Add number_of_results parameter to vector search components

src/backend/base/langflow/components/vectorsearch/ChromaSearch.py

@@ -48,6 +48,11 @@ class ChromaSearchComponent(LCVectorStoreComponent):
                 "display_name": "Server SSL Enabled",
                 "advanced": True,
             },
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(
@@ -57,6 +62,7 @@ class ChromaSearchComponent(LCVectorStoreComponent):
         collection_name: str,
         embedding: Embeddings,
         chroma_server_ssl_enabled: bool,
+        number_of_results: int = 4,
         index_directory: Optional[str] = None,
         chroma_server_cors_allow_origins: Optional[str] = None,
         chroma_server_host: Optional[str] = None,
@@ -102,4 +108,4 @@ class ChromaSearchComponent(LCVectorStoreComponent):
             client_settings=chroma_settings,
         )
 
-        return self.search_with_vector_store(input_value, search_type, vector_store)
+        return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)

src/backend/base/langflow/components/vectorsearch/FAISSSearch.py

@@ -21,6 +21,11 @@ class FAISSSearchComponent(LCVectorStoreComponent):
             },
             "input_value": {"display_name": "Input"},
             "index_name": {"display_name": "Index Name"},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(
@@ -28,6 +33,7 @@ class FAISSSearchComponent(LCVectorStoreComponent):
         input_value: Text,
         embedding: Embeddings,
         folder_path: str,
+        number_of_results: int = 4,
         index_name: str = "langflow_index",
     ) -> List[Record]:
         if not folder_path:
@@ -38,5 +44,5 @@ class FAISSSearchComponent(LCVectorStoreComponent):
             raise ValueError("Failed to load the FAISS index.")
 
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type="similarity"
+            vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py

@@ -23,6 +23,11 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
             "index_name": {"display_name": "Index Name"},
             "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
             "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(  # type: ignore[override]
@@ -30,6 +35,7 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
         input_value: Text,
         search_type: str,
         embedding: Embeddings,
+        number_of_results: int = 4,
         collection_name: str = "",
         db_name: str = "",
         index_name: str = "",
@@ -47,5 +53,5 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
         if not vector_store:
             raise ValueError("Failed to create MongoDB Atlas Vector Store")
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type=search_type
+            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/PineconeSearch.py

@@ -38,6 +38,11 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
                 "default": 1,
                 "advanced": True,
             },
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(  # type: ignore[override]
@@ -46,6 +51,7 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
         embedding: Embeddings,
         pinecone_env: str,
         text_key: str = "text",
+        number_of_results: int = 4,
         pool_threads: int = 4,
         index_name: Optional[str] = None,
         pinecone_api_key: Optional[str] = None,
@@ -66,5 +72,5 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
             raise ValueError("Failed to load the Pinecone index.")
 
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type=search_type
+            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/QdrantSearch.py

@@ -41,6 +41,11 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
             "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
             "timeout": {"display_name": "Timeout", "advanced": True},
             "url": {"display_name": "URL", "advanced": True},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(  # type: ignore[override]
@@ -48,6 +53,7 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
         input_value: Text,
         embedding: Embeddings,
         collection_name: str,
+        number_of_results: int = 4,
         search_type: str = "similarity",
         api_key: Optional[str] = None,
         content_payload_key: str = "page_content",
@@ -88,5 +94,5 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
             raise ValueError("Failed to load the Qdrant index.")
 
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type=search_type
+            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/RedisSearch.py

@@ -39,6 +39,11 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
                 "advanced": False,
             },
             "redis_index_name": {"display_name": "Redis Index", "advanced": False},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(  # type: ignore[override]
@@ -48,6 +53,7 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
         embedding: Embeddings,
         redis_server_url: str,
         redis_index_name: str,
+        number_of_results: int = 4,
         schema: Optional[str] = None,
     ) -> List[Record]:
         """
@@ -72,5 +78,5 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
             raise ValueError("Failed to load the Redis index.")
 
         return self.search_with_vector_store(
-            input_value=input_value, search_type=search_type, vector_store=vector_store
+            input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py

@@ -26,6 +26,11 @@ class SupabaseSearchComponent(LCVectorStoreComponent):
             "supabase_service_key": {"display_name": "Supabase Service Key"},
             "supabase_url": {"display_name": "Supabase URL"},
             "table_name": {"display_name": "Table Name", "advanced": True},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(
@@ -33,6 +38,7 @@ class SupabaseSearchComponent(LCVectorStoreComponent):
         input_value: Text,
         search_type: str,
         embedding: Embeddings,
+        number_of_results: int = 4,
         query_name: str = "",
         supabase_service_key: str = "",
         supabase_url: str = "",
@@ -45,4 +51,4 @@ class SupabaseSearchComponent(LCVectorStoreComponent):
             table_name=table_name,
             query_name=query_name,
         )
-        return self.search_with_vector_store(input_value, search_type, vector_store)
+        return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)

src/backend/base/langflow/components/vectorsearch/VectaraSearch.py

@@ -34,6 +34,11 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
             "display_name": "Files Url",
             "info": "Make vectara object using url of files (optional)",
         },
+        "number_of_results": {
+            "display_name": "Number of Results",
+            "info": "Number of results to return.",
+            "advanced": True,
+        },
     }
 
     def build(  # type: ignore[override]
@@ -43,6 +48,7 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
         vectara_customer_id: str,
         vectara_corpus_id: str,
         vectara_api_key: str,
+        number_of_results: int = 4,
     ) -> List[Record]:
         source = "Langflow"
         vector_store = Vectara(
@@ -56,5 +62,5 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
             raise ValueError("Failed to create Vectara Vector Store")
 
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type=search_type
+            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py

@@ -49,7 +49,11 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
             "field_type": "bool",
             "advanced": True,
         },
-        "code": {"show": False},
+        "number_of_results": {
+            "display_name": "Number of Results",
+            "info": "Number of results to return.",
+            "advanced": True,
+        },
     }
 
     def build(  # type: ignore[override]
@@ -57,6 +61,7 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
         input_value: Text,
         search_type: str,
         url: str,
+        number_of_results: int = 4,
         search_by_text: bool = False,
         api_key: Optional[str] = None,
         index_name: Optional[str] = None,
@@ -77,5 +82,5 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
             raise ValueError("Failed to load the Weaviate index.")
 
         return self.search_with_vector_store(
-            vector_store=vector_store, input_value=input_value, search_type=search_type
+            vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
         )

src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py

@@ -33,6 +33,11 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
             },
             "collection_name": {"display_name": "Table", "advanced": False},
             "input_value": {"display_name": "Input"},
+            "number_of_results": {
+                "display_name": "Number of Results",
+                "info": "Number of results to return.",
+                "advanced": True,
+            },
         }
 
     def build(  # type: ignore[override]
@@ -42,6 +47,7 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
         search_type: str,
         pg_server_url: str,
         collection_name: str,
+        number_of_results: int = 4,
     ) -> List[Record]:
         """
         Builds the Vector Store or BaseRetriever object.
@@ -64,5 +70,5 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
         except Exception as e:
             raise RuntimeError(f"Failed to build PGVector: {e}")
         return self.search_with_vector_store(
-            input_value=input_value, search_type=search_type, vector_store=vector_store
+            input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
         )