diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
index 097fdbbc2..c629cdb3e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
@@ -358,7 +358,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        client: Optional[Any] = None,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            client=client,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
+                                "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,
@@ -851,7 +851,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,
@@ -877,7 +877,7 @@
                                 "display_name": "Max Tokens",
                                 "advanced": true,
                                 "dynamic": false,
-                                "info": "",
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
                                 "load_from_db": false,
                                 "title_case": false
                             },
@@ -1106,7 +1106,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,
@@ -1491,7 +1491,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"path\": {\n                \"display_name\": \"Path\",\n                \"field_type\": \"file\",\n                \"file_types\": TEXT_FILE_TYPES,\n                \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n            },\n            \"silent_errors\": {\n                \"display_name\": \"Silent Errors\",\n                \"advanced\": True,\n                \"info\": \"If true, errors will not raise an exception.\",\n            },\n        }\n\n    def load_file(self, path: str, silent_errors: bool = False) -> Record:\n        resolved_path = self.resolve_path(path)\n        path_obj = Path(resolved_path)\n        extension = path_obj.suffix[1:].lower()\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n        record = parse_text_file_to_record(resolved_path, silent_errors)\n        self.status = record if record else \"No data\"\n        return record or Record()\n\n    def build(\n        self,\n        path: str,\n        silent_errors: bool = False,\n    ) -> Record:\n        record = self.load_file(path, silent_errors)\n        self.status = record\n        return record\n",
+                                "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"path\": {\n                \"display_name\": \"Path\",\n                \"field_type\": \"file\",\n                \"file_types\": TEXT_FILE_TYPES,\n                \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n            },\n            \"silent_errors\": {\n                \"display_name\": \"Silent Errors\",\n                \"advanced\": True,\n                \"info\": \"If true, errors will not raise an exception.\",\n            },\n        }\n\n    def load_file(self, path: str, silent_errors: bool = False) -> Record:\n        resolved_path = self.resolve_path(path)\n        path_obj = Path(resolved_path)\n        extension = path_obj.suffix[1:].lower()\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n        record = parse_text_file_to_record(resolved_path, silent_errors)\n        self.status = record if record else \"No data\"\n        return record or Record()\n\n    def build(\n        self,\n        path: str,\n        silent_errors: bool = False,\n    ) -> Record:\n        record = self.load_file(path, silent_errors)\n        self.status = record\n        return record\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,
@@ -1631,7 +1631,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from typing import Optional\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Input\",\n                \"info\": \"The texts to split.\",\n                \"input_types\": [\"Document\", \"Record\"],\n            },\n            \"separators\": {\n                \"display_name\": \"Separators\",\n                \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\n                \"display_name\": \"Chunk Size\",\n                \"info\": \"The maximum length of each chunk.\",\n                \"field_type\": \"int\",\n                \"value\": 1000,\n            },\n            \"chunk_overlap\": {\n                \"display_name\": \"Chunk Overlap\",\n                \"info\": \"The amount of overlap between chunks.\",\n                \"field_type\": \"int\",\n                \"value\": 200,\n            },\n            \"code\": {\"show\": False},\n        }\n\n    def build(\n        self,\n        inputs: list[Document],\n        separators: Optional[list[str]] = None,\n        chunk_size: Optional[int] = 1000,\n        chunk_overlap: Optional[int] = 200,\n    ) -> list[Record]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n            length_function (function): The function to use to calculate the length of the text.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if separators == \"\":\n            separators = None\n        elif separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            separators = [unescape_string(x) for x in separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(chunk_size, str):\n            chunk_size = int(chunk_size)\n        if isinstance(chunk_overlap, str):\n            chunk_overlap = int(chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=separators,\n            chunk_size=chunk_size,\n            chunk_overlap=chunk_overlap,\n        )\n        documents = []\n        for _input in inputs:\n            if isinstance(_input, Record):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        records = self.to_records(docs)\n        self.repr_value = build_loader_repr_from_records(records)\n        return records\n",
+                                "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Input\",\n                \"info\": \"The texts to split.\",\n                \"input_types\": [\"Document\", \"Record\"],\n            },\n            \"separators\": {\n                \"display_name\": \"Separators\",\n                \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\n                \"display_name\": \"Chunk Size\",\n                \"info\": \"The maximum length of each chunk.\",\n                \"field_type\": \"int\",\n                \"value\": 1000,\n            },\n            \"chunk_overlap\": {\n                \"display_name\": \"Chunk Overlap\",\n                \"info\": \"The amount of overlap between chunks.\",\n                \"field_type\": \"int\",\n                \"value\": 200,\n            },\n            \"code\": {\"show\": False},\n        }\n\n    def build(\n        self,\n        inputs: list[Document],\n        separators: Optional[list[str]] = None,\n        chunk_size: Optional[int] = 1000,\n        chunk_overlap: Optional[int] = 200,\n    ) -> list[Record]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n            length_function (function): The function to use to calculate the length of the text.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if separators == \"\":\n            separators = None\n        elif separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            separators = [unescape_string(x) for x in separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(chunk_size, str):\n            chunk_size = int(chunk_size)\n        if isinstance(chunk_overlap, str):\n            chunk_overlap = int(chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=separators,\n            chunk_size=chunk_size,\n            chunk_overlap=chunk_overlap,\n        )\n        documents = []\n        for _input in inputs:\n            if isinstance(_input, Record):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        records = self.to_records(docs)\n        self.repr_value = build_loader_repr_from_records(records)\n        return records\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,
@@ -2632,7 +2632,7 @@
                                 "list": false,
                                 "show": true,
                                 "multiline": true,
-                                "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        client: Optional[Any] = None,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            client=client,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
+                                "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
                                 "fileTypes": [],
                                 "file_path": "",
                                 "password": false,