OasisDB: A minimal and lightweight vector database

golang llm machine-learning rag vector vector-database vector-search
13 Open Issues Need Help Last updated: Sep 8, 2025

Open Issues Need Help

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enhancement help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 3/5
bug help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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).

Complexity: 3/5
help wanted good first issue test

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search
enhancement help wanted good first issue test

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search
enhancement help wanted good first issue test

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 4/5
enhancement help wanted good first issue

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 2/5
enhancement help wanted good first issue

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 3/5
help wanted good first issue

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search
enhancement help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 3/5
enhancement help wanted good first issue

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search
enhancement help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 4/5
enhancement help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search

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.

Complexity: 4/5
help wanted

OasisDB: A minimal and lightweight vector database

Go
#golang#llm#machine-learning#rag#vector#vector-database#vector-search