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56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
"""Embedding utilities for testing."""
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import math
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import random
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from collections import Counter, defaultdict
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from typing import Any
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from langchain_core.embeddings import Embeddings
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class CharacterEmbeddings(Embeddings):
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"""Simple character-frequency based embeddings using random projections."""
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def __init__(self, dims: int = 50, seed: int = 42):
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"""Initialize with embedding dimensions and random seed."""
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self._rng = random.Random(seed)
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self.dims = dims
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# Create projection vector for each character lazily
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self._char_projections: defaultdict[str, list[float]] = defaultdict(
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lambda: [
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self._rng.gauss(0, 1 / math.sqrt(self.dims)) for _ in range(self.dims)
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]
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)
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def _embed_one(self, text: str) -> list[float]:
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"""Embed a single text."""
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counts = Counter(text)
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total = sum(counts.values())
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if total == 0:
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return [0.0] * self.dims
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embedding = [0.0] * self.dims
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for char, count in counts.items():
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weight = count / total
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char_proj = self._char_projections[char]
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for i, proj in enumerate(char_proj):
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embedding[i] += weight * proj
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norm = math.sqrt(sum(x * x for x in embedding))
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if norm > 0:
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embedding = [x / norm for x in embedding]
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return embedding
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def embed_documents(self, texts: list[str]) -> list[list[float]]:
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"""Embed a list of documents."""
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return [self._embed_one(text) for text in texts]
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def embed_query(self, text: str) -> list[float]:
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"""Embed a query string."""
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return self._embed_one(text)
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def __eq__(self, other: Any) -> bool:
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return isinstance(other, CharacterEmbeddings) and self.dims == other.dims
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