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refactor: 移除 SPLADE 和 hybrid_cascade，精简搜索架构

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删除 SPLADE 稀疏神经搜索后端和 hybrid_cascade 策略，
将搜索架构从 6 种后端简化为 4 种（FTS Exact/Fuzzy, Binary Vector, Dense Vector, LSP）。

主要变更：
- 删除 splade_encoder.py, splade_index.py, migration_009 等 4 个文件
- 移除 config.py 中 SPLADE 相关配置（enable_splade, splade_model 等）
- DEFAULT_WEIGHTS 改为 FTS 权重 {exact:0.25, fuzzy:0.1, vector:0.5, lsp:0.15}
- 删除 hybrid_cascade_search()，所有 cascade fallback 改为 self.search()
- API fusion_strategy='hybrid' 向后兼容映射到 binary_rerank
- 删除 CLI index_splade/splade_status 命令和 --method splade
- 更新测试、基准测试和文档
This commit is contained in:
catlog22
2026-02-08 12:07:41 +08:00
parent 72d2ae750b
commit 71faaf43a8
22 changed files with 126 additions and 2883 deletions
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42
codex-lens/benchmarks/method_contribution_analysis.py
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@@ -1,7 +1,7 @@
"""Analysis script for hybrid search method contribution and storage architecture.
This script analyzes:
1. Individual method contribution in hybrid search (FTS/SPLADE/Vector)
1. Individual method contribution in hybrid search (FTS/Vector)
2. Storage architecture conflicts between different retrieval methods
3. FTS + Rerank fusion experiment
"""
@@ -24,9 +24,7 @@ from codexlens.search.ranking import (
reciprocal_rank_fusion,
cross_encoder_rerank,
DEFAULT_WEIGHTS,
FTS_FALLBACK_WEIGHTS,
)
from codexlens.search.hybrid_search import THREE_WAY_WEIGHTS
from codexlens.entities import SearchResult
@@ -117,15 +115,7 @@ def analyze_storage_architecture(index_path: Path) -> Dict[str, Any]:
"binary cascade search."
)
# 2. Check SPLADE index status
if "splade_posting_list" in tables:
splade_count = results["tables"]["splade_posting_list"]["row_count"]
if splade_count == 0:
results["recommendations"].append(
"SPLADE tables exist but empty. Run SPLADE indexing to enable sparse retrieval."
)
# 3. Check FTS tables
# 2. Check FTS tables
fts_tables = [t for t in tables if t.startswith("files_fts")]
if len(fts_tables) >= 2:
results["recommendations"].append(
@@ -163,10 +153,9 @@ def analyze_method_contributions(
# Run each method independently
methods = {
"fts_exact": {"fuzzy": False, "vector": False, "splade": False},
"fts_fuzzy": {"fuzzy": True, "vector": False, "splade": False},
"vector": {"fuzzy": False, "vector": True, "splade": False},
"splade": {"fuzzy": False, "vector": False, "splade": True},
"fts_exact": {"fuzzy": False, "vector": False},
"fts_fuzzy": {"fuzzy": True, "vector": False},
"vector": {"fuzzy": False, "vector": True},
}
method_results: Dict[str, List[SearchResult]] = {}
@@ -178,7 +167,6 @@ def analyze_method_contributions(
# Set config to disable/enable specific backends
engine._config = type('obj', (object,), {
'use_fts_fallback': method_name.startswith("fts"),
'enable_splade': method_name == "splade",
'embedding_use_gpu': True,
})()
@@ -186,13 +174,13 @@ def analyze_method_contributions(
if method_name == "fts_exact":
# Force FTS fallback mode with fuzzy disabled
engine.weights = FTS_FALLBACK_WEIGHTS.copy()
engine.weights = DEFAULT_WEIGHTS.copy()
results_list = engine.search(
index_path, query, limit=limit,
enable_fuzzy=False, enable_vector=False, pure_vector=False
)
elif method_name == "fts_fuzzy":
engine.weights = FTS_FALLBACK_WEIGHTS.copy()
engine.weights = DEFAULT_WEIGHTS.copy()
results_list = engine.search(
index_path, query, limit=limit,
enable_fuzzy=True, enable_vector=False, pure_vector=False
@@ -202,12 +190,6 @@ def analyze_method_contributions(
index_path, query, limit=limit,
enable_fuzzy=False, enable_vector=True, pure_vector=True
)
elif method_name == "splade":
engine.weights = {"splade": 1.0}
results_list = engine.search(
index_path, query, limit=limit,
enable_fuzzy=False, enable_vector=False, pure_vector=False
)
else:
results_list = []
@@ -259,7 +241,7 @@ def analyze_method_contributions(
# Compute RRF with each method's contribution
rrf_map = {}
for name, results in method_results.items():
if results and name in ["fts_exact", "splade", "vector"]:
if results and name in ["fts_exact", "vector"]:
# Rename for RRF
rrf_name = name.replace("fts_exact", "exact")
rrf_map[rrf_name] = results
@@ -310,7 +292,7 @@ def experiment_fts_rerank_fusion(
"""Experiment: FTS + Rerank fusion vs standard hybrid.
Compares:
1. Standard Hybrid (SPLADE + Vector RRF)
1. Standard Hybrid (FTS + Vector RRF)
2. FTS + CrossEncoder Rerank -> then fuse with Vector
"""
results = {
@@ -336,11 +318,10 @@ def experiment_fts_rerank_fusion(
"strategies": {}
}
# Strategy 1: Standard Hybrid (SPLADE + Vector)
# Strategy 1: Standard Hybrid (FTS + Vector)
try:
engine = HybridSearchEngine(weights=DEFAULT_WEIGHTS)
engine._config = type('obj', (object,), {
'enable_splade': True,
'use_fts_fallback': False,
'embedding_use_gpu': True,
})()
@@ -364,10 +345,9 @@ def experiment_fts_rerank_fusion(
# Strategy 2: FTS + Rerank -> Fuse with Vector
try:
# Step 1: Get FTS results (coarse)
fts_engine = HybridSearchEngine(weights=FTS_FALLBACK_WEIGHTS)
fts_engine = HybridSearchEngine(weights=DEFAULT_WEIGHTS)
fts_engine._config = type('obj', (object,), {
'use_fts_fallback': True,
'enable_splade': False,
'embedding_use_gpu': True,
})()