Make filters to apply before semantic search configurable

Details
--
- The filters to apply are configured for each type in the search controller
- Muliple filters can be applied on the query, entries etc before search
- The asymmetric query method now just applies the passed filters to the
  query, entries and embeddings before semantic search is performed

Reason
--
This abstraction will simplify adding other pre-search filters. E.g datetime filter

src/main.py

@@ -17,6 +17,7 @@ from src.utils.cli import cli
 from src.utils.config import SearchType, SearchModels, ProcessorConfigModel, ConversationProcessorConfigModel
 from src.utils.rawconfig import FullConfig
 from src.processor.conversation.gpt import converse, extract_search_type, message_to_log, message_to_prompt, understand, summarize
+from src.search_filter.explicit_filter import explicit_filter
 
 # Application Global State
 config = FullConfig()
@@ -58,14 +59,14 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None):
 
     if (t == SearchType.Notes or t == None) and model.notes_search:
         # query notes
-        hits, entries = asymmetric.query(user_query, model.notes_search, device=device)
+        hits, entries = asymmetric.query(user_query, model.notes_search, device=device, filters=[explicit_filter])
 
         # collate and return results
         return asymmetric.collate_results(hits, entries, results_count)
 
     if (t == SearchType.Music or t == None) and model.music_search:
         # query music library
-        hits, entries = asymmetric.query(user_query, model.music_search, device=device)
+        hits, entries = asymmetric.query(user_query, model.music_search, device=device, filters=[explicit_filter])
 
         # collate and return results
         return asymmetric.collate_results(hits, entries, results_count)

src/search_type/asymmetric.py

@@ -94,7 +94,7 @@ def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, d
     return corpus_embeddings
 
 
-def query(raw_query: str, model: TextSearchModel, device=torch.device('cpu')):
+def query(raw_query: str, model: TextSearchModel, device=torch.device('cpu'), filters: list = []):
     "Search all notes for entries that answer the query"
 
     # Copy original embeddings, entries to filter them for query
@@ -102,8 +102,9 @@ def query(raw_query: str, model: TextSearchModel, device=torch.device('cpu')):
     corpus_embeddings = deepcopy(model.corpus_embeddings)
     entries = deepcopy(model.entries)
 
-    # Filter to entries that contain all required_words and no blocked_words
+    # Filter query, entries and embeddings before semantic search
-    query, entries, corpus_embeddings = explicit_filter(query, entries, corpus_embeddings)
+    for filter in filters:
+        query, entries, corpus_embeddings = filter(query, entries, corpus_embeddings)
     if entries is None or len(entries) == 0:
         return {}