Cassandra: support for non-AstraDB databases (#2380)

* Added support for Contact points
* Reworded some parameters depending on astra / local cassandra usage
* Added username

Successor of https://github.com/langflow-ai/langflow/pull/2206 but with
the new framework

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

@@ -4,6 +4,7 @@ from langchain_community.vectorstores import Cassandra
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.helpers.data import docs_to_data
+from langflow.inputs import DictInput
 from langflow.io import (
     DataInput,
     DropdownInput,
@@ -23,24 +24,30 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
     icon = "Cassandra"
 
     inputs = [
+        MessageTextInput(name="database_ref",
+                         display_name="Contact Points / Astra Database ID",
+                         info="Contact points for the database (or AstraDB database ID)",
+                         required=True),
+        MessageTextInput(name="username",
+                         display_name="Username",
+                         info="Username for the database (leave empty for AstraDB)."),
         SecretStrInput(
             name="token",
-            display_name="Token",
+            display_name="Password / AstraDB Token",
-            info="Authentication token for accessing Cassandra on Astra DB.",
+            info="User password for the database (or AstraDB token).",
-            required=True,
+            required=True
-        ),
-        MessageTextInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
-        MessageTextInput(
-            name="table_name",
-            display_name="Table Name",
-            info="The name of the table where vectors will be stored.",
-            required=True,
         ),
         MessageTextInput(
             name="keyspace",
             display_name="Keyspace",
-            info="Optional key space within Astra DB. The keyspace should already be created.",
+            info="Table Keyspace (or AstraDB namespace).",
-            advanced=False,
+            required=True,
+        ),
+        MessageTextInput(
+            name="table_name",
+            display_name="Table Name",
+            info="The name of the table (or AstraDB collection) where vectors will be stored.",
+            required=True,
         ),
         IntInput(
             name="ttl_seconds",
@@ -69,6 +76,13 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
             value="Sync",
             advanced=True,
         ),
+        DictInput(
+            name="cluster_kwargs",
+            display_name="Cluster arguments",
+            info="Optional dictionary of additional keyword arguments for the Cassandra cluster.",
+            advanced=True,
+            is_list=True
+        ),
         MultilineInput(name="search_query", display_name="Search Query"),
         DataInput(
             name="ingest_data",
@@ -96,10 +110,35 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
                 "Could not import cassio integration package. " "Please install it with `pip install cassio`."
             )
 
-        cassio.init(
+        from uuid import UUID
-            database_id=self.database_id,
+
-            token=self.token,
+        database_ref = self.database_ref
-        )
+
+        try:
+            UUID(self.database_ref)
+            is_astra = True
+        except ValueError:
+            is_astra = False
+            if "," in self.database_ref:
+                # use a copy because we can't change the type of the parameter
+                database_ref = self.database_ref.split(",")
+
+        if is_astra:
+            cassio.init(
+                database_id=database_ref,
+                token=self.token,
+                cluster_kwargs=self.cluster_kwargs,
+            )
+        else:
+            cassio.init(
+                contact_points=database_ref,
+                username=self.username,
+                password=self.token,
+                cluster_kwargs=self.cluster_kwargs,
+            )
+
+        if not self.ttl_seconds:
+            self.ttl_seconds = None
 
         documents = []