fix

src/backend/base/langflow/components/vectorstores/AstraDB.py

@@ -1,5 +1,6 @@
 from loguru import logger
 
+from langchain_core.vectorstores import VectorStore
 from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers import docs_to_data
 from langflow.inputs import FloatInput, DictInput
@@ -22,6 +23,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
     documentation: str = "https://python.langchain.com/docs/integrations/vectorstores/astradb"
     icon: str = "AstraDB"
 
+    _cached_vectorstore: VectorStore = None
+
     inputs = [
         StrInput(
             name="collection_name",
@@ -158,6 +161,8 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
     ]
 
     def _build_vector_store_no_ingest(self):
+        if self._cached_vectorstore:
+            return self._cached_vectorstore
         try:
             from langchain_astradb import AstraDBVectorStore
             from langchain_astradb.utils.astradb import SetupMode
@@ -216,13 +221,13 @@ def _build_vector_store_no_ingest(self):
         except Exception as e:
             raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
 
+        self._cached_vectorstore = vector_store
+
         return vector_store
 
     def build_vector_store(self):
         vector_store = self._build_vector_store_no_ingest()
-        if hasattr(self, "ingest_data") and self.ingest_data:
-            logger.debug("Ingesting data into the Vector Store.")
-            self._add_documents_to_vector_store(vector_store)
+        self._add_documents_to_vector_store(vector_store)
         return vector_store
 
     def _add_documents_to_vector_store(self, vector_store):
@@ -233,7 +238,7 @@ def _add_documents_to_vector_store(self, vector_store):
             else:
                 raise ValueError("Vector Store Inputs must be Data objects.")
 
-        if documents and self.embedding is not None:
+        if documents:
             logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
             try:
                 vector_store.add_documents(documents)
@@ -252,6 +257,7 @@ def _map_search_type(self):
 
     def search_documents(self) -> list[Data]:
         vector_store = self._build_vector_store_no_ingest()
+        self._add_documents_to_vector_store(vector_store)
 
         logger.debug(f"Search input: {self.search_input}")
         logger.debug(f"Search type: {self.search_type}")

src/backend/base/langflow/components/vectorstores/Cassandra.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import List
 
 from langchain_community.vectorstores import Cassandra
 
@@ -15,6 +15,7 @@
     SecretStrInput,
 )
 from langflow.schema import Data
+from loguru import logger
 
 
 class CassandraVectorStoreComponent(LCVectorStoreComponent):
@@ -23,6 +24,8 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/cassandra"
     icon = "Cassandra"
 
+    _cached_vectorstore: Cassandra = None
+
     inputs = [
         MessageTextInput(
             name="database_ref",
@@ -131,11 +134,14 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
     ]
 
     def build_vector_store(self) -> Cassandra:
-        return self._build_cassandra(ingest=True)
+        return self._build_cassandra()
 
-    def _build_cassandra(self, ingest: bool) -> Cassandra:
+    def _build_cassandra(self) -> Cassandra:
+        if self._cached_vectorstore:
+            return self._cached_vectorstore
         try:
             import cassio
+            from langchain_community.utilities.cassandra import SetupMode
         except ImportError:
             raise ImportError(
                 "Could not import cassio integration package. " "Please install it with `pip install cassio`."
@@ -167,43 +173,48 @@ def _build_cassandra(self, ingest: bool) -> Cassandra:
                 password=self.token,
                 cluster_kwargs=self.cluster_kwargs,
             )
-        ttl_seconds: Optional[int] = self.ttl_seconds
-
         documents = []
 
-        if ingest:
-            for _input in self.ingest_data or []:
-                if isinstance(_input, Data):
-                    documents.append(_input.to_lc_document())
-                else:
-                    documents.append(_input)
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                documents.append(_input)
 
         if self.enable_body_search:
             body_index_options = [("index_analyzer", "STANDARD")]
         else:
             body_index_options = None
 
+        if self.setup_mode == "Off":
+            setup_mode = SetupMode.OFF
+        elif self.setup_mode == "Sync":
+            setup_mode = SetupMode.SYNC
+        else:
+            setup_mode = SetupMode.ASYNC
+
         if documents:
+            logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
             table = Cassandra.from_documents(
                 documents=documents,
                 embedding=self.embedding,
                 table_name=self.table_name,
                 keyspace=self.keyspace,
-                ttl_seconds=ttl_seconds,
+                ttl_seconds=self.ttl_seconds or None,
                 batch_size=self.batch_size,
                 body_index_options=body_index_options,
             )
-
         else:
+            logger.debug("No documents to add to the Vector Store.")
             table = Cassandra(
                 embedding=self.embedding,
                 table_name=self.table_name,
                 keyspace=self.keyspace,
-                ttl_seconds=ttl_seconds,
+                ttl_seconds=self.ttl_seconds or None,
                 body_index_options=body_index_options,
-                setup_mode=self.setup_mode,
+                setup_mode=setup_mode,
             )
-
+        self._cached_vectorstore = table
         return table
 
     def _map_search_type(self):
@@ -215,13 +226,19 @@ def _map_search_type(self):
             return "similarity"
 
     def search_documents(self) -> List[Data]:
-        vector_store = self._build_cassandra(ingest=False)
+        vector_store = self._build_cassandra()
+
+        logger.debug(f"Search input: {self.search_query}")
+        logger.debug(f"Search type: {self.search_type}")
+        logger.debug(f"Number of results: {self.number_of_results}")
 
         if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
             try:
                 search_type = self._map_search_type()
                 search_args = self._build_search_args()
 
+                logger.debug(f"Search args: {str(search_args)}")
+
                 docs = vector_store.search(query=self.search_query, search_type=search_type, **search_args)
             except KeyError as e:
                 if "content" in str(e):
@@ -231,6 +248,8 @@ def search_documents(self) -> List[Data]:
                 else:
                     raise e
 
+            logger.debug(f"Retrieved documents: {len(docs)}")
+
             data = docs_to_data(docs)
             self.status = data
             return data