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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Similarity detection for skill deduplication."""
from __future__ import annotations
import importlib
import logging
import threading
from typing import TYPE_CHECKING, List, Optional, Tuple
from ..protocols.deduplication import DeduplicationConfig
if TYPE_CHECKING:
from ..core.skillbook import Skill
from ..core.skillbook import Skillbook
logger = logging.getLogger(__name__)
def _has(module: str) -> bool:
"""Return True if *module* can be imported."""
try:
importlib.import_module(module)
return True
except ImportError:
return False
class SimilarityDetector:
"""Detect similar skill pairs using cosine similarity on embeddings."""
def __init__(self, config: DeduplicationConfig | None = None) -> None:
self.config = config or DeduplicationConfig()
self._model: object | None = None # lazy sentence-transformers model
self._model_lock = threading.Lock()
# ------------------------------------------------------------------
# Single / batch embedding computation
# ------------------------------------------------------------------
def compute_embedding(self, text: str) -> Optional[List[float]]:
"""Compute embedding for a single text."""
if self.config.embedding_provider == "litellm":
return self._embed_litellm(text)
return self._embed_st(text)
def compute_embeddings_batch(self, texts: List[str]) -> List[Optional[List[float]]]:
"""Compute embeddings for multiple texts (more efficient)."""
if not texts:
return []
if self.config.embedding_provider == "litellm":
return self._embed_batch_litellm(texts)
return self._embed_batch_st(texts)
# ------------------------------------------------------------------
# LiteLLM provider
# ------------------------------------------------------------------
def _embed_litellm(self, text: str) -> Optional[List[float]]:
if not _has("litellm"):
logger.warning("LiteLLM not available for embeddings")
return None
try:
import litellm
response = litellm.embedding(
model=self.config.embedding_model, input=[text]
)
return response.data[0]["embedding"]
except Exception as e:
logger.warning(
"Failed to compute embedding via LiteLLM (%s): %s", type(e).__name__, e
)
return None
def _embed_batch_litellm(self, texts: List[str]) -> List[Optional[List[float]]]:
if not _has("litellm"):
logger.warning("LiteLLM not available for embeddings")
return [None] * len(texts)
try:
import litellm
response = litellm.embedding(model=self.config.embedding_model, input=texts)
return [item["embedding"] for item in response.data]
except Exception as e:
logger.warning(
"Failed to compute batch embeddings via LiteLLM (%s): %s",
type(e).__name__,
e,
)
return [None] * len(texts)
# ------------------------------------------------------------------
# sentence-transformers provider
# ------------------------------------------------------------------
def _embed_st(self, text: str) -> Optional[List[float]]:
if not _has("sentence_transformers"):
logger.warning("sentence-transformers not available for embeddings")
return None
try:
model = self._get_st_model()
embedding = model.encode(text, convert_to_numpy=True)
return embedding.tolist()
except Exception as e:
logger.warning(
"Failed to compute embedding via sentence-transformers (%s): %s",
type(e).__name__,
e,
)
return None
def _embed_batch_st(self, texts: List[str]) -> List[Optional[List[float]]]:
if not _has("sentence_transformers"):
logger.warning("sentence-transformers not available for embeddings")
return [None] * len(texts)
try:
model = self._get_st_model()
embeddings = model.encode(texts, convert_to_numpy=True)
return [emb.tolist() for emb in embeddings]
except Exception as e:
logger.warning(
"Failed to compute batch embeddings via sentence-transformers (%s): %s",
type(e).__name__,
e,
)
return [None] * len(texts)
def _get_st_model(self):
"""Lazy-load the sentence-transformers model (thread-safe)."""
if self._model is None:
with self._model_lock:
if self._model is None: # double-check after acquiring lock
from sentence_transformers import SentenceTransformer
self._model = SentenceTransformer(self.config.local_model_name)
return self._model
# ------------------------------------------------------------------
# Cosine similarity
# ------------------------------------------------------------------
def cosine_similarity(self, a: List[float], b: List[float]) -> float:
"""Compute cosine similarity between two embedding vectors."""
if not _has("numpy"):
# Pure-Python fallback
dot = sum(x * y for x, y in zip(a, b))
norm_a = sum(x * x for x in a) ** 0.5
norm_b = sum(x * x for x in b) ** 0.5
if norm_a == 0 or norm_b == 0:
return 0.0
return dot / (norm_a * norm_b)
import numpy as np
a_arr = np.array(a)
b_arr = np.array(b)
dot = np.dot(a_arr, b_arr)
norm_a = np.linalg.norm(a_arr)
norm_b = np.linalg.norm(b_arr)
if norm_a == 0 or norm_b == 0:
return 0.0
return float(dot / (norm_a * norm_b))
# ------------------------------------------------------------------
# High-level API
# ------------------------------------------------------------------
def ensure_embeddings(self, skillbook: "Skillbook") -> int:
"""Ensure all active skills have embeddings computed.
Returns:
Number of new embeddings computed.
"""
needs = [s for s in skillbook.skills() if s.embedding is None]
if not needs:
return 0
texts = [s.embedding_text() for s in needs]
embeddings = self.compute_embeddings_batch(texts)
count = 0
for skill, embedding in zip(needs, embeddings):
if embedding is not None:
skill.embedding = embedding
count += 1
logger.info("Computed %d embeddings for skills", count)
return count
def detect_similar_pairs(
self,
skillbook: "Skillbook",
threshold: float | None = None,
) -> List[Tuple["Skill", "Skill", float]]:
"""Find all skill pairs with similarity >= *threshold*.
Returns:
Sorted list of ``(skill_a, skill_b, similarity)`` tuples
(descending by score).
"""
threshold = threshold or self.config.similarity_threshold
similar_pairs: List[Tuple["Skill", "Skill", float]] = []
skills = skillbook.skills(include_invalid=False)
if self.config.within_section_only:
sections: dict[str, list] = {}
for skill in skills:
sections.setdefault(skill.section, []).append(skill)
for section_skills in sections.values():
similar_pairs.extend(
self._find_similar(section_skills, skillbook, threshold)
)
else:
similar_pairs = self._find_similar(skills, skillbook, threshold)
similar_pairs.sort(key=lambda x: x[2], reverse=True)
return similar_pairs
def _find_similar(
self,
skills: List["Skill"],
skillbook: "Skillbook",
threshold: float,
) -> List[Tuple["Skill", "Skill", float]]:
pairs: List[Tuple["Skill", "Skill", float]] = []
for i, skill_a in enumerate(skills):
if skill_a.embedding is None:
continue
for skill_b in skills[i + 1 :]:
if skill_b.embedding is None:
continue
if skillbook.has_keep_decision(skill_a.id, skill_b.id):
continue
sim = self.cosine_similarity(skill_a.embedding, skill_b.embedding)
if sim >= threshold:
pairs.append((skill_a, skill_b, sim))
return pairs
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