L2 distance

- We create a `faiss.IndexFlatL2` index, which uses the L2 distance metric to measure similarity. 3. **Add Embeddings to the Index**: - We add the document embeddings to the index using the `add` method. 4. **Generate a Random Query…

This example demonstrates how to use FAISS for efficient similarity search on a large dataset of document embeddings. By leveraging FAISS, you can achieve significant improvements in both memory usage and search performance. [Turn 4860] Us…

- **Device ID**: The `0` in `faiss.index_cpu_to_gpu(gpu_res, 0, cpu_index)` refers to the GPU device ID. If you have multiple GPUs, you can specify a different device ID. - **Efficiency**: Using a GPU can significantly speed up the index…

index = faiss.IndexFlatL2(embedding_dim) # Add the document embeddings to the index index.add(document_embeddings) # Generate a random query embedding query_embedding = np.random.rand(1, embedding_dim).astype('float32') # Search the inde…

Use matrix factorization techniques, such as Singular Value Decomposition (SVD) or Non-negative Matrix Factorization (NMF), to impute missing values. ### Example Implementation Let's implement a predictive imputation method using a simple…