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Tests for batch embedding pre-computation in _perform_kg_search().
Verifies that kg_query batches all needed embeddings (query, ll_keywords,
hl_keywords) into a single embedding API call instead of 3 sequential calls.
"""
from unittest.mock import AsyncMock, MagicMock
import numpy as np
import pytest
from lightrag.base import QueryParam
def _make_mock_embedding_func(dim=1536):
"""Create a mock async embedding function that returns distinct vectors per input."""
async def _embed(texts, **kwargs):
return np.array(
[np.full(dim, i + 1, dtype=np.float32) for i in range(len(texts))]
)
mock = AsyncMock(side_effect=_embed)
return mock
def _make_mock_kv_storage(embedding_func, global_config=None):
mock = MagicMock()
mock.embedding_func = embedding_func
mock.global_config = global_config or {"kg_chunk_pick_method": "VECTOR"}
return mock
def _make_mock_vdb():
"""Create a mock VDB whose query() records the query_embedding it receives."""
mock = AsyncMock()
mock.query = AsyncMock(return_value=[])
mock.cosine_better_than_threshold = 0.2
return mock
def _make_mock_graph():
mock = AsyncMock()
return mock
@pytest.mark.offline
@pytest.mark.asyncio
async def test_hybrid_mode_batches_embeddings():
"""In hybrid mode with both keywords, embedding_func should be called exactly once."""
from lightrag.operate import _perform_kg_search
embed_func = _make_mock_embedding_func()
text_chunks_db = _make_mock_kv_storage(embed_func)
entities_vdb = _make_mock_vdb()
relationships_vdb = _make_mock_vdb()
knowledge_graph = _make_mock_graph()
query_param = QueryParam(mode="hybrid", top_k=5)
await _perform_kg_search(
query="test query",
ll_keywords="entity1, entity2",
hl_keywords="theme1, theme2",
knowledge_graph_inst=knowledge_graph,
entities_vdb=entities_vdb,
relationships_vdb=relationships_vdb,
text_chunks_db=text_chunks_db,
query_param=query_param,
)
# The embedding function should be called exactly once with all 3 texts batched
assert (
embed_func.call_count == 1
), f"Expected 1 batched embedding call, got {embed_func.call_count}"
call_args = embed_func.call_args[0][0]
assert len(call_args) == 3, f"Expected 3 texts in batch, got {len(call_args)}"
assert call_args == ["test query", "entity1, entity2", "theme1, theme2"]
@pytest.mark.offline
@pytest.mark.asyncio
async def test_hybrid_mode_passes_embeddings_to_vdbs():
"""Pre-computed embeddings should be forwarded to entities and relationships VDB queries."""
from lightrag.operate import _perform_kg_search
embed_func = _make_mock_embedding_func()
text_chunks_db = _make_mock_kv_storage(embed_func)
entities_vdb = _make_mock_vdb()
relationships_vdb = _make_mock_vdb()
knowledge_graph = _make_mock_graph()
query_param = QueryParam(mode="hybrid", top_k=5)
await _perform_kg_search(
query="test query",
ll_keywords="entity keywords",
hl_keywords="theme keywords",
knowledge_graph_inst=knowledge_graph,
entities_vdb=entities_vdb,
relationships_vdb=relationships_vdb,
text_chunks_db=text_chunks_db,
query_param=query_param,
)
# entities_vdb.query should receive ll_embedding (index 1 → all 2s)
entities_call = entities_vdb.query.call_args
assert entities_call is not None, "entities_vdb.query was not called"
ll_embedding = entities_call.kwargs.get("query_embedding")
assert ll_embedding is not None, "ll_embedding was not passed to entities_vdb.query"
assert np.all(
ll_embedding == 2.0
), f"Expected ll_embedding=[2,2,...], got {ll_embedding[:3]}"
# relationships_vdb.query should receive hl_embedding (index 2 → all 3s)
rel_call = relationships_vdb.query.call_args
assert rel_call is not None, "relationships_vdb.query was not called"
hl_embedding = rel_call.kwargs.get("query_embedding")
assert (
hl_embedding is not None
), "hl_embedding was not passed to relationships_vdb.query"
assert np.all(
hl_embedding == 3.0
), f"Expected hl_embedding=[3,3,...], got {hl_embedding[:3]}"
@pytest.mark.offline
@pytest.mark.asyncio
async def test_local_mode_skips_hl_keywords():
"""In local mode, should only embed query + ll_keywords (skip hl_keywords)."""
from lightrag.operate import _perform_kg_search
embed_func = _make_mock_embedding_func()
text_chunks_db = _make_mock_kv_storage(embed_func)
entities_vdb = _make_mock_vdb()
relationships_vdb = _make_mock_vdb()
knowledge_graph = _make_mock_graph()
query_param = QueryParam(mode="local", top_k=5)
await _perform_kg_search(
query="test query",
ll_keywords="entity keywords",
hl_keywords="theme keywords",
knowledge_graph_inst=knowledge_graph,
entities_vdb=entities_vdb,
relationships_vdb=relationships_vdb,
text_chunks_db=text_chunks_db,
query_param=query_param,
)
assert embed_func.call_count == 1
call_args = embed_func.call_args[0][0]
assert len(call_args) == 2, f"Expected 2 texts (query + ll), got {len(call_args)}"
assert "theme keywords" not in call_args
@pytest.mark.offline
@pytest.mark.asyncio
async def test_global_mode_skips_ll_keywords():
"""In global mode, should only embed query + hl_keywords (skip ll_keywords)."""
from lightrag.operate import _perform_kg_search
embed_func = _make_mock_embedding_func()
text_chunks_db = _make_mock_kv_storage(embed_func)
entities_vdb = _make_mock_vdb()
relationships_vdb = _make_mock_vdb()
knowledge_graph = _make_mock_graph()
query_param = QueryParam(mode="global", top_k=5)
await _perform_kg_search(
query="test query",
ll_keywords="entity keywords",
hl_keywords="theme keywords",
knowledge_graph_inst=knowledge_graph,
entities_vdb=entities_vdb,
relationships_vdb=relationships_vdb,
text_chunks_db=text_chunks_db,
query_param=query_param,
)
assert embed_func.call_count == 1
call_args = embed_func.call_args[0][0]
assert len(call_args) == 2, f"Expected 2 texts (query + hl), got {len(call_args)}"
assert "entity keywords" not in call_args
@pytest.mark.offline
@pytest.mark.asyncio
async def test_embedding_failure_falls_back_gracefully():
"""If batch embedding fails, VDB queries should still work (fallback to individual calls)."""
from lightrag.operate import _perform_kg_search
embed_func = AsyncMock(side_effect=RuntimeError("API error"))
text_chunks_db = _make_mock_kv_storage(embed_func)
entities_vdb = _make_mock_vdb()
relationships_vdb = _make_mock_vdb()
knowledge_graph = _make_mock_graph()
query_param = QueryParam(mode="hybrid", top_k=5)
# Should not raise — graceful degradation
await _perform_kg_search(
query="test query",
ll_keywords="entity keywords",
hl_keywords="theme keywords",
knowledge_graph_inst=knowledge_graph,
entities_vdb=entities_vdb,
relationships_vdb=relationships_vdb,
text_chunks_db=text_chunks_db,
query_param=query_param,
)
# VDB queries should still be called (with query_embedding=None fallback)
entities_call = entities_vdb.query.call_args
assert entities_call is not None
assert entities_call.kwargs.get("query_embedding") is None
rel_call = relationships_vdb.query.call_args
assert rel_call is not None
assert rel_call.kwargs.get("query_embedding") is None
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