Spaces:
Sleeping
Sleeping
File size: 5,847 Bytes
bb04c5f | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | # searcher/search_engine.py
import yaml
from searcher.query_understanding import QueryUnderstanding
from searcher.dense_retriever import DenseRetriever
from searcher.sparse_retriever import SparseRetriever
from searcher.fusion_ranker import FusionRanker
from searcher.reranker import Reranker
from searcher.facet_filter import FacetFilter
from searcher.highlighter import Highlighter
class SearchEngine:
"""
Orchestrates the full search pipeline end-to-end:
raw query
→ QueryUnderstanding (expand + rewrite)
→ DenseRetriever (semantic FAISS search)
→ SparseRetriever (BM25 lexical search)
→ FusionRanker (RRF merge)
→ Reranker (cross-encoder precision)
→ FacetFilter (type / date / size / directory)
→ Highlighter (preview + HTML highlights)
→ final results
"""
def __init__(self, config_path="config.yaml"):
self.config_path = config_path
with open(config_path) as f:
self.config = yaml.safe_load(f)
self.query_understanding = QueryUnderstanding(config_path)
self.dense_retriever = DenseRetriever(config_path)
self.sparse_retriever = SparseRetriever(config_path)
self.fusion_ranker = FusionRanker(k=60)
self.reranker = Reranker(config_path)
self.facet_filter = FacetFilter()
self.highlighter = Highlighter(preview_words=30)
self.candidate_k = self.config.get("candidate_k", 20)
self.final_k = self.config.get("top_k", 5)
def search(
self,
query: str,
top_k: int = None,
file_type: list[str] = None,
date_after=None,
date_before=None,
min_size: int = None,
max_size: int = None,
directory: str = None,
) -> dict:
"""
Run the full search pipeline.
Args:
query — natural language user query
top_k — number of final results (overrides config)
file_type — e.g. [".pdf", ".docx"]
date_after — datetime; exclude older files
date_before — datetime; exclude newer files
min_size — min file size in bytes
max_size — max file size in bytes
directory — restrict to this directory
Returns:
dict:
query_info — dict from QueryUnderstanding
results — list of final result dicts, each with:
filepath, chunk_text, chunk_index,
preview, preview_html,
dense_score (if present),
sparse_score (if present),
rrf_score, rerank_score
"""
k = top_k or self.final_k
# Step 1 — query understanding
query_info = self.query_understanding.process(query)
query_info.setdefault("original", query)
query_info.setdefault("expanded", query)
query_info.setdefault("rewritten", query)
# Step 2 — dense retrieval (uses expanded query for better semantic reach)
dense_results = self.dense_retriever.retrieve(
query_info["expanded"], top_k=self.candidate_k
)
# Step 3 — sparse retrieval (uses rewritten query; expansion hurts BM25)
sparse_results = self.sparse_retriever.retrieve(
query_info["rewritten"], top_k=self.candidate_k
)
# Step 4 — RRF fusion
fused = self.fusion_ranker.fuse(dense_results, sparse_results, top_k=self.candidate_k)
# Step 5 — cross-encoder reranking
reranked = self.reranker.rerank(query_info["original"], fused, top_k=k * 2)
# Step 6 — facet filtering
filtered = self.facet_filter.filter(
reranked,
file_type=file_type,
date_after=date_after,
date_before=date_before,
min_size=min_size,
max_size=max_size,
directory=directory,
)
# Trim to top_k after filtering
final = filtered[:k]
# Step 7 — highlight previews
final = self.highlighter.annotate(final, query_info["original"])
for r in final:
if "preview" not in r or not r["preview"]:
r["preview"] = r.get("chunk_text", "")[:200]
return {
"query_info": query_info,
"results": final or [],
}
if __name__ == "__main__":
engine = SearchEngine()
while True:
query = input("\n🔍 Enter your search query (or type 'exit'): ")
if query.lower() == "exit":
print("Exiting search engine...")
break
output = engine.search(query, top_k=3)
print(f"\nQuery : {output['query_info']['original']}")
print(f"Expanded : {output['query_info']['expanded']}")
print(f"Results : {len(output['results'])}\n")
for i, r in enumerate(output["results"], 1):
print(f"--- Result {i} ---")
print(f"File : {r['filepath']}")
print(f"Preview : {r['preview']}")
# Handle safe printing of scores
rrf = r.get('rrf_score')
rerank = r.get('rerank_score')
if rrf is not None:
print(f"RRF : {rrf:.5f}")
else:
print("RRF : n/a")
if rerank is not None:
print(f"Rerank : {rerank:.4f}")
else:
print("Rerank : n/a")
print() |