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- `faiss.omp_set_num_threads(8)` enables multi-threading to take advantage of multiple CPU cores. Adjust the number of threads based on your CPU capabilities. 4. **Training the Index**: - The index needs to be trained on the data before adding vectors. 5. **Adding Vectors**: - Vectors are added to the index after training. 6. **Searching**: - The `search` method is used to find the nearest neighbors. ### Additional Tips - **Batch Processing**: If you are adding vectors in batches, consider adding them in smaller chunks to reduce memory usage. - **Disk-Based Indexing**: If memory is still a constraint, consider using disk-based indexing methods like `IndexIDMap` or `IndexPreTransform`. - **Profiling**: Use profiling tools to measure the performance and identify bottlenecks. ### Debugging Steps Summary 1. **Check System Memory**: Ensure your system has enough memory to handle the dataset and indexing process. 2. **Reduce Dataset Size**: Try with a smaller subset of your dataset to see if the issue persists. 3. **Adjust Parameters**: Experiment with different values for `nlist`, `M`, and `nbits` to find the optimal balance between speed and memory usage.