Make cross-encoder re-rank results if query param set on /search API

- Improve search speed by ~10x
  Tested on corpus of 125K lines, 12.5K entries

- Allow cross-encoder to re-rank results by settings &?r=true when querying /search API
  - It's an optional param that default to False
  - Earlier all results were re-ranked by cross-encoder
  - Making this configurable allows for much faster results, if desired
    but for lower accuracy

src/search_type/text_search.py

@@ -63,7 +63,7 @@ def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, d
     return corpus_embeddings
 
 
-def query(raw_query: str, model: TextSearchModel, device='cpu', filters: list = [], verbose=0):
+def query(raw_query: str, model: TextSearchModel, rank_results=False, device='cpu', filters: list = [], verbose=0):
     "Search for entries that answer the query"
     query = raw_query
 
@@ -108,21 +108,23 @@ def query(raw_query: str, model: TextSearchModel, device='cpu', filters: list =
         print(f"Search Time: {end - start:.3f} seconds")
 
     # Score all retrieved entries using the cross-encoder
-    start = time.time()
-    cross_inp = [[query, entries[hit['corpus_id']]['compiled']] for hit in hits]
-    cross_scores = model.cross_encoder.predict(cross_inp)
-    end = time.time()
-    if verbose > 1:
-        print(f"Cross-Encoder Predict Time: {end - start:.3f} seconds")
+    if rank_results:
+        start = time.time()
+        cross_inp = [[query, entries[hit['corpus_id']]['compiled']] for hit in hits]
+        cross_scores = model.cross_encoder.predict(cross_inp)
+        end = time.time()
+        if verbose > 1:
+            print(f"Cross-Encoder Predict Time: {end - start:.3f} seconds")
 
-    # Store cross-encoder scores in results dictionary for ranking
-    for idx in range(len(cross_scores)):
-        hits[idx]['cross-score'] = cross_scores[idx]
+        # Store cross-encoder scores in results dictionary for ranking
+        for idx in range(len(cross_scores)):
+            hits[idx]['cross-score'] = cross_scores[idx]
 
     # Order results by cross-encoder score followed by bi-encoder score
     start = time.time()
     hits.sort(key=lambda x: x['score'], reverse=True) # sort by bi-encoder score
-    hits.sort(key=lambda x: x['cross-score'], reverse=True) # sort by cross-encoder score
+    if rank_results:
+        hits.sort(key=lambda x: x['cross-score'], reverse=True) # sort by cross-encoder score
     end = time.time()
     if verbose > 1:
         print(f"Rank Time: {end - start:.3f} seconds")
@@ -152,7 +154,7 @@ def collate_results(hits, entries, count=5):
     return [
         {
             "entry": entries[hit['corpus_id']]['raw'],
-            "score": f"{hit['cross-score']:.3f}"
+            "score": f"{hit['cross-score'] if 'cross-score' in hit else hit['score']:.3f}"
         }
         for hit
         in hits[0:count]]