Split text_search.query logic into separate methods for modularity

The query method had become too big.

Extract out filter, score, sort and deduplicate logic used by
text_search.query into separate methods.

This should improve readabilty of code.

src/search_type/text_search.py

@@ -85,34 +85,15 @@ def compute_embeddings(entries_with_ids: list[tuple[int, Entry]], bi_encoder, em
     return corpus_embeddings
 
 
-def query(raw_query: str, model: TextSearchModel, rank_results=False):
+def query(raw_query: str, model: TextSearchModel, rank_results: bool = False):
     "Search for entries that answer the query"
     query, entries, corpus_embeddings = raw_query, model.entries, model.corpus_embeddings
 
     # Filter query, entries and embeddings before semantic search
-    start_filter = time.time()
-    included_entry_indices = set(range(len(entries)))
-    filters_in_query = [filter for filter in model.filters if filter.can_filter(query)]
-    for filter in filters_in_query:
-        query, included_entry_indices_by_filter = filter.apply(query, entries)
-        included_entry_indices.intersection_update(included_entry_indices_by_filter)
-
-    # Get entries (and associated embeddings) satisfying all filters
-    if not included_entry_indices:
-        return [], []
-    else:
-        start = time.time()
-        entries = [entries[id] for id in included_entry_indices]
-        corpus_embeddings = torch.index_select(corpus_embeddings, 0, torch.tensor(list(included_entry_indices), device=state.device))
-        end = time.time()
-        logger.debug(f"Keep entries satisfying all filters: {end - start} seconds")
-
-    end_filter = time.time()
-    logger.debug(f"Total Filter Time: {end_filter - start_filter:.3f} seconds on device: {state.device}")
-
+    query, entries, corpus_embeddings = apply_filters(query, entries, corpus_embeddings, model.filters)
+    # If no entries left after filtering, return empty results
     if entries is None or len(entries) == 0:
         return [], []
-
     # If query only had filters it'll be empty now. So short-circuit and return results.
     if query.strip() == "":
         hits = [{"corpus_id": id, "score": 1.0} for id, _ in enumerate(entries)]
@@ -133,34 +114,13 @@ def query(raw_query: str, model: TextSearchModel, rank_results=False):
 
     # 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)
-        end = time.time()
-        logger.debug(f"Cross-Encoder Predict Time: {end - start:.3f} seconds on device: {state.device}")
-
-        # Store cross-encoder scores in results dictionary for ranking
-        for idx in range(len(cross_scores)):
-            hits[idx]['cross-score'] = cross_scores[idx]
+        hits = cross_encoder_score(model.cross_encoder, query, entries, hits)
 
     # 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()
-    logger.debug(f"Rank Time: {end - start:.3f} seconds on device: {state.device}")
+    hits = sort_results(rank_results, hits)
 
     # Deduplicate entries by raw entry text before showing to users
-    # Compiled entries are split by max tokens supported by ML models.
-    # This can result in duplicate hits, entries shown to user.
-    start = time.time()
-    seen, original_hits_count = set(), len(hits)
-    hits = [hit for hit in hits
-            if entries[hit['corpus_id']].raw not in seen and not seen.add(entries[hit['corpus_id']].raw)]
-    duplicate_hits = original_hits_count - len(hits)
-    end = time.time()
-    logger.debug(f"Deduplication Time: {end - start:.3f} seconds. Removed {duplicate_hits} duplicates")
+    hits = deduplicate_results(entries, hits)
 
     return hits, entries
 
@@ -219,3 +179,68 @@ def setup(text_to_jsonl: Type[TextToJsonl], config: TextContentConfig, search_co
         filter.load(entries, regenerate=regenerate)
 
     return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, filters, top_k)
+
+
+def apply_filters(query: str, entries: list[Entry], corpus_embeddings: torch.Tensor, filters: list[BaseFilter]) -> tuple[str, list[Entry], torch.Tensor]:
+    '''Filter query, entries and embeddings before semantic search'''
+    start_filter = time.time()
+    included_entry_indices = set(range(len(entries)))
+    filters_in_query = [filter for filter in filters if filter.can_filter(query)]
+    for filter in filters_in_query:
+        query, included_entry_indices_by_filter = filter.apply(query, entries)
+        included_entry_indices.intersection_update(included_entry_indices_by_filter)
+
+    # Get entries (and associated embeddings) satisfying all filters
+    if not included_entry_indices:
+        return '', [], torch.tensor([], device=state.device)
+    else:
+        start = time.time()
+        entries = [entries[id] for id in included_entry_indices]
+        corpus_embeddings = torch.index_select(corpus_embeddings, 0, torch.tensor(list(included_entry_indices), device=state.device))
+        end = time.time()
+        logger.debug(f"Keep entries satisfying all filters: {end - start} seconds")
+
+    end_filter = time.time()
+    logger.debug(f"Total Filter Time: {end_filter - start_filter:.3f} seconds on device: {state.device}")
+
+    return query, entries, corpus_embeddings
+
+
+def cross_encoder_score(cross_encoder: CrossEncoder, query: str, entries: list[Entry], hits: list[dict]) -> list[dict]:
+    '''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 = cross_encoder.predict(cross_inp)
+    end = time.time()
+    logger.debug(f"Cross-Encoder Predict Time: {end - start:.3f} seconds on device: {state.device}")
+
+    # Store cross-encoder scores in results dictionary for ranking
+    for idx in range(len(cross_scores)):
+        hits[idx]['cross-score'] = cross_scores[idx]
+
+    return hits
+
+
+def sort_results(rank_results: bool, hits: list[dict]) -> list[dict]:
+    '''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()
+    logger.debug(f"Rank Time: {end - start:.3f} seconds on device: {state.device}")
+    return hits
+
+
+def deduplicate_results(entries: list[Entry], hits: list[dict]) -> list[dict]:
+    '''Deduplicate entries by raw entry text before showing to users
+    Compiled entries are split by max tokens supported by ML models.
+    This can result in duplicate hits, entries shown to user.'''
+    start = time.time()
+    seen, original_hits_count = set(), len(hits)
+    hits = [hit for hit in hits
+            if entries[hit['corpus_id']].raw not in seen and not seen.add(entries[hit['corpus_id']].raw)]
+    duplicate_hits = original_hits_count - len(hits)
+    end = time.time()
+    logger.debug(f"Deduplication Time: {end - start:.3f} seconds. Removed {duplicate_hits} duplicates")
+    return hits