify the codebase to recognize Leetcode as a valid problem source and allow users to search for Leetcode problems.
Benefits:
Expands the problem dataset.
Provides users with more problems to search through, improving the utility of the semantic search engine.
Steps to Reproduce
Scrape Leetcode problem data.
Format problems into the existing JSON structure.
Run the processing pipeline to generate summaries, embeddings, and detect languages.
Perform a search and verify that Leetcode problems are included in search results.
Description
Currently, the project supports problems from sources like vjudge and AtCoder, but it does not include Leetcode problems. Adding Leetcode problems to the dataset will expand the available problem set and improve the semantic search functionality.
Proposed Changes:
problems/
folder: Ensure that problems are added with appropriate naming conventions (e.g.,problems/leetcode_100.json
).build_summary
,build_embedding
,build_locale
) for Leetcode problems.Benefits:
Steps to Reproduce
Additional Information
Related Issues
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