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4f6e61d | 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 | import numpy as np
import json
from typing import List, Dict, Any, Tuple
from sqlalchemy import select, func
from sqlalchemy.ext.asyncio import AsyncSession
from backend.db.models.comment import Comment
from backend.services.ml_model import toxicity_detector
async def find_similar_comments(
db: AsyncSession,
text: str,
limit: int = 10,
classification: int = None
) -> List[Dict[str, Any]]:
"""
Find comments similar to the given text using vector similarity
Args:
db: Database session
text: Text to find similar comments for
limit: Maximum number of similar comments to return
classification: Filter by classification (optional)
Returns:
List of similar comments with similarity scores
"""
# Get vector representation of the text
embedding = await toxicity_detector.get_embeddings(text)
# Build query to get comments
query = select(Comment)
# Add classification filter if provided
if classification is not None:
query = query.filter(Comment.classification_result == classification)
# Execute query
result = await db.execute(query)
comments = result.scalars().all()
# Calculate similarities manually
comments_with_distances = []
for comment in comments:
try:
# Get the vector from content_vector_json
if hasattr(comment, "content_vector_json") and comment.content_vector_json:
comment_vector = np.array(json.loads(comment.content_vector_json))
elif hasattr(comment, "content_vector") and comment.content_vector:
comment_vector = np.array(comment.content_vector)
else:
continue
# Calculate L2 distance
distance = np.linalg.norm(embedding - comment_vector)
comments_with_distances.append((comment, distance))
except (TypeError, ValueError, json.JSONDecodeError):
# Skip comments with invalid vectors
continue
# Sort by distance (lowest first)
comments_with_distances.sort(key=lambda x: x[1])
# Take only the top 'limit' results
comments_with_distances = comments_with_distances[:limit]
# Format results
similar_comments = []
for comment, distance in comments_with_distances:
# Convert distance to similarity score (0-100)
similarity = max(0, min(100, 100 * (1 - distance / 10)))
similar_comments.append({
"id": comment.id,
"content": comment.content,
"classification": comment.classification_result,
"platform": comment.source_platform,
"similarity_score": round(similarity, 2),
"created_at": comment.created_at.isoformat() if comment.created_at else None
})
return similar_comments
async def cluster_comments(
db: AsyncSession,
limit: int = 100,
classification: int = None
) -> List[Dict[str, Any]]:
"""
Perform basic clustering on comments to find groups of similar content
Args:
db: Database session
limit: Maximum number of comments to analyze
classification: Filter by classification (optional)
Returns:
List of comment clusters
"""
# Build query to get comments
query = select(Comment).limit(limit)
# Add classification filter if provided
if classification is not None:
query = query.filter(Comment.classification_result == classification)
# Execute query
result = await db.execute(query)
comments = result.scalars().all()
# Extract comment vectors
comment_vectors = []
for comment in comments:
try:
# Get vector from content_vector_json or content_vector
if hasattr(comment, "content_vector_json") and comment.content_vector_json:
vector = np.array(json.loads(comment.content_vector_json))
elif hasattr(comment, "content_vector") and comment.content_vector:
vector = np.array(comment.content_vector)
else:
continue
comment_vectors.append({
"id": comment.id,
"content": comment.content,
"classification": comment.classification_result,
"platform": comment.source_platform,
"vector": vector,
"created_at": comment.created_at
})
except (TypeError, ValueError, json.JSONDecodeError):
# Skip comments with invalid vectors
continue
# If too few comments, return them all as one cluster
if len(comment_vectors) < 5:
return [{
"cluster_id": 0,
"size": len(comment_vectors),
"comments": [
{
"id": c["id"],
"content": c["content"],
"classification": c["classification"],
"platform": c["platform"],
"created_at": c["created_at"].isoformat() if c["created_at"] else None
}
for c in comment_vectors
]
}]
# Simple clustering based on vector similarity
# This is a basic implementation - in a real system, you'd use a more sophisticated algorithm
clusters = []
assigned_comments = set()
# For each unassigned comment, find similar ones to form a cluster
for i, comment in enumerate(comment_vectors):
if comment["id"] in assigned_comments:
continue
# This comment becomes the centroid of a new cluster
cluster_comments = [comment]
assigned_comments.add(comment["id"])
# Find similar comments
for j, other_comment in enumerate(comment_vectors):
if other_comment["id"] in assigned_comments:
continue
# Calculate similarity
vec1 = comment["vector"]
vec2 = other_comment["vector"]
distance = np.linalg.norm(vec1 - vec2)
similarity = max(0, min(100, 100 * (1 - distance / 10)))
# If similar enough, add to cluster
if similarity > 70: # Threshold for similarity
cluster_comments.append(other_comment)
assigned_comments.add(other_comment["id"])
# Only create clusters with at least 2 comments
if len(cluster_comments) > 1:
clusters.append({
"cluster_id": len(clusters),
"size": len(cluster_comments),
"comments": [
{
"id": c["id"],
"content": c["content"],
"classification": c["classification"],
"platform": c["platform"],
"created_at": c["created_at"].isoformat() if c["created_at"] else None
}
for c in cluster_comments
]
})
# Add remaining comments as individual clusters
remaining = [c for c in comment_vectors if c["id"] not in assigned_comments]
if remaining:
clusters.append({
"cluster_id": len(clusters),
"size": len(remaining),
"comments": [
{
"id": c["id"],
"content": c["content"],
"classification": c["classification"],
"platform": c["platform"],
"created_at": c["created_at"].isoformat() if c["created_at"] else None
}
for c in remaining
]
})
return clusters |