diff --git a/src/backend/base/langflow/components/vectorstores/astradb.py b/src/backend/base/langflow/components/vectorstores/astradb.py
index d5a6d1a93..009be6df1 100644
--- a/src/backend/base/langflow/components/vectorstores/astradb.py
+++ b/src/backend/base/langflow/components/vectorstores/astradb.py
@@ -127,7 +127,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
             name="api_endpoint",
             display_name="API Endpoint",
             info="The API endpoint for the Astra DB instance.",
-            advanced=True,
+            show=os.getenv("LANGFLOW_HOST") is not None,  # TODO: Clean up all examples of these
         ),
         DropdownInput(
             name="database_name",
@@ -145,6 +145,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
             ],
             value="",
             combobox=True,
+            show=os.getenv("LANGFLOW_HOST") is None,
         ),
         DropdownInput(
             name="collection_name",
@@ -165,6 +166,15 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
             ],
             value="",
         ),
+        DropdownInput(
+            name="vectorize_choice",
+            display_name="Embedding Model or Astra Vectorize",
+            info="Choose an embedding model or use Astra Vectorize.",
+            options=["Embedding Model", "Astra Vectorize"],
+            value="Embedding Model",
+            show=os.getenv("LANGFLOW_HOST") is not None,
+            real_time_refresh=True,
+        ),
         StrInput(
             name="keyspace",
             display_name="Keyspace",
@@ -490,6 +500,16 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
             return []
 
     def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):
+        if field_name == "vectorize_choice":
+            if field_value == "Astra Vectorize":
+                build_config["embedding_model"]["show"] = False
+                build_config["embedding_model"]["required"] = False
+            else:
+                build_config["embedding_model"]["show"] = True
+                build_config["embedding_model"]["required"] = True
+
+            return build_config
+
         if not self.token or not self.token.startswith("AstraCS:"):
             build_config["database_name"]["info"] = "Add a Valid Token to Select a Database"
         else:
@@ -594,7 +614,11 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
             raise ImportError(msg) from e
 
         # Get the embedding model and additional params
-        embedding_params = {"embedding": self.embedding_model} if self.embedding_model else {}
+        embedding_params = (
+            {"embedding": self.embedding_model}
+            if self.embedding_model and self.vectorize_choice == "Embedding Model"
+            else {}
+        )
         additional_params = self.astradb_vectorstore_kwargs or {}
 
         # Get Langflow version and platform information
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json
index 82e4172aa..03e897d12 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
@@ -7,7 +7,7 @@
         "data": {
           "sourceHandle": {
             "dataType": "ParseData",
-            "id": "ParseData-JV2Ry",
+            "id": "ParseData-dvw00",
             "name": "text",
             "output_types": [
               "Message"
@@ -15,7 +15,7 @@
           },
           "targetHandle": {
             "fieldName": "context",
-            "id": "Prompt-WhGNl",
+            "id": "Prompt-II4kG",
             "inputTypes": [
               "Message",
               "Text"
@@ -23,11 +23,11 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ParseData-JV2Ry{œdataTypeœ:œParseDataœ,œidœ:œParseData-JV2Ryœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-WhGNl{œfieldNameœ:œcontextœ,œidœ:œPrompt-WhGNlœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ParseData-JV2Ry",
-        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-JV2Ryœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-WhGNl",
-        "targetHandle": "{œfieldNameœ: œcontextœ, œidœ: œPrompt-WhGNlœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ParseData-dvw00{œdataTypeœ:œParseDataœ,œidœ:œParseData-dvw00œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-II4kG{œfieldNameœ:œcontextœ,œidœ:œPrompt-II4kGœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ParseData-dvw00",
+        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-dvw00œ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-II4kG",
+        "targetHandle": "{œfieldNameœ: œcontextœ, œidœ: œPrompt-II4kGœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "animated": false,
@@ -35,7 +35,7 @@
         "data": {
           "sourceHandle": {
             "dataType": "ChatInput",
-            "id": "ChatInput-xWTbO",
+            "id": "ChatInput-czq7J",
             "name": "message",
             "output_types": [
               "Message"
@@ -43,7 +43,7 @@
           },
           "targetHandle": {
             "fieldName": "question",
-            "id": "Prompt-WhGNl",
+            "id": "Prompt-II4kG",
             "inputTypes": [
               "Message",
               "Text"
@@ -51,11 +51,11 @@
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-xWTbO{œdataTypeœ:œChatInputœ,œidœ:œChatInput-xWTbOœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-WhGNl{œfieldNameœ:œquestionœ,œidœ:œPrompt-WhGNlœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-xWTbO",
-        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-xWTbOœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "Prompt-WhGNl",
-        "targetHandle": "{œfieldNameœ: œquestionœ, œidœ: œPrompt-WhGNlœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ChatInput-czq7J{œdataTypeœ:œChatInputœ,œidœ:œChatInput-czq7Jœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-II4kG{œfieldNameœ:œquestionœ,œidœ:œPrompt-II4kGœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-czq7J",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-czq7Jœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-II4kG",
+        "targetHandle": "{œfieldNameœ: œquestionœ, œidœ: œPrompt-II4kGœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "animated": false,
@@ -63,7 +63,7 @@
         "data": {
           "sourceHandle": {
             "dataType": "File",
-            "id": "File-zHNC8",
+            "id": "File-x7QbL",
             "name": "data",
             "output_types": [
               "Data"
@@ -71,154 +71,25 @@
           },
           "targetHandle": {
             "fieldName": "data_inputs",
-            "id": "SplitText-TfND9",
+            "id": "SplitText-TCpIk",
             "inputTypes": [
               "Data"
             ],
             "type": "other"
           }
         },
-        "id": "reactflow__edge-File-zHNC8{œdataTypeœ:œFileœ,œidœ:œFile-zHNC8œ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-SplitText-TfND9{œfieldNameœ:œdata_inputsœ,œidœ:œSplitText-TfND9œ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
-        "source": "File-zHNC8",
-        "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-zHNC8œ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
-        "target": "SplitText-TfND9",
-        "targetHandle": "{œfieldNameœ: œdata_inputsœ, œidœ: œSplitText-TfND9œ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+        "id": "reactflow__edge-File-x7QbL{œdataTypeœ:œFileœ,œidœ:œFile-x7QbLœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-SplitText-TCpIk{œfieldNameœ:œdata_inputsœ,œidœ:œSplitText-TCpIkœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+        "source": "File-x7QbL",
+        "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-x7QbLœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+        "target": "SplitText-TCpIk",
+        "targetHandle": "{œfieldNameœ: œdata_inputsœ, œidœ: œSplitText-TCpIkœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
       },
       {
         "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "OpenAIEmbeddings",
-            "id": "OpenAIEmbeddings-RXyh4",
-            "name": "embeddings",
-            "output_types": [
-              "Embeddings"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "embedding_model",
-            "id": "AstraDB-eCpm7",
-            "inputTypes": [
-              "Embeddings"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "reactflow__edge-OpenAIEmbeddings-RXyh4{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-RXyh4œ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-eCpm7{œfieldNameœ:œembedding_modelœ,œidœ:œAstraDB-eCpm7œ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
-        "source": "OpenAIEmbeddings-RXyh4",
-        "sourceHandle": "{œdataTypeœ: œOpenAIEmbeddingsœ, œidœ: œOpenAIEmbeddings-RXyh4œ, œnameœ: œembeddingsœ, œoutput_typesœ: [œEmbeddingsœ]}",
-        "target": "AstraDB-eCpm7",
-        "targetHandle": "{œfieldNameœ: œembedding_modelœ, œidœ: œAstraDB-eCpm7œ, œinputTypesœ: [œEmbeddingsœ], œtypeœ: œotherœ}"
-      },
-      {
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "SplitText",
-            "id": "SplitText-TfND9",
-            "name": "chunks",
-            "output_types": [
-              "Data"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "ingest_data",
-            "id": "AstraDB-eCpm7",
-            "inputTypes": [
-              "Data"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "reactflow__edge-SplitText-TfND9{œdataTypeœ:œSplitTextœ,œidœ:œSplitText-TfND9œ,œnameœ:œchunksœ,œoutput_typesœ:[œDataœ]}-AstraDB-eCpm7{œfieldNameœ:œingest_dataœ,œidœ:œAstraDB-eCpm7œ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
-        "source": "SplitText-TfND9",
-        "sourceHandle": "{œdataTypeœ: œSplitTextœ, œidœ: œSplitText-TfND9œ, œnameœ: œchunksœ, œoutput_typesœ: [œDataœ]}",
-        "target": "AstraDB-eCpm7",
-        "targetHandle": "{œfieldNameœ: œingest_dataœ, œidœ: œAstraDB-eCpm7œ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
-      },
-      {
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "ChatInput",
-            "id": "ChatInput-xWTbO",
-            "name": "message",
-            "output_types": [
-              "Message"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "search_query",
-            "id": "AstraDB-2ETzC",
-            "inputTypes": [
-              "Message"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "reactflow__edge-ChatInput-xWTbO{œdataTypeœ:œChatInputœ,œidœ:œChatInput-xWTbOœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-AstraDB-2ETzC{œfieldNameœ:œsearch_queryœ,œidœ:œAstraDB-2ETzCœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-xWTbO",
-        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-xWTbOœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "AstraDB-2ETzC",
-        "targetHandle": "{œfieldNameœ: œsearch_queryœ, œidœ: œAstraDB-2ETzCœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
-      },
-      {
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "AstraDB",
-            "id": "AstraDB-2ETzC",
-            "name": "search_results",
-            "output_types": [
-              "Data"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "data",
-            "id": "ParseData-JV2Ry",
-            "inputTypes": [
-              "Data"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "reactflow__edge-AstraDB-2ETzC{œdataTypeœ:œAstraDBœ,œidœ:œAstraDB-2ETzCœ,œnameœ:œsearch_resultsœ,œoutput_typesœ:[œDataœ]}-ParseData-JV2Ry{œfieldNameœ:œdataœ,œidœ:œParseData-JV2Ryœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
-        "source": "AstraDB-2ETzC",
-        "sourceHandle": "{œdataTypeœ: œAstraDBœ, œidœ: œAstraDB-2ETzCœ, œnameœ: œsearch_resultsœ, œoutput_typesœ: [œDataœ]}",
-        "target": "ParseData-JV2Ry",
-        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-JV2Ryœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
-      },
-      {
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "OpenAIEmbeddings",
-            "id": "OpenAIEmbeddings-RcSEE",
-            "name": "embeddings",
-            "output_types": [
-              "Embeddings"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "embedding_model",
-            "id": "AstraDB-2ETzC",
-            "inputTypes": [
-              "Embeddings"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "reactflow__edge-OpenAIEmbeddings-RcSEE{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-RcSEEœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-2ETzC{œfieldNameœ:œembedding_modelœ,œidœ:œAstraDB-2ETzCœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
-        "source": "OpenAIEmbeddings-RcSEE",
-        "sourceHandle": "{œdataTypeœ: œOpenAIEmbeddingsœ, œidœ: œOpenAIEmbeddings-RcSEEœ, œnameœ: œembeddingsœ, œoutput_typesœ: [œEmbeddingsœ]}",
-        "target": "AstraDB-2ETzC",
-        "targetHandle": "{œfieldNameœ: œembedding_modelœ, œidœ: œAstraDB-2ETzCœ, œinputTypesœ: [œEmbeddingsœ], œtypeœ: œotherœ}"
-      },
-      {
         "data": {
           "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-WhGNl",
+            "id": "Prompt-II4kG",
             "name": "prompt",
             "output_types": [
               "Message"
@@ -226,24 +97,25 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-IqFJR",
+            "id": "OpenAIModel-O6wY2",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "xy-edge__Prompt-WhGNl{œdataTypeœ:œPromptœ,œidœ:œPrompt-WhGNlœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-IqFJR{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-IqFJRœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "Prompt-WhGNl",
-        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-WhGNlœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "OpenAIModel-IqFJR",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-IqFJRœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-II4kG{œdataTypeœ:œPromptœ,œidœ:œPrompt-II4kGœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-O6wY2{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-O6wY2œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-II4kG",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-II4kGœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-O6wY2",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-O6wY2œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-IqFJR",
+            "id": "OpenAIModel-O6wY2",
             "name": "text_output",
             "output_types": [
               "Message"
@@ -251,18 +123,147 @@
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "ChatOutput-zeZUc",
+            "id": "ChatOutput-IWGUu",
             "inputTypes": [
               "Message"
             ],
             "type": "str"
           }
         },
-        "id": "xy-edge__OpenAIModel-IqFJR{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-IqFJRœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-zeZUc{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-zeZUcœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-IqFJR",
-        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-IqFJRœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
-        "target": "ChatOutput-zeZUc",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-zeZUcœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-O6wY2{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-O6wY2œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-IWGUu{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-IWGUuœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-O6wY2",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-O6wY2œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-IWGUu",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-IWGUuœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIEmbeddings",
+            "id": "OpenAIEmbeddings-4daFu",
+            "name": "embeddings",
+            "output_types": [
+              "Embeddings"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "embedding_model",
+            "id": "AstraDB-kVUuJ",
+            "inputTypes": [
+              "Embeddings"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-OpenAIEmbeddings-4daFu{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-4daFuœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-kVUuJ{œfieldNameœ:œembedding_modelœ,œidœ:œAstraDB-kVUuJœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
+        "source": "OpenAIEmbeddings-4daFu",
+        "sourceHandle": "{œdataTypeœ: œOpenAIEmbeddingsœ, œidœ: œOpenAIEmbeddings-4daFuœ, œnameœ: œembeddingsœ, œoutput_typesœ: [œEmbeddingsœ]}",
+        "target": "AstraDB-kVUuJ",
+        "targetHandle": "{œfieldNameœ: œembedding_modelœ, œidœ: œAstraDB-kVUuJœ, œinputTypesœ: [œEmbeddingsœ], œtypeœ: œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "ChatInput",
+            "id": "ChatInput-czq7J",
+            "name": "message",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "search_query",
+            "id": "AstraDB-kVUuJ",
+            "inputTypes": [
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-ChatInput-czq7J{œdataTypeœ:œChatInputœ,œidœ:œChatInput-czq7Jœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-AstraDB-kVUuJ{œfieldNameœ:œsearch_queryœ,œidœ:œAstraDB-kVUuJœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-czq7J",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-czq7Jœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "AstraDB-kVUuJ",
+        "targetHandle": "{œfieldNameœ: œsearch_queryœ, œidœ: œAstraDB-kVUuJœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIEmbeddings",
+            "id": "OpenAIEmbeddings-1YeCO",
+            "name": "embeddings",
+            "output_types": [
+              "Embeddings"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "embedding_model",
+            "id": "AstraDB-h03SC",
+            "inputTypes": [
+              "Embeddings"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-OpenAIEmbeddings-1YeCO{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-1YeCOœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-h03SC{œfieldNameœ:œembedding_modelœ,œidœ:œAstraDB-h03SCœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
+        "source": "OpenAIEmbeddings-1YeCO",
+        "sourceHandle": "{œdataTypeœ: œOpenAIEmbeddingsœ, œidœ: œOpenAIEmbeddings-1YeCOœ, œnameœ: œembeddingsœ, œoutput_typesœ: [œEmbeddingsœ]}",
+        "target": "AstraDB-h03SC",
+        "targetHandle": "{œfieldNameœ: œembedding_modelœ, œidœ: œAstraDB-h03SCœ, œinputTypesœ: [œEmbeddingsœ], œtypeœ: œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "SplitText",
+            "id": "SplitText-TCpIk",
+            "name": "chunks",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "ingest_data",
+            "id": "AstraDB-h03SC",
+            "inputTypes": [
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-SplitText-TCpIk{œdataTypeœ:œSplitTextœ,œidœ:œSplitText-TCpIkœ,œnameœ:œchunksœ,œoutput_typesœ:[œDataœ]}-AstraDB-h03SC{œfieldNameœ:œingest_dataœ,œidœ:œAstraDB-h03SCœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+        "source": "SplitText-TCpIk",
+        "sourceHandle": "{œdataTypeœ: œSplitTextœ, œidœ: œSplitText-TCpIkœ, œnameœ: œchunksœ, œoutput_typesœ: [œDataœ]}",
+        "target": "AstraDB-h03SC",
+        "targetHandle": "{œfieldNameœ: œingest_dataœ, œidœ: œAstraDB-h03SCœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+      },
+      {
+        "data": {
+          "sourceHandle": {
+            "dataType": "AstraDB",
+            "id": "AstraDB-kVUuJ",
+            "name": "search_results",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "data",
+            "id": "ParseData-dvw00",
+            "inputTypes": [
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "xy-edge__AstraDB-kVUuJ{œdataTypeœ:œAstraDBœ,œidœ:œAstraDB-kVUuJœ,œnameœ:œsearch_resultsœ,œoutput_typesœ:[œDataœ]}-ParseData-dvw00{œfieldNameœ:œdataœ,œidœ:œParseData-dvw00œ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+        "source": "AstraDB-kVUuJ",
+        "sourceHandle": "{œdataTypeœ: œAstraDBœ, œidœ: œAstraDB-kVUuJœ, œnameœ: œsearch_resultsœ, œoutput_typesœ: [œDataœ]}",
+        "target": "ParseData-dvw00",
+        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-dvw00œ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
       }
     ],
     "nodes": [
@@ -270,7 +271,7 @@
         "data": {
           "description": "Get chat inputs from the Playground.",
           "display_name": "Chat Input",
-          "id": "ChatInput-xWTbO",
+          "id": "ChatInput-czq7J",
           "node": {
             "base_classes": [
               "Message"
@@ -533,10 +534,10 @@
         },
         "dragging": false,
         "height": 234,
-        "id": "ChatInput-xWTbO",
+        "id": "ChatInput-czq7J",
         "measured": {
           "height": 234,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 743.9745420290319,
@@ -554,7 +555,7 @@
         "data": {
           "description": "Convert Data into plain text following a specified template.",
           "display_name": "Parse Data",
-          "id": "ParseData-JV2Ry",
+          "id": "ParseData-dvw00",
           "node": {
             "base_classes": [
               "Message"
@@ -686,10 +687,10 @@
         },
         "dragging": false,
         "height": 350,
-        "id": "ParseData-JV2Ry",
+        "id": "ParseData-dvw00",
         "measured": {
           "height": 350,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 1606.0595305373527,
@@ -707,7 +708,7 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-WhGNl",
+          "id": "Prompt-II4kG",
           "node": {
             "base_classes": [
               "Message"
@@ -865,10 +866,10 @@
         },
         "dragging": false,
         "height": 433,
-        "id": "Prompt-WhGNl",
+        "id": "Prompt-II4kG",
         "measured": {
           "height": 433,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 1977.9097981422992,
@@ -886,7 +887,7 @@
         "data": {
           "description": "Split text into chunks based on specified criteria.",
           "display_name": "Split Text",
-          "id": "SplitText-TfND9",
+          "id": "SplitText-TCpIk",
           "node": {
             "base_classes": [
               "Data"
@@ -1031,10 +1032,10 @@
         },
         "dragging": false,
         "height": 475,
-        "id": "SplitText-TfND9",
+        "id": "SplitText-TCpIk",
         "measured": {
           "height": 475,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 1683.4543896546102,
@@ -1050,7 +1051,7 @@
       },
       {
         "data": {
-          "id": "note-Dv6pY",
+          "id": "note-axLgL",
           "node": {
             "description": "## 🐕 2. Retriever Flow\n\nThis flow answers your questions with contextual data retrieved from your vector database.\n\nOpen the **Playground** and ask, \n\n```\nWhat is this document about?\n```\n",
             "display_name": "",
@@ -1063,10 +1064,10 @@
         },
         "dragging": false,
         "height": 324,
-        "id": "note-Dv6pY",
+        "id": "note-axLgL",
         "measured": {
           "height": 324,
-          "width": 328
+          "width": 325
         },
         "position": {
           "x": 374.388314931542,
@@ -1087,7 +1088,7 @@
       },
       {
         "data": {
-          "id": "note-Ln4xi",
+          "id": "note-qNiWx",
           "node": {
             "description": "## 📖 README\n\nLoad your data into a vector database with the 📚 **Load Data** flow, and then use your data as chat context with the 🐕 **Retriever** flow.\n\n**🚨 Add your OpenAI API key as a global variable to easily add it to all of the OpenAI components in this flow.** \n\n**Quick start**\n1. Run the 📚 **Load Data** flow.\n2. Run the 🐕 **Retriever** flow.\n\n**Next steps** \n\n- Experiment by changing the prompt and the loaded data to see how the bot's responses change. \n\nFor more info, see the [Langflow docs](https://docs.langflow.org/starter-projects-vector-store-rag).",
             "display_name": "Read Me",
@@ -1100,10 +1101,10 @@
         },
         "dragging": false,
         "height": 527,
-        "id": "note-Ln4xi",
+        "id": "note-qNiWx",
         "measured": {
           "height": 527,
-          "width": 328
+          "width": 325
         },
         "position": {
           "x": 94.28986613312418,
@@ -1126,7 +1127,7 @@
         "data": {
           "description": "Display a chat message in the Playground.",
           "display_name": "Chat Output",
-          "id": "ChatOutput-zeZUc",
+          "id": "ChatOutput-IWGUu",
           "node": {
             "base_classes": [
               "Message"
@@ -1387,10 +1388,10 @@
         },
         "dragging": false,
         "height": 234,
-        "id": "ChatOutput-zeZUc",
+        "id": "ChatOutput-IWGUu",
         "measured": {
           "height": 234,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 2738.611008351098,
@@ -1406,7 +1407,7 @@
       },
       {
         "data": {
-          "id": "OpenAIEmbeddings-RcSEE",
+          "id": "OpenAIEmbeddings-4daFu",
           "node": {
             "base_classes": [
               "Embeddings"
@@ -1885,10 +1886,10 @@
         },
         "dragging": false,
         "height": 320,
-        "id": "OpenAIEmbeddings-RcSEE",
+        "id": "OpenAIEmbeddings-4daFu",
         "measured": {
           "height": 320,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 825.435626932521,
@@ -1904,7 +1905,7 @@
       },
       {
         "data": {
-          "id": "note-vRlKb",
+          "id": "note-O3Xoo",
           "node": {
             "description": "## 📚 1. Load Data Flow\n\nRun this first! Load data from a local file and embed it into the vector database.\n\nSelect a Database and a Collection, or create new ones. \n\nClick ▶️ **Run component** on the **Astra DB** component to load your data.\n\n* If you're using OSS Langflow, add your Astra DB Application Token to the Astra DB component.\n\n#### Next steps:\n Experiment by changing the prompt and the contextual data to see how the retrieval flow's responses change.",
             "display_name": "",
@@ -1917,10 +1918,10 @@
         },
         "dragging": false,
         "height": 50,
-        "id": "note-vRlKb",
+        "id": "note-O3Xoo",
         "measured": {
           "height": 50,
-          "width": 328
+          "width": 325
         },
         "position": {
           "x": 955.3277857006676,
@@ -1940,7 +1941,7 @@
       },
       {
         "data": {
-          "id": "OpenAIEmbeddings-RXyh4",
+          "id": "OpenAIEmbeddings-1YeCO",
           "node": {
             "base_classes": [
               "Embeddings"
@@ -2419,10 +2420,10 @@
         },
         "dragging": false,
         "height": 320,
-        "id": "OpenAIEmbeddings-RXyh4",
+        "id": "OpenAIEmbeddings-1YeCO",
         "measured": {
           "height": 320,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 1690.9220896443658,
@@ -2438,7 +2439,7 @@
       },
       {
         "data": {
-          "id": "File-zHNC8",
+          "id": "File-x7QbL",
           "node": {
             "base_classes": [
               "Data"
@@ -2663,10 +2664,10 @@
         },
         "dragging": false,
         "height": 367,
-        "id": "File-zHNC8",
+        "id": "File-x7QbL",
         "measured": {
           "height": 367,
-          "width": 360
+          "width": 320
         },
         "position": {
           "x": 1318.9043936921921,
@@ -2682,7 +2683,7 @@
       },
       {
         "data": {
-          "id": "note-Sfs1f",
+          "id": "note-zAehE",
           "node": {
             "description": "### 💡 Add your OpenAI API key here 👇",
             "display_name": "",
@@ -2695,10 +2696,10 @@
         },
         "dragging": false,
         "height": 324,
-        "id": "note-Sfs1f",
+        "id": "note-zAehE",
         "measured": {
           "height": 324,
-          "width": 326
+          "width": 324
         },
         "position": {
           "x": 1692.2322233423606,
@@ -2714,7 +2715,7 @@
       },
       {
         "data": {
-          "id": "note-CWV0c",
+          "id": "note-DoYpw",
           "node": {
             "description": "### 💡 Add your OpenAI API key here 👇",
             "display_name": "",
@@ -2727,10 +2728,10 @@
         },
         "dragging": false,
         "height": 324,
-        "id": "note-CWV0c",
+        "id": "note-DoYpw",
         "measured": {
           "height": 324,
-          "width": 326
+          "width": 324
         },
         "position": {
           "x": 824.1003268813427,
@@ -2746,7 +2747,7 @@
       },
       {
         "data": {
-          "id": "note-ClCtT",
+          "id": "note-CwGTd",
           "node": {
             "description": "### 💡 Add your OpenAI API key here 👇",
             "display_name": "",
@@ -2759,10 +2760,10 @@
         },
         "dragging": false,
         "height": 324,
-        "id": "note-ClCtT",
+        "id": "note-CwGTd",
         "measured": {
           "height": 324,
-          "width": 326
+          "width": 324
         },
         "position": {
           "x": 2350.297636215281,
@@ -2778,919 +2779,7 @@
       },
       {
         "data": {
-          "id": "AstraDB-eCpm7",
-          "node": {
-            "base_classes": [
-              "Data",
-              "DataFrame"
-            ],
-            "beta": false,
-            "conditional_paths": [],
-            "custom_fields": {},
-            "description": "Ingest and search documents in Astra DB",
-            "display_name": "Astra DB",
-            "documentation": "https://docs.datastax.com/en/langflow/astra-components.html",
-            "edited": false,
-            "field_order": [
-              "token",
-              "environment",
-              "api_endpoint",
-              "database_name",
-              "collection_name",
-              "keyspace",
-              "embedding_model",
-              "ingest_data",
-              "search_query",
-              "number_of_results",
-              "search_type",
-              "search_score_threshold",
-              "advanced_search_filter",
-              "content_field",
-              "deletion_field",
-              "ignore_invalid_documents",
-              "astradb_vectorstore_kwargs"
-            ],
-            "frozen": false,
-            "icon": "AstraDB",
-            "legacy": false,
-            "metadata": {},
-            "minimized": false,
-            "output_types": [],
-            "outputs": [
-              {
-                "allows_loop": false,
-                "cache": true,
-                "display_name": "Search Results",
-                "method": "search_documents",
-                "name": "search_results",
-                "required_inputs": [
-                  "collection_name",
-                  "database_name",
-                  "embedding_model",
-                  "token"
-                ],
-                "selected": "Data",
-                "types": [
-                  "Data"
-                ],
-                "value": "__UNDEFINED__"
-              },
-              {
-                "allows_loop": false,
-                "cache": true,
-                "display_name": "DataFrame",
-                "method": "as_dataframe",
-                "name": "dataframe",
-                "required_inputs": [],
-                "selected": "DataFrame",
-                "types": [
-                  "DataFrame"
-                ],
-                "value": "__UNDEFINED__"
-              }
-            ],
-            "pinned": false,
-            "template": {
-              "_type": "Component",
-              "advanced_search_filter": {
-                "_input_type": "NestedDictInput",
-                "advanced": true,
-                "display_name": "Search Metadata Filter",
-                "dynamic": false,
-                "info": "Optional dictionary of filters to apply to the search query.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "advanced_search_filter",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "api_endpoint": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "API Endpoint",
-                "dynamic": false,
-                "info": "The API endpoint for the Astra DB instance.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "api_endpoint",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "astradb_vectorstore_kwargs": {
-                "_input_type": "NestedDictInput",
-                "advanced": true,
-                "display_name": "AstraDBVectorStore Parameters",
-                "dynamic": false,
-                "info": "Optional dictionary of additional parameters for the AstraDBVectorStore.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "astradb_vectorstore_kwargs",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\n\nfrom astrapy import AstraDBAdmin, DataAPIClient, Database\nfrom langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import FloatInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.datastax.com/en/langflow/astra-components.html\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    @dataclass\n    class NewDatabaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new database in Astra DB.\",\n                        \"display_name\": \"Create New Database\",\n                        \"field_order\": [\"new_database_name\", \"cloud_provider\", \"region\"],\n                        \"template\": {\n                            \"new_database_name\": StrInput(\n                                name=\"new_database_name\",\n                                display_name=\"New Database Name\",\n                                info=\"Name of the new database to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"cloud_provider\": DropdownInput(\n                                name=\"cloud_provider\",\n                                display_name=\"Cloud Provider\",\n                                info=\"Cloud provider for the new database.\",\n                                options=[\"Amazon Web Services\", \"Google Cloud Platform\", \"Microsoft Azure\"],\n                                required=True,\n                            ),\n                            \"region\": DropdownInput(\n                                name=\"region\",\n                                display_name=\"Region\",\n                                info=\"Region for the new database.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    @dataclass\n    class NewCollectionInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new collection in Astra DB.\",\n                        \"display_name\": \"Create New Collection\",\n                        \"field_order\": [\n                            \"new_collection_name\",\n                            \"embedding_generation_provider\",\n                            \"embedding_generation_model\",\n                        ],\n                        \"template\": {\n                            \"new_collection_name\": StrInput(\n                                name=\"new_collection_name\",\n                                display_name=\"New Collection Name\",\n                                info=\"Name of the new collection to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"embedding_generation_provider\": DropdownInput(\n                                name=\"embedding_generation_provider\",\n                                display_name=\"Embedding Generation Provider\",\n                                info=\"Provider to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                            \"embedding_generation_model\": DropdownInput(\n                                name=\"embedding_generation_model\",\n                                display_name=\"Embedding Generation Model\",\n                                info=\"Model to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        StrInput(\n            name=\"environment\",\n            display_name=\"Environment\",\n            info=\"The environment for the Astra DB API Endpoint.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"The API endpoint for the Astra DB instance.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"database_name\",\n            display_name=\"Database\",\n            info=\"Select a database in Astra DB.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewDatabaseInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"collections\": 0,\n                }\n            ],\n            value=\"\",\n            combobox=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewCollectionInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"provider\": None,\n                    \"model\": None,\n                    \"records\": 0,\n                    \"icon\": \"\",\n                }\n            ],\n            value=\"\",\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n            required=True,\n        ),\n        *LCVectorStoreComponent.inputs,\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n    ]\n\n    @classmethod\n    def map_cloud_providers(cls):\n        return {\n            \"Amazon Web Services\": {\n                \"id\": \"aws\",\n                \"regions\": [\"us-east-2\", \"ap-south-1\", \"eu-west-1\"],\n            },\n            \"Google Cloud Platform\": {\n                \"id\": \"gcp\",\n                \"regions\": [\"us-east1\"],\n            },\n            \"Microsoft Azure\": {\n                \"id\": \"azure\",\n                \"regions\": [\"westus3\"],\n            },\n        }\n\n    @classmethod\n    def create_database_api(\n        cls,\n        token: str,\n        new_database_name: str,\n        cloud_provider: str,\n        region: str,\n    ):\n        client = DataAPIClient(token=token)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Call the create database function\n        return admin_client.create_database(\n            name=new_database_name,\n            cloud_provider=cloud_provider,\n            region=region,\n        )\n\n    @classmethod\n    def create_collection_api(\n        cls,\n        token: str,\n        database_name: str,\n        new_collection_name: str,\n        dimension: int | None = None,\n        embedding_generation_provider: str | None = None,\n        embedding_generation_model: str | None = None,\n    ):\n        client = DataAPIClient(token=token)\n        api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)\n\n        # Get the database object\n        database = client.get_database(api_endpoint=api_endpoint, token=token)\n\n        # Build vectorize options, if needed\n        vectorize_options = None\n        if not dimension:\n            vectorize_options = CollectionVectorServiceOptions(\n                provider=embedding_generation_provider,\n                model_name=embedding_generation_model,\n                authentication=None,\n                parameters=None,\n            )\n\n        # Create the collection\n        return database.create_collection(\n            name=new_collection_name,\n            dimension=dimension,\n            service=vectorize_options,\n        )\n\n    @classmethod\n    def get_database_list_static(cls, token: str, environment: str | None = None):\n        client = DataAPIClient(token=token, environment=environment)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Get the list of databases\n        db_list = list(admin_client.list_databases())\n\n        # Generate the api endpoint for each database\n        return {\n            db.info.name: {\n                \"api_endpoint\": (api_endpoint := f\"https://{db.info.id}-{db.info.region}.apps.astra.datastax.com\"),\n                \"collections\": len(\n                    list(\n                        client.get_database(\n                            api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace\n                        ).list_collection_names(keyspace=db.info.keyspace)\n                    )\n                ),\n            }\n            for db in db_list\n        }\n\n    def get_database_list(self):\n        return self.get_database_list_static(token=self.token, environment=self.environment)\n\n    @classmethod\n    def get_api_endpoint_static(\n        cls,\n        token: str,\n        environment: str | None = None,\n        api_endpoint: str | None = None,\n        database_name: str | None = None,\n    ):\n        # Check if an api endpoint is provided\n        if api_endpoint:\n            return api_endpoint\n\n        # Check if the database_name is like a url\n        if database_name and database_name.startswith(\"https://\"):\n            return database_name\n\n        # If the database is not set, nothing we can do.\n        if not database_name:\n            return None\n\n        # Otherwise, get the URL from the database list\n        return cls.get_database_list_static(token=token, environment=environment).get(database_name).get(\"api_endpoint\")\n\n    def get_api_endpoint(self):\n        return self.get_api_endpoint_static(\n            token=self.token,\n            environment=self.environment,\n            api_endpoint=self.api_endpoint,\n            database_name=self.database_name,\n        )\n\n    def get_keyspace(self):\n        keyspace = self.keyspace\n\n        if keyspace:\n            return keyspace.strip()\n\n        return None\n\n    def get_database_object(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n\n            return client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def collection_exists(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n            database = client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n\n            return self.collection_name in list(database.list_collection_names(keyspace=self.get_keyspace()))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting collection status: {e}\")\n\n            return False\n\n    def collection_data(self, collection_name: str, database: Database | None = None):\n        try:\n            if not database:\n                client = DataAPIClient(token=self.token, environment=self.environment)\n\n                database = client.get_database(\n                    api_endpoint=self.get_api_endpoint(),\n                    token=self.token,\n                    keyspace=self.get_keyspace(),\n                )\n\n            collection = database.get_collection(collection_name, keyspace=self.get_keyspace())\n\n            return collection.estimated_document_count()\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error checking collection data: {e}\")\n\n            return None\n\n    def get_vectorize_providers(self):\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            admin = AstraDBAdmin(token=self.token)\n            db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())\n\n            # Get the list of embedding providers\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                # TODO: https://astra.datastax.com/api/v2/graphql\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return {}\n\n    def _initialize_database_options(self):\n        try:\n            return [\n                {\"name\": name, \"collections\": info[\"collections\"]} for name, info in self.get_database_list().items()\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching databases: {e}\")\n\n            return []\n\n    def _initialize_collection_options(self):\n        database = self.get_database_object()\n        if database is None:\n            return []\n\n        try:\n            collection_list = list(database.list_collections(keyspace=self.get_keyspace()))\n\n            return [\n                {\n                    \"name\": col.name,\n                    \"records\": self.collection_data(collection_name=col.name, database=database),\n                    \"provider\": (\n                        col.options.vector.service.provider\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                    \"icon\": \"\",\n                    \"model\": (\n                        col.options.vector.service.model_name\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                }\n                for col in collection_list\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return []\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if not self.token or not self.token.startswith(\"AstraCS:\"):\n            build_config[\"database_name\"][\"info\"] = \"Add a Valid Token to Select a Database\"\n        else:\n            build_config[\"database_name\"][\"info\"] = \"Select a Database from Astra DB\"\n\n        # Refresh the database name options\n        if field_name in [\"token\", \"environment\"] or not build_config[\"database_name\"][\"options\"]:\n            # Reset the list of collections\n            build_config[\"collection_name\"][\"options\"] = []\n            build_config[\"collection_name\"][\"options_metadata\"] = []\n            build_config[\"database_name\"][\"value\"] = []\n\n            # Get the list of databases\n            database_options = self._initialize_database_options()\n            build_config[\"database_name\"][\"options\"] = [db[\"name\"] for db in database_options]\n            build_config[\"database_name\"][\"options_metadata\"] = [\n                {k: v for k, v in db.items() if k not in [\"name\"]} for db in database_options\n            ]\n\n            # Get list of regions for a given cloud provider\n            \"\"\"\n            cloud_provider = (\n                build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"cloud_provider\"][\n                    \"value\"\n                ]\n                or \"Amazon Web Services\"\n            )\n            build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"region\"][\n                \"options\"\n            ] = self.map_cloud_providers()[cloud_provider][\"regions\"]\n            \"\"\"\n\n            return build_config\n\n        # Refresh the collection name options\n        if field_name in [\"database_name\", \"api_endpoint\"] or not build_config[\"collection_name\"][\"options\"]:\n            build_config[\"collection_name\"][\"value\"] = None\n\n            # Reset the list of collections\n            collection_options = self._initialize_collection_options()\n            build_config[\"collection_name\"][\"options\"] = [col[\"name\"] for col in collection_options]\n            build_config[\"collection_name\"][\"options_metadata\"] = [\n                {k: v for k, v in col.items() if k not in [\"name\"]} for col in collection_options\n            ]\n\n            return build_config\n\n        # Hide embedding model option if opriona_metadata provider is not null\n        if field_name == \"collection_name\":\n            # Find location of the name in the options list\n            index_of_name = build_config[\"collection_name\"][\"options\"].index(field_value)\n            value_of_provider = build_config[\"collection_name\"][\"options_metadata\"][index_of_name][\"provider\"]\n\n            if value_of_provider:\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_model\"][\"required\"] = True\n\n        # For the final step, get the list of vectorize providers\n        \"\"\"\n        vectorize_providers = self.get_vectorize_providers()\n        if not vectorize_providers:\n            return build_config\n\n        # Allow the user to see the embedding provider options\n        provider_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_provider\"\n        ][\"options\"]\n        if not provider_options:\n            # If the collection is set, allow user to see embedding options\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"options\"] = [\"Bring your own\", \"Nvidia\", *[key for key in vectorize_providers if key != \"Nvidia\"]]\n\n        # And allow the user to see the models based on a selected provider\n        model_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_model\"\n        ][\"options\"]\n        if not model_options:\n            embedding_provider = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"value\"]\n\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_model\"\n            ][\"options\"] = vectorize_providers.get(embedding_provider, [[], []])[1]\n        \"\"\"\n\n        return build_config\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Get the embedding model and additional params\n        embedding_params = {\"embedding\": self.embedding_model} if self.embedding_model else {}\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": self.collection_exists(),  # TODO: May want to expose this option\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params and self.collection_data(collection_name=self.collection_name) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=self.get_api_endpoint(),\n                namespace=self.get_keyspace(),\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=self.get_keyspace())\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except Exception as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
-              },
-              "collection_name": {
-                "_input_type": "DropdownInput",
-                "advanced": false,
-                "combobox": false,
-                "dialog_inputs": {},
-                "display_name": "Collection",
-                "dynamic": false,
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "name": "collection_name",
-                "options": [],
-                "options_metadata": [],
-                "placeholder": "",
-                "real_time_refresh": true,
-                "refresh_button": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "content_field": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Content Field",
-                "dynamic": false,
-                "info": "Field to use as the text content field for the vector store.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "content_field",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "database_name": {
-                "_input_type": "DropdownInput",
-                "advanced": false,
-                "combobox": true,
-                "dialog_inputs": {},
-                "display_name": "Database",
-                "dynamic": false,
-                "info": "Select a database in Astra DB.",
-                "name": "database_name",
-                "options": [],
-                "options_metadata": [
-                  {
-                    "collections": 0
-                  }
-                ],
-                "placeholder": "",
-                "real_time_refresh": true,
-                "refresh_button": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "deletion_field": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Deletion Based On Field",
-                "dynamic": false,
-                "info": "When this parameter is provided, documents in the target collection with metadata field values matching the input metadata field value will be deleted before new data is loaded.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "deletion_field",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "embedding_model": {
-                "_input_type": "HandleInput",
-                "advanced": false,
-                "display_name": "Embedding Model",
-                "dynamic": false,
-                "info": "Allows an embedding model configuration.",
-                "input_types": [
-                  "Embeddings"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "embedding_model",
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
-              "environment": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Environment",
-                "dynamic": false,
-                "info": "The environment for the Astra DB API Endpoint.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "environment",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "ignore_invalid_documents": {
-                "_input_type": "BoolInput",
-                "advanced": true,
-                "display_name": "Ignore Invalid Documents",
-                "dynamic": false,
-                "info": "Boolean flag to determine whether to ignore invalid documents at runtime.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "ignore_invalid_documents",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "bool",
-                "value": false
-              },
-              "ingest_data": {
-                "_input_type": "DataInput",
-                "advanced": false,
-                "display_name": "Ingest Data",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Data"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "ingest_data",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
-              "keyspace": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Keyspace",
-                "dynamic": false,
-                "info": "Optional keyspace within Astra DB to use for the collection.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "keyspace",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "number_of_results": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Number of Search Results",
-                "dynamic": false,
-                "info": "Number of search results to return.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "number_of_results",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": 4
-              },
-              "search_query": {
-                "_input_type": "MultilineInput",
-                "advanced": false,
-                "display_name": "Search Query",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "multiline": true,
-                "name": "search_query",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": true,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "search_score_threshold": {
-                "_input_type": "FloatInput",
-                "advanced": true,
-                "display_name": "Search Score Threshold",
-                "dynamic": false,
-                "info": "Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "search_score_threshold",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "float",
-                "value": 0
-              },
-              "search_type": {
-                "_input_type": "DropdownInput",
-                "advanced": true,
-                "combobox": false,
-                "dialog_inputs": {},
-                "display_name": "Search Type",
-                "dynamic": false,
-                "info": "Search type to use",
-                "name": "search_type",
-                "options": [
-                  "Similarity",
-                  "Similarity with score threshold",
-                  "MMR (Max Marginal Relevance)"
-                ],
-                "options_metadata": [],
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": "Similarity"
-              },
-              "token": {
-                "_input_type": "SecretStrInput",
-                "advanced": false,
-                "display_name": "Astra DB Application Token",
-                "dynamic": false,
-                "info": "Authentication token for accessing Astra DB.",
-                "input_types": [],
-                "load_from_db": true,
-                "name": "token",
-                "password": true,
-                "placeholder": "",
-                "real_time_refresh": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              }
-            },
-            "tool_mode": false
-          },
-          "showNode": true,
-          "type": "AstraDB"
-        },
-        "dragging": false,
-        "id": "AstraDB-eCpm7",
-        "measured": {
-          "height": 686,
-          "width": 360
-        },
-        "position": {
-          "x": 2053.72248912555,
-          "y": 1456.27751087445
-        },
-        "selected": false,
-        "type": "genericNode"
-      },
-      {
-        "data": {
-          "id": "AstraDB-2ETzC",
-          "node": {
-            "base_classes": [
-              "Data",
-              "DataFrame"
-            ],
-            "beta": false,
-            "conditional_paths": [],
-            "custom_fields": {},
-            "description": "Ingest and search documents in Astra DB",
-            "display_name": "Astra DB",
-            "documentation": "https://docs.datastax.com/en/langflow/astra-components.html",
-            "edited": false,
-            "field_order": [
-              "token",
-              "environment",
-              "api_endpoint",
-              "database_name",
-              "collection_name",
-              "keyspace",
-              "embedding_model",
-              "ingest_data",
-              "search_query",
-              "number_of_results",
-              "search_type",
-              "search_score_threshold",
-              "advanced_search_filter",
-              "content_field",
-              "deletion_field",
-              "ignore_invalid_documents",
-              "astradb_vectorstore_kwargs"
-            ],
-            "frozen": false,
-            "icon": "AstraDB",
-            "legacy": false,
-            "metadata": {},
-            "minimized": false,
-            "output_types": [],
-            "outputs": [
-              {
-                "allows_loop": false,
-                "cache": true,
-                "display_name": "Search Results",
-                "method": "search_documents",
-                "name": "search_results",
-                "required_inputs": [
-                  "collection_name",
-                  "database_name",
-                  "embedding_model",
-                  "token"
-                ],
-                "selected": "Data",
-                "types": [
-                  "Data"
-                ],
-                "value": "__UNDEFINED__"
-              },
-              {
-                "allows_loop": false,
-                "cache": true,
-                "display_name": "DataFrame",
-                "method": "as_dataframe",
-                "name": "dataframe",
-                "required_inputs": [],
-                "selected": "DataFrame",
-                "types": [
-                  "DataFrame"
-                ],
-                "value": "__UNDEFINED__"
-              }
-            ],
-            "pinned": false,
-            "template": {
-              "_type": "Component",
-              "advanced_search_filter": {
-                "_input_type": "NestedDictInput",
-                "advanced": true,
-                "display_name": "Search Metadata Filter",
-                "dynamic": false,
-                "info": "Optional dictionary of filters to apply to the search query.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "advanced_search_filter",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "api_endpoint": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "API Endpoint",
-                "dynamic": false,
-                "info": "The API endpoint for the Astra DB instance.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "api_endpoint",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "astradb_vectorstore_kwargs": {
-                "_input_type": "NestedDictInput",
-                "advanced": true,
-                "display_name": "AstraDBVectorStore Parameters",
-                "dynamic": false,
-                "info": "Optional dictionary of additional parameters for the AstraDBVectorStore.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "astradb_vectorstore_kwargs",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\n\nfrom astrapy import AstraDBAdmin, DataAPIClient, Database\nfrom langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import FloatInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.datastax.com/en/langflow/astra-components.html\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    @dataclass\n    class NewDatabaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new database in Astra DB.\",\n                        \"display_name\": \"Create New Database\",\n                        \"field_order\": [\"new_database_name\", \"cloud_provider\", \"region\"],\n                        \"template\": {\n                            \"new_database_name\": StrInput(\n                                name=\"new_database_name\",\n                                display_name=\"New Database Name\",\n                                info=\"Name of the new database to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"cloud_provider\": DropdownInput(\n                                name=\"cloud_provider\",\n                                display_name=\"Cloud Provider\",\n                                info=\"Cloud provider for the new database.\",\n                                options=[\"Amazon Web Services\", \"Google Cloud Platform\", \"Microsoft Azure\"],\n                                required=True,\n                            ),\n                            \"region\": DropdownInput(\n                                name=\"region\",\n                                display_name=\"Region\",\n                                info=\"Region for the new database.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    @dataclass\n    class NewCollectionInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new collection in Astra DB.\",\n                        \"display_name\": \"Create New Collection\",\n                        \"field_order\": [\n                            \"new_collection_name\",\n                            \"embedding_generation_provider\",\n                            \"embedding_generation_model\",\n                        ],\n                        \"template\": {\n                            \"new_collection_name\": StrInput(\n                                name=\"new_collection_name\",\n                                display_name=\"New Collection Name\",\n                                info=\"Name of the new collection to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"embedding_generation_provider\": DropdownInput(\n                                name=\"embedding_generation_provider\",\n                                display_name=\"Embedding Generation Provider\",\n                                info=\"Provider to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                            \"embedding_generation_model\": DropdownInput(\n                                name=\"embedding_generation_model\",\n                                display_name=\"Embedding Generation Model\",\n                                info=\"Model to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        StrInput(\n            name=\"environment\",\n            display_name=\"Environment\",\n            info=\"The environment for the Astra DB API Endpoint.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"The API endpoint for the Astra DB instance.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"database_name\",\n            display_name=\"Database\",\n            info=\"Select a database in Astra DB.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewDatabaseInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"collections\": 0,\n                }\n            ],\n            value=\"\",\n            combobox=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewCollectionInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"provider\": None,\n                    \"model\": None,\n                    \"records\": 0,\n                    \"icon\": \"\",\n                }\n            ],\n            value=\"\",\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n            required=True,\n        ),\n        *LCVectorStoreComponent.inputs,\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n    ]\n\n    @classmethod\n    def map_cloud_providers(cls):\n        return {\n            \"Amazon Web Services\": {\n                \"id\": \"aws\",\n                \"regions\": [\"us-east-2\", \"ap-south-1\", \"eu-west-1\"],\n            },\n            \"Google Cloud Platform\": {\n                \"id\": \"gcp\",\n                \"regions\": [\"us-east1\"],\n            },\n            \"Microsoft Azure\": {\n                \"id\": \"azure\",\n                \"regions\": [\"westus3\"],\n            },\n        }\n\n    @classmethod\n    def create_database_api(\n        cls,\n        token: str,\n        new_database_name: str,\n        cloud_provider: str,\n        region: str,\n    ):\n        client = DataAPIClient(token=token)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Call the create database function\n        return admin_client.create_database(\n            name=new_database_name,\n            cloud_provider=cloud_provider,\n            region=region,\n        )\n\n    @classmethod\n    def create_collection_api(\n        cls,\n        token: str,\n        database_name: str,\n        new_collection_name: str,\n        dimension: int | None = None,\n        embedding_generation_provider: str | None = None,\n        embedding_generation_model: str | None = None,\n    ):\n        client = DataAPIClient(token=token)\n        api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)\n\n        # Get the database object\n        database = client.get_database(api_endpoint=api_endpoint, token=token)\n\n        # Build vectorize options, if needed\n        vectorize_options = None\n        if not dimension:\n            vectorize_options = CollectionVectorServiceOptions(\n                provider=embedding_generation_provider,\n                model_name=embedding_generation_model,\n                authentication=None,\n                parameters=None,\n            )\n\n        # Create the collection\n        return database.create_collection(\n            name=new_collection_name,\n            dimension=dimension,\n            service=vectorize_options,\n        )\n\n    @classmethod\n    def get_database_list_static(cls, token: str, environment: str | None = None):\n        client = DataAPIClient(token=token, environment=environment)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Get the list of databases\n        db_list = list(admin_client.list_databases())\n\n        # Generate the api endpoint for each database\n        return {\n            db.info.name: {\n                \"api_endpoint\": (api_endpoint := f\"https://{db.info.id}-{db.info.region}.apps.astra.datastax.com\"),\n                \"collections\": len(\n                    list(\n                        client.get_database(\n                            api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace\n                        ).list_collection_names(keyspace=db.info.keyspace)\n                    )\n                ),\n            }\n            for db in db_list\n        }\n\n    def get_database_list(self):\n        return self.get_database_list_static(token=self.token, environment=self.environment)\n\n    @classmethod\n    def get_api_endpoint_static(\n        cls,\n        token: str,\n        environment: str | None = None,\n        api_endpoint: str | None = None,\n        database_name: str | None = None,\n    ):\n        # Check if an api endpoint is provided\n        if api_endpoint:\n            return api_endpoint\n\n        # Check if the database_name is like a url\n        if database_name and database_name.startswith(\"https://\"):\n            return database_name\n\n        # If the database is not set, nothing we can do.\n        if not database_name:\n            return None\n\n        # Otherwise, get the URL from the database list\n        return cls.get_database_list_static(token=token, environment=environment).get(database_name).get(\"api_endpoint\")\n\n    def get_api_endpoint(self):\n        return self.get_api_endpoint_static(\n            token=self.token,\n            environment=self.environment,\n            api_endpoint=self.api_endpoint,\n            database_name=self.database_name,\n        )\n\n    def get_keyspace(self):\n        keyspace = self.keyspace\n\n        if keyspace:\n            return keyspace.strip()\n\n        return None\n\n    def get_database_object(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n\n            return client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def collection_exists(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n            database = client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n\n            return self.collection_name in list(database.list_collection_names(keyspace=self.get_keyspace()))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting collection status: {e}\")\n\n            return False\n\n    def collection_data(self, collection_name: str, database: Database | None = None):\n        try:\n            if not database:\n                client = DataAPIClient(token=self.token, environment=self.environment)\n\n                database = client.get_database(\n                    api_endpoint=self.get_api_endpoint(),\n                    token=self.token,\n                    keyspace=self.get_keyspace(),\n                )\n\n            collection = database.get_collection(collection_name, keyspace=self.get_keyspace())\n\n            return collection.estimated_document_count()\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error checking collection data: {e}\")\n\n            return None\n\n    def get_vectorize_providers(self):\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            admin = AstraDBAdmin(token=self.token)\n            db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())\n\n            # Get the list of embedding providers\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                # TODO: https://astra.datastax.com/api/v2/graphql\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return {}\n\n    def _initialize_database_options(self):\n        try:\n            return [\n                {\"name\": name, \"collections\": info[\"collections\"]} for name, info in self.get_database_list().items()\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching databases: {e}\")\n\n            return []\n\n    def _initialize_collection_options(self):\n        database = self.get_database_object()\n        if database is None:\n            return []\n\n        try:\n            collection_list = list(database.list_collections(keyspace=self.get_keyspace()))\n\n            return [\n                {\n                    \"name\": col.name,\n                    \"records\": self.collection_data(collection_name=col.name, database=database),\n                    \"provider\": (\n                        col.options.vector.service.provider\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                    \"icon\": \"\",\n                    \"model\": (\n                        col.options.vector.service.model_name\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                }\n                for col in collection_list\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return []\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if not self.token or not self.token.startswith(\"AstraCS:\"):\n            build_config[\"database_name\"][\"info\"] = \"Add a Valid Token to Select a Database\"\n        else:\n            build_config[\"database_name\"][\"info\"] = \"Select a Database from Astra DB\"\n\n        # Refresh the database name options\n        if field_name in [\"token\", \"environment\"] or not build_config[\"database_name\"][\"options\"]:\n            # Reset the list of collections\n            build_config[\"collection_name\"][\"options\"] = []\n            build_config[\"collection_name\"][\"options_metadata\"] = []\n            build_config[\"database_name\"][\"value\"] = []\n\n            # Get the list of databases\n            database_options = self._initialize_database_options()\n            build_config[\"database_name\"][\"options\"] = [db[\"name\"] for db in database_options]\n            build_config[\"database_name\"][\"options_metadata\"] = [\n                {k: v for k, v in db.items() if k not in [\"name\"]} for db in database_options\n            ]\n\n            # Get list of regions for a given cloud provider\n            \"\"\"\n            cloud_provider = (\n                build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"cloud_provider\"][\n                    \"value\"\n                ]\n                or \"Amazon Web Services\"\n            )\n            build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"region\"][\n                \"options\"\n            ] = self.map_cloud_providers()[cloud_provider][\"regions\"]\n            \"\"\"\n\n            return build_config\n\n        # Refresh the collection name options\n        if field_name in [\"database_name\", \"api_endpoint\"] or not build_config[\"collection_name\"][\"options\"]:\n            build_config[\"collection_name\"][\"value\"] = None\n\n            # Reset the list of collections\n            collection_options = self._initialize_collection_options()\n            build_config[\"collection_name\"][\"options\"] = [col[\"name\"] for col in collection_options]\n            build_config[\"collection_name\"][\"options_metadata\"] = [\n                {k: v for k, v in col.items() if k not in [\"name\"]} for col in collection_options\n            ]\n\n            return build_config\n\n        # Hide embedding model option if opriona_metadata provider is not null\n        if field_name == \"collection_name\":\n            # Find location of the name in the options list\n            index_of_name = build_config[\"collection_name\"][\"options\"].index(field_value)\n            value_of_provider = build_config[\"collection_name\"][\"options_metadata\"][index_of_name][\"provider\"]\n\n            if value_of_provider:\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_model\"][\"required\"] = True\n\n        # For the final step, get the list of vectorize providers\n        \"\"\"\n        vectorize_providers = self.get_vectorize_providers()\n        if not vectorize_providers:\n            return build_config\n\n        # Allow the user to see the embedding provider options\n        provider_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_provider\"\n        ][\"options\"]\n        if not provider_options:\n            # If the collection is set, allow user to see embedding options\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"options\"] = [\"Bring your own\", \"Nvidia\", *[key for key in vectorize_providers if key != \"Nvidia\"]]\n\n        # And allow the user to see the models based on a selected provider\n        model_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_model\"\n        ][\"options\"]\n        if not model_options:\n            embedding_provider = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"value\"]\n\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_model\"\n            ][\"options\"] = vectorize_providers.get(embedding_provider, [[], []])[1]\n        \"\"\"\n\n        return build_config\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Get the embedding model and additional params\n        embedding_params = {\"embedding\": self.embedding_model} if self.embedding_model else {}\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": self.collection_exists(),  # TODO: May want to expose this option\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params and self.collection_data(collection_name=self.collection_name) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=self.get_api_endpoint(),\n                namespace=self.get_keyspace(),\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=self.get_keyspace())\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except Exception as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
-              },
-              "collection_name": {
-                "_input_type": "DropdownInput",
-                "advanced": false,
-                "combobox": false,
-                "dialog_inputs": {},
-                "display_name": "Collection",
-                "dynamic": false,
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "name": "collection_name",
-                "options": [],
-                "options_metadata": [],
-                "placeholder": "",
-                "real_time_refresh": true,
-                "refresh_button": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "content_field": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Content Field",
-                "dynamic": false,
-                "info": "Field to use as the text content field for the vector store.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "content_field",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "database_name": {
-                "_input_type": "DropdownInput",
-                "advanced": false,
-                "combobox": true,
-                "dialog_inputs": {},
-                "display_name": "Database",
-                "dynamic": false,
-                "info": "Select a database in Astra DB.",
-                "name": "database_name",
-                "options": [],
-                "options_metadata": [
-                  {
-                    "collections": 1
-                  }
-                ],
-                "placeholder": "",
-                "real_time_refresh": true,
-                "refresh_button": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": []
-              },
-              "deletion_field": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Deletion Based On Field",
-                "dynamic": false,
-                "info": "When this parameter is provided, documents in the target collection with metadata field values matching the input metadata field value will be deleted before new data is loaded.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "deletion_field",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "embedding_model": {
-                "_input_type": "HandleInput",
-                "advanced": false,
-                "display_name": "Embedding Model",
-                "dynamic": false,
-                "info": "Allows an embedding model configuration.",
-                "input_types": [
-                  "Embeddings"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "embedding_model",
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
-              "environment": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Environment",
-                "dynamic": false,
-                "info": "The environment for the Astra DB API Endpoint.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "environment",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "ignore_invalid_documents": {
-                "_input_type": "BoolInput",
-                "advanced": true,
-                "display_name": "Ignore Invalid Documents",
-                "dynamic": false,
-                "info": "Boolean flag to determine whether to ignore invalid documents at runtime.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "ignore_invalid_documents",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "bool",
-                "value": false
-              },
-              "ingest_data": {
-                "_input_type": "DataInput",
-                "advanced": false,
-                "display_name": "Ingest Data",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Data"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "ingest_data",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
-              "keyspace": {
-                "_input_type": "StrInput",
-                "advanced": true,
-                "display_name": "Keyspace",
-                "dynamic": false,
-                "info": "Optional keyspace within Astra DB to use for the collection.",
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "keyspace",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "number_of_results": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Number of Search Results",
-                "dynamic": false,
-                "info": "Number of search results to return.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "number_of_results",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": 4
-              },
-              "search_query": {
-                "_input_type": "MultilineInput",
-                "advanced": false,
-                "display_name": "Search Query",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "multiline": true,
-                "name": "search_query",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": true,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "search_score_threshold": {
-                "_input_type": "FloatInput",
-                "advanced": true,
-                "display_name": "Search Score Threshold",
-                "dynamic": false,
-                "info": "Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "search_score_threshold",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "float",
-                "value": 0
-              },
-              "search_type": {
-                "_input_type": "DropdownInput",
-                "advanced": true,
-                "combobox": false,
-                "dialog_inputs": {},
-                "display_name": "Search Type",
-                "dynamic": false,
-                "info": "Search type to use",
-                "name": "search_type",
-                "options": [
-                  "Similarity",
-                  "Similarity with score threshold",
-                  "MMR (Max Marginal Relevance)"
-                ],
-                "options_metadata": [],
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": "Similarity"
-              },
-              "token": {
-                "_input_type": "SecretStrInput",
-                "advanced": false,
-                "display_name": "Astra DB Application Token",
-                "dynamic": false,
-                "info": "Authentication token for accessing Astra DB.",
-                "input_types": [],
-                "load_from_db": true,
-                "name": "token",
-                "password": true,
-                "placeholder": "",
-                "real_time_refresh": true,
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              }
-            },
-            "tool_mode": false
-          },
-          "showNode": true,
-          "type": "AstraDB"
-        },
-        "dragging": false,
-        "id": "AstraDB-2ETzC",
-        "measured": {
-          "height": 686,
-          "width": 360
-        },
-        "position": {
-          "x": 1211.81052392379,
-          "y": 596.2478494450424
-        },
-        "selected": false,
-        "type": "genericNode"
-      },
-      {
-        "data": {
-          "id": "OpenAIModel-IqFJR",
+          "id": "OpenAIModel-O6wY2",
           "node": {
             "base_classes": [
               "LanguageModel",
@@ -4020,10 +3109,10 @@
           "type": "OpenAIModel"
         },
         "dragging": false,
-        "id": "OpenAIModel-IqFJR",
+        "id": "OpenAIModel-O6wY2",
         "measured": {
-          "height": 734,
-          "width": 360
+          "height": 656,
+          "width": 320
         },
         "position": {
           "x": 2365.714820732046,
@@ -4031,17 +3120,971 @@
         },
         "selected": false,
         "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "AstraDB-kVUuJ",
+          "node": {
+            "base_classes": [
+              "Data",
+              "DataFrame"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Ingest and search documents in Astra DB",
+            "display_name": "Astra DB",
+            "documentation": "https://docs.datastax.com/en/langflow/astra-components.html",
+            "edited": false,
+            "field_order": [
+              "token",
+              "environment",
+              "api_endpoint",
+              "database_name",
+              "collection_name",
+              "vectorize_choice",
+              "keyspace",
+              "embedding_model",
+              "ingest_data",
+              "search_query",
+              "number_of_results",
+              "search_type",
+              "search_score_threshold",
+              "advanced_search_filter",
+              "content_field",
+              "deletion_field",
+              "ignore_invalid_documents",
+              "astradb_vectorstore_kwargs"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "legacy": false,
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Search Results",
+                "method": "search_documents",
+                "name": "search_results",
+                "required_inputs": [
+                  "collection_name",
+                  "database_name",
+                  "embedding_model",
+                  "token"
+                ],
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "DataFrame",
+                "method": "as_dataframe",
+                "name": "dataframe",
+                "required_inputs": [],
+                "selected": "DataFrame",
+                "types": [
+                  "DataFrame"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "advanced_search_filter": {
+                "_input_type": "NestedDictInput",
+                "advanced": true,
+                "display_name": "Search Metadata Filter",
+                "dynamic": false,
+                "info": "Optional dictionary of filters to apply to the search query.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "advanced_search_filter",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "NestedDict",
+                "value": {}
+              },
+              "api_endpoint": {
+                "_input_type": "StrInput",
+                "advanced": false,
+                "display_name": "API Endpoint",
+                "dynamic": false,
+                "info": "The API endpoint for the Astra DB instance.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "api_endpoint",
+                "placeholder": "",
+                "required": false,
+                "show": false,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "astradb_vectorstore_kwargs": {
+                "_input_type": "NestedDictInput",
+                "advanced": true,
+                "display_name": "AstraDBVectorStore Parameters",
+                "dynamic": false,
+                "info": "Optional dictionary of additional parameters for the AstraDBVectorStore.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "astradb_vectorstore_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "NestedDict",
+                "value": {}
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\n\nfrom astrapy import AstraDBAdmin, DataAPIClient, Database\nfrom langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import FloatInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.datastax.com/en/langflow/astra-components.html\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    @dataclass\n    class NewDatabaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new database in Astra DB.\",\n                        \"display_name\": \"Create New Database\",\n                        \"field_order\": [\"new_database_name\", \"cloud_provider\", \"region\"],\n                        \"template\": {\n                            \"new_database_name\": StrInput(\n                                name=\"new_database_name\",\n                                display_name=\"New Database Name\",\n                                info=\"Name of the new database to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"cloud_provider\": DropdownInput(\n                                name=\"cloud_provider\",\n                                display_name=\"Cloud Provider\",\n                                info=\"Cloud provider for the new database.\",\n                                options=[\"Amazon Web Services\", \"Google Cloud Platform\", \"Microsoft Azure\"],\n                                required=True,\n                            ),\n                            \"region\": DropdownInput(\n                                name=\"region\",\n                                display_name=\"Region\",\n                                info=\"Region for the new database.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    @dataclass\n    class NewCollectionInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new collection in Astra DB.\",\n                        \"display_name\": \"Create New Collection\",\n                        \"field_order\": [\n                            \"new_collection_name\",\n                            \"embedding_generation_provider\",\n                            \"embedding_generation_model\",\n                        ],\n                        \"template\": {\n                            \"new_collection_name\": StrInput(\n                                name=\"new_collection_name\",\n                                display_name=\"New Collection Name\",\n                                info=\"Name of the new collection to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"embedding_generation_provider\": DropdownInput(\n                                name=\"embedding_generation_provider\",\n                                display_name=\"Embedding Generation Provider\",\n                                info=\"Provider to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                            \"embedding_generation_model\": DropdownInput(\n                                name=\"embedding_generation_model\",\n                                display_name=\"Embedding Generation Model\",\n                                info=\"Model to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        StrInput(\n            name=\"environment\",\n            display_name=\"Environment\",\n            info=\"The environment for the Astra DB API Endpoint.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"The API endpoint for the Astra DB instance.\",\n            show=os.getenv(\"LANGFLOW_HOST\") is not None,  # TODO: Clean up all examples of these\n        ),\n        DropdownInput(\n            name=\"database_name\",\n            display_name=\"Database\",\n            info=\"Select a database in Astra DB.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewDatabaseInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"collections\": 0,\n                }\n            ],\n            value=\"\",\n            combobox=True,\n            show=os.getenv(\"LANGFLOW_HOST\") is None,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewCollectionInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"provider\": None,\n                    \"model\": None,\n                    \"records\": 0,\n                    \"icon\": \"\",\n                }\n            ],\n            value=\"\",\n        ),\n        DropdownInput(\n            name=\"vectorize_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Choose an embedding model or use Astra Vectorize.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            value=\"Embedding Model\",\n            show=os.getenv(\"LANGFLOW_HOST\") is not None,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n            required=True,\n        ),\n        *LCVectorStoreComponent.inputs,\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n    ]\n\n    @classmethod\n    def map_cloud_providers(cls):\n        return {\n            \"Amazon Web Services\": {\n                \"id\": \"aws\",\n                \"regions\": [\"us-east-2\", \"ap-south-1\", \"eu-west-1\"],\n            },\n            \"Google Cloud Platform\": {\n                \"id\": \"gcp\",\n                \"regions\": [\"us-east1\"],\n            },\n            \"Microsoft Azure\": {\n                \"id\": \"azure\",\n                \"regions\": [\"westus3\"],\n            },\n        }\n\n    @classmethod\n    def create_database_api(\n        cls,\n        token: str,\n        new_database_name: str,\n        cloud_provider: str,\n        region: str,\n    ):\n        client = DataAPIClient(token=token)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Call the create database function\n        return admin_client.create_database(\n            name=new_database_name,\n            cloud_provider=cloud_provider,\n            region=region,\n        )\n\n    @classmethod\n    def create_collection_api(\n        cls,\n        token: str,\n        database_name: str,\n        new_collection_name: str,\n        dimension: int | None = None,\n        embedding_generation_provider: str | None = None,\n        embedding_generation_model: str | None = None,\n    ):\n        client = DataAPIClient(token=token)\n        api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)\n\n        # Get the database object\n        database = client.get_database(api_endpoint=api_endpoint, token=token)\n\n        # Build vectorize options, if needed\n        vectorize_options = None\n        if not dimension:\n            vectorize_options = CollectionVectorServiceOptions(\n                provider=embedding_generation_provider,\n                model_name=embedding_generation_model,\n                authentication=None,\n                parameters=None,\n            )\n\n        # Create the collection\n        return database.create_collection(\n            name=new_collection_name,\n            dimension=dimension,\n            service=vectorize_options,\n        )\n\n    @classmethod\n    def get_database_list_static(cls, token: str, environment: str | None = None):\n        client = DataAPIClient(token=token, environment=environment)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Get the list of databases\n        db_list = list(admin_client.list_databases())\n\n        # Generate the api endpoint for each database\n        return {\n            db.info.name: {\n                \"api_endpoint\": (api_endpoint := f\"https://{db.info.id}-{db.info.region}.apps.astra.datastax.com\"),\n                \"collections\": len(\n                    list(\n                        client.get_database(\n                            api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace\n                        ).list_collection_names(keyspace=db.info.keyspace)\n                    )\n                ),\n            }\n            for db in db_list\n        }\n\n    def get_database_list(self):\n        return self.get_database_list_static(token=self.token, environment=self.environment)\n\n    @classmethod\n    def get_api_endpoint_static(\n        cls,\n        token: str,\n        environment: str | None = None,\n        api_endpoint: str | None = None,\n        database_name: str | None = None,\n    ):\n        # Check if an api endpoint is provided\n        if api_endpoint:\n            return api_endpoint\n\n        # Check if the database_name is like a url\n        if database_name and database_name.startswith(\"https://\"):\n            return database_name\n\n        # If the database is not set, nothing we can do.\n        if not database_name:\n            return None\n\n        # Otherwise, get the URL from the database list\n        return cls.get_database_list_static(token=token, environment=environment).get(database_name).get(\"api_endpoint\")\n\n    def get_api_endpoint(self):\n        return self.get_api_endpoint_static(\n            token=self.token,\n            environment=self.environment,\n            api_endpoint=self.api_endpoint,\n            database_name=self.database_name,\n        )\n\n    def get_keyspace(self):\n        keyspace = self.keyspace\n\n        if keyspace:\n            return keyspace.strip()\n\n        return None\n\n    def get_database_object(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n\n            return client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def collection_exists(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n            database = client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n\n            return self.collection_name in list(database.list_collection_names(keyspace=self.get_keyspace()))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting collection status: {e}\")\n\n            return False\n\n    def collection_data(self, collection_name: str, database: Database | None = None):\n        try:\n            if not database:\n                client = DataAPIClient(token=self.token, environment=self.environment)\n\n                database = client.get_database(\n                    api_endpoint=self.get_api_endpoint(),\n                    token=self.token,\n                    keyspace=self.get_keyspace(),\n                )\n\n            collection = database.get_collection(collection_name, keyspace=self.get_keyspace())\n\n            return collection.estimated_document_count()\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error checking collection data: {e}\")\n\n            return None\n\n    def get_vectorize_providers(self):\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            admin = AstraDBAdmin(token=self.token)\n            db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())\n\n            # Get the list of embedding providers\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                # TODO: https://astra.datastax.com/api/v2/graphql\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return {}\n\n    def _initialize_database_options(self):\n        try:\n            return [\n                {\"name\": name, \"collections\": info[\"collections\"]} for name, info in self.get_database_list().items()\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching databases: {e}\")\n\n            return []\n\n    def _initialize_collection_options(self):\n        database = self.get_database_object()\n        if database is None:\n            return []\n\n        try:\n            collection_list = list(database.list_collections(keyspace=self.get_keyspace()))\n\n            return [\n                {\n                    \"name\": col.name,\n                    \"records\": self.collection_data(collection_name=col.name, database=database),\n                    \"provider\": (\n                        col.options.vector.service.provider\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                    \"icon\": \"\",\n                    \"model\": (\n                        col.options.vector.service.model_name\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                }\n                for col in collection_list\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return []\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"vectorize_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"show\"] = False\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"show\"] = True\n                build_config[\"embedding_model\"][\"required\"] = True\n\n            return build_config\n\n        if not self.token or not self.token.startswith(\"AstraCS:\"):\n            build_config[\"database_name\"][\"info\"] = \"Add a Valid Token to Select a Database\"\n        else:\n            build_config[\"database_name\"][\"info\"] = \"Select a Database from Astra DB\"\n\n        # Refresh the database name options\n        if field_name in [\"token\", \"environment\"] or not build_config[\"database_name\"][\"options\"]:\n            # Reset the list of collections\n            build_config[\"collection_name\"][\"options\"] = []\n            build_config[\"collection_name\"][\"options_metadata\"] = []\n            build_config[\"database_name\"][\"value\"] = []\n\n            # Get the list of databases\n            database_options = self._initialize_database_options()\n            build_config[\"database_name\"][\"options\"] = [db[\"name\"] for db in database_options]\n            build_config[\"database_name\"][\"options_metadata\"] = [\n                {k: v for k, v in db.items() if k not in [\"name\"]} for db in database_options\n            ]\n\n            # Get list of regions for a given cloud provider\n            \"\"\"\n            cloud_provider = (\n                build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"cloud_provider\"][\n                    \"value\"\n                ]\n                or \"Amazon Web Services\"\n            )\n            build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"region\"][\n                \"options\"\n            ] = self.map_cloud_providers()[cloud_provider][\"regions\"]\n            \"\"\"\n\n            return build_config\n\n        # Refresh the collection name options\n        if field_name in [\"database_name\", \"api_endpoint\"] or not build_config[\"collection_name\"][\"options\"]:\n            build_config[\"collection_name\"][\"value\"] = None\n\n            # Reset the list of collections\n            collection_options = self._initialize_collection_options()\n            build_config[\"collection_name\"][\"options\"] = [col[\"name\"] for col in collection_options]\n            build_config[\"collection_name\"][\"options_metadata\"] = [\n                {k: v for k, v in col.items() if k not in [\"name\"]} for col in collection_options\n            ]\n\n            return build_config\n\n        # Hide embedding model option if opriona_metadata provider is not null\n        if field_name == \"collection_name\":\n            # Find location of the name in the options list\n            index_of_name = build_config[\"collection_name\"][\"options\"].index(field_value)\n            value_of_provider = build_config[\"collection_name\"][\"options_metadata\"][index_of_name][\"provider\"]\n\n            if value_of_provider:\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_model\"][\"required\"] = True\n\n        # For the final step, get the list of vectorize providers\n        \"\"\"\n        vectorize_providers = self.get_vectorize_providers()\n        if not vectorize_providers:\n            return build_config\n\n        # Allow the user to see the embedding provider options\n        provider_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_provider\"\n        ][\"options\"]\n        if not provider_options:\n            # If the collection is set, allow user to see embedding options\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"options\"] = [\"Bring your own\", \"Nvidia\", *[key for key in vectorize_providers if key != \"Nvidia\"]]\n\n        # And allow the user to see the models based on a selected provider\n        model_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_model\"\n        ][\"options\"]\n        if not model_options:\n            embedding_provider = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"value\"]\n\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_model\"\n            ][\"options\"] = vectorize_providers.get(embedding_provider, [[], []])[1]\n        \"\"\"\n\n        return build_config\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Get the embedding model and additional params\n        embedding_params = (\n            {\"embedding\": self.embedding_model}\n            if self.embedding_model and self.vectorize_choice == \"Embedding Model\"\n            else {}\n        )\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": self.collection_exists(),  # TODO: May want to expose this option\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params and self.collection_data(collection_name=self.collection_name) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=self.get_api_endpoint(),\n                namespace=self.get_keyspace(),\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=self.get_keyspace())\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except Exception as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+              },
+              "collection_name": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Collection",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "name": "collection_name",
+                "options": [],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "refresh_button": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "content_field": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Content Field",
+                "dynamic": false,
+                "info": "Field to use as the text content field for the vector store.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "content_field",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "database_name": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": true,
+                "dialog_inputs": {},
+                "display_name": "Database",
+                "dynamic": false,
+                "info": "Select a database in Astra DB.",
+                "name": "database_name",
+                "options": [],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "refresh_button": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": []
+              },
+              "deletion_field": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Deletion Based On Field",
+                "dynamic": false,
+                "info": "When this parameter is provided, documents in the target collection with metadata field values matching the input metadata field value will be deleted before new data is loaded.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "deletion_field",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "embedding_model": {
+                "_input_type": "HandleInput",
+                "advanced": false,
+                "display_name": "Embedding Model",
+                "dynamic": false,
+                "info": "Allows an embedding model configuration.",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "embedding_model",
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
+              "environment": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Environment",
+                "dynamic": false,
+                "info": "The environment for the Astra DB API Endpoint.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "environment",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "ignore_invalid_documents": {
+                "_input_type": "BoolInput",
+                "advanced": true,
+                "display_name": "Ignore Invalid Documents",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to ignore invalid documents at runtime.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ignore_invalid_documents",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "bool",
+                "value": false
+              },
+              "ingest_data": {
+                "_input_type": "DataInput",
+                "advanced": false,
+                "display_name": "Ingest Data",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Data"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ingest_data",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
+              "keyspace": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Keyspace",
+                "dynamic": false,
+                "info": "Optional keyspace within Astra DB to use for the collection.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "keyspace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "number_of_results": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "Number of Search Results",
+                "dynamic": false,
+                "info": "Number of search results to return.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "number_of_results",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 4
+              },
+              "search_query": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "display_name": "Search Query",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "search_query",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "search_score_threshold": {
+                "_input_type": "FloatInput",
+                "advanced": true,
+                "display_name": "Search Score Threshold",
+                "dynamic": false,
+                "info": "Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "search_score_threshold",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "float",
+                "value": 0
+              },
+              "search_type": {
+                "_input_type": "DropdownInput",
+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Search Type",
+                "dynamic": false,
+                "info": "Search type to use",
+                "name": "search_type",
+                "options": [
+                  "Similarity",
+                  "Similarity with score threshold",
+                  "MMR (Max Marginal Relevance)"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "Similarity"
+              },
+              "token": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "ASTRA_DB_APPLICATION_TOKEN"
+              },
+              "vectorize_choice": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Embedding Model or Astra Vectorize",
+                "dynamic": false,
+                "info": "Choose an embedding model or use Astra Vectorize.",
+                "name": "vectorize_choice",
+                "options": [
+                  "Embedding Model",
+                  "Astra Vectorize"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": false,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "Embedding Model"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "AstraDB"
+        },
+        "dragging": false,
+        "id": "AstraDB-kVUuJ",
+        "measured": {
+          "height": 614,
+          "width": 320
+        },
+        "position": {
+          "x": 1220.74449782511,
+          "y": 600
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "AstraDB-h03SC",
+          "node": {
+            "base_classes": [
+              "Data",
+              "DataFrame"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Ingest and search documents in Astra DB",
+            "display_name": "Astra DB",
+            "documentation": "https://docs.datastax.com/en/langflow/astra-components.html",
+            "edited": false,
+            "field_order": [
+              "token",
+              "environment",
+              "api_endpoint",
+              "database_name",
+              "collection_name",
+              "vectorize_choice",
+              "keyspace",
+              "embedding_model",
+              "ingest_data",
+              "search_query",
+              "number_of_results",
+              "search_type",
+              "search_score_threshold",
+              "advanced_search_filter",
+              "content_field",
+              "deletion_field",
+              "ignore_invalid_documents",
+              "astradb_vectorstore_kwargs"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "legacy": false,
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Search Results",
+                "method": "search_documents",
+                "name": "search_results",
+                "required_inputs": [
+                  "collection_name",
+                  "database_name",
+                  "embedding_model",
+                  "token"
+                ],
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "DataFrame",
+                "method": "as_dataframe",
+                "name": "dataframe",
+                "required_inputs": [],
+                "selected": "DataFrame",
+                "types": [
+                  "DataFrame"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "advanced_search_filter": {
+                "_input_type": "NestedDictInput",
+                "advanced": true,
+                "display_name": "Search Metadata Filter",
+                "dynamic": false,
+                "info": "Optional dictionary of filters to apply to the search query.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "advanced_search_filter",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "NestedDict",
+                "value": {}
+              },
+              "api_endpoint": {
+                "_input_type": "StrInput",
+                "advanced": false,
+                "display_name": "API Endpoint",
+                "dynamic": false,
+                "info": "The API endpoint for the Astra DB instance.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "api_endpoint",
+                "placeholder": "",
+                "required": false,
+                "show": false,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "astradb_vectorstore_kwargs": {
+                "_input_type": "NestedDictInput",
+                "advanced": true,
+                "display_name": "AstraDBVectorStore Parameters",
+                "dynamic": false,
+                "info": "Optional dictionary of additional parameters for the AstraDBVectorStore.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "astradb_vectorstore_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "NestedDict",
+                "value": {}
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\n\nfrom astrapy import AstraDBAdmin, DataAPIClient, Database\nfrom langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import FloatInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.datastax.com/en/langflow/astra-components.html\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    @dataclass\n    class NewDatabaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new database in Astra DB.\",\n                        \"display_name\": \"Create New Database\",\n                        \"field_order\": [\"new_database_name\", \"cloud_provider\", \"region\"],\n                        \"template\": {\n                            \"new_database_name\": StrInput(\n                                name=\"new_database_name\",\n                                display_name=\"New Database Name\",\n                                info=\"Name of the new database to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"cloud_provider\": DropdownInput(\n                                name=\"cloud_provider\",\n                                display_name=\"Cloud Provider\",\n                                info=\"Cloud provider for the new database.\",\n                                options=[\"Amazon Web Services\", \"Google Cloud Platform\", \"Microsoft Azure\"],\n                                required=True,\n                            ),\n                            \"region\": DropdownInput(\n                                name=\"region\",\n                                display_name=\"Region\",\n                                info=\"Region for the new database.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    @dataclass\n    class NewCollectionInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new collection in Astra DB.\",\n                        \"display_name\": \"Create New Collection\",\n                        \"field_order\": [\n                            \"new_collection_name\",\n                            \"embedding_generation_provider\",\n                            \"embedding_generation_model\",\n                        ],\n                        \"template\": {\n                            \"new_collection_name\": StrInput(\n                                name=\"new_collection_name\",\n                                display_name=\"New Collection Name\",\n                                info=\"Name of the new collection to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"embedding_generation_provider\": DropdownInput(\n                                name=\"embedding_generation_provider\",\n                                display_name=\"Embedding Generation Provider\",\n                                info=\"Provider to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                            \"embedding_generation_model\": DropdownInput(\n                                name=\"embedding_generation_model\",\n                                display_name=\"Embedding Generation Model\",\n                                info=\"Model to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        StrInput(\n            name=\"environment\",\n            display_name=\"Environment\",\n            info=\"The environment for the Astra DB API Endpoint.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"The API endpoint for the Astra DB instance.\",\n            show=os.getenv(\"LANGFLOW_HOST\") is not None,  # TODO: Clean up all examples of these\n        ),\n        DropdownInput(\n            name=\"database_name\",\n            display_name=\"Database\",\n            info=\"Select a database in Astra DB.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewDatabaseInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"collections\": 0,\n                }\n            ],\n            value=\"\",\n            combobox=True,\n            show=os.getenv(\"LANGFLOW_HOST\") is None,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewCollectionInput()),\n            options=[],\n            options_metadata=[\n                {\n                    \"provider\": None,\n                    \"model\": None,\n                    \"records\": 0,\n                    \"icon\": \"\",\n                }\n            ],\n            value=\"\",\n        ),\n        DropdownInput(\n            name=\"vectorize_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Choose an embedding model or use Astra Vectorize.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            value=\"Embedding Model\",\n            show=os.getenv(\"LANGFLOW_HOST\") is not None,\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n            required=True,\n        ),\n        *LCVectorStoreComponent.inputs,\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n    ]\n\n    @classmethod\n    def map_cloud_providers(cls):\n        return {\n            \"Amazon Web Services\": {\n                \"id\": \"aws\",\n                \"regions\": [\"us-east-2\", \"ap-south-1\", \"eu-west-1\"],\n            },\n            \"Google Cloud Platform\": {\n                \"id\": \"gcp\",\n                \"regions\": [\"us-east1\"],\n            },\n            \"Microsoft Azure\": {\n                \"id\": \"azure\",\n                \"regions\": [\"westus3\"],\n            },\n        }\n\n    @classmethod\n    def create_database_api(\n        cls,\n        token: str,\n        new_database_name: str,\n        cloud_provider: str,\n        region: str,\n    ):\n        client = DataAPIClient(token=token)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Call the create database function\n        return admin_client.create_database(\n            name=new_database_name,\n            cloud_provider=cloud_provider,\n            region=region,\n        )\n\n    @classmethod\n    def create_collection_api(\n        cls,\n        token: str,\n        database_name: str,\n        new_collection_name: str,\n        dimension: int | None = None,\n        embedding_generation_provider: str | None = None,\n        embedding_generation_model: str | None = None,\n    ):\n        client = DataAPIClient(token=token)\n        api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)\n\n        # Get the database object\n        database = client.get_database(api_endpoint=api_endpoint, token=token)\n\n        # Build vectorize options, if needed\n        vectorize_options = None\n        if not dimension:\n            vectorize_options = CollectionVectorServiceOptions(\n                provider=embedding_generation_provider,\n                model_name=embedding_generation_model,\n                authentication=None,\n                parameters=None,\n            )\n\n        # Create the collection\n        return database.create_collection(\n            name=new_collection_name,\n            dimension=dimension,\n            service=vectorize_options,\n        )\n\n    @classmethod\n    def get_database_list_static(cls, token: str, environment: str | None = None):\n        client = DataAPIClient(token=token, environment=environment)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Get the list of databases\n        db_list = list(admin_client.list_databases())\n\n        # Generate the api endpoint for each database\n        return {\n            db.info.name: {\n                \"api_endpoint\": (api_endpoint := f\"https://{db.info.id}-{db.info.region}.apps.astra.datastax.com\"),\n                \"collections\": len(\n                    list(\n                        client.get_database(\n                            api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace\n                        ).list_collection_names(keyspace=db.info.keyspace)\n                    )\n                ),\n            }\n            for db in db_list\n        }\n\n    def get_database_list(self):\n        return self.get_database_list_static(token=self.token, environment=self.environment)\n\n    @classmethod\n    def get_api_endpoint_static(\n        cls,\n        token: str,\n        environment: str | None = None,\n        api_endpoint: str | None = None,\n        database_name: str | None = None,\n    ):\n        # Check if an api endpoint is provided\n        if api_endpoint:\n            return api_endpoint\n\n        # Check if the database_name is like a url\n        if database_name and database_name.startswith(\"https://\"):\n            return database_name\n\n        # If the database is not set, nothing we can do.\n        if not database_name:\n            return None\n\n        # Otherwise, get the URL from the database list\n        return cls.get_database_list_static(token=token, environment=environment).get(database_name).get(\"api_endpoint\")\n\n    def get_api_endpoint(self):\n        return self.get_api_endpoint_static(\n            token=self.token,\n            environment=self.environment,\n            api_endpoint=self.api_endpoint,\n            database_name=self.database_name,\n        )\n\n    def get_keyspace(self):\n        keyspace = self.keyspace\n\n        if keyspace:\n            return keyspace.strip()\n\n        return None\n\n    def get_database_object(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n\n            return client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def collection_exists(self):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n            database = client.get_database(\n                api_endpoint=self.get_api_endpoint(),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n\n            return self.collection_name in list(database.list_collection_names(keyspace=self.get_keyspace()))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting collection status: {e}\")\n\n            return False\n\n    def collection_data(self, collection_name: str, database: Database | None = None):\n        try:\n            if not database:\n                client = DataAPIClient(token=self.token, environment=self.environment)\n\n                database = client.get_database(\n                    api_endpoint=self.get_api_endpoint(),\n                    token=self.token,\n                    keyspace=self.get_keyspace(),\n                )\n\n            collection = database.get_collection(collection_name, keyspace=self.get_keyspace())\n\n            return collection.estimated_document_count()\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error checking collection data: {e}\")\n\n            return None\n\n    def get_vectorize_providers(self):\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            admin = AstraDBAdmin(token=self.token)\n            db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())\n\n            # Get the list of embedding providers\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                # TODO: https://astra.datastax.com/api/v2/graphql\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return {}\n\n    def _initialize_database_options(self):\n        try:\n            return [\n                {\"name\": name, \"collections\": info[\"collections\"]} for name, info in self.get_database_list().items()\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching databases: {e}\")\n\n            return []\n\n    def _initialize_collection_options(self):\n        database = self.get_database_object()\n        if database is None:\n            return []\n\n        try:\n            collection_list = list(database.list_collections(keyspace=self.get_keyspace()))\n\n            return [\n                {\n                    \"name\": col.name,\n                    \"records\": self.collection_data(collection_name=col.name, database=database),\n                    \"provider\": (\n                        col.options.vector.service.provider\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                    \"icon\": \"\",\n                    \"model\": (\n                        col.options.vector.service.model_name\n                        if col.options.vector and col.options.vector.service\n                        else None\n                    ),\n                }\n                for col in collection_list\n            ]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return []\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"vectorize_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"show\"] = False\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"show\"] = True\n                build_config[\"embedding_model\"][\"required\"] = True\n\n            return build_config\n\n        if not self.token or not self.token.startswith(\"AstraCS:\"):\n            build_config[\"database_name\"][\"info\"] = \"Add a Valid Token to Select a Database\"\n        else:\n            build_config[\"database_name\"][\"info\"] = \"Select a Database from Astra DB\"\n\n        # Refresh the database name options\n        if field_name in [\"token\", \"environment\"] or not build_config[\"database_name\"][\"options\"]:\n            # Reset the list of collections\n            build_config[\"collection_name\"][\"options\"] = []\n            build_config[\"collection_name\"][\"options_metadata\"] = []\n            build_config[\"database_name\"][\"value\"] = []\n\n            # Get the list of databases\n            database_options = self._initialize_database_options()\n            build_config[\"database_name\"][\"options\"] = [db[\"name\"] for db in database_options]\n            build_config[\"database_name\"][\"options_metadata\"] = [\n                {k: v for k, v in db.items() if k not in [\"name\"]} for db in database_options\n            ]\n\n            # Get list of regions for a given cloud provider\n            \"\"\"\n            cloud_provider = (\n                build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"cloud_provider\"][\n                    \"value\"\n                ]\n                or \"Amazon Web Services\"\n            )\n            build_config[\"database_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"region\"][\n                \"options\"\n            ] = self.map_cloud_providers()[cloud_provider][\"regions\"]\n            \"\"\"\n\n            return build_config\n\n        # Refresh the collection name options\n        if field_name in [\"database_name\", \"api_endpoint\"] or not build_config[\"collection_name\"][\"options\"]:\n            build_config[\"collection_name\"][\"value\"] = None\n\n            # Reset the list of collections\n            collection_options = self._initialize_collection_options()\n            build_config[\"collection_name\"][\"options\"] = [col[\"name\"] for col in collection_options]\n            build_config[\"collection_name\"][\"options_metadata\"] = [\n                {k: v for k, v in col.items() if k not in [\"name\"]} for col in collection_options\n            ]\n\n            return build_config\n\n        # Hide embedding model option if opriona_metadata provider is not null\n        if field_name == \"collection_name\":\n            # Find location of the name in the options list\n            index_of_name = build_config[\"collection_name\"][\"options\"].index(field_value)\n            value_of_provider = build_config[\"collection_name\"][\"options_metadata\"][index_of_name][\"provider\"]\n\n            if value_of_provider:\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_model\"][\"required\"] = False\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_model\"][\"required\"] = True\n\n        # For the final step, get the list of vectorize providers\n        \"\"\"\n        vectorize_providers = self.get_vectorize_providers()\n        if not vectorize_providers:\n            return build_config\n\n        # Allow the user to see the embedding provider options\n        provider_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_provider\"\n        ][\"options\"]\n        if not provider_options:\n            # If the collection is set, allow user to see embedding options\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"options\"] = [\"Bring your own\", \"Nvidia\", *[key for key in vectorize_providers if key != \"Nvidia\"]]\n\n        # And allow the user to see the models based on a selected provider\n        model_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_model\"\n        ][\"options\"]\n        if not model_options:\n            embedding_provider = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"value\"]\n\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_model\"\n            ][\"options\"] = vectorize_providers.get(embedding_provider, [[], []])[1]\n        \"\"\"\n\n        return build_config\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Get the embedding model and additional params\n        embedding_params = (\n            {\"embedding\": self.embedding_model}\n            if self.embedding_model and self.vectorize_choice == \"Embedding Model\"\n            else {}\n        )\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": self.collection_exists(),  # TODO: May want to expose this option\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params and self.collection_data(collection_name=self.collection_name) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=self.get_api_endpoint(),\n                namespace=self.get_keyspace(),\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object()\n                collection = database.get_collection(self.collection_name, keyspace=self.get_keyspace())\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except Exception as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+              },
+              "collection_name": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Collection",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "name": "collection_name",
+                "options": [],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "refresh_button": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "content_field": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Content Field",
+                "dynamic": false,
+                "info": "Field to use as the text content field for the vector store.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "content_field",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "database_name": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": true,
+                "dialog_inputs": {},
+                "display_name": "Database",
+                "dynamic": false,
+                "info": "Select a database in Astra DB.",
+                "name": "database_name",
+                "options": [],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "refresh_button": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": []
+              },
+              "deletion_field": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Deletion Based On Field",
+                "dynamic": false,
+                "info": "When this parameter is provided, documents in the target collection with metadata field values matching the input metadata field value will be deleted before new data is loaded.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "deletion_field",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "embedding_model": {
+                "_input_type": "HandleInput",
+                "advanced": false,
+                "display_name": "Embedding Model",
+                "dynamic": false,
+                "info": "Allows an embedding model configuration.",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "embedding_model",
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
+              "environment": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Environment",
+                "dynamic": false,
+                "info": "The environment for the Astra DB API Endpoint.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "environment",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "ignore_invalid_documents": {
+                "_input_type": "BoolInput",
+                "advanced": true,
+                "display_name": "Ignore Invalid Documents",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to ignore invalid documents at runtime.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ignore_invalid_documents",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "bool",
+                "value": false
+              },
+              "ingest_data": {
+                "_input_type": "DataInput",
+                "advanced": false,
+                "display_name": "Ingest Data",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Data"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ingest_data",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
+              "keyspace": {
+                "_input_type": "StrInput",
+                "advanced": true,
+                "display_name": "Keyspace",
+                "dynamic": false,
+                "info": "Optional keyspace within Astra DB to use for the collection.",
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "keyspace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
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+              "number_of_results": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "Number of Search Results",
+                "dynamic": false,
+                "info": "Number of search results to return.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "number_of_results",
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+                "required": false,
+                "show": true,
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+                "advanced": false,
+                "display_name": "Search Query",
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+                "info": "",
+                "input_types": [
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+                "list_add_label": "Add More",
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+                "multiline": true,
+                "name": "search_query",
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+                "advanced": true,
+                "display_name": "Search Score Threshold",
+                "dynamic": false,
+                "info": "Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "search_score_threshold",
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+                "required": false,
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+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Search Type",
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+                "info": "Search type to use",
+                "name": "search_type",
+                "options": [
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+                "show": true,
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+                "type": "str",
+                "value": "Similarity"
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+              "token": {
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+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "ASTRA_DB_APPLICATION_TOKEN"
+              },
+              "vectorize_choice": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Embedding Model or Astra Vectorize",
+                "dynamic": false,
+                "info": "Choose an embedding model or use Astra Vectorize.",
+                "name": "vectorize_choice",
+                "options": [
+                  "Embedding Model",
+                  "Astra Vectorize"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": false,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "Embedding Model"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "AstraDB"
+        },
+        "dragging": false,
+        "id": "AstraDB-h03SC",
+        "measured": {
+          "height": 614,
+          "width": 320
+        },
+        "position": {
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+          "y": 1452.6057423878851
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+        "type": "genericNode"
       }
     ],
     "viewport": {
-      "x": 47.8442601004615,
-      "y": -178.71161058286998,
-      "zoom": 0.47333491993960286
+      "x": 111.19696210801396,
+      "y": -163.1714705321857,
+      "zoom": 0.43217536168398524
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   },
   "description": "Load your data for chat context with Retrieval Augmented Generation.",
   "endpoint_name": null,
-  "id": "823f3806-1a62-4561-adbf-897d056b49bb",
+  "id": "2ac4865f-686e-4c3b-a0d5-7665b454db88",
   "is_component": false,
   "last_tested_version": "1.1.1",
   "name": "Vector Store RAG",