chore: temp

srctag/cli.py

@@ -41,13 +41,15 @@ def prepare():
 @click.option("--output-path", default="", help="Output file path for CSV")
 @click.option("--file-level", default=FileLevelEnum.FILE.value, help="Scan file level, FILE or DIR, default to FILE")
 @click.option("--st-model", default="", help="Sentence Transformer Model")
-def tag(repo_root, max_depth_limit, include_regex, tags_file, output_path, file_level, st_model):
+@click.option("--commit-include-regex", default="", help="Commit message include regex pattern")
+def tag(repo_root, max_depth_limit, include_regex, tags_file, output_path, file_level, st_model, commit_include_regex):
     """ tag your repo """
     collector = Collector()
     collector.config.repo_root = repo_root
     collector.config.max_depth_limit = max_depth_limit
     collector.config.include_regex = include_regex
     collector.config.file_level = file_level
+    collector.config.commit_include_regex = commit_include_regex
 
     ctx = collector.collect_metadata()
     storage = Storage()

srctag/storage.py

@@ -57,7 +57,7 @@ def init_chroma(self):
                 model_name=self.config.st_model_name
             ),
             # dis range: [0, 1]
-            metadata={"hnsw:space": "cosine"}
+            metadata={"hnsw:space": "l2"}
         )
 
     def process_file_ctx(self, file: FileContext, collection: Collection):

srctag/tagger.py

@@ -79,8 +79,11 @@ def tag(self, storage: Storage) -> TagResult:
 
             metadatas: typing.List[Metadata] = query_result["metadatas"][0]
             # https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/chroma.py
+            # https://stats.stackexchange.com/questions/158279/how-i-can-convert-distance-euclidean-to-similarity-score
             distances: typing.List[float] = query_result["distances"][0]
-            normalized_scores = [1 - each for each in distances]
+            normalized_scores = [
+                1.0 / (1.0 + x) for x in distances
+            ]
 
             for each_metadata, each_score in zip(metadatas, normalized_scores):
                 each_file_name = each_metadata[MetadataConstant.KEY_SOURCE]
@@ -100,8 +103,8 @@ def tag(self, storage: Storage) -> TagResult:
                 each_file_tag_result[each_tag] = each_score
             else:
                 # has been touched by other commits
-                # merge these scores
-                each_file_tag_result[each_tag] += each_score
+                # keep the closest one
+                each_file_tag_result[each_tag] = max(each_score, each_file_tag_result[each_tag])
         # END tag_results
 
         scores_df = pd.DataFrame.from_dict(ret, orient="index")

tests/test_api.py

@@ -72,6 +72,8 @@ def test_query(setup_tagger):
 
     tags_series = tag_result.tags_by_file("examples/write.py")
     assert len(tags_series) == len(all_tags)
+    tags_series = tags_series[tags_series > 0.5][:5]
+    assert len(tags_series) == 1
     for k, v in tags_series.items():
         logger.info(f"tag: {k}, score: {v}")