generated from coulomb/repo-seed
tegwick
e499edba90
Copy markitect.llm module into standalone llm_connect package. All markitect.* imports replaced with llm_connect.* equivalents. LLMError base class inlined (no markitect.exceptions dependency). Verified: from llm_connect import create_adapter works. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""
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Pure-Python vector similarity utilities.
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No external dependencies — uses :mod:`math` only. Sufficient for the
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current entity scale (~100s). numpy can be substituted later if needed.
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"""
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import math
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def cosine_similarity(a: list[float], b: list[float]) -> float:
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"""Cosine similarity between two vectors.
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Returns a float in [-1, 1]. Returns 0.0 if either vector has
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zero magnitude (to avoid division by zero).
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"""
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dot = sum(x * y for x, y in zip(a, b))
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mag_a = math.sqrt(sum(x * x for x in a))
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mag_b = math.sqrt(sum(x * x for x in b))
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if mag_a == 0.0 or mag_b == 0.0:
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return 0.0
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return dot / (mag_a * mag_b)
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def similarity_matrix(embeddings: list[list[float]]) -> list[list[float]]:
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"""Build an NxN cosine similarity matrix.
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``matrix[i][j]`` is the cosine similarity between
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``embeddings[i]`` and ``embeddings[j]``.
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"""
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n = len(embeddings)
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mat: list[list[float]] = [[0.0] * n for _ in range(n)]
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for i in range(n):
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mat[i][i] = 1.0
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for j in range(i + 1, n):
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sim = cosine_similarity(embeddings[i], embeddings[j])
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mat[i][j] = sim
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mat[j][i] = sim
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return mat
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def find_similar_pairs(
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embeddings: dict[str, list[float]],
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threshold: float = 0.80,
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) -> list[tuple[str, str, float]]:
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"""Find all pairs with cosine similarity >= *threshold*.
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Args:
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embeddings: Mapping of slug → embedding vector.
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threshold: Minimum similarity to include (default 0.80).
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Returns:
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List of ``(slug_a, slug_b, similarity)`` tuples sorted by
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similarity descending.
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"""
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slugs = sorted(embeddings)
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pairs: list[tuple[str, str, float]] = []
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for i, slug_a in enumerate(slugs):
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for slug_b in slugs[i + 1:]:
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sim = cosine_similarity(embeddings[slug_a], embeddings[slug_b])
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if sim >= threshold:
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pairs.append((slug_a, slug_b, sim))
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pairs.sort(key=lambda t: t[2], reverse=True)
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return pairs
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