A lil service that creates embeddings of posts, profiles, and avatars to store them in Qdrant
add posts, profiles, and avatars
+1751
-13
+4
config.py
+4
config.py
···
4
4
class Config(BaseSettings):
5
5
qdrant_url: str = "http://localhost:6333"
6
6
qdrant_profile_collection_name: str = "bluesky_profile"
7
+
qdrant_avatar_collection_name: str = "bluesky_avatar"
8
+
qdrant_post_collection_name: str = "bluesky_post"
7
9
8
10
embedding_model: str = "all-MiniLM-L6-v2"
9
11
embedding_device: str = "cuda"
···
13
15
kafka_topic: str = "firehose_events"
14
16
kafka_group_id: str = "skyembed"
15
17
kafka_auto_offset_reset: str = "latest"
18
+
19
+
retina_host: str = "http://localhost:7075"
16
20
17
21
model_config = SettingsConfigDict(
18
22
env_file=".env",
-10
consumer.py
-10
consumer.py
+254
database.py
+254
database.py
···
1
+
from dataclasses import dataclass
2
+
import logging
3
+
from typing import List, Optional
4
+
import uuid
5
+
6
+
from config import CONFIG
7
+
from qdrant_client import QdrantClient
8
+
from qdrant_client.models import (
9
+
Distance,
10
+
FieldCondition,
11
+
Filter,
12
+
MatchValue,
13
+
Payload,
14
+
PayloadSchemaType,
15
+
PointStruct,
16
+
VectorParams,
17
+
)
18
+
19
+
logger = logging.getLogger(__name__)
20
+
21
+
22
+
@dataclass
23
+
class Result:
24
+
did: str
25
+
payload: Optional[Payload]
26
+
score: Optional[float]
27
+
28
+
29
+
@dataclass
30
+
class ResultWithVector(Result):
31
+
vector: PointStruct
32
+
33
+
34
+
class QdrantService:
35
+
def __init__(self) -> None:
36
+
self.client = None
37
+
38
+
def initialized(self):
39
+
return self.client is not None
40
+
41
+
def initialize(self) -> None:
42
+
logger.info(f"Connecting to Qdrant at {CONFIG.qdrant_url}")
43
+
44
+
self.client = QdrantClient(
45
+
url=CONFIG.qdrant_url,
46
+
)
47
+
48
+
self.profile_collection_name = CONFIG.qdrant_profile_collection_name
49
+
self.avatar_collection_name = CONFIG.qdrant_avatar_collection_name
50
+
self.post_collection_name = CONFIG.qdrant_post_collection_name
51
+
self._ensure_collections_exist()
52
+
53
+
def _ensure_collections_exist(self):
54
+
profile_coll_exists = self.client.collection_exists(
55
+
self.profile_collection_name
56
+
)
57
+
avatar_coll_exists = self.client.collection_exists(self.avatar_collection_name)
58
+
post_coll_exists = self.client.collection_exists(self.post_collection_name)
59
+
60
+
if not profile_coll_exists:
61
+
logger.info(f"Creating profile collection: {self.profile_collection_name}")
62
+
self.client.create_collection(
63
+
collection_name=self.profile_collection_name,
64
+
vectors_config=VectorParams(
65
+
size=CONFIG.embedding_size,
66
+
distance=Distance.COSINE,
67
+
),
68
+
)
69
+
self.client.create_payload_index(
70
+
collection_name=self.profile_collection_name,
71
+
field_name="did",
72
+
field_schema=PayloadSchemaType.KEYWORD,
73
+
)
74
+
logger.info("Collection created successfully")
75
+
76
+
if not avatar_coll_exists:
77
+
logger.info(f"Creating avatar collection: {self.avatar_collection_name}")
78
+
self.client.create_collection(
79
+
collection_name=self.avatar_collection_name,
80
+
vectors_config=VectorParams(
81
+
# PDQ vectors have a size of 256
82
+
size=256,
83
+
# Qdrant doesn't support hamming distance, so we'll use euclidian distance and
84
+
# use the square root of the selected max distance for lookups
85
+
distance=Distance.EUCLID,
86
+
),
87
+
)
88
+
self.client.create_payload_index(
89
+
collection_name=self.avatar_collection_name,
90
+
field_name="did",
91
+
field_schema=PayloadSchemaType.KEYWORD,
92
+
)
93
+
94
+
if not post_coll_exists:
95
+
logger.info(f"Creating post collection: {self.post_collection_name}")
96
+
self.client.create_collection(
97
+
collection_name=self.post_collection_name,
98
+
vectors_config=VectorParams(
99
+
size=CONFIG.embedding_size,
100
+
distance=Distance.COSINE,
101
+
),
102
+
)
103
+
self.client.create_payload_index(
104
+
collection_name=self.post_collection_name,
105
+
field_name="uri",
106
+
field_schema=PayloadSchemaType.KEYWORD,
107
+
)
108
+
logger.info("Collection created successfully")
109
+
110
+
def upsert_profile(self, did: str, description: str, vector: List[float]):
111
+
try:
112
+
payload = {
113
+
"did": did,
114
+
"description": description,
115
+
}
116
+
117
+
existing = self.client.scroll(
118
+
collection_name=self.profile_collection_name,
119
+
scroll_filter=Filter(
120
+
must=[FieldCondition(key="did", match=MatchValue(value=did))]
121
+
),
122
+
)
123
+
124
+
if existing and existing[0] and len(existing[0]) > 0:
125
+
point_id = existing[0][0].id
126
+
else:
127
+
point_id = str(uuid.uuid4())
128
+
129
+
point = PointStruct(
130
+
id=point_id,
131
+
vector=vector,
132
+
payload=payload,
133
+
)
134
+
135
+
self.client.upsert(
136
+
collection_name=self.profile_collection_name,
137
+
points=[point],
138
+
)
139
+
140
+
return True
141
+
except Exception as e:
142
+
logger.error(f"Error upserting profile: {e}")
143
+
return False
144
+
145
+
def upsert_avatar(self, did: str, cid: str, vector: List[float]):
146
+
try:
147
+
payload = {
148
+
"did": did,
149
+
"cid": cid,
150
+
}
151
+
152
+
existing = self.client.scroll(
153
+
collection_name=self.avatar_collection_name,
154
+
scroll_filter=Filter(
155
+
must=[FieldCondition(key="did", match=MatchValue(value=did))]
156
+
),
157
+
)
158
+
159
+
if existing and existing[0] and len(existing[0]) > 0:
160
+
point_id = existing[0][0].id
161
+
else:
162
+
point_id = str(uuid.uuid4())
163
+
164
+
point = PointStruct(
165
+
id=point_id,
166
+
vector=vector,
167
+
payload=payload,
168
+
)
169
+
170
+
self.client.upsert(
171
+
collection_name=self.avatar_collection_name,
172
+
points=[point],
173
+
)
174
+
175
+
return True
176
+
except Exception as e:
177
+
logger.error(f"Error upserting avatar: {e}")
178
+
return False
179
+
180
+
def upsert_post(self, did: str, uri: str, text: str, vector: List[float]):
181
+
try:
182
+
payload = {
183
+
"did": did,
184
+
"uri": uri,
185
+
"text": text,
186
+
}
187
+
188
+
# we don't care about upserting these
189
+
point_id = str(uuid.uuid4())
190
+
191
+
point = PointStruct(
192
+
id=point_id,
193
+
vector=vector,
194
+
payload=payload,
195
+
)
196
+
197
+
self.client.upsert(
198
+
collection_name=self.post_collection_name,
199
+
points=[point],
200
+
)
201
+
202
+
return True
203
+
except Exception as e:
204
+
logger.error(f"Error upserting post: {e}")
205
+
return False
206
+
207
+
def search_similar(
208
+
self,
209
+
collection_name: str,
210
+
query_vector: List[float],
211
+
limit: int = 10,
212
+
score_threshold: Optional[float] = None,
213
+
filter_conditions: Optional[Filter] = None,
214
+
) -> Optional[List[Result]]:
215
+
try:
216
+
results = self.client.query_points(
217
+
collection_name=collection_name,
218
+
query=query_vector,
219
+
query_filter=filter_conditions,
220
+
limit=limit,
221
+
score_threshold=score_threshold,
222
+
with_payload=True,
223
+
).points
224
+
225
+
return [
226
+
Result(
227
+
did=hit.payload.get("did"),
228
+
payload=hit.payload,
229
+
score=hit.score,
230
+
)
231
+
for hit in results
232
+
]
233
+
except Exception as e:
234
+
logger.error(f"Error searching for similar vectors: {e}")
235
+
236
+
def get_profile_by_did(self, did: str) -> Optional[ResultWithVector]:
237
+
result = self.client.scroll(
238
+
collection_name=self.profile_collection_name,
239
+
scroll_filter=Filter(
240
+
must=[FieldCondition(key="did", match=MatchValue(value=did))]
241
+
),
242
+
)
243
+
244
+
if result and result[0] and len(result[0]) > 0:
245
+
point = result[0][0]
246
+
return ResultWithVector(
247
+
did=point.payload["did"],
248
+
payload=point.payload,
249
+
vector=point.vector,
250
+
score=1.0,
251
+
)
252
+
253
+
254
+
QDRANT_SERVICE = QdrantService()
+20
docker-compose.yaml
+20
docker-compose.yaml
···
1
+
services:
2
+
qdrant:
3
+
image: qdrant/qdrant:latest
4
+
ports:
5
+
- "6333:6333"
6
+
- "6334:6334"
7
+
volumes:
8
+
- qdrant_storage:/qdrant/storage
9
+
environment:
10
+
- QDRANT_HOST=0.0.0.0
11
+
restart: unless-stopped
12
+
13
+
retina:
14
+
image: ghcr.io/haileyok/osprey-atproto/retina:main-3604991
15
+
ports:
16
+
- "7075:8080"
17
+
restart: unless-stopped
18
+
19
+
volumes:
20
+
qdrant_storage:
+42
embedder.py
+42
embedder.py
···
1
+
import logging
2
+
from typing import List
3
+
4
+
from sentence_transformers import SentenceTransformer
5
+
import torch
6
+
7
+
from config import CONFIG
8
+
9
+
10
+
logger = logging.getLogger(__name__)
11
+
12
+
13
+
class EmbeddingService:
14
+
def __init__(self) -> None:
15
+
self.model = None
16
+
17
+
def initialized(self):
18
+
return self.model is not None
19
+
20
+
def initialize(self) -> None:
21
+
device = CONFIG.embedding_device
22
+
23
+
if device == "cuda" and not torch.cuda.is_available():
24
+
device = "cpu"
25
+
logger.warning("CUDA requested but not availaable, falling back to CPU")
26
+
27
+
if device == "cuda":
28
+
logger.info("Using CUDA")
29
+
else:
30
+
logger.info("Using CPU")
31
+
32
+
self.model = SentenceTransformer(CONFIG.embedding_model, device=device)
33
+
34
+
def encode(self, text: str) -> List[float]:
35
+
if not text or not text.strip():
36
+
return [0.0] * CONFIG.embedding_size
37
+
38
+
vector = self.model.encode(text, convert_to_numpy=True)
39
+
return vector.tolist()
40
+
41
+
42
+
EMBEDDING_SERVICE = EmbeddingService()
+78
-2
indexer.py
+78
-2
indexer.py
···
1
1
import logging
2
2
3
+
from database import QDRANT_SERVICE
4
+
from embedder import EMBEDDING_SERVICE
5
+
from retina import RETINA_CLIENT, binary_to_float_vector, hex_to_binary
3
6
from models import AtKafkaEvent
4
7
5
8
···
14
17
if not event.operation:
15
18
return
16
19
17
-
if event.operation.collection != "app.bsky.actor.profile":
20
+
if event.operation.action == "delete":
18
21
return
19
22
20
-
logger.info(f"{event}")
23
+
if event.operation.collection == "app.bsky.actor.profile":
24
+
self._handle_profile(event)
25
+
elif event.operation.collection == "app.bsky.feed.post":
26
+
self._handle_post(event)
27
+
28
+
def _handle_profile(self, event: AtKafkaEvent):
29
+
record = event.operation.record
30
+
31
+
description = record.get("description")
32
+
if not isinstance(description, str):
33
+
return
34
+
35
+
if not description or not description.strip():
36
+
return
37
+
38
+
vector = EMBEDDING_SERVICE.encode(description)
39
+
40
+
QDRANT_SERVICE.upsert_profile(
41
+
did=event.did, description=description, vector=vector
42
+
)
43
+
44
+
try:
45
+
avatar = record.get("avatar")
46
+
47
+
if not isinstance(avatar, dict):
48
+
return
49
+
50
+
ref = avatar.get("ref")
51
+
52
+
if not isinstance(ref, dict):
53
+
return
54
+
55
+
link = ref.get("$link")
56
+
if not isinstance(link, str):
57
+
return
58
+
59
+
resp = RETINA_CLIENT.get_image_hash(event.did, link)
60
+
except Exception as e:
61
+
logger.error(f"Failed to get avatar hash: {e}")
62
+
return
63
+
64
+
if resp.quality_too_low:
65
+
logger.info("avatar quality was too low")
66
+
return
67
+
68
+
if not resp.hash:
69
+
logger.error("no hash in response")
70
+
return
71
+
72
+
bin = hex_to_binary(resp.hash)
73
+
vector = binary_to_float_vector(bin)
74
+
75
+
QDRANT_SERVICE.upsert_avatar(
76
+
did=event.did,
77
+
cid=link,
78
+
vector=vector,
79
+
)
80
+
81
+
def _handle_post(self, event: AtKafkaEvent):
82
+
text = event.operation.record.get("text")
83
+
if not isinstance(text, str):
84
+
return
85
+
86
+
if not text or not text.strip():
87
+
return
88
+
89
+
vector = EMBEDDING_SERVICE.encode(text)
90
+
91
+
QDRANT_SERVICE.upsert_post(
92
+
did=event.did,
93
+
uri=f"at://{event.did}/app.bsky.feed.post/{event.operation.rkey}",
94
+
text=text,
95
+
vector=vector,
96
+
)
+6
-1
main.py
+6
-1
main.py
···
7
7
8
8
import click
9
9
from aiokafka import AIOKafkaConsumer
10
-
from aiokafka.errors import KafkaError
11
10
12
11
from config import CONFIG
12
+
from database import QDRANT_SERVICE
13
+
from embedder import EMBEDDING_SERVICE
13
14
from indexer import Indexer
14
15
from models import AtKafkaEvent
15
16
···
91
92
):
92
93
signal.signal(signal.SIGINT, signal_handler)
93
94
signal.signal(signal.SIGTERM, signal_handler)
95
+
96
+
EMBEDDING_SERVICE.initialize()
97
+
98
+
QDRANT_SERVICE.initialize()
94
99
95
100
try:
96
101
asyncio.run(
+6
pyproject.toml
+6
pyproject.toml
+46
retina.py
+46
retina.py
···
1
+
from typing import List, Optional
2
+
from pydantic import BaseModel, Field
3
+
from config import CONFIG
4
+
5
+
import requests
6
+
7
+
8
+
class RetinaResult(BaseModel):
9
+
hash: Optional[str]
10
+
binary: Optional[str]
11
+
quality_too_low: Optional[bool] = Field(alias="qualityTooLow")
12
+
13
+
14
+
class RetinaClient:
15
+
def __init__(self) -> None:
16
+
self._retina_host = CONFIG.retina_host
17
+
18
+
def get_image_hash(self, did: str, cid: str):
19
+
resp = requests.post(
20
+
f"{self._retina_host}/api/hash",
21
+
json={
22
+
"did": did,
23
+
"cid": cid,
24
+
},
25
+
)
26
+
resp.raise_for_status()
27
+
28
+
return RetinaResult.model_validate_json(resp.text)
29
+
30
+
31
+
RETINA_CLIENT = RetinaClient()
32
+
33
+
34
+
def hex_to_binary(hex: str):
35
+
return bytes.fromhex(hex)
36
+
37
+
38
+
def binary_to_float_vector(bin: bytes):
39
+
vector_data: List[float] = []
40
+
for byte in bin:
41
+
for j in range(8):
42
+
if (byte >> (7 - j)) & 1 == 1:
43
+
vector_data.append(1.0)
44
+
else:
45
+
vector_data.append(0.0)
46
+
return vector_data
+162
search.py
+162
search.py
···
1
+
import logging
2
+
import sys
3
+
from typing import List
4
+
import click
5
+
from rich.console import Console
6
+
from rich.table import Table
7
+
from rich.panel import Panel
8
+
from rich import box
9
+
10
+
from config import CONFIG
11
+
from database import QDRANT_SERVICE, Result
12
+
from embedder import EMBEDDING_SERVICE
13
+
14
+
15
+
logging.basicConfig(
16
+
level=logging.INFO,
17
+
format=logging.BASIC_FORMAT,
18
+
)
19
+
20
+
logger = logging.getLogger(__name__)
21
+
22
+
console = Console()
23
+
24
+
25
+
def display_results(
26
+
type: str, query: str, results: List[Result], show_more: bool = False
27
+
):
28
+
"""
29
+
A lil guy that turns the results into a table for viewing
30
+
"""
31
+
32
+
if not results:
33
+
console.print("[yellow]No similar profiles found.[/yellow]")
34
+
return
35
+
36
+
console.print(Panel(f"[bold blue]Query: {query}[/bold blue]", box=box.ROUNDED))
37
+
console.print()
38
+
39
+
table = Table(
40
+
title=f"Found {len(results)} similar results",
41
+
box=box.ROUNDED,
42
+
header_style="bold magenta",
43
+
expand=True,
44
+
show_header=True,
45
+
show_lines=True,
46
+
)
47
+
48
+
table.add_column("#", style="dim", width=4)
49
+
table.add_column("DID", style="cyan", width=35)
50
+
table.add_column("Similarity", justify="right", style="green", width=10)
51
+
52
+
if show_more:
53
+
table.add_column("More", style="white", overflow="fold")
54
+
55
+
for idx, result in enumerate(results, 1):
56
+
similarity_percent = f"{result.score or 0.0 * 100:.4f}%"
57
+
58
+
row: List[str] = [
59
+
str(idx),
60
+
result.did,
61
+
similarity_percent,
62
+
]
63
+
64
+
if show_more and result.payload is not None:
65
+
more = None
66
+
if type == "profile":
67
+
more = result.payload.get("description")
68
+
69
+
if more is not None:
70
+
row.append(more)
71
+
72
+
table.add_row(*row)
73
+
74
+
console.print(table)
75
+
console.print()
76
+
77
+
78
+
@click.group()
79
+
def main():
80
+
pass
81
+
82
+
83
+
@main.command()
84
+
@click.argument("query", required=False)
85
+
@click.option(
86
+
"--type",
87
+
default="profile",
88
+
show_default=True,
89
+
)
90
+
@click.option(
91
+
"--did",
92
+
)
93
+
@click.option(
94
+
"--limit",
95
+
default=10,
96
+
show_default=True,
97
+
)
98
+
@click.option(
99
+
"--threshold",
100
+
default=0.7,
101
+
type=float,
102
+
show_default=True,
103
+
)
104
+
@click.option(
105
+
"--show-more",
106
+
is_flag=True,
107
+
default=False,
108
+
show_default=True,
109
+
)
110
+
def search(
111
+
query: str,
112
+
type: str,
113
+
did: str,
114
+
limit: int,
115
+
threshold: float,
116
+
show_more: bool,
117
+
):
118
+
# TODO: would be nice if these were flags instead
119
+
if type != "profile":
120
+
raise Exception("invalid type")
121
+
122
+
QDRANT_SERVICE.initialize()
123
+
124
+
try:
125
+
if type == "profile":
126
+
if not query:
127
+
console.print("[cyan]Looking up profile...[/cyan]")
128
+
profile = QDRANT_SERVICE.get_profile_by_did(did)
129
+
130
+
if not profile:
131
+
console.print(f"[red]Profile not found: {did}[/red]")
132
+
sys.exit(1)
133
+
134
+
description = profile.payload.get("description")
135
+
query_vector = profile.vector
136
+
137
+
console.print("[green]Found profile[/green]")
138
+
else:
139
+
EMBEDDING_SERVICE.initialize()
140
+
141
+
description = query
142
+
query_vector = EMBEDDING_SERVICE.encode(query)
143
+
144
+
console.print("[cyan]Looking up similar profiles...[/cyan]")
145
+
146
+
results = QDRANT_SERVICE.search_similar(
147
+
collection_name=CONFIG.qdrant_profile_collection_name,
148
+
query_vector=query_vector,
149
+
limit=limit,
150
+
score_threshold=threshold,
151
+
)
152
+
153
+
display_results(type, description, results, show_more)
154
+
155
+
except Exception as e:
156
+
console.print(f"[red]Error: {e}[/red]")
157
+
logger.error(f"Search error: {e}", exc_info=True)
158
+
sys.exit(1)
159
+
160
+
161
+
if __name__ == "__main__":
162
+
main()
+1133
uv.lock
+1133
uv.lock
···
1
1
version = 1
2
2
revision = 3
3
3
requires-python = ">=3.12"
4
+
resolution-markers = [
5
+
"python_full_version >= '3.14'",
6
+
"python_full_version == '3.13.*'",
7
+
"python_full_version < '3.13'",
8
+
]
4
9
5
10
[[package]]
6
11
name = "aiokafka"
···
34
39
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
35
40
wheels = [
36
41
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
42
+
]
43
+
44
+
[[package]]
45
+
name = "anyio"
46
+
version = "4.12.0"
47
+
source = { registry = "https://pypi.org/simple" }
48
+
dependencies = [
49
+
{ name = "idna" },
50
+
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
51
+
]
52
+
sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
53
+
wheels = [
54
+
{ url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
37
55
]
38
56
39
57
[[package]]
···
46
64
]
47
65
48
66
[[package]]
67
+
name = "certifi"
68
+
version = "2025.11.12"
69
+
source = { registry = "https://pypi.org/simple" }
70
+
sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
71
+
wheels = [
72
+
{ url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" },
73
+
]
74
+
75
+
[[package]]
76
+
name = "charset-normalizer"
77
+
version = "3.4.4"
78
+
source = { registry = "https://pypi.org/simple" }
79
+
sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
80
+
wheels = [
81
+
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82
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83
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{ url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558, upload-time = "2025-10-14T04:40:55.677Z" },
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295
1264
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···
302
1271
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1281
307
1282
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312
1287
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