Use verbosity level instead of bool across application

For consistent, more granular verbosity controls across app
Allows user to increase verbosity by passing -vvv flags passed to main.py

main.py

@@ -37,9 +37,9 @@ def search(q: str, n: Optional[int] = 5, t: Optional[str] = 'notes'):
 if __name__ == '__main__':
     # Setup Argument Parser
     parser = argparse.ArgumentParser(description="Expose API for Semantic Search")
-    parser.add_argument('--compressed-jsonl', '-j', required=True, type=pathlib.Path, help="Compressed JSONL formatted notes file to compute embeddings from")
-    parser.add_argument('--embeddings', '-e', required=True, type=pathlib.Path, help="File to save/load model embeddings to/from")
-    parser.add_argument('--verbose', action='store_true', default=False, help="Show verbose conversion logs. Default: false")
+    parser.add_argument('--compressed-jsonl', '-j', type=pathlib.Path, default=pathlib.Path(".notes.jsonl.gz"), help="Compressed JSONL formatted notes file to compute embeddings from")
+    parser.add_argument('--embeddings', '-e', type=pathlib.Path, default=pathlib.Path(".notes_embeddings.pt"), help="File to save/load model embeddings to/from")
+    parser.add_argument('--verbose', action='count', help="Show verbose conversion logs. Default: 0")
     args = parser.parse_args()
 
     # Initialize Model

processor/org_mode/org_to_jsonl.py

@@ -11,9 +11,9 @@ import gzip
 
 
 # Define Functions
-def org_to_jsonl(org_files, org_file_filter, output_path, verbose=False):
+def org_to_jsonl(org_files, org_file_filter, output_path, verbose=0):
     # Get Org Files to Process
-    org_files = get_org_files(args.input_files, args.input_filter)
+    org_files = get_org_files(args.input_files, args.input_filter, verbose)
 
     # Extract Entries from specified Org files
     entries = extract_org_entries(org_files)
@@ -59,7 +59,7 @@ def load_jsonl(input_path, verbose=0):
     return data
 
 
-def get_org_files(org_files=None, org_file_filter=None):
+def get_org_files(org_files=None, org_file_filter=None, verbose=0):
     "Get Org files to process"
     absolute_org_files, filtered_org_files = set(), set()
     if org_files:
@@ -75,7 +75,7 @@ def get_org_files(org_files=None, org_file_filter=None):
     if any(files_with_non_org_extensions):
         print(f"[Warning] There maybe non org-mode files in the input set: {files_with_non_org_extensions}")
 
-    if args.verbose:
+    if args.verbose > 0:
         print(f'Processing files: {all_org_files}')
 
     return all_org_files

search_type/asymmetric.py

@@ -20,7 +20,7 @@ def initialize_model():
     return bi_encoder, cross_encoder, top_k
 
 
-def extract_entries(notesfile, verbose=False):
+def extract_entries(notesfile, verbose=0):
     "Load entries from compressed jsonl"
     entries = []
     with gzip.open(get_absolute_path(notesfile), 'rt', encoding='utf8') as jsonl:
@@ -34,24 +34,24 @@ def extract_entries(notesfile, verbose=False):
             note_string = f'{note["Title"]}\t{note["Tags"] if "Tags" in note else ""}\n{note["Body"] if "Body" in note else ""}'
             entries.extend([note_string])
 
-    if verbose:
+    if verbose > 0:
         print(f"Loaded {len(entries)} entries from {notesfile}")
 
     return entries
 
 
-def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, verbose=False):
+def compute_embeddings(entries, bi_encoder, embeddings_file, regenerate=False, verbose=0):
     "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
     # Load pre-computed embeddings from file if exists
     if embeddings_file.exists() and not regenerate:
         corpus_embeddings = torch.load(get_absolute_path(embeddings_file))
-        if verbose:
+        if verbose > 0:
             print(f"Loaded embeddings from {embeddings_file}")
 
     else:  # Else compute the corpus_embeddings from scratch, which can take a while
         corpus_embeddings = bi_encoder.encode(entries, convert_to_tensor=True, show_progress_bar=True)
         torch.save(corpus_embeddings, get_absolute_path(embeddings_file))
-        if verbose:
+        if verbose > 0:
             print(f"Computed embeddings and save them to {embeddings_file}")
 
     return corpus_embeddings
@@ -147,7 +147,7 @@ if __name__ == '__main__':
     parser.add_argument('--embeddings', '-e', required=True, type=pathlib.Path, help="File to save/load model embeddings to/from")
     parser.add_argument('--results-count', '-n', default=5, type=int, help="Number of results to render. Default: 5")
     parser.add_argument('--interactive', action='store_true', default=False, help="Interactive mode allows user to run queries on the model. Default: true")
-    parser.add_argument('--verbose', action='store_true', default=False, help="Show verbose conversion logs. Default: false")
+    parser.add_argument('--verbose', action='count', help="Show verbose conversion logs. Default: 0")
     args = parser.parse_args()
 
     # Initialize Model