Vector Store RAG - Ingest.json

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                "value": "from langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data, DataFrame\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n    display_name: str = \"Split Text\"\n    description: str = \"Split text into chunks based on specified criteria.\"\n    icon = \"scissors-line-dashed\"\n    name = \"SplitText\"\n\n    inputs = [\n        HandleInput(\n            name=\"data_inputs\",\n            display_name=\"Data Inputs\",\n            info=\"The data to split.\",\n            input_types=[\"Data\"],\n            is_list=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"chunk_overlap\",\n            display_name=\"Chunk Overlap\",\n            info=\"Number of characters to overlap between chunks.\",\n            value=200,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"The maximum number of characters in each chunk.\",\n            value=1000,\n        ),\n        MessageTextInput(\n            name=\"separator\",\n            display_name=\"Separator\",\n            info=\"The character to split on. Defaults to newline.\",\n            value=\"\\n\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def _docs_to_data(self, docs):\n        return [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n\n    def split_text(self) -> list[Data]:\n        separator = unescape_string(self.separator)\n\n        documents = [_input.to_lc_document() for _input in self.data_inputs if isinstance(_input, Data)]\n\n        splitter = CharacterTextSplitter(\n            chunk_overlap=self.chunk_overlap,\n            chunk_size=self.chunk_size,\n            separator=separator,\n        )\n        docs = splitter.split_documents(documents)\n        data = self._docs_to_data(docs)\n        self.status = data\n        return data\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.split_text())\n"
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            "description": "Ingest and search documents in Astra DB",
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              "search_query",
              "number_of_results",
              "search_type",
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              "ignore_invalid_documents",
              "astradb_vectorstore_kwargs"
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                "name": "search_results",
                "hidden": null,
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                "combobox": true,
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                "info": "The Database / API Endpoint for the Astra DB instance.",
                "name": "Database",
                "options": [
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                "options_metadata": [
                  {
                    "collections": 4,
                    "api_endpoint": "https://ab82863b-337c-461a-9457-8a198f5b2e48-us-east-2.apps.astra.datastax.com"
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                "trace_as_metadata": true,
                "type": "str",
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              "astradb_vectorstore_kwargs": {
                "_input_type": "NestedDictInput",
                "advanced": true,
                "display_name": "AstraDBVectorStore Parameters",
                "dynamic": false,
                "info": "Optional dictionary of additional parameters for the AstraDBVectorStore.",
                "list": false,
                "list_add_label": "Add More",
                "name": "astradb_vectorstore_kwargs",
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              "autodetect_collection": {
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                "advanced": true,
                "display_name": "Autodetect Collection",
                "dynamic": false,
                "info": "Boolean flag to determine whether to autodetect the collection.",
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                "name": "autodetect_collection",
                "placeholder": "",
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                "value": "import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\n\nfrom astrapy import AstraDBAdmin, DataAPIClient, Database\nfrom langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import FloatInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Ingest and search documents in Astra DB\"\n    documentation: str = \"https://docs.datastax.com/en/langflow/astra-components.html\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    @dataclass\n    class NewDatabaseInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new database in Astra DB.\",\n                        \"display_name\": \"Create New Database\",\n                        \"field_order\": [\"new_database_name\", \"cloud_provider\", \"region\"],\n                        \"template\": {\n                            \"new_database_name\": StrInput(\n                                name=\"new_database_name\",\n                                display_name=\"New Database Name\",\n                                info=\"Name of the new database to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"cloud_provider\": DropdownInput(\n                                name=\"cloud_provider\",\n                                display_name=\"Cloud Provider\",\n                                info=\"Cloud provider for the new database.\",\n                                options=[\"Amazon Web Services\", \"Google Cloud Platform\", \"Microsoft Azure\"],\n                                required=True,\n                            ),\n                            \"region\": DropdownInput(\n                                name=\"region\",\n                                display_name=\"Region\",\n                                info=\"Region for the new database.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    @dataclass\n    class NewCollectionInput:\n        functionality: str = \"create\"\n        fields: dict[str, dict] = field(\n            default_factory=lambda: {\n                \"data\": {\n                    \"node\": {\n                        \"description\": \"Create a new collection in Astra DB.\",\n                        \"display_name\": \"Create New Collection\",\n                        \"field_order\": [\n                            \"new_collection_name\",\n                            \"embedding_generation_provider\",\n                            \"embedding_generation_model\",\n                        ],\n                        \"template\": {\n                            \"new_collection_name\": StrInput(\n                                name=\"new_collection_name\",\n                                display_name=\"New Collection Name\",\n                                info=\"Name of the new collection to create in Astra DB.\",\n                                required=True,\n                            ),\n                            \"embedding_generation_provider\": DropdownInput(\n                                name=\"embedding_generation_provider\",\n                                display_name=\"Embedding Generation Provider\",\n                                info=\"Provider to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                            \"embedding_generation_model\": DropdownInput(\n                                name=\"embedding_generation_model\",\n                                display_name=\"Embedding Generation Model\",\n                                info=\"Model to use for generating embeddings.\",\n                                options=[],\n                                required=True,\n                            ),\n                        },\n                    },\n                }\n            }\n        )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        StrInput(\n            name=\"environment\",\n            display_name=\"Environment\",\n            info=\"The environment for the Astra DB API Endpoint.\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\",\n            info=\"The Database / API Endpoint for the Astra DB instance.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            combobox=True,\n        ),\n        StrInput(\n            name=\"d_api_endpoint\",\n            display_name=\"Database API Endpoint\",\n            info=\"The API Endpoint for the Astra DB instance. Supercedes database selection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            # dialog_inputs=asdict(NewCollectionInput()),\n            combobox=True,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Choose an embedding model or use Astra Vectorize.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            value=\"Embedding Model\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Specify the Embedding Model. Not required for Astra Vectorize collections.\",\n            required=False,\n        ),\n        *LCVectorStoreComponent.inputs,\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Search Results\",\n            info=\"Number of search results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"autodetect_collection\",\n            display_name=\"Autodetect Collection\",\n            info=\"Boolean flag to determine whether to autodetect the collection.\",\n            advanced=True,\n            value=True,\n        ),\n        StrInput(\n            name=\"content_field\",\n            display_name=\"Content Field\",\n            info=\"Field to use as the text content field for the vector store.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"deletion_field\",\n            display_name=\"Deletion Based On Field\",\n            info=\"When this parameter is provided, documents in the target collection with \"\n            \"metadata field values matching the input metadata field value will be deleted \"\n            \"before new data is loaded.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"ignore_invalid_documents\",\n            display_name=\"Ignore Invalid Documents\",\n            info=\"Boolean flag to determine whether to ignore invalid documents at runtime.\",\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"astradb_vectorstore_kwargs\",\n            display_name=\"AstraDBVectorStore Parameters\",\n            info=\"Optional dictionary of additional parameters for the AstraDBVectorStore.\",\n            advanced=True,\n        ),\n    ]\n\n    @classmethod\n    def map_cloud_providers(cls):\n        return {\n            \"Amazon Web Services\": {\n                \"id\": \"aws\",\n                \"regions\": [\"us-east-2\", \"ap-south-1\", \"eu-west-1\"],\n            },\n            \"Google Cloud Platform\": {\n                \"id\": \"gcp\",\n                \"regions\": [\"us-east1\"],\n            },\n            \"Microsoft Azure\": {\n                \"id\": \"azure\",\n                \"regions\": [\"westus3\"],\n            },\n        }\n\n    @classmethod\n    def create_database_api(\n        cls,\n        token: str,\n        new_database_name: str,\n        cloud_provider: str,\n        region: str,\n    ):\n        client = DataAPIClient(token=token)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Call the create database function\n        return admin_client.create_database(\n            name=new_database_name,\n            cloud_provider=cloud_provider,\n            region=region,\n        )\n\n    @classmethod\n    def create_collection_api(\n        cls,\n        token: str,\n        database_name: str,\n        new_collection_name: str,\n        dimension: int | None = None,\n        embedding_generation_provider: str | None = None,\n        embedding_generation_model: str | None = None,\n    ):\n        client = DataAPIClient(token=token)\n        api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)\n\n        # Get the database object\n        database = client.get_database(api_endpoint=api_endpoint, token=token)\n\n        # Build vectorize options, if needed\n        vectorize_options = None\n        if not dimension:\n            vectorize_options = CollectionVectorServiceOptions(\n                provider=embedding_generation_provider,\n                model_name=embedding_generation_model,\n                authentication=None,\n                parameters=None,\n            )\n\n        # Create the collection\n        return database.create_collection(\n            name=new_collection_name,\n            dimension=dimension,\n            service=vectorize_options,\n        )\n\n    @classmethod\n    def get_database_list_static(cls, token: str, environment: str | None = None):\n        client = DataAPIClient(token=token, environment=environment)\n\n        # Get the admin object\n        admin_client = client.get_admin(token=token)\n\n        # Get the list of databases\n        db_list = list(admin_client.list_databases())\n\n        # Set the environment properly\n        env_string = \"\"\n        if environment and environment != \"prod\":\n            env_string = f\"-{environment}\"\n\n        # Generate the api endpoint for each database\n        db_info_dict = {}\n        for db in db_list:\n            try:\n                api_endpoint = f\"https://{db.info.id}-{db.info.region}.apps.astra{env_string}.datastax.com\"\n                db_info_dict[db.info.name] = {\n                    \"api_endpoint\": api_endpoint,\n                    \"collections\": len(\n                        list(\n                            client.get_database(\n                                api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace\n                            ).list_collection_names(keyspace=db.info.keyspace)\n                        )\n                    ),\n                }\n            except Exception:  # noqa: BLE001, S110\n                pass\n\n        return db_info_dict\n\n    def get_database_list(self):\n        return self.get_database_list_static(token=self.token, environment=self.environment)\n\n    @classmethod\n    def get_api_endpoint_static(\n        cls,\n        token: str,\n        environment: str | None = None,\n        api_endpoint: str | None = None,\n        database_name: str | None = None,\n    ):\n        # If the api_endpoint is set, return it\n        if api_endpoint:\n            return api_endpoint\n\n        # Check if the database_name is like a url\n        if database_name and database_name.startswith(\"https://\"):\n            return database_name\n\n        # If the database is not set, nothing we can do.\n        if not database_name:\n            return None\n\n        # Otherwise, get the URL from the database list\n        return cls.get_database_list_static(token=token, environment=environment).get(database_name).get(\"api_endpoint\")\n\n    def get_api_endpoint(self, *, api_endpoint: str | None = None):\n        return self.get_api_endpoint_static(\n            token=self.token,\n            environment=self.environment,\n            api_endpoint=api_endpoint or self.d_api_endpoint,\n            database_name=self.api_endpoint,\n        )\n\n    def get_keyspace(self):\n        keyspace = self.keyspace\n\n        if keyspace:\n            return keyspace.strip()\n\n        return None\n\n    def get_database_object(self, api_endpoint: str | None = None):\n        try:\n            client = DataAPIClient(token=self.token, environment=self.environment)\n\n            return client.get_database(\n                api_endpoint=self.get_api_endpoint(api_endpoint=api_endpoint),\n                token=self.token,\n                keyspace=self.get_keyspace(),\n            )\n        except Exception as e:\n            msg = f\"Error fetching database object: {e}\"\n            raise ValueError(msg) from e\n\n    def collection_data(self, collection_name: str, database: Database | None = None):\n        try:\n            if not database:\n                client = DataAPIClient(token=self.token, environment=self.environment)\n\n                database = client.get_database(\n                    api_endpoint=self.get_api_endpoint(),\n                    token=self.token,\n                    keyspace=self.get_keyspace(),\n                )\n\n            collection = database.get_collection(collection_name, keyspace=self.get_keyspace())\n\n            return collection.estimated_document_count()\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error checking collection data: {e}\")\n\n            return None\n\n    def get_vectorize_providers(self):\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            admin = AstraDBAdmin(token=self.token)\n            db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())\n\n            # Get the list of embedding providers\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                # TODO: https://astra.datastax.com/api/v2/graphql\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return {}\n\n    def _initialize_database_options(self):\n        try:\n            return [\n                {\n                    \"name\": name,\n                    \"collections\": info[\"collections\"],\n                    \"api_endpoint\": info[\"api_endpoint\"],\n                }\n                for name, info in self.get_database_list().items()\n            ]\n        except Exception as e:\n            msg = f\"Error fetching database options: {e}\"\n            raise ValueError(msg) from e\n\n    def _initialize_collection_options(self, api_endpoint: str | None = None):\n        # Retrieve the database object\n        database = self.get_database_object(api_endpoint=api_endpoint)\n\n        # Get the list of collections\n        collection_list = list(database.list_collections(keyspace=self.get_keyspace()))\n\n        # Return the list of collections and metadata associated\n        return [\n            {\n                \"name\": col.name,\n                \"records\": self.collection_data(collection_name=col.name, database=database),\n                \"provider\": (\n                    col.options.vector.service.provider if col.options.vector and col.options.vector.service else None\n                ),\n                \"icon\": \"\",\n                \"model\": (\n                    col.options.vector.service.model_name if col.options.vector and col.options.vector.service else None\n                ),\n            }\n            for col in collection_list\n        ]\n\n    def reset_collection_list(self, build_config: dict):\n        # Get the list of options we have based on the token provided\n        collection_options = self._initialize_collection_options()\n\n        # If we retrieved options based on the token, show the dropdown\n        build_config[\"collection_name\"][\"options\"] = [col[\"name\"] for col in collection_options]\n        build_config[\"collection_name\"][\"options_metadata\"] = [\n            {k: v for k, v in col.items() if k not in [\"name\"]} for col in collection_options\n        ]\n\n        # Reset the selected collection\n        build_config[\"collection_name\"][\"value\"] = \"\"\n\n        return build_config\n\n    def reset_database_list(self, build_config: dict):\n        # Get the list of options we have based on the token provided\n        database_options = self._initialize_database_options()\n\n        # If we retrieved options based on the token, show the dropdown\n        build_config[\"api_endpoint\"][\"options\"] = [db[\"name\"] for db in database_options]\n        build_config[\"api_endpoint\"][\"options_metadata\"] = [\n            {k: v for k, v in db.items() if k not in [\"name\"]} for db in database_options\n        ]\n\n        # Reset the selected database\n        build_config[\"api_endpoint\"][\"value\"] = \"\"\n\n        return build_config\n\n    def reset_build_config(self, build_config: dict):\n        # Reset the list of databases we have based on the token provided\n        build_config[\"api_endpoint\"][\"options\"] = []\n        build_config[\"api_endpoint\"][\"options_metadata\"] = []\n        build_config[\"api_endpoint\"][\"value\"] = \"\"\n        build_config[\"api_endpoint\"][\"name\"] = \"Database\"\n\n        # Reset the list of collections and metadata associated\n        build_config[\"collection_name\"][\"options\"] = []\n        build_config[\"collection_name\"][\"options_metadata\"] = []\n        build_config[\"collection_name\"][\"value\"] = \"\"\n\n        return build_config\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # When the component first executes, this is the update refresh call\n        first_run = field_name == \"collection_name\" and not field_value and not build_config[\"api_endpoint\"][\"options\"]\n\n        # If the token has not been provided, simply return\n        if not self.token:\n            return self.reset_build_config(build_config)\n\n        # If this is the first execution of the component, reset and build database list\n        if first_run or field_name in [\"token\", \"environment\"]:\n            # Reset the build config to ensure we are starting fresh\n            build_config = self.reset_build_config(build_config)\n            build_config = self.reset_database_list(build_config)\n\n            # Get list of regions for a given cloud provider\n            \"\"\"\n            cloud_provider = (\n                build_config[\"api_endpoint\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"cloud_provider\"][\n                    \"value\"\n                ]\n                or \"Amazon Web Services\"\n            )\n            build_config[\"api_endpoint\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\"region\"][\n                \"options\"\n            ] = self.map_cloud_providers()[cloud_provider][\"regions\"]\n            \"\"\"\n\n            return build_config\n\n        # Refresh the collection name options\n        if field_name == \"api_endpoint\":\n            # If missing, refresh the database options\n            if not build_config[\"api_endpoint\"][\"options\"] or not field_value:\n                return self.update_build_config(build_config, field_value=self.token, field_name=\"token\")\n\n            # Set the underlying api endpoint value of the database\n            if field_value in build_config[\"api_endpoint\"][\"options\"]:\n                index_of_name = build_config[\"api_endpoint\"][\"options\"].index(field_value)\n                build_config[\"d_api_endpoint\"][\"value\"] = build_config[\"api_endpoint\"][\"options_metadata\"][\n                    index_of_name\n                ][\"api_endpoint\"]\n            else:\n                build_config[\"d_api_endpoint\"][\"value\"] = \"\"\n\n            # Reset the list of collections we have based on the token provided\n            return self.reset_collection_list(build_config)\n\n        # Hide embedding model option if opriona_metadata provider is not null\n        if field_name == \"collection_name\" and field_value:\n            # Assume we will be autodetecting the collection:\n            build_config[\"autodetect_collection\"][\"value\"] = True\n\n            # Set the options for collection name to be the field value if its a new collection\n            if field_value not in build_config[\"collection_name\"][\"options\"]:\n                # Add the new collection to the list of options\n                build_config[\"collection_name\"][\"options\"].append(field_value)\n                build_config[\"collection_name\"][\"options_metadata\"].append(\n                    {\"records\": 0, \"provider\": None, \"icon\": \"\", \"model\": None}\n                )\n\n                # Ensure that autodetect collection is set to False, since its a new collection\n                build_config[\"autodetect_collection\"][\"value\"] = False\n\n            # Find the position of the selected collection to align with metadata\n            index_of_name = build_config[\"collection_name\"][\"options\"].index(field_value)\n            value_of_provider = build_config[\"collection_name\"][\"options_metadata\"][index_of_name][\"provider\"]\n\n            # If we were able to determine the Vectorize provider, set it accordingly\n            if value_of_provider:\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_choice\"][\"value\"] = \"Astra Vectorize\"\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n\n        # For the final step, get the list of vectorize providers\n        \"\"\"\n        vectorize_providers = self.get_vectorize_providers()\n        if not vectorize_providers:\n            return build_config\n\n        # Allow the user to see the embedding provider options\n        provider_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_provider\"\n        ][\"options\"]\n        if not provider_options:\n            # If the collection is set, allow user to see embedding options\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"options\"] = [\"Bring your own\", \"Nvidia\", *[key for key in vectorize_providers if key != \"Nvidia\"]]\n\n        # And allow the user to see the models based on a selected provider\n        model_options = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n            \"embedding_generation_model\"\n        ][\"options\"]\n        if not model_options:\n            embedding_provider = build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_provider\"\n            ][\"value\"]\n\n            build_config[\"collection_name\"][\"dialog_inputs\"][\"fields\"][\"data\"][\"node\"][\"template\"][\n                \"embedding_generation_model\"\n            ][\"options\"] = vectorize_providers.get(embedding_provider, [[], []])[1]\n        \"\"\"\n\n        return build_config\n\n    @check_cached_vector_store\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        # Get the embedding model and additional params\n        embedding_params = (\n            {\"embedding\": self.embedding_model}\n            if self.embedding_model and self.embedding_choice == \"Embedding Model\"\n            else {}\n        )\n\n        # Get the additional parameters\n        additional_params = self.astradb_vectorstore_kwargs or {}\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"AWS_EXECUTION_ENV\") == \"AWS_ECS_FARGATE\":  # TODO: More precise way of detecting\n            langflow_prefix = \"ds-\"\n\n        # Get the database object\n        database = self.get_database_object(api_endpoint=self.d_api_endpoint)\n        autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection\n\n        # Bundle up the auto-detect parameters\n        autodetect_params = {\n            \"autodetect_collection\": autodetect,\n            \"content_field\": (\n                self.content_field\n                if self.content_field and embedding_params\n                else (\n                    \"page_content\"\n                    if embedding_params\n                    and self.collection_data(collection_name=self.collection_name, database=database) == 0\n                    else None\n                )\n            ),\n            \"ignore_invalid_documents\": self.ignore_invalid_documents,\n        }\n\n        # Attempt to build the Vector Store object\n        try:\n            vector_store = AstraDBVectorStore(\n                # Astra DB Authentication Parameters\n                token=self.token,\n                api_endpoint=database.api_endpoint,\n                namespace=database.keyspace,\n                collection_name=self.collection_name,\n                environment=self.environment,\n                # Astra DB Usage Tracking Parameters\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                # Astra DB Vector Store Parameters\n                **autodetect_params,\n                **embedding_params,\n                **additional_params,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        # Add documents to the vector store\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents and self.deletion_field:\n            self.log(f\"Deleting documents where {self.deletion_field}\")\n            try:\n                database = self.get_database_object(api_endpoint=self.d_api_endpoint)\n                collection = database.get_collection(self.collection_name, keyspace=database.keyspace)\n                delete_values = list({doc.metadata[self.deletion_field] for doc in documents})\n                self.log(f\"Deleting documents where {self.deletion_field} matches {delete_values}.\")\n                collection.delete_many({f\"metadata.{self.deletion_field}\": {\"$in\": delete_values}})\n            except Exception as e:\n                msg = f\"Error deleting documents from AstraDBVectorStore based on '{self.deletion_field}': {e}\"\n                raise ValueError(msg) from e\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        search_type_mapping = {\n            \"Similarity with score threshold\": \"similarity_score_threshold\",\n            \"MMR (Max Marginal Relevance)\": \"mmr\",\n        }\n\n        return search_type_mapping.get(self.search_type, \"similarity\")\n\n    def _build_search_args(self):\n        query = self.search_query if isinstance(self.search_query, str) and self.search_query.strip() else None\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_query}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
              },
              "collection_name": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": true,
                "dialog_inputs": {},
                "display_name": "Collection",
                "dynamic": false,
                "info": "The name of the collection within Astra DB where the vectors will be stored.",
                "name": "collection_name",
                "options": [],
                "options_metadata": [],
                "placeholder": "",
                "real_time_refresh": true,
                "refresh_button": true,
                "required": true,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "content_field": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "Content Field",
                "dynamic": false,
                "info": "Field to use as the text content field for the vector store.",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "content_field",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "d_api_endpoint": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "Database API Endpoint",
                "dynamic": false,
                "info": "The API Endpoint for the Astra DB instance. Supercedes database selection.",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "d_api_endpoint",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "https://ab82863b-337c-461a-9457-8a198f5b2e48-us-east-2.apps.astra.datastax.com"
              },
              "deletion_field": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "Deletion Based On Field",
                "dynamic": false,
                "info": "When this parameter is provided, documents in the target collection with metadata field values matching the input metadata field value will be deleted before new data is loaded.",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "deletion_field",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "embedding_choice": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Embedding Model or Astra Vectorize",
                "dynamic": false,
                "info": "Choose an embedding model or use Astra Vectorize.",
                "name": "embedding_choice",
                "options": [
                  "Embedding Model",
                  "Astra Vectorize"
                ],
                "options_metadata": [],
                "placeholder": "",
                "real_time_refresh": true,
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Astra Vectorize"
              },
              "embedding_model": {
                "_input_type": "HandleInput",
                "advanced": true,
                "display_name": "Embedding Model",
                "dynamic": false,
                "info": "Specify the Embedding Model. Not required for Astra Vectorize collections.",
                "input_types": [
                  "Embeddings"
                ],
                "list": false,
                "list_add_label": "Add More",
                "name": "embedding_model",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "environment": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "Environment",
                "dynamic": false,
                "info": "The environment for the Astra DB API Endpoint.",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "environment",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "ignore_invalid_documents": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Ignore Invalid Documents",
                "dynamic": false,
                "info": "Boolean flag to determine whether to ignore invalid documents at runtime.",
                "list": false,
                "list_add_label": "Add More",
                "name": "ignore_invalid_documents",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": false
              },
              "ingest_data": {
                "_input_type": "DataInput",
                "advanced": false,
                "display_name": "Ingest Data",
                "dynamic": false,
                "info": "",
                "input_types": [
                  "Data"
                ],
                "list": false,
                "list_add_label": "Add More",
                "name": "ingest_data",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "keyspace": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "Keyspace",
                "dynamic": false,
                "info": "Optional keyspace within Astra DB to use for the collection.",
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "name": "keyspace",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "number_of_results": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Number of Search Results",
                "dynamic": false,
                "info": "Number of search results to return.",
                "list": false,
                "list_add_label": "Add More",
                "name": "number_of_results",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 4
              },
              "search_query": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "display_name": "Search Query",
                "dynamic": false,
                "info": "",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "list_add_label": "Add More",
                "load_from_db": false,
                "multiline": true,
                "name": "search_query",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "search_score_threshold": {
                "_input_type": "FloatInput",
                "advanced": true,
                "display_name": "Search Score Threshold",
                "dynamic": false,
                "info": "Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')",
                "list": false,
                "list_add_label": "Add More",
                "name": "search_score_threshold",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "float",
                "value": 0
              },
              "search_type": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "dialog_inputs": {},
                "display_name": "Search Type",
                "dynamic": false,
                "info": "Search type to use",
                "name": "search_type",
                "options": [
                  "Similarity",
                  "Similarity with score threshold",
                  "MMR (Max Marginal Relevance)"
                ],
                "options_metadata": [],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Similarity"
              },
              "token": {
                "_input_type": "SecretStrInput",
                "advanced": false,
                "display_name": "Astra DB Application Token",
                "dynamic": false,
                "info": "Authentication token for accessing Astra DB.",
                "input_types": [],
                "load_from_db": true,
                "name": "token",
                "password": true,
                "placeholder": "",
                "real_time_refresh": true,
                "required": true,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": ""
              }
            },
            "tool_mode": false
          },
          "showNode": true,
          "type": "AstraDB"
        },
        "dragging": false,
        "id": "AstraDB-wMwg6",
        "measured": {
          "height": 570,
          "width": 320
        },
        "position": {
          "x": 2058.637102277477,
          "y": 1322.853863666889
        },
        "selected": false,
        "type": "genericNode"
      },
      {
        "id": "TextInput-BQ7yq",
        "type": "genericNode",
        "position": {
          "x": 889.8057753386661,
          "y": 1469.4740672949424
        },
        "data": {
          "node": {
            "template": {
              "_type": "Component",
              "code": {
                "type": "code",
                "required": true,
                "placeholder": "",
                "list": false,
                "show": true,
                "multiline": true,
                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        return Message(\n            text=self.input_value,\n        )\n",
                "fileTypes": [],
                "file_path": "",
                "password": false,
                "name": "code",
                "advanced": true,
                "dynamic": true,
                "info": "",
                "load_from_db": false,
                "title_case": false
              },
              "input_value": {
                "tool_mode": false,
                "trace_as_input": true,
                "multiline": true,
                "trace_as_metadata": true,
                "load_from_db": false,
                "list": false,
                "list_add_label": "Add More",
                "required": false,
                "placeholder": "",
                "show": true,
                "name": "input_value",
                "value": "The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse (\\\"Norman\\\" comes from \\\"Norseman\\\") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.\\nThe Norman dynasty had a major political, cultural and military impact on medieval Europe and even the Near East. The Normans were famed for their martial spirit and eventually for their Christian piety, becoming exponents of the Catholic orthodoxy into which they assimilated. They adopted the Gallo-Romance language of the Frankish land they settled, their dialect becoming known as Norman, Normaund or Norman French, an important literary language. The Duchy of Normandy, which they formed by treaty with the French crown, was a great fief of medieval France, and under Richard I of Normandy was forged into a cohesive and formidable principality in feudal tenure. The Normans are noted both for their culture, such as their unique Romanesque architecture and musical traditions, and for their significant military accomplishments and innovations. Norman adventurers founded the Kingdom of Sicily under Roger II after conquering southern Italy on the Saracens and Byzantines, and an expedition on behalf of their duke, William the Conqueror, led to the Norman conquest of England at the Battle of Hastings in 1066. Norman cultural and military influence spread from these new European centres to the Crusader states of the Near East, where their prince Bohemond I founded the Principality of Antioch in the Levant, to Scotland and Wales in Great Britain, to Ireland, and to the coasts of north Africa and the Canary Islands.\\nThe English name \\\"Normans\\\" comes from the French words Normans/Normanz, plural of Normant, modern French normand, which is itself borrowed from Old Low Franconian Nortmann \\\"Northman\\\" or directly from Old Norse Nor\\u00f0ma\\u00f0r, Latinized variously as Nortmannus, Normannus, or Nordmannus (recorded in Medieval Latin, 9th century) to mean \\\"Norseman, Viking\\\".\\nIn the course of the 10th century, the initially destructive incursions of Norse war bands into the rivers of France evolved into more permanent encampments that included local women and personal property. The Duchy of Normandy, which began in 911 as a fiefdom, was established by the treaty of Saint-Clair-sur-Epte between King Charles III of West Francia and the famed Viking ruler Rollo, and was situated in the former Frankish kingdom of Neustria. The treaty offered Rollo and his men the French lands between the river Epte and the Atlantic coast in exchange for their protection against further Viking incursions. The area corresponded to the northern part of present-day Upper Normandy down to the river Seine, but the Duchy would eventually extend west beyond the Seine. The territory was roughly equivalent to the old province of Rouen, and reproduced the Roman administrative structure of Gallia Lugdunensis II (part of the former Gallia Lugdunensis).\\nBefore Rollo's arrival, its populations did not differ from Picardy or the \\u00cele-de-France, which were considered \\\"Frankish\\\". Earlier Viking settlers had begun arriving in the 880s, but were divided between colonies in the east (Roumois and Pays de Caux) around the low Seine valley and in the west in the Cotentin Peninsula, and were separated by traditional pagii, where the population remained about the same with almost no foreign settlers. Rollo's contingents who raided and ultimately settled Normandy and parts of the Atlantic coast included Danes, Norwegians, Norse\\u2013Gaels, Orkney Vikings, possibly Swedes, and Anglo-Danes from the English Danelaw under Norse control.\\nThe descendants of Rollo's Vikings and their Frankish wives would replace the Norse religion and Old Norse language with Catholicism (Christianity) and the Gallo-Romance language of the local people, blending their maternal Frankish heritage with Old Norse traditions and customs to synthesize a unique \\\"Norman\\\" culture in the north of France. The Norman language was forged by the adoption of the indigenous langue d'o\\u00efl branch of Romance by a Norse-speaking ruling class, and it developed into the regional language that survives today.\\nThe Normans thereafter adopted the growing feudal doctrines of the rest of France, and worked them into a functional hierarchical system in both Normandy and in England. The new Norman rulers were culturally and ethnically distinct from the old French aristocracy, most of whom traced their lineage to Franks of the Carolingian dynasty. Most Norman knights remained poor and land-hungry, and by 1066 Normandy had been exporting fighting horsemen for more than a generation. Many Normans of Italy, France and England eventually served as avid Crusaders under the Italo-Norman prince Bohemund I and the Anglo-Norman king Richard the Lion-Heart.\\nSoon after the Normans began to enter Italy, they entered the Byzantine Empire and then Armenia, fighting against the Pechenegs, the Bulgars, and especially the Seljuk Turks. Norman mercenaries were first encouraged to come to the south by the Lombards to act against the Byzantines, but they soon fought in Byzantine service in Sicily. They were prominent alongside Varangian and Lombard contingents in the Sicilian campaign of George Maniaces in 1038\\u201340. There is debate whether the Normans in Greek service actually were from Norman Italy, and it now seems likely only a few came from there. It is also unknown how many of the \\\"Franks\\\", as the Byzantines called them, were Normans and not other Frenchmen.\\nOne of the first Norman mercenaries to serve as a Byzantine general was Herv\\u00e9 in the 1050s. By then however, there were already Norman mercenaries serving as far away as Trebizond and Georgia. They were based at Malatya and Edessa, under the Byzantine duke of Antioch, Isaac Komnenos. In the 1060s, Robert Crispin led the Normans of Edessa against the Turks. Roussel de Bailleul even tried to carve out an independent state in Asia Minor with support from the local population, but he was stopped by the Byzantine general Alexius Komnenos.\\nSome Normans joined Turkish forces to aid in the destruction of the Armenians vassal-states of Sassoun and Taron in far eastern Anatolia. Later, many took up service with the Armenian state further south in Cilicia and the Taurus Mountains. A Norman named Oursel led a force of \\\"Franks\\\" into the upper Euphrates valley in northern Syria. From 1073 to 1074, 8,000 of the 20,000 troops of the Armenian general Philaretus Brachamius were Normans\\u2014formerly of Oursel\\u2014led by Raimbaud. They even lent their ethnicity to the name of their castle: Afranji, meaning \\\"Franks.\\\" The known trade between Amalfi and Antioch and between Bari and Tarsus may be related to the presence of Italo-Normans in those cities while Amalfi and Bari were under Norman rule in Italy.\\nSeveral families of Byzantine Greece were of Norman mercenary origin during the period of the Comnenian Restoration, when Byzantine emperors were seeking out western European warriors. The Raoulii were descended from an Italo-Norman named Raoul, the Petraliphae were descended from a Pierre d'Aulps, and that group of Albanian clans known as the Maniakates were descended from Normans who served under George Maniaces in the Sicilian expedition of 1038.\\nRobert Guiscard, an other Norman adventurer previously elevated to the dignity of count of Apulia as the result of his military successes, ultimately drove the Byzantines out of southern Italy. Having obtained the consent of pope Gregory VII and acting as his vassal, Robert continued his campaign conquering the Balkan peninsula as a foothold for western feudal lords and the Catholic Church. After allying himself with Croatia and the Catholic cities of Dalmatia, in 1081 he led an army of 30,000 men in 300 ships landing on the southern shores of Albania, capturing Valona, Kanina, Jericho (Orikumi), and reaching Butrint after numerous pillages. They joined the fleet that had previously conquered Corfu and attacked Dyrrachium from land and sea, devastating everything along the way. Under these harsh circumstances, the locals accepted the call of emperor Alexius I Comnenus to join forces with the Byzantines against the Normans. The Albanian forces could not take part in the ensuing battle because it had started before their arrival. Immediately before the battle, the Venetian fleet had secured a victory in the coast surrounding the city. Forced to retreat, Alexius ceded the command to a high Albanian official named Comiscortes in the service of Byzantium. The city's garrison resisted until February 1082, when Dyrrachium was betrayed to the Normans by the Venetian and Amalfitan merchants who had settled there. The Normans were now free to penetrate into the hinterland; they took Ioannina and some minor cities in southwestern Macedonia and Thessaly before appearing at the gates of Thessalonica. Dissension among the high ranks coerced the Normans to retreat to Italy. They lost Dyrrachium, Valona, and Butrint in 1085, after the death of Robert.\\nA few years after the First Crusade, in 1107, the Normans under the command of Bohemond, Robert's son, landed in Valona and besieged Dyrrachium using the most sophisticated military equipment of the time, but to no avail. Meanwhile, they occupied Petrela, the citadel of Mili at the banks of the river Deabolis, Gllavenica (Ballsh), Kanina and Jericho. This time, the Albanians sided with the Normans, dissatisfied by the heavy taxes the Byzantines had imposed upon them. With their help, the Normans secured the Arbanon passes and opened their way to Dibra. The lack of supplies, disease and Byzantine resistance forced Bohemond to retreat from his campaign and sign a peace treaty with the Byzantines in the city of Deabolis.\\nThe further decline of Byzantine state-of-affairs paved the road to a third attack in 1185, when a large Norman army invaded Dyrrachium, owing to the betrayal of high Byzantine officials. Some time later, Dyrrachium\\u2014one of the most important naval bases of the Adriatic\\u2014fell again to Byzantine hands.\\nThe Normans were in contact with England from an early date. Not only were their original Viking brethren still ravaging the English coasts, they occupied most of the important ports opposite England across the English Channel. This relationship eventually produced closer ties of blood through the marriage of Emma, sister of Duke Richard II of Normandy, and King Ethelred II of England. Because of this, Ethelred fled to Normandy in 1013, when he was forced from his kingdom by Sweyn Forkbeard. His stay in Normandy (until 1016) influenced him and his sons by Emma, who stayed in Normandy after Cnut the Great's conquest of the isle.\\nWhen finally Edward the Confessor returned from his father's refuge in 1041, at the invitation of his half-brother Harthacnut, he brought with him a Norman-educated mind. He also brought many Norman counsellors and fighters, some of whom established an English cavalry force. This concept never really took root, but it is a typical example of the attitudes of Edward. He appointed Robert of Jumi\\u00e8ges archbishop of Canterbury and made Ralph the Timid earl of Hereford. He invited his brother-in-law Eustace II, Count of Boulogne to his court in 1051, an event which resulted in the greatest of early conflicts between Saxon and Norman and ultimately resulted in the exile of Earl Godwin of Wessex.\\nIn 1066, Duke William II of Normandy conquered England killing King Harold II at the Battle of Hastings. The invading Normans and their descendants replaced the Anglo-Saxons as the ruling class of England. The nobility of England were part of a single Normans culture and many had lands on both sides of the channel. Early Norman kings of England, as Dukes of Normandy, owed homage to the King of France for their land on the continent. They considered England to be their most important holding (it brought with it the title of King\\u2014an important status symbol).\\nEventually, the Normans merged with the natives, combining languages and traditions. In the course of the Hundred Years' War, the Norman aristocracy often identified themselves as English. The Anglo-Norman language became distinct from the Latin language, something that was the subject of some humour by Geoffrey Chaucer. The Anglo-Norman language was eventually absorbed into the Anglo-Saxon language of their subjects (see Old English) and influenced it, helping (along with the Norse language of the earlier Anglo-Norse settlers and the Latin used by the church) in the development of Middle English. It in turn evolved into Modern English.\\nThe Normans had a profound effect on Irish culture and history after their invasion at Bannow Bay in 1169. Initially the Normans maintained a distinct culture and ethnicity. Yet, with time, they came to be subsumed into Irish culture to the point that it has been said that they became \\\"more Irish than the Irish themselves.\\\" The Normans settled mostly in an area in the east of Ireland, later known as the Pale, and also built many fine castles and settlements, including Trim Castle and Dublin Castle. Both cultures intermixed, borrowing from each other's language, culture and outlook. Norman descendants today can be recognised by their surnames. Names such as French, (De) Roche, Devereux, D'Arcy, Treacy and Lacy are particularly common in the southeast of Ireland, especially in the southern part of County Wexford where the first Norman settlements were established. Other Norman names such as Furlong predominate there. Another common Norman-Irish name was Morell (Murrell) derived from the French Norman name Morel. Other names beginning with Fitz (from the Norman for son) indicate Norman ancestry. These included Fitzgerald, FitzGibbons (Gibbons) dynasty, Fitzmaurice. Other families bearing such surnames as Barry (de Barra) and De B\\u00farca (Burke) are also of Norman extraction.\\nOne of the claimants of the English throne opposing William the Conqueror, Edgar Atheling, eventually fled to Scotland. King Malcolm III of Scotland married Edgar's sister Margaret, and came into opposition to William who had already disputed Scotland's southern borders. William invaded Scotland in 1072, riding as far as Abernethy where he met up with his fleet of ships. Malcolm submitted, paid homage to William and surrendered his son Duncan as a hostage, beginning a series of arguments as to whether the Scottish Crown owed allegiance to the King of England.\\nNormans came into Scotland, building castles and founding noble families who would provide some future kings, such as Robert the Bruce, as well as founding a considerable number of the Scottish clans. King David I of Scotland, whose elder brother Alexander I had married Sybilla of Normandy, was instrumental in introducing Normans and Norman culture to Scotland, part of the process some scholars call the \\\"Davidian Revolution\\\". Having spent time at the court of Henry I of England (married to David's sister Maud of Scotland), and needing them to wrestle the kingdom from his half-brother M\\u00e1el Coluim mac Alaxandair, David had to reward many with lands. The process was continued under David's successors, most intensely of all under William the Lion. The Norman-derived feudal system was applied in varying degrees to most of Scotland. Scottish families of the names Bruce, Gray, Ramsay, Fraser, Ogilvie, Montgomery, Sinclair, Pollock, Burnard, Douglas and Gordon to name but a few, and including the later royal House of Stewart, can all be traced back to Norman ancestry.\\nEven before the Norman Conquest of England, the Normans had come into contact with Wales. Edward the Confessor had set up the aforementioned Ralph as earl of Hereford and charged him with defending the Marches and warring with the Welsh. In these original ventures, the Normans failed to make any headway into Wales.\\nSubsequent to the Conquest, however, the Marches came completely under the dominance of William's most trusted Norman barons, including Bernard de Neufmarch\\u00e9, Roger of Montgomery in Shropshire and Hugh Lupus in Cheshire. These Normans began a long period of slow conquest during which almost all of Wales was at some point subject to Norman interference. Norman words, such as baron (barwn), first entered Welsh at that time.\\nThe legendary religious zeal of the Normans was exercised in religious wars long before the First Crusade carved out a Norman principality in Antioch. They were major foreign participants in the Reconquista in Iberia. In 1018, Roger de Tosny travelled to the Iberian Peninsula to carve out a state for himself from Moorish lands, but failed. In 1064, during the War of Barbastro, William of Montreuil led the papal army and took a huge booty.\\nIn 1096, Crusaders passing by the siege of Amalfi were joined by Bohemond of Taranto and his nephew Tancred with an army of Italo-Normans. Bohemond was the de facto leader of the Crusade during its passage through Asia Minor. After the successful Siege of Antioch in 1097, Bohemond began carving out an independent principality around that city. Tancred was instrumental in the conquest of Jerusalem and he worked for the expansion of the Crusader kingdom in Transjordan and the region of Galilee.[citation needed]\\nThe conquest of Cyprus by the Anglo-Norman forces of the Third Crusade opened a new chapter in the history of the island, which would be under Western European domination for the following 380 years. Although not part of a planned operation, the conquest had much more permanent results than initially expected.\\nIn April 1191 Richard the Lion-hearted left Messina with a large fleet in order to reach Acre. But a storm dispersed the fleet. After some searching, it was discovered that the boat carrying his sister and his fianc\\u00e9e Berengaria was anchored on the south coast of Cyprus, together with the wrecks of several other ships, including the treasure ship. Survivors of the wrecks had been taken prisoner by the island's despot Isaac Komnenos. On 1 May 1191, Richard's fleet arrived in the port of Limassol on Cyprus. He ordered Isaac to release the prisoners and the treasure. Isaac refused, so Richard landed his troops and took Limassol.\\nVarious princes of the Holy Land arrived in Limassol at the same time, in particular Guy de Lusignan. All declared their support for Richard provided that he support Guy against his rival Conrad of Montferrat. The local barons abandoned Isaac, who considered making peace with Richard, joining him on the crusade, and offering his daughter in marriage to the person named by Richard. But Isaac changed his mind and tried to escape. Richard then proceeded to conquer the whole island, his troops being led by Guy de Lusignan. Isaac surrendered and was confined with silver chains, because Richard had promised that he would not place him in irons. By 1 June, Richard had conquered the whole island. His exploit was well publicized and contributed to his reputation; he also derived significant financial gains from the conquest of the island. Richard left for Acre on 5 June, with his allies. Before his departure, he named two of his Norman generals, Richard de Camville and Robert de Thornham, as governors of Cyprus.\\nBetween 1402 and 1405, the expedition led by the Norman noble Jean de Bethencourt and the Poitevine Gadifer de la Salle conquered the Canarian islands of Lanzarote, Fuerteventura and El Hierro off the Atlantic coast of Africa. Their troops were gathered in Normandy, Gascony and were later reinforced by Castilian colonists.\\nBethencourt took the title of King of the Canary Islands, as vassal to Henry III of Castile. In 1418, Jean's nephew Maciot de Bethencourt sold the rights to the islands to Enrique P\\u00e9rez de Guzm\\u00e1n, 2nd Count de Niebla.\\nThe customary law of Normandy was developed between the 10th and 13th centuries and survives today through the legal systems of Jersey and Guernsey in the Channel Islands. Norman customary law was transcribed in two customaries in Latin by two judges for use by them and their colleagues: These are the Tr\\u00e8s ancien coutumier (Very ancient customary), authored between 1200 and 1245; and the Grand coutumier de Normandie (Great customary of Normandy, originally Summa de legibus Normanniae in curia la\\u00efcali), authored between 1235 and 1245.\\nNorman architecture typically stands out as a new stage in the architectural history of the regions they subdued. They spread a unique Romanesque idiom to England and Italy, and the encastellation of these regions with keeps in their north French style fundamentally altered the military landscape. Their style was characterised by rounded arches, particularly over windows and doorways, and massive proportions.\\nIn England, the period of Norman architecture immediately succeeds that of the Anglo-Saxon and precedes the Early Gothic. In southern Italy, the Normans incorporated elements of Islamic, Lombard, and Byzantine building techniques into their own, initiating a unique style known as Norman-Arab architecture within the Kingdom of Sicily.\\nIn the visual arts, the Normans did not have the rich and distinctive traditions of the cultures they conquered. However, in the early 11th century the dukes began a programme of church reform, encouraging the Cluniac reform of monasteries and patronising intellectual pursuits, especially the proliferation of scriptoria and the reconstitution of a compilation of lost illuminated manuscripts. The church was utilised by the dukes as a unifying force for their disparate duchy. The chief monasteries taking part in this \\\"renaissance\\\" of Norman art and scholarship were Mont-Saint-Michel, F\\u00e9camp, Jumi\\u00e8ges, Bec, Saint-Ouen, Saint-Evroul, and Saint-Wandrille. These centres were in contact with the so-called \\\"Winchester school\\\", which channeled a pure Carolingian artistic tradition to Normandy. In the final decade of the 11th and first of the 12th century, Normandy experienced a golden age of illustrated manuscripts, but it was brief and the major scriptoria of Normandy ceased to function after the midpoint of the century.\\nThe French Wars of Religion in the 16th century and French Revolution in the 18th successively destroyed much of what existed in the way of the architectural and artistic remnant of this Norman creativity. The former, with their violence, caused the wanton destruction of many Norman edifices; the latter, with its assault on religion, caused the purposeful destruction of religious objects of any type, and its destabilisation of society resulted in rampant pillaging.\\nBy far the most famous work of Norman art is the Bayeux Tapestry, which is not a tapestry but a work of embroidery. It was commissioned by Odo, the Bishop of Bayeux and first Earl of Kent, employing natives from Kent who were learned in the Nordic traditions imported in the previous half century by the Danish Vikings.\\nIn Britain, Norman art primarily survives as stonework or metalwork, such as capitals and baptismal fonts. In southern Italy, however, Norman artwork survives plentifully in forms strongly influenced by its Greek, Lombard, and Arab forebears. Of the royal regalia preserved in Palermo, the crown is Byzantine in style and the coronation cloak is of Arab craftsmanship with Arabic inscriptions. Many churches preserve sculptured fonts, capitals, and more importantly mosaics, which were common in Norman Italy and drew heavily on the Greek heritage. Lombard Salerno was a centre of ivorywork in the 11th century and this continued under Norman domination. Finally should be noted the intercourse between French Crusaders traveling to the Holy Land who brought with them French artefacts with which to gift the churches at which they stopped in southern Italy amongst their Norman cousins. For this reason many south Italian churches preserve works from France alongside their native pieces.\\nNormandy was the site of several important developments in the history of classical music in the 11th century. F\\u00e9camp Abbey and Saint-Evroul Abbey were centres of musical production and education. At F\\u00e9camp, under two Italian abbots, William of Volpiano and John of Ravenna, the system of denoting notes by letters was developed and taught. It is still the most common form of pitch representation in English- and German-speaking countries today. Also at F\\u00e9camp, the staff, around which neumes were oriented, was first developed and taught in the 11th century. Under the German abbot Isembard, La Trinit\\u00e9-du-Mont became a centre of musical composition.\\nAt Saint Evroul, a tradition of singing had developed and the choir achieved fame in Normandy. Under the Norman abbot Robert de Grantmesnil, several monks of Saint-Evroul fled to southern Italy, where they were patronised by Robert Guiscard and established a Latin monastery at Sant'Eufemia. There they continued the tradition of singing.\\n\"",
                "display_name": "Text",
                "advanced": false,
                "input_types": [
                  "Message"
                ],
                "dynamic": false,
                "info": "Text to be passed as input.",
                "title_case": false,
                "type": "str",
                "_input_type": "MultilineInput"
              }
            },
            "description": "Get text inputs from the Playground.",
            "icon": "type",
            "base_classes": [
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            ],
            "display_name": "Text Input",
            "documentation": "",
            "minimized": false,
            "custom_fields": {},
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            "pinned": false,
            "conditional_paths": [],
            "frozen": false,
            "outputs": [
              {
                "types": [
                  "Message"
                ],
                "selected": "Message",
                "name": "text",
                "display_name": "Message",
                "method": "text_response",
                "value": "__UNDEFINED__",
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              }
            ],
            "field_order": [
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            "edited": false,
            "metadata": {},
            "tool_mode": false,
            "category": "inputs",
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