diff --git a/src/backend/base/langflow/components/vectorstores/astradb.py b/src/backend/base/langflow/components/vectorstores/astradb.py
index 050f5d450..4c12d3321 100644
--- a/src/backend/base/langflow/components/vectorstores/astradb.py
+++ b/src/backend/base/langflow/components/vectorstores/astradb.py
@@ -633,7 +633,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
         setup_mode = autodetect_params["setup_mode"]
 
         # Get the embedding model
-        embedding_dict = {"embedding": self.embedding_model}
+        embedding_dict = {"embedding": self.embedding_model} if self.embedding_choice == "Embedding Model" else {}
 
         # Use the embedding model if the choice is set to "Embedding Model"
         if self.embedding_choice == "Astra Vectorize" and not autodetect:
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 c4a48630e..d3e6bde86 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	
@@ -3201,7 +3201,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\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 = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\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            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=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            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\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        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of 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        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\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        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    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\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                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 self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Update the choice of embedding model based on collection name\n        if field_name == \"collection_name\":\n            # Detect if it is a new collection\n            is_new_collection = field_value == \"+ Create new collection\"\n\n            # Set the advanced and required fields based on the collection choice\n            build_config[\"embedding_choice\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"value\": \"Embedding Model\" if is_new_collection else build_config[\"embedding_choice\"].get(\"value\"),\n                }\n            )\n\n            # Set the advanced field for the embedding model\n            build_config[\"embedding_model\"][\"advanced\"] = not is_new_collection\n\n            # Set the advanced and required fields for the new collection name\n            build_config[\"collection_name_new\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"required\": is_new_collection,\n                    \"value\": \"\" if not is_new_collection else build_config[\"collection_name_new\"].get(\"value\"),\n                }\n            )\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                # Remove unnecessary fields when a service is set\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": True},\n                    \"embedding_choice\": {\"value\": \"Astra Vectorize\"},\n                }\n            else:\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": False},\n                    \"embedding_provider\": {\"advanced\": False},\n                    \"embedding_choice\": {\"value\": \"Embedding Model\"},\n                }\n\n            # Apply updates to the build_config\n            for key, value in updates.items():\n                build_config[key].update(value)\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\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        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        # Initialize parameters based on the collection name\n        is_new_collection = self.collection_name == \"+ Create new collection\"\n\n        # Build the list of autodetect parameters\n        autodetect_params = {\n            \"autodetect\": not is_new_collection,\n            \"metric_value\": self.metric if is_new_collection else None,\n            \"metadata_indexing_include\": (\n                [s for s in self.metadata_indexing_include if s] or None if is_new_collection else None\n            ),\n            \"metadata_indexing_exclude\": (\n                [s for s in self.metadata_indexing_exclude if s] or None if is_new_collection else None\n            ),\n            \"collection_indexing_policy\": (\n                orjson.dumps(self.collection_indexing_policy)\n                if is_new_collection and self.collection_indexing_policy\n                else None\n            ),\n            \"setup_mode\": setup_mode_value if is_new_collection else None,\n        }\n\n        # Unpack parameters\n        autodetect = autodetect_params[\"autodetect\"]\n        metric_value = autodetect_params[\"metric_value\"]\n        metadata_indexing_include = autodetect_params[\"metadata_indexing_include\"]\n        metadata_indexing_exclude = autodetect_params[\"metadata_indexing_exclude\"]\n        collection_indexing_policy = autodetect_params[\"collection_indexing_policy\"]\n        setup_mode = autodetect_params[\"setup_mode\"]\n\n        # Get the embedding model\n        embedding_dict = {\"embedding\": self.embedding_model}\n\n        # Use the embedding model if the choice is set to \"Embedding Model\"\n        if self.embedding_choice == \"Astra Vectorize\" and not autodetect:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=getattr(self, \"embedding_provider\", None) or None,\n                model=getattr(self, \"model\", None) or None,\n                z_01_model_parameters=getattr(self, \"z_01_model_parameters\", None) or None,\n                z_02_api_key_name=getattr(self, \"z_02_api_key_name\", None) or None,\n                z_03_provider_api_key=getattr(self, \"z_03_provider_api_key\", None) or None,\n                z_04_authentication=getattr(self, \"z_04_authentication\", {}) or {},\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get the running environment for Langflow\n        environment = (\n            parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n            if getattr(self, \"api_endpoint\", None)\n            else None\n        )\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        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                content_field=self.content_field or None,\n                ignore_invalid_documents=self.ignore_invalid_documents,\n                environment=environment,\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\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:\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        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\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 or 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\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\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_input}\")\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        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\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 = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\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            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=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            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\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        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of 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        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\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        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    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\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                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 self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Update the choice of embedding model based on collection name\n        if field_name == \"collection_name\":\n            # Detect if it is a new collection\n            is_new_collection = field_value == \"+ Create new collection\"\n\n            # Set the advanced and required fields based on the collection choice\n            build_config[\"embedding_choice\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"value\": \"Embedding Model\" if is_new_collection else build_config[\"embedding_choice\"].get(\"value\"),\n                }\n            )\n\n            # Set the advanced field for the embedding model\n            build_config[\"embedding_model\"][\"advanced\"] = not is_new_collection\n\n            # Set the advanced and required fields for the new collection name\n            build_config[\"collection_name_new\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"required\": is_new_collection,\n                    \"value\": \"\" if not is_new_collection else build_config[\"collection_name_new\"].get(\"value\"),\n                }\n            )\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                # Remove unnecessary fields when a service is set\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": True},\n                    \"embedding_choice\": {\"value\": \"Astra Vectorize\"},\n                }\n            else:\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": False},\n                    \"embedding_provider\": {\"advanced\": False},\n                    \"embedding_choice\": {\"value\": \"Embedding Model\"},\n                }\n\n            # Apply updates to the build_config\n            for key, value in updates.items():\n                build_config[key].update(value)\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\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        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        # Initialize parameters based on the collection name\n        is_new_collection = self.collection_name == \"+ Create new collection\"\n\n        # Build the list of autodetect parameters\n        autodetect_params = {\n            \"autodetect\": not is_new_collection,\n            \"metric_value\": self.metric if is_new_collection else None,\n            \"metadata_indexing_include\": (\n                [s for s in self.metadata_indexing_include if s] or None if is_new_collection else None\n            ),\n            \"metadata_indexing_exclude\": (\n                [s for s in self.metadata_indexing_exclude if s] or None if is_new_collection else None\n            ),\n            \"collection_indexing_policy\": (\n                orjson.dumps(self.collection_indexing_policy)\n                if is_new_collection and self.collection_indexing_policy\n                else None\n            ),\n            \"setup_mode\": setup_mode_value if is_new_collection else None,\n        }\n\n        # Unpack parameters\n        autodetect = autodetect_params[\"autodetect\"]\n        metric_value = autodetect_params[\"metric_value\"]\n        metadata_indexing_include = autodetect_params[\"metadata_indexing_include\"]\n        metadata_indexing_exclude = autodetect_params[\"metadata_indexing_exclude\"]\n        collection_indexing_policy = autodetect_params[\"collection_indexing_policy\"]\n        setup_mode = autodetect_params[\"setup_mode\"]\n\n        # Get the embedding model\n        embedding_dict = {\"embedding\": self.embedding_model} if self.embedding_choice == \"Embedding Model\" else {}\n\n        # Use the embedding model if the choice is set to \"Embedding Model\"\n        if self.embedding_choice == \"Astra Vectorize\" and not autodetect:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=getattr(self, \"embedding_provider\", None) or None,\n                model=getattr(self, \"model\", None) or None,\n                z_01_model_parameters=getattr(self, \"z_01_model_parameters\", None) or None,\n                z_02_api_key_name=getattr(self, \"z_02_api_key_name\", None) or None,\n                z_03_provider_api_key=getattr(self, \"z_03_provider_api_key\", None) or None,\n                z_04_authentication=getattr(self, \"z_04_authentication\", {}) or {},\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get the running environment for Langflow\n        environment = (\n            parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n            if getattr(self, \"api_endpoint\", None)\n            else None\n        )\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        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                content_field=self.content_field or None,\n                ignore_invalid_documents=self.ignore_invalid_documents,\n                environment=environment,\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\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:\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        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\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 or 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\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\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_input}\")\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        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_indexing_policy": {
                 "_input_type": "StrInput",
@@ -3781,7 +3781,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\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 = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\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            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=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            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\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        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of 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        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\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        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    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\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                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 self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Update the choice of embedding model based on collection name\n        if field_name == \"collection_name\":\n            # Detect if it is a new collection\n            is_new_collection = field_value == \"+ Create new collection\"\n\n            # Set the advanced and required fields based on the collection choice\n            build_config[\"embedding_choice\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"value\": \"Embedding Model\" if is_new_collection else build_config[\"embedding_choice\"].get(\"value\"),\n                }\n            )\n\n            # Set the advanced field for the embedding model\n            build_config[\"embedding_model\"][\"advanced\"] = not is_new_collection\n\n            # Set the advanced and required fields for the new collection name\n            build_config[\"collection_name_new\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"required\": is_new_collection,\n                    \"value\": \"\" if not is_new_collection else build_config[\"collection_name_new\"].get(\"value\"),\n                }\n            )\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                # Remove unnecessary fields when a service is set\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": True},\n                    \"embedding_choice\": {\"value\": \"Astra Vectorize\"},\n                }\n            else:\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": False},\n                    \"embedding_provider\": {\"advanced\": False},\n                    \"embedding_choice\": {\"value\": \"Embedding Model\"},\n                }\n\n            # Apply updates to the build_config\n            for key, value in updates.items():\n                build_config[key].update(value)\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\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        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        # Initialize parameters based on the collection name\n        is_new_collection = self.collection_name == \"+ Create new collection\"\n\n        # Build the list of autodetect parameters\n        autodetect_params = {\n            \"autodetect\": not is_new_collection,\n            \"metric_value\": self.metric if is_new_collection else None,\n            \"metadata_indexing_include\": (\n                [s for s in self.metadata_indexing_include if s] or None if is_new_collection else None\n            ),\n            \"metadata_indexing_exclude\": (\n                [s for s in self.metadata_indexing_exclude if s] or None if is_new_collection else None\n            ),\n            \"collection_indexing_policy\": (\n                orjson.dumps(self.collection_indexing_policy)\n                if is_new_collection and self.collection_indexing_policy\n                else None\n            ),\n            \"setup_mode\": setup_mode_value if is_new_collection else None,\n        }\n\n        # Unpack parameters\n        autodetect = autodetect_params[\"autodetect\"]\n        metric_value = autodetect_params[\"metric_value\"]\n        metadata_indexing_include = autodetect_params[\"metadata_indexing_include\"]\n        metadata_indexing_exclude = autodetect_params[\"metadata_indexing_exclude\"]\n        collection_indexing_policy = autodetect_params[\"collection_indexing_policy\"]\n        setup_mode = autodetect_params[\"setup_mode\"]\n\n        # Get the embedding model\n        embedding_dict = {\"embedding\": self.embedding_model}\n\n        # Use the embedding model if the choice is set to \"Embedding Model\"\n        if self.embedding_choice == \"Astra Vectorize\" and not autodetect:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=getattr(self, \"embedding_provider\", None) or None,\n                model=getattr(self, \"model\", None) or None,\n                z_01_model_parameters=getattr(self, \"z_01_model_parameters\", None) or None,\n                z_02_api_key_name=getattr(self, \"z_02_api_key_name\", None) or None,\n                z_03_provider_api_key=getattr(self, \"z_03_provider_api_key\", None) or None,\n                z_04_authentication=getattr(self, \"z_04_authentication\", {}) or {},\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get the running environment for Langflow\n        environment = (\n            parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n            if getattr(self, \"api_endpoint\", None)\n            else None\n        )\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        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                content_field=self.content_field or None,\n                ignore_invalid_documents=self.ignore_invalid_documents,\n                environment=environment,\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\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:\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        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\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 or 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\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\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_input}\")\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        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\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 = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\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            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=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            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\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        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n            tool_mode=True,\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of 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        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\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        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    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\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                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 self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Update the choice of embedding model based on collection name\n        if field_name == \"collection_name\":\n            # Detect if it is a new collection\n            is_new_collection = field_value == \"+ Create new collection\"\n\n            # Set the advanced and required fields based on the collection choice\n            build_config[\"embedding_choice\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"value\": \"Embedding Model\" if is_new_collection else build_config[\"embedding_choice\"].get(\"value\"),\n                }\n            )\n\n            # Set the advanced field for the embedding model\n            build_config[\"embedding_model\"][\"advanced\"] = not is_new_collection\n\n            # Set the advanced and required fields for the new collection name\n            build_config[\"collection_name_new\"].update(\n                {\n                    \"advanced\": not is_new_collection,\n                    \"required\": is_new_collection,\n                    \"value\": \"\" if not is_new_collection else build_config[\"collection_name_new\"].get(\"value\"),\n                }\n            )\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                # Remove unnecessary fields when a service is set\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": True},\n                    \"embedding_choice\": {\"value\": \"Astra Vectorize\"},\n                }\n            else:\n                # Update the providers mapping\n                updates = {\n                    \"embedding_model\": {\"advanced\": False},\n                    \"embedding_provider\": {\"advanced\": False},\n                    \"embedding_choice\": {\"value\": \"Embedding Model\"},\n                }\n\n            # Apply updates to the build_config\n            for key, value in updates.items():\n                build_config[key].update(value)\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\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        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        # Initialize parameters based on the collection name\n        is_new_collection = self.collection_name == \"+ Create new collection\"\n\n        # Build the list of autodetect parameters\n        autodetect_params = {\n            \"autodetect\": not is_new_collection,\n            \"metric_value\": self.metric if is_new_collection else None,\n            \"metadata_indexing_include\": (\n                [s for s in self.metadata_indexing_include if s] or None if is_new_collection else None\n            ),\n            \"metadata_indexing_exclude\": (\n                [s for s in self.metadata_indexing_exclude if s] or None if is_new_collection else None\n            ),\n            \"collection_indexing_policy\": (\n                orjson.dumps(self.collection_indexing_policy)\n                if is_new_collection and self.collection_indexing_policy\n                else None\n            ),\n            \"setup_mode\": setup_mode_value if is_new_collection else None,\n        }\n\n        # Unpack parameters\n        autodetect = autodetect_params[\"autodetect\"]\n        metric_value = autodetect_params[\"metric_value\"]\n        metadata_indexing_include = autodetect_params[\"metadata_indexing_include\"]\n        metadata_indexing_exclude = autodetect_params[\"metadata_indexing_exclude\"]\n        collection_indexing_policy = autodetect_params[\"collection_indexing_policy\"]\n        setup_mode = autodetect_params[\"setup_mode\"]\n\n        # Get the embedding model\n        embedding_dict = {\"embedding\": self.embedding_model} if self.embedding_choice == \"Embedding Model\" else {}\n\n        # Use the embedding model if the choice is set to \"Embedding Model\"\n        if self.embedding_choice == \"Astra Vectorize\" and not autodetect:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=getattr(self, \"embedding_provider\", None) or None,\n                model=getattr(self, \"model\", None) or None,\n                z_01_model_parameters=getattr(self, \"z_01_model_parameters\", None) or None,\n                z_02_api_key_name=getattr(self, \"z_02_api_key_name\", None) or None,\n                z_03_provider_api_key=getattr(self, \"z_03_provider_api_key\", None) or None,\n                z_04_authentication=getattr(self, \"z_04_authentication\", {}) or {},\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get the running environment for Langflow\n        environment = (\n            parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n            if getattr(self, \"api_endpoint\", None)\n            else None\n        )\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        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                content_field=self.content_field or None,\n                ignore_invalid_documents=self.ignore_invalid_documents,\n                environment=environment,\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\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:\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        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\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 or 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\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\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_input}\")\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        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_indexing_policy": {
                 "_input_type": "StrInput",