add configuable funcname

examples/main_cli_example.py

@@ -69,7 +69,7 @@ async def main():
     
     query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead?"
     print(f"You: {query}")
-    chat_response = chat.ask(query, recompute_beighbor_embeddings=True)
+    chat_response = chat.ask(query, top_k=10, recompute_beighbor_embeddings=True)
     print(f"Leann: {chat_response}")
 
 if __name__ == "__main__":

packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py

@@ -246,7 +246,7 @@ class DiskannSearcher(LeannBackendSearcherInterface):
             raise
 
     def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, any]:
-        complexity = kwargs.get("complexity", 100)
+        complexity = kwargs.get("complexity", 32)
         beam_width = kwargs.get("beam_width", 4)
         
         USE_DEFERRED_FETCH = kwargs.get("USE_DEFERRED_FETCH", False)

packages/leann-core/src/leann/api.py

@@ -139,9 +139,36 @@ class LeannChat:
             self.openai_client = openai.OpenAI(api_key=api_key)
         return self.openai_client
         
-    def ask(self, question: str, **kwargs):
-        # 1. 检索
-        results = self.searcher.search(question, top_k=5, **kwargs)
+    def ask(self, question: str, top_k=5, **kwargs):
+        """
+        Additional keyword arguments (kwargs) for advanced search customization. Example usage:
+            chat.ask(
+                "What is ANN?",
+                top_k=10,
+                complexity=64,
+                beam_width=8,
+                USE_DEFERRED_FETCH=True,
+                skip_search_reorder=True,
+                recompute_beighbor_embeddings=True,
+                dedup_node_dis=True,
+                prune_ratio=0.1,
+                batch_recompute=True,
+                global_pruning=True
+            )
+        
+        Supported kwargs:
+            - complexity (int): Search complexity parameter (default: 32)
+            - beam_width (int): Beam width for search (default: 4)
+            - USE_DEFERRED_FETCH (bool): Enable deferred fetch mode (default: False)
+            - skip_search_reorder (bool): Skip search reorder step (default: False)
+            - recompute_beighbor_embeddings (bool): Enable ZMQ embedding server for neighbor recomputation (default: False)
+            - dedup_node_dis (bool): Deduplicate nodes by distance (default: False)
+            - prune_ratio (float): Pruning ratio for search (default: 0.0)
+            - batch_recompute (bool): Enable batch recomputation (default: False)
+            - global_pruning (bool): Enable global pruning (default: False)
+        """
+
+        results = self.searcher.search(question, top_k=top_k, **kwargs)
         context = "\n\n".join([r['text'] for r in results])
 
         # 2. 构建 Prompt