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- Introduce BaseAsTokenCounter and EstimatedAsTokenCounter for token estimation - Add AsMsgStat and AsBlockStat schema for message statistics tracking - Implement FileIO class with read/write/append/edit operations - Create file utility functions for safe async file reading and truncation - Add MemorySearch component for semantic search in memory files - Register new component types in ComponentEnum and update imports - Add constants for default host, port, and truncation limits - Create BaseService abstract base class for service implementations - Implement BaseStep with component accessors and lifecycle management - Add proper __all__ exports for all new modules and components
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
"""Vector similarity computation utilities.
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Provides functions for calculating cosine similarity between vectors,
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with support for both single vectors and batch operations using NumPy.
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"""
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import numpy as np
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def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
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"""Calculate the cosine similarity between two numeric vectors.
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Cosine similarity measures the cosine of the angle between two vectors,
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returning a value between -1 (opposite) and 1 (identical direction).
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Args:
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vec1: First vector as a list of floats.
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vec2: Second vector as a list of floats.
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Returns:
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Cosine similarity value in range [-1.0, 1.0].
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Returns 0.0 if either vector has zero magnitude.
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Raises:
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ValueError: If vectors have different lengths.
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Examples:
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>>> cosine_similarity([1.0, 0.0], [1.0, 0.0])
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1.0
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>>> cosine_similarity([1.0, 0.0], [0.0, 1.0])
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0.0
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>>> cosine_similarity([1.0, 1.0], [-1.0, -1.0])
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-1.0
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"""
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if len(vec1) != len(vec2):
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raise ValueError(f"Vectors must have same length: {len(vec1)} != {len(vec2)}")
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dot_product = sum(a * b for a, b in zip(vec1, vec2))
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magnitude1 = sum(a * a for a in vec1) ** 0.5
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magnitude2 = sum(b * b for b in vec2) ** 0.5
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if magnitude1 == 0 or magnitude2 == 0:
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return 0.0
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return dot_product / (magnitude1 * magnitude2)
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def batch_cosine_similarity(nd_array1: np.ndarray, nd_array2: np.ndarray) -> np.ndarray:
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"""Calculate cosine similarity matrix between two batches of vectors.
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Efficiently computes pairwise cosine similarities using matrix operations.
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Args:
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nd_array1: Matrix of shape (batch_size1, emb_size) representing
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the first batch of embedding vectors.
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nd_array2: Matrix of shape (batch_size2, emb_size) representing
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the second batch of embedding vectors.
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Returns:
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Similarity matrix of shape (batch_size1, batch_size2) where
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result[i, j] is the cosine similarity between nd_array1[i] and
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nd_array2[j]. Values are in range [-1.0, 1.0].
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Raises:
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ValueError: If embedding dimensions don't match between arrays.
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Examples:
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>>> import numpy as np
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>>> arr1 = np.array([[1.0, 0.0], [0.0, 1.0]])
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>>> arr2 = np.array([[1.0, 0.0], [1.0, 1.0]])
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>>> batch_cosine_similarity(arr1, arr2)
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array([[1. , 0.70710678],
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[0. , 0.70710678]])
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"""
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if nd_array1.shape[1] != nd_array2.shape[1]:
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raise ValueError(
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f"Embedding dimensions must match: {nd_array1.shape[1]} != {nd_array2.shape[1]}",
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)
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# Compute dot products: (batch_size1, emb_size) @ (emb_size, batch_size2)
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# Result shape: (batch_size1, batch_size2)
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dot_products = np.dot(nd_array1, nd_array2.T)
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# Compute L2 norms for each vector
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norms1 = np.linalg.norm(nd_array1, axis=1) # Shape: (batch_size1,)
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norms2 = np.linalg.norm(nd_array2, axis=1) # Shape: (batch_size2,)
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# Compute outer product of norms: (batch_size1, 1) @ (1, batch_size2)
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# Result shape: (batch_size1, batch_size2)
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norm_products = np.outer(norms1, norms2)
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# Avoid division by zero
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norm_products = np.where(norm_products == 0, 1e-10, norm_products)
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# Compute cosine similarities
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return dot_products / norm_products
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