179b048f07
- 引入了一个新的指标计算模块，用于检索和答案评估。 -通过整合基准生成和评估指标来简化评估服务。 -为新指标和基准生成功能添加了单元测试。 - 更新了前端组件，以利用新的图标，并改进了评估基准的样式。 -将与评估相关的逻辑整合为专门的“知识/评估”结构，以更好地组织。
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import os
|
|
|
|
import pytest
|
|
|
|
os.environ.setdefault("OPENAI_API_KEY", "test-key")
|
|
|
|
from yuxi.knowledge.eval.benchmark_generation import (
|
|
build_benchmark_generation_prompt,
|
|
clamp_neighbors_count,
|
|
collect_kb_chunks,
|
|
cosine_similarity,
|
|
select_neighbor_indices,
|
|
)
|
|
|
|
|
|
class FakeKnowledgeBase:
|
|
files_meta = {
|
|
"file_a": {"database_id": "db_1"},
|
|
"file_b": {"database_id": "db_2"},
|
|
}
|
|
|
|
async def get_file_content(self, db_id, fid):
|
|
return {
|
|
"lines": [
|
|
{"id": f"{fid}_chunk", "content": "内容", "chunk_order_index": 0},
|
|
]
|
|
}
|
|
|
|
|
|
def test_clamp_neighbors_count():
|
|
assert clamp_neighbors_count(-1) == 0
|
|
assert clamp_neighbors_count(3) == 3
|
|
assert clamp_neighbors_count(11) == 10
|
|
|
|
|
|
def test_select_neighbor_indices_orders_by_cosine_similarity():
|
|
embeddings = [[1.0, 0.0], [0.9, 0.1], [0.0, 1.0]]
|
|
norms = [1.0, cosine_similarity(embeddings[1], embeddings[1], 1.0, 1.0) ** 0.5, 1.0]
|
|
|
|
assert select_neighbor_indices(0, embeddings, norms, 1) == [1]
|
|
|
|
|
|
def test_build_benchmark_generation_prompt_contains_required_schema():
|
|
prompt = build_benchmark_generation_prompt([("chunk_1", "片段内容")])
|
|
|
|
assert "片段ID=chunk_1" in prompt
|
|
assert "query、gold_answer、gold_chunk_ids" in prompt
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_collect_kb_chunks_filters_database_id():
|
|
chunks = await collect_kb_chunks(FakeKnowledgeBase(), "db_1")
|
|
|
|
assert chunks == [{"id": "file_a_chunk", "content": "内容", "file_id": "file_a", "chunk_index": 0}]
|