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PEFT
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preference-learning
qlora
agent
personalization
association-engine
Instructions to use feiertu/hermes-association-engine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use feiertu/hermes-association-engine with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 2,263 Bytes
767b8b9 | 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 | """测试 refine_scene."""
import uuid
import pytest
from hermes_core.embedder import Embedder
from hermes_core.db import init_db, upsert_scope, insert_record
from hermes_core.types import Record, Scope, Dimension
from hermes_core.refiner import refine_scene
pytestmark = pytest.mark.requires_embedder
@pytest.fixture
def embedder():
return Embedder()
def _uid():
return f"u_{uuid.uuid4().hex[:8]}"
def test_refine_narrow_creates_new_scope(embedder):
uid = _uid()
conn = init_db(uid)
s = Scope(id="scope_a", label="后端开发",
centroid=embedder.encode("后端开发"), record_count=1)
upsert_scope(conn, s)
insert_record(conn, Record(
id=f"rec_{uuid.uuid4().hex[:8]}", user_id=uid, scope_id="scope_a",
scope_label="后端开发",
dimensions=[Dimension(key="language", value="Java", context="")],
))
rid = conn.execute("SELECT id FROM records LIMIT 1").fetchone()["id"]
conn.close()
result = refine_scene(
user_id=uid, record_id=rid,
scope_desc="游戏Mod开发",
direction="narrow", embedder=embedder,
)
assert result["status"] == "refined"
assert result["new_scope_id"] != result["old_scope_id"] # 新 scope
def test_refine_broaden_reassigns_to_existing(embedder):
uid = _uid()
conn = init_db(uid)
s1 = Scope(id="scope_broad", label="软件开发",
centroid=embedder.encode("软件开发"), record_count=3)
s2 = Scope(id="scope_narrow", label="React前端",
centroid=embedder.encode("React前端"), record_count=1)
upsert_scope(conn, s1)
upsert_scope(conn, s2)
insert_record(conn, Record(
id=f"rec_{uuid.uuid4().hex[:8]}", user_id=uid, scope_id="scope_narrow",
scope_label="React前端",
dimensions=[Dimension(key="framework", value="React", context="")],
))
rid = conn.execute("SELECT id FROM records LIMIT 1").fetchone()["id"]
conn.close()
result = refine_scene(
user_id=uid, record_id=rid,
scope_desc="软件开发",
direction="broaden", embedder=embedder,
)
assert result["status"] == "refined"
# broaden 应被重新分配到 scope_broad
assert result["new_scope_id"] == "scope_broad"
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