Disable ability to call <text>_to_jsonl, <type>_search packages directly

- This code is de-synced with expected args by above scripts
- Better to remove unused capabilitity that needlessly increases
  maintainance burden

src/processor/ledger/beancount_to_jsonl.py

@@ -128,16 +128,3 @@ def convert_transactions_to_maps(entries: list[str], transaction_to_file_map) ->
 def convert_transaction_maps_to_jsonl(entries: list[dict]) -> str:
     "Convert each Beancount transaction dictionary to JSON and collate as JSONL"
     return ''.join([f'{json.dumps(entry_dict, ensure_ascii=False)}\n' for entry_dict in entries])
-
-
-if __name__ == '__main__':
-    # Setup Argument Parser
-    parser = argparse.ArgumentParser(description="Map Beancount transactions into (compressed) JSONL format")
-    parser.add_argument('--output-file', '-o', type=pathlib.Path, required=True, help="Output file for (compressed) JSONL formatted transactions. Expected file extensions: jsonl or jsonl.gz")
-    parser.add_argument('--input-files', '-i', nargs='*', help="List of beancount files to process")
-    parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for beancount files to process")
-    parser.add_argument('--verbose', '-v', action='count', default=0, help="Show verbose conversion logs, Default: 0")
-    args = parser.parse_args()
-
-    # Map transactions in beancount files to (compressed) JSONL formatted file
-    beancount_to_jsonl(args.input_files, args.input_filter, args.output_file, args.verbose)

src/processor/markdown/markdown_to_jsonl.py

@@ -127,16 +127,3 @@ def convert_markdown_entries_to_maps(entries: list[str], entry_to_file_map) -> l
 def convert_markdown_maps_to_jsonl(entries):
     "Convert each Markdown entries to JSON and collate as JSONL"
     return ''.join([f'{json.dumps(entry_dict, ensure_ascii=False)}\n' for entry_dict in entries])
-
-
-if __name__ == '__main__':
-    # Setup Argument Parser
-    parser = argparse.ArgumentParser(description="Map Markdown entries into (compressed) JSONL format")
-    parser.add_argument('--output-file', '-o', type=pathlib.Path, required=True, help="Output file for (compressed) JSONL formatted notes. Expected file extensions: jsonl or jsonl.gz")
-    parser.add_argument('--input-files', '-i', nargs='*', help="List of markdown files to process")
-    parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for markdown files to process")
-    parser.add_argument('--verbose', '-v', action='count', default=0, help="Show verbose conversion logs, Default: 0")
-    args = parser.parse_args()
-
-    # Map notes in Markdown files to (compressed) JSONL formatted file
-    markdown_to_jsonl(args.input_files, args.input_filter, args.output_file, args.verbose)

src/processor/org_mode/org_to_jsonl.py

@@ -153,16 +153,3 @@ def convert_org_nodes_to_entries(entries: list[orgnode.Orgnode], entry_to_file_m
 def convert_org_entries_to_jsonl(entries: list[dict]) -> str:
     "Convert each Org-Mode entry to JSON and collate as JSONL"
     return ''.join([f'{json.dumps(entry_dict, ensure_ascii=False)}\n' for entry_dict in entries])
-
-
-if __name__ == '__main__':
-    # Setup Argument Parser
-    parser = argparse.ArgumentParser(description="Map Org-Mode notes into (compressed) JSONL format")
-    parser.add_argument('--output-file', '-o', type=pathlib.Path, required=True, help="Output file for (compressed) JSONL formatted notes. Expected file extensions: jsonl or jsonl.gz")
-    parser.add_argument('--input-files', '-i', nargs='*', help="List of org-mode files to process")
-    parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for org-mode files to process")
-    parser.add_argument('--verbose', '-v', action='count', default=0, help="Show verbose conversion logs, Default: 0")
-    args = parser.parse_args()
-
-    # Map notes in Org-Mode files to (compressed) JSONL formatted file
-    org_to_jsonl(args.input_files, args.input_filter, args.output_file, args.verbose)

src/search_type/image_search.py

@@ -263,30 +263,3 @@ def setup(config: ImageContentConfig, search_config: ImageSearchConfig, regenera
                             image_embeddings,
                             image_metadata_embeddings,
                             encoder)
-
-
-if __name__ == '__main__':
-    # Setup Argument Parser
-    parser = argparse.ArgumentParser(description="Semantic Search on Images")
-    parser.add_argument('--image-directory', '-i', required=True, type=pathlib.Path, help="Image directory to query")
-    parser.add_argument('--embeddings-file', '-e', default='image_embeddings.pt', type=pathlib.Path, help="File to save/load model embeddings to/from. Default: ./embeddings.pt")
-    parser.add_argument('--regenerate', action='store_true', default=False, help="Regenerate embeddings of Images in Image Directory . Default: false")
-    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='count', default=0, help="Show verbose conversion logs. Default: 0")
-    args = parser.parse_args()
-
-    image_names, image_embeddings, image_metadata_embeddings, model = setup(args.image_directory, args.embeddings_file, regenerate=args.regenerate)
-
-    # Run User Queries on Entries in Interactive Mode
-    while args.interactive:
-        # get query from user
-        user_query = input("Enter your query: ")
-        if user_query == "exit":
-            exit(0)
-
-        # query images
-        hits = query(user_query, image_embeddings, image_metadata_embeddings, model, args.results_count, args.verbose)
-
-        # render results
-        render_results(hits, image_names, args.image_directory, count=args.results_count)

src/search_type/text_search.py

@@ -204,32 +204,3 @@ def setup(text_to_jsonl, config: TextContentConfig, search_config: TextSearchCon
         filter.load(entries, regenerate=regenerate)
 
     return TextSearchModel(entries, corpus_embeddings, bi_encoder, cross_encoder, filters, top_k)
-
-
-if __name__ == '__main__':
-    # Setup Argument Parser
-    parser = argparse.ArgumentParser(description="Map Text files into (compressed) JSONL format")
-    parser.add_argument('--input-files', '-i', nargs='*', help="List of Text files to process")
-    parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for Text files to process")
-    parser.add_argument('--compressed-jsonl', '-j', type=pathlib.Path, default=pathlib.Path("text.jsonl.gz"), help="Compressed JSONL to compute embeddings from")
-    parser.add_argument('--embeddings', '-e', type=pathlib.Path, default=pathlib.Path("text_embeddings.pt"), help="File to save/load model embeddings to/from")
-    parser.add_argument('--regenerate', action='store_true', default=False, help="Regenerate embeddings from text files. Default: false")
-    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='count', default=0, help="Show verbose conversion logs. Default: 0")
-    args = parser.parse_args()
-
-    entries, corpus_embeddings, bi_encoder, cross_encoder, top_k = setup(args.input_files, args.input_filter, args.compressed_jsonl, args.embeddings, args.regenerate)
-
-    # Run User Queries on Entries in Interactive Mode
-    while args.interactive:
-        # get query from user
-        user_query = input("Enter your query: ")
-        if user_query == "exit":
-            exit(0)
-
-        # query notes
-        hits = query(user_query, corpus_embeddings, entries, bi_encoder, cross_encoder, top_k)
-
-        # render results
-        render_results(hits, entries, count=args.results_count)