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/main.py

@@ -59,7 +59,7 @@ async def config_data(updated_config: FullConfig):
     return config
 
 @app.get('/search')
-def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
+def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Optional[bool] = False):
     if q is None or q == '':
         print(f'No query param (q) passed in API call to initiate search')
         return {}
@@ -72,7 +72,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
     if (t == SearchType.Org or t == None) and model.orgmode_search:
         # query org-mode notes
         query_start = time.time()
-        hits, entries = text_search.query(user_query, model.orgmode_search, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=verbose)
+        hits, entries = text_search.query(user_query, model.orgmode_search, rank_results=r, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=verbose)
         query_end = time.time()
 
         # collate and return results
@@ -83,7 +83,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
     if (t == SearchType.Music or t == None) and model.music_search:
         # query music library
         query_start = time.time()
-        hits, entries = text_search.query(user_query, model.music_search, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=verbose)
+        hits, entries = text_search.query(user_query, model.music_search, rank_results=r, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=verbose)
         query_end = time.time()
 
         # collate and return results
@@ -94,7 +94,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
     if (t == SearchType.Markdown or t == None) and model.orgmode_search:
         # query markdown files
         query_start = time.time()
-        hits, entries = text_search.query(user_query, model.markdown_search, device=device, filters=[ExplicitFilter(), DateFilter()], verbose=verbose)
+        hits, entries = text_search.query(user_query, model.markdown_search, rank_results=r, device=device, filters=[ExplicitFilter(), DateFilter()], verbose=verbose)
         query_end = time.time()
 
         # collate and return results
@@ -105,7 +105,7 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
     if (t == SearchType.Ledger or t == None) and model.ledger_search:
         # query transactions
         query_start = time.time()
-        hits, entries = text_search.query(user_query, model.ledger_search, filters=[ExplicitFilter(), DateFilter()], verbose=verbose)
+        hits, entries = text_search.query(user_query, model.ledger_search, rank_results=r, device=device, filters=[ExplicitFilter(), DateFilter()], verbose=verbose)
         query_end = time.time()
 
         # collate and return results

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
 
@@ -108,6 +108,7 @@ 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
+    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)
@@ -122,6 +123,7 @@ def query(raw_query: str, model: TextSearchModel, device='cpu', filters: list =
     # 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
+    if rank_results:
         hits.sort(key=lambda x: x['cross-score'], reverse=True) # sort by cross-encoder score
     end = time.time()
     if verbose > 1:
@@ -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]]

tests/test_asymmetric_search.py

@@ -29,7 +29,8 @@ def test_asymmetric_search(content_config: ContentConfig, search_config: SearchC
     # Act
     hits, entries = text_search.query(
         query,
-        model = model.notes_search)
+        model = model.notes_search,
+        rank_results=True)
 
     results = text_search.collate_results(
         hits,

tests/test_client.py

@@ -119,7 +119,7 @@ def test_notes_search(content_config: ContentConfig, search_config: SearchConfig
     user_query = "How to git install application?"
 
     # Act
-    response = client.get(f"/search?q={user_query}&n=1&t=org")
+    response = client.get(f"/search?q={user_query}&n=1&t=org&r=true")
 
     # Assert
     assert response.status_code == 200