Open Issues Need Help
View All on GitHubOasisDB: A minimal and lightweight vector database
AI Summary: The task is to fix a bug in the OasisDB vector database. The `SearchDocuments` function in `document.go` is causing a panic when no documents are found during a search. The fix involves adding a check to ensure the results array is not empty before accessing its elements. The fix should also handle cases where some documents might not be found gracefully, without causing the entire search to fail.
OasisDB: A minimal and lightweight vector database
AI Summary: Add more unit tests to the OasisDB vector database project, focusing on improving test coverage. This involves writing new unit tests using a preferred testing framework (implied to be vibe).
OasisDB: A minimal and lightweight vector database
OasisDB: A minimal and lightweight vector database
OasisDB: A minimal and lightweight vector database
AI Summary: Implement scalar quantization for the IVF (Inverted File Index) vector index within the OasisDB project. This involves adding a new quantization method to improve the efficiency and storage requirements of the IVF index.
OasisDB: A minimal and lightweight vector database
AI Summary: Implement a new FLAT index type for the OasisDB vector database. This involves adding the functionality to store vectors and perform brute-force similarity searches without any sophisticated indexing structures.
OasisDB: A minimal and lightweight vector database
AI Summary: Develop and integrate a practical use case (e.g., RAG or image search) demonstrating OasisDB's capabilities, and contribute it back to the OasisDB repository.
OasisDB: A minimal and lightweight vector database
OasisDB: A minimal and lightweight vector database
AI Summary: Update the `searchDocument` function in the OasisDB vector database to accept documents as input parameters instead of vectors. This involves modifying the function's signature and potentially its internal logic to handle document processing before performing the vector search.
OasisDB: A minimal and lightweight vector database
OasisDB: A minimal and lightweight vector database
AI Summary: Implement filter query functionality in the OasisDB vector database. This involves choosing an appropriate filtering method (post-filter, pre-filter, or other) and integrating it into the existing codebase, ensuring compatibility with the database's architecture and API.
OasisDB: A minimal and lightweight vector database
AI Summary: Implement benchmark tests for the OasisDB vector database using a dataset like sift1m. This involves writing scripts to run the benchmarks and potentially modifying the existing codebase to support the testing framework.
OasisDB: A minimal and lightweight vector database