normalized_query_vector

4. **Building the Index**: We use Faiss to build an index of the document vectors. The index is optimized for inner product similarity. 5. **Searching and Retrieving**: We encode the query into a vector, normalize it, and search the index t…

print(f"Vectors shape: {vectors.shape}") print(f"Normalized vectors shape: {normalized_vectors.shape}") print(f"Query vector shape: {query_vector.shape}") print(f"Normalized query vector shape: {normalized_query_vector.shape}") ``` ### Sum…

return vectors # Define the FAISS index dimension = 128 index = faiss.IndexFlatL2(dimension) # Example vectors with missing data vectors = np.random.rand(5000, dimension) vectors[np.random.rand(*vectors.shape) < 0.1] = np.nan # Intro…

# Example query vector with different dimensions query_vector = np.random.rand(120) # Query vector with 120 dimensions # Pad query vector to the target dimension padded_query_vector = pad_vectors(query_vector.reshape(1, -1), dimension) #…

model = LinearRegression() model.fit(observed_vectors[:, :-1], observed_vectors[:, -1]) # Predict missing values predicted_values = model.predict(missing_vectors[:, :-1]) vectors[missing_mask] = predicted_values …

raise ValueError(f"Mismatched dimensions: Expected {dimension}, got {normalized_query_vector.shape[1]}") # Perform search distances, indices = index.search(normalized_query_vector, k=10) # Print results print(f"Distances: {distances}"…