fix: update executor parameter (jina-ai#180)

* fix: update parameter

* fix: fix import

* fix: fix error

* fix: fix limit

* fix: fix match_args

annlite/executor.py

@@ -18,6 +18,7 @@ class AnnLiteIndexer(Executor):
     :param n_dim: Dimensionality of vectors to index
     :param metric: Distance metric type. Can be 'euclidean', 'inner_product', or 'cosine'
     :param limit: Number of results to get for each query document in search
+    :param match_args: the arguments to `DocumentArray`'s match function
     :param data_path: the workspace of the AnnLiteIndexer.
     :param ef_construction: The construction time/accuracy trade-off
     :param ef_search: The query time accuracy/speed trade-off
@@ -37,6 +38,7 @@ def __init__(
         n_dim: int = 0,
         metric: str = 'cosine',
         limit: int = 10,
+        match_args: Optional[Dict] = None,
         data_path: Optional[str] = None,
         ef_construction: Optional[int] = None,
         ef_search: Optional[int] = None,
@@ -58,8 +60,10 @@ def __init__(
             raise ValueError('Please specify the dimension of the vectors to index!')
 
         self.metric = metric
-        self.limit = limit
+        self.match_args = match_args or {}
         self.include_metadata = include_metadata
+        if limit:
+            self.match_args.update({'limit': limit})
 
         self.index_access_paths = index_access_paths
         if 'index_traversal_paths' in kwargs:
@@ -250,10 +254,13 @@ def search(
         if not docs:
             return
 
-        limit = int(parameters.get('limit', self.limit))
-        search_filter = parameters.get('filter', None)
         access_paths = parameters.get('access_paths', self.search_access_paths)
         flat_docs = docs[access_paths]
+        match_args = (
+            {**self.match_args, **parameters}
+            if parameters is not None
+            else self.match_args
+        )
 
         with self._index_lock:
             if len(self._data_buffer) > 0:
@@ -262,7 +269,7 @@ def search(
                     'Please wait for the pending documents to be indexed.'
                 )
 
-            flat_docs.match(self._index, filter=search_filter, limit=limit)
+            flat_docs.match(self._index, **match_args)
 
     @requests(on='/filter')
     def filter(self, parameters: Dict, **kwargs):