my prefect server setup
prefect-metrics.waow.tech
python
orchestration
add phi memory compaction pipeline
ingest flow now fetches observations + interactions from phi's TurboPuffer
namespaces alongside github/tangled data. new dbt models stage and profile
per-user data. new compact flow (triggers on transform, parallel with brief)
synthesizes relationship summaries via haiku and writes them back to
TurboPuffer where phi reads them at conversation time.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
+1055
-4
+51
analytics/models/enrichment/int_phi_user_profiles.sql
+51
analytics/models/enrichment/int_phi_user_profiles.sql
···
1
+
WITH obs_counts AS (
2
+
SELECT
3
+
handle,
4
+
COUNT(DISTINCT observation_id) AS observation_count,
5
+
MIN(created_at) AS first_observation,
6
+
MAX(created_at) AS latest_observation
7
+
FROM {{ ref('stg_phi_observations') }}
8
+
GROUP BY handle
9
+
),
10
+
tag_stats AS (
11
+
SELECT
12
+
handle,
13
+
tag,
14
+
COUNT(*) AS tag_count
15
+
FROM (
16
+
SELECT handle, UNNEST(tags) AS tag
17
+
FROM {{ ref('stg_phi_observations') }}
18
+
)
19
+
GROUP BY handle, tag
20
+
),
21
+
top_tags AS (
22
+
SELECT
23
+
handle,
24
+
LIST(tag ORDER BY tag_count DESC)[:5] AS top_tags
25
+
FROM tag_stats
26
+
GROUP BY handle
27
+
),
28
+
ix_stats AS (
29
+
SELECT
30
+
handle,
31
+
COUNT(*) AS interaction_count,
32
+
MIN(created_at) AS first_interaction,
33
+
MAX(created_at) AS last_interaction
34
+
FROM {{ ref('stg_phi_interactions') }}
35
+
GROUP BY handle
36
+
)
37
+
SELECT
38
+
o.handle,
39
+
o.observation_count,
40
+
COALESCE(i.interaction_count, 0) AS interaction_count,
41
+
o.first_observation AS first_seen,
42
+
i.last_interaction,
43
+
t.top_tags,
44
+
GREATEST(0.0, 1.0 - DATEDIFF('day',
45
+
COALESCE(i.last_interaction, o.latest_observation)::DATE,
46
+
CURRENT_DATE) / 30.0
47
+
) AS recency_score
48
+
FROM obs_counts o
49
+
LEFT JOIN top_tags t ON o.handle = t.handle
50
+
LEFT JOIN ix_stats i ON o.handle = i.handle
51
+
WHERE o.observation_count >= 3
+2
analytics/models/staging/_sources.yml
+2
analytics/models/staging/_sources.yml
+5
analytics/models/staging/stg_phi_interactions.sql
+5
analytics/models/staging/stg_phi_interactions.sql
+5
analytics/models/staging/stg_phi_observations.sql
+5
analytics/models/staging/stg_phi_observations.sql
+235
flows/compact.py
+235
flows/compact.py
···
1
+
"""
2
+
Synthesize per-user relationship summaries from phi's observations + interactions.
3
+
4
+
Reads from dbt mart (int_phi_user_profiles) and staging models, sends to LLM,
5
+
writes summaries back to TurboPuffer where phi can consume them at conversation time.
6
+
7
+
Triggers on transform completion (parallel with brief).
8
+
"""
9
+
10
+
import hashlib
11
+
import os
12
+
import shutil
13
+
from datetime import datetime, timedelta, timezone
14
+
15
+
import duckdb
16
+
import turbopuffer
17
+
from openai import OpenAI
18
+
from pydantic_ai import Agent
19
+
from pydantic_ai.models.anthropic import AnthropicModel
20
+
from pydantic_ai.providers.anthropic import AnthropicProvider
21
+
from prefect import flow, get_run_logger, task
22
+
from prefect.blocks.system import Secret
23
+
from prefect.cache_policies import CachePolicy
24
+
from prefect.context import TaskRunContext
25
+
26
+
from mps.phi import clean_handle
27
+
28
+
SYSTEM_PROMPT = """\
29
+
you synthesize relationship summaries for a bluesky bot named phi.
30
+
given observations and recent interactions with a user, produce a dense
31
+
paragraph that captures: who this person is, what they care about right
32
+
now, the tone of the relationship, and any notable patterns.
33
+
34
+
write as notes to phi's future self. use lowercase. be honest about
35
+
uncertainty. if the relationship is thin, say so — a thin summary beats
36
+
a fabricated one. include concrete details (projects, interests, topics)
37
+
not just vibes.
38
+
"""
39
+
40
+
41
+
class ByObservationsHash(CachePolicy):
42
+
"""Cache compact result by handle + observations content hash."""
43
+
44
+
def compute_key(
45
+
self,
46
+
task_ctx: TaskRunContext,
47
+
inputs: dict,
48
+
flow_parameters: dict,
49
+
**kwargs,
50
+
) -> str | None:
51
+
handle = inputs.get("handle")
52
+
observations_text = inputs.get("observations_text")
53
+
if not handle or not observations_text:
54
+
return None
55
+
h = hashlib.md5(observations_text.encode()).hexdigest()[:12]
56
+
return f"compact/{handle}/{h}"
57
+
58
+
59
+
@task
60
+
def snapshot_db(db_path: str) -> str:
61
+
"""Snapshot DuckDB once to avoid exclusive flock (same pattern as brief)."""
62
+
snap = "/tmp/compact_analytics_snapshot.duckdb"
63
+
shutil.copy2(db_path, snap)
64
+
return snap
65
+
66
+
67
+
@task
68
+
def load_user_profiles(snap_path: str) -> list[dict]:
69
+
"""Read per-user profiles from the dbt enrichment model."""
70
+
db = duckdb.connect(snap_path, read_only=True)
71
+
rows = db.execute(
72
+
"SELECT handle, observation_count, interaction_count, "
73
+
"first_seen, last_interaction, top_tags, recency_score "
74
+
"FROM int_phi_user_profiles ORDER BY recency_score DESC"
75
+
).fetchall()
76
+
db.close()
77
+
78
+
columns = [
79
+
"handle", "observation_count", "interaction_count",
80
+
"first_seen", "last_interaction", "top_tags", "recency_score",
81
+
]
82
+
return [dict(zip(columns, row)) for row in rows]
83
+
84
+
85
+
@task
86
+
def load_user_observations(snap_path: str, handle: str) -> str:
87
+
"""Read observations for a specific user, formatted as text."""
88
+
db = duckdb.connect(snap_path, read_only=True)
89
+
rows = db.execute(
90
+
"SELECT content, tags, created_at FROM stg_phi_observations "
91
+
"WHERE handle = ? ORDER BY created_at DESC",
92
+
[handle],
93
+
).fetchall()
94
+
db.close()
95
+
96
+
lines = []
97
+
for content, tags, created_at in rows:
98
+
tag_str = f" [{', '.join(tags)}]" if tags else ""
99
+
lines.append(f"- {content}{tag_str} ({created_at})")
100
+
return "\n".join(lines)
101
+
102
+
103
+
@task
104
+
def load_user_interactions(snap_path: str, handle: str) -> str:
105
+
"""Read interactions for a specific user, formatted as text."""
106
+
db = duckdb.connect(snap_path, read_only=True)
107
+
rows = db.execute(
108
+
"SELECT content, created_at FROM stg_phi_interactions "
109
+
"WHERE handle = ? ORDER BY created_at DESC LIMIT 20",
110
+
[handle],
111
+
).fetchall()
112
+
db.close()
113
+
114
+
lines = []
115
+
for content, created_at in rows:
116
+
lines.append(f"[{created_at}]\n{content}")
117
+
return "\n\n".join(lines)
118
+
119
+
120
+
def _format_stats(profile: dict) -> str:
121
+
tags = ", ".join(profile.get("top_tags") or [])
122
+
return (
123
+
f"observations: {profile['observation_count']}, "
124
+
f"interactions: {profile['interaction_count']}, "
125
+
f"first seen: {profile['first_seen']}, "
126
+
f"last interaction: {profile.get('last_interaction') or 'never'}, "
127
+
f"top tags: [{tags}], "
128
+
f"recency: {profile['recency_score']:.2f}"
129
+
)
130
+
131
+
132
+
@task(
133
+
cache_policy=ByObservationsHash(),
134
+
cache_expiration=timedelta(hours=4),
135
+
persist_result=True,
136
+
result_serializer="json",
137
+
)
138
+
async def synthesize_summary(
139
+
handle: str,
140
+
stats_text: str,
141
+
observations_text: str,
142
+
interactions_text: str,
143
+
api_key: str,
144
+
) -> str:
145
+
"""LLM synthesis of a relationship summary. Cached by observations hash."""
146
+
model = AnthropicModel("claude-haiku-4-5", provider=AnthropicProvider(api_key=api_key))
147
+
agent = Agent(model, system_prompt=SYSTEM_PROMPT, name="phi-compactor")
148
+
149
+
prompt = (
150
+
f"user: @{handle}\n"
151
+
f"stats: {stats_text}\n\n"
152
+
f"observations:\n{observations_text}\n\n"
153
+
f"recent interactions:\n{interactions_text}"
154
+
)
155
+
result = await agent.run(prompt)
156
+
return result.output
157
+
158
+
159
+
def _summary_id(handle: str) -> str:
160
+
"""Stable, deterministic ID for a user's relationship summary."""
161
+
return f"summary-{clean_handle(handle)}"
162
+
163
+
164
+
@task
165
+
def write_summary_to_turbopuffer(
166
+
tpuf_key: str,
167
+
openai_key: str,
168
+
handle: str,
169
+
summary: str,
170
+
):
171
+
"""Embed summary and upsert to the user's TurboPuffer namespace as kind=summary."""
172
+
openai_client = OpenAI(api_key=openai_key)
173
+
embedding = openai_client.embeddings.create(
174
+
model="text-embedding-3-small", input=summary,
175
+
).data[0].embedding
176
+
177
+
client = turbopuffer.Turbopuffer(api_key=tpuf_key, region="gcp-us-central1")
178
+
ns_name = f"phi-users-{clean_handle(handle)}"
179
+
ns = client.namespace(ns_name)
180
+
181
+
ns.write(
182
+
upsert_rows=[
183
+
{
184
+
"id": _summary_id(handle),
185
+
"vector": embedding,
186
+
"kind": "summary",
187
+
"content": summary,
188
+
"tags": [],
189
+
"created_at": datetime.now(timezone.utc).isoformat(),
190
+
}
191
+
],
192
+
distance_metric="cosine_distance",
193
+
schema={
194
+
"kind": {"type": "string", "filterable": True},
195
+
"content": {"type": "string", "full_text_search": True},
196
+
"tags": {"type": "[]string", "filterable": True},
197
+
"created_at": {"type": "string"},
198
+
},
199
+
)
200
+
201
+
202
+
@flow(name="compact", log_prints=True)
203
+
async def compact():
204
+
"""Synthesize per-user relationship summaries from phi's memory."""
205
+
logger = get_run_logger()
206
+
db_path = os.environ.get(
207
+
"ANALYTICS_DB_PATH",
208
+
os.environ.get("PREFECT_LOCAL_STORAGE_PATH", "/tmp") + "/analytics.duckdb",
209
+
)
210
+
tpuf_key = (await Secret.load("turbopuffer-api-key")).get()
211
+
openai_key = (await Secret.load("openai-api-key")).get()
212
+
anthropic_key = (await Secret.load("anthropic-api-key")).get()
213
+
214
+
snap_path = snapshot_db(db_path)
215
+
216
+
profiles = load_user_profiles(snap_path)
217
+
logger.info(f"found {len(profiles)} users above threshold")
218
+
219
+
for profile in profiles:
220
+
handle = profile["handle"]
221
+
obs_text = load_user_observations(snap_path, handle)
222
+
ix_text = load_user_interactions(snap_path, handle)
223
+
stats_text = _format_stats(profile)
224
+
225
+
summary = await synthesize_summary(
226
+
handle, stats_text, obs_text, ix_text, anthropic_key,
227
+
)
228
+
write_summary_to_turbopuffer(tpuf_key, openai_key, handle, summary)
229
+
logger.info(f"@{handle}: compacted {profile['observation_count']} observations")
230
+
231
+
232
+
if __name__ == "__main__":
233
+
import asyncio
234
+
235
+
asyncio.run(compact())
+102
-4
flows/ingest.py
+102
-4
flows/ingest.py
···
21
21
from prefect.cache_policies import CachePolicy
22
22
from prefect.context import TaskRunContext
23
23
24
-
from mps.db import write_github_issues, write_tangled_items
24
+
from mps.db import (
25
+
write_github_issues,
26
+
write_phi_interactions,
27
+
write_phi_observations,
28
+
write_tangled_items,
29
+
)
25
30
from mps.github import IssueOrPR, IssueRef, gh_headers
31
+
from mps.phi import PhiInteraction, PhiObservation, restore_handle
26
32
from mps.tangled import PDS_BASE, TangledItem, fetch_items, fetch_repo_at_uris
27
33
28
34
GITHUB_API = "https://api.github.com"
···
178
184
return items
179
185
180
186
187
+
# --- phi tasks ---
188
+
189
+
USER_NS_PREFIX = "phi-users-"
190
+
191
+
192
+
@task
193
+
def fetch_phi_memory(tpuf_key: str) -> tuple[list[PhiObservation], list[PhiInteraction]]:
194
+
"""Fetch observations and interactions from all phi-users-* TurboPuffer namespaces."""
195
+
import turbopuffer
196
+
197
+
logger = get_run_logger()
198
+
client = turbopuffer.Turbopuffer(api_key=tpuf_key, region="gcp-us-central1")
199
+
200
+
observations: list[PhiObservation] = []
201
+
interactions: list[PhiInteraction] = []
202
+
203
+
page = client.namespaces(prefix=USER_NS_PREFIX)
204
+
ns_ids = [ns.id for ns in page.namespaces]
205
+
logger.info(f"found {len(ns_ids)} phi user namespaces")
206
+
207
+
for ns_id in ns_ids:
208
+
handle = restore_handle(ns_id)
209
+
ns = client.namespace(ns_id)
210
+
211
+
# fetch observations
212
+
try:
213
+
resp = ns.query(
214
+
rank_by=("vector", "ANN", [0.5] * 1536),
215
+
top_k=200,
216
+
filters={"kind": ["Eq", "observation"]},
217
+
include_attributes=["content", "tags", "created_at"],
218
+
)
219
+
if resp.rows:
220
+
for row in resp.rows:
221
+
observations.append(PhiObservation(
222
+
handle=handle,
223
+
observation_id=str(row.id),
224
+
content=row.content,
225
+
tags=getattr(row, "tags", []) or [],
226
+
created_at=getattr(row, "created_at", ""),
227
+
))
228
+
except Exception as e:
229
+
if "not found" not in str(e).lower():
230
+
logger.warning(f"failed to fetch observations for {ns_id}: {e}")
231
+
232
+
# fetch interactions
233
+
try:
234
+
resp = ns.query(
235
+
rank_by=("vector", "ANN", [0.5] * 1536),
236
+
top_k=200,
237
+
filters={"kind": ["Eq", "interaction"]},
238
+
include_attributes=["content", "created_at"],
239
+
)
240
+
if resp.rows:
241
+
for row in resp.rows:
242
+
interactions.append(PhiInteraction(
243
+
handle=handle,
244
+
interaction_id=str(row.id),
245
+
content=row.content,
246
+
created_at=getattr(row, "created_at", ""),
247
+
))
248
+
except Exception as e:
249
+
if "not found" not in str(e).lower():
250
+
logger.warning(f"failed to fetch interactions for {ns_id}: {e}")
251
+
252
+
logger.info(f"fetched {len(observations)} observations, {len(interactions)} interactions")
253
+
return observations, interactions
254
+
255
+
256
+
@task
257
+
def persist_phi(
258
+
observations: list[PhiObservation],
259
+
interactions: list[PhiInteraction],
260
+
) -> tuple[int, int]:
261
+
db = _db_path()
262
+
obs_count = write_phi_observations(observations, db) if observations else 0
263
+
ix_count = write_phi_interactions(interactions, db) if interactions else 0
264
+
return obs_count, ix_count
265
+
266
+
181
267
# --- persist tasks ---
182
268
183
269
···
204
290
@flow(name="ingest", log_prints=True)
205
291
def ingest(only_unread: bool = True):
206
292
"""
207
-
Fetch GitHub and tangled.org data concurrently, then persist sequentially.
293
+
Fetch GitHub, tangled.org, and phi memory concurrently, then persist sequentially.
208
294
"""
209
295
logger = get_run_logger()
210
296
211
297
token = load_token()
212
298
213
-
# kick off tangled fetch immediately (no deps)
299
+
# kick off tangled + phi fetches immediately (no deps on github token)
214
300
tangled_future = fetch_all_tangled_items.submit()
215
301
302
+
tpuf_key = Secret.load("turbopuffer-api-key").get()
303
+
phi_future = fetch_phi_memory.submit(tpuf_key)
304
+
216
305
# github fetches need the token
217
306
notif_refs = fetch_notifications(token, only_unread=only_unread)
218
307
authored_refs = fetch_authored_items(token)
···
233
322
gh_items = [r for r in futures.result() if r is not None]
234
323
logger.info(f"resolved {len(gh_items)} github issues/PRs")
235
324
236
-
# wait for tangled fetch
325
+
# wait for tangled + phi fetches
237
326
tangled_items = tangled_future.result()
238
327
logger.info(f"fetched {len(tangled_items)} tangled items")
328
+
329
+
phi_observations, phi_interactions = phi_future.result()
239
330
240
331
# sequential writes — same process, no DuckDB lock contention
241
332
if gh_items:
···
245
336
if tangled_items:
246
337
total = persist_tangled(tangled_items)
247
338
logger.info(f"persisted {len(tangled_items)} tangled rows; {total} total in raw_tangled_items")
339
+
340
+
if phi_observations or phi_interactions:
341
+
obs_total, ix_total = persist_phi(phi_observations, phi_interactions)
342
+
logger.info(
343
+
f"persisted {len(phi_observations)} phi observations ({obs_total} total), "
344
+
f"{len(phi_interactions)} interactions ({ix_total} total)"
345
+
)
248
346
249
347
250
348
if __name__ == "__main__":
+2
packages/mps/pyproject.toml
+2
packages/mps/pyproject.toml
+62
packages/mps/src/mps/db.py
+62
packages/mps/src/mps/db.py
···
4
4
5
5
import duckdb
6
6
7
+
from mps.phi import PhiInteraction, PhiObservation
8
+
7
9
8
10
def write_github_issues(items: list, db_path: str) -> int:
9
11
"""Upsert IssueOrPR items into raw_github_issues. Returns total row count."""
···
68
70
count = con.execute("SELECT count(*) FROM raw_tangled_items").fetchone()[0]
69
71
con.close()
70
72
return count
73
+
74
+
75
+
def write_phi_observations(items: list[PhiObservation], db_path: str) -> int:
76
+
"""Upsert phi observations into raw_phi_observations. Returns total row count."""
77
+
con = duckdb.connect(db_path)
78
+
con.execute("""
79
+
CREATE TABLE IF NOT EXISTS raw_phi_observations (
80
+
handle VARCHAR, observation_id VARCHAR,
81
+
content VARCHAR, tags VARCHAR[],
82
+
created_at VARCHAR,
83
+
fetched_at TIMESTAMP DEFAULT now(),
84
+
PRIMARY KEY (observation_id)
85
+
)
86
+
""")
87
+
rows = [
88
+
(
89
+
item.handle, item.observation_id,
90
+
item.content, item.tags,
91
+
item.created_at,
92
+
datetime.datetime.now(datetime.UTC),
93
+
)
94
+
for item in items
95
+
]
96
+
if rows:
97
+
con.executemany(
98
+
"INSERT OR REPLACE INTO raw_phi_observations VALUES (?, ?, ?, ?, ?, ?)",
99
+
rows,
100
+
)
101
+
count = con.execute("SELECT count(*) FROM raw_phi_observations").fetchone()[0]
102
+
con.close()
103
+
return count
104
+
105
+
106
+
def write_phi_interactions(items: list[PhiInteraction], db_path: str) -> int:
107
+
"""Upsert phi interactions into raw_phi_interactions. Returns total row count."""
108
+
con = duckdb.connect(db_path)
109
+
con.execute("""
110
+
CREATE TABLE IF NOT EXISTS raw_phi_interactions (
111
+
handle VARCHAR, interaction_id VARCHAR,
112
+
content VARCHAR, created_at VARCHAR,
113
+
fetched_at TIMESTAMP DEFAULT now(),
114
+
PRIMARY KEY (interaction_id)
115
+
)
116
+
""")
117
+
rows = [
118
+
(
119
+
item.handle, item.interaction_id,
120
+
item.content, item.created_at,
121
+
datetime.datetime.now(datetime.UTC),
122
+
)
123
+
for item in items
124
+
]
125
+
if rows:
126
+
con.executemany(
127
+
"INSERT OR REPLACE INTO raw_phi_interactions VALUES (?, ?, ?, ?, ?)",
128
+
rows,
129
+
)
130
+
count = con.execute("SELECT count(*) FROM raw_phi_interactions").fetchone()[0]
131
+
con.close()
132
+
return count
+30
packages/mps/src/mps/phi.py
+30
packages/mps/src/mps/phi.py
···
1
+
"""Data models for phi bot memory (TurboPuffer observations + interactions)."""
2
+
3
+
from dataclasses import dataclass, field
4
+
5
+
6
+
def clean_handle(handle: str) -> str:
7
+
"""Sanitize handle to match bot's namespace naming convention."""
8
+
return handle.replace(".", "_").replace("@", "").replace("-", "_")
9
+
10
+
11
+
def restore_handle(ns_id: str) -> str:
12
+
"""Reverse-map a namespace ID back to a handle (best-effort)."""
13
+
return ns_id.removeprefix("phi-users-").replace("_", ".")
14
+
15
+
16
+
@dataclass
17
+
class PhiObservation:
18
+
handle: str
19
+
observation_id: str
20
+
content: str
21
+
tags: list[str] = field(default_factory=list)
22
+
created_at: str = ""
23
+
24
+
25
+
@dataclass
26
+
class PhiInteraction:
27
+
handle: str
28
+
interaction_id: str
29
+
content: str # "user: ...\nbot: ..."
30
+
created_at: str = ""
+11
prefect.yaml
+11
prefect.yaml
···
73
73
match_related:
74
74
prefect.resource.name: "transform"
75
75
prefect.resource.role: "deployment"
76
+
77
+
- name: compact
78
+
entrypoint: flows/compact.py:compact
79
+
work_pool: *k8s
80
+
triggers:
81
+
- type: event
82
+
expect:
83
+
- "prefect.flow-run.Completed"
84
+
match_related:
85
+
prefect.resource.name: "transform"
86
+
prefect.resource.role: "deployment"
+550
uv.lock
+550
uv.lock
···
27
27
]
28
28
29
29
[[package]]
30
+
name = "aiohappyeyeballs"
31
+
version = "2.6.1"
32
+
source = { registry = "https://pypi.org/simple" }
33
+
sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
34
+
wheels = [
35
+
{ url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
36
+
]
37
+
38
+
[[package]]
39
+
name = "aiohttp"
40
+
version = "3.13.3"
41
+
source = { registry = "https://pypi.org/simple" }
42
+
dependencies = [
43
+
{ name = "aiohappyeyeballs" },
44
+
{ name = "aiosignal" },
45
+
{ name = "attrs" },
46
+
{ name = "frozenlist" },
47
+
{ name = "multidict" },
48
+
{ name = "propcache" },
49
+
{ name = "yarl" },
50
+
]
51
+
sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
52
+
wheels = [
53
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882
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883
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722
884
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723
885
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724
886
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···
800
962
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801
963
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802
964
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965
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803
966
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805
968
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···
812
975
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813
976
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814
977
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978
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815
979
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817
981
]
···
820
984
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821
985
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822
986
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987
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823
988
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824
989
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825
990
]
···
1246
1411
dependencies = [
1247
1412
{ name = "duckdb" },
1248
1413
{ name = "httpx" },
1414
+
{ name = "openai" },
1249
1415
{ name = "prefect" },
1250
1416
{ name = "pydantic" },
1251
1417
{ name = "pydantic-ai-slim", extra = ["anthropic", "prefect"] },
1418
+
{ name = "turbopuffer" },
1252
1419
]
1253
1420
1254
1421
[package.metadata]
1255
1422
requires-dist = [
1256
1423
{ name = "duckdb", specifier = ">=1.0" },
1257
1424
{ name = "httpx", specifier = ">=0.27" },
1425
+
{ name = "openai" },
1258
1426
{ name = "prefect", specifier = ">=3.0" },
1259
1427
{ name = "pydantic", specifier = ">=2" },
1260
1428
{ name = "pydantic-ai-slim", extras = ["anthropic", "prefect"] },
1429
+
{ name = "turbopuffer" },
1261
1430
]
1262
1431
1263
1432
[[package]]
···
1296
1465
]
1297
1466
1298
1467
[[package]]
1468
+
name = "multidict"
1469
+
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1470
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1471
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1472
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1547
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1548
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1299
1549
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1550
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1301
1551
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1336
1586
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1337
1587
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1589
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1591
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1339
1608
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1340
1609
1341
1610
[[package]]
···
1537
1806
]
1538
1807
1539
1808
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1809
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1955
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1956
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1957
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1958
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1959
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1960
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1961
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1962
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1963
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1964
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1965
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1966
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1967
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1968
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
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1979
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1980
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1981
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1982
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1983
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1984
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1985
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1986
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
+
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2002
+
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2003
+
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2004
+
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1573
2005
]
1574
2006
1575
2007
[[package]]
···
2285
2717
]
2286
2718
2287
2719
[[package]]
2720
+
name = "tqdm"
2721
+
version = "4.67.3"
2722
+
source = { registry = "https://pypi.org/simple" }
2723
+
dependencies = [
2724
+
{ name = "colorama", marker = "sys_platform == 'win32'" },
2725
+
]
2726
+
sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" }
2727
+
wheels = [
2728
+
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2729
+
]
2730
+
2731
+
[[package]]
2732
+
name = "turbopuffer"
2733
+
version = "1.19.0"
2734
+
source = { registry = "https://pypi.org/simple" }
2735
+
dependencies = [
2736
+
{ name = "aiohttp" },
2737
+
{ name = "anyio" },
2738
+
{ name = "distro" },
2739
+
{ name = "httpx" },
2740
+
{ name = "orjson" },
2741
+
{ name = "pybase64" },
2742
+
{ name = "pydantic" },
2743
+
{ name = "sniffio" },
2744
+
{ name = "typing-extensions" },
2745
+
]
2746
+
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2747
+
wheels = [
2748
+
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2749
+
]
2750
+
2751
+
[[package]]
2288
2752
name = "ty"
2289
2753
version = "0.0.23"
2290
2754
source = { registry = "https://pypi.org/simple" }
···
2499
2963
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2500
2964
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2501
2965
{ url = "https://files.pythonhosted.org/packages/91/fb/1b112af72e5a2b5ac66b5a6a478cc220117224c1481a2b1a35e87222a791/whenever-0.9.5-py3-none-any.whl", hash = "sha256:cc600303e703d57cc42c5db1c81bbc3becb8ef6fde5c46aee5d68c292239fc4b", size = 64873, upload-time = "2026-01-11T19:47:49.585Z" },
2966
+
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2967
+
2968
+
[[package]]
2969
+
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2970
+
version = "1.23.0"
2971
+
source = { registry = "https://pypi.org/simple" }
2972
+
dependencies = [
2973
+
{ name = "idna" },
2974
+
{ name = "multidict" },
2975
+
{ name = "propcache" },
2976
+
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2977
+
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2978
+
wheels = [
2979
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2980
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2981
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2982
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2983
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2984
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2985
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2986
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2987
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2988
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2989
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2990
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2991
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2992
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2993
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2994
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2995
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2996
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2997
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2998
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2999
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3000
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3001
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3002
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3003
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3006
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3007
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