fix: refactor bundle and component names (#9140)

* Changed Youtube to YouTube

* Updated AIML to AI/ML API

* Updated AI/ML Model name

* Changed Anthropic description

* updated arXiv name

* Set Astra Vectorize as legacy

* Updated get env var

* Updates firecrawl names

* Updated Icon for Google Gen AI embeddings

* Add Space on Home Assistant

* Updated Hugging Face name

* Updated Maritalk Name

* Updated Not Diamond name

* Updated ScrapeGraph names

* Changed SearchApi Name

* Changed TwelveLabs naming

* Updated AstraDB naming

* Updated VertexAI naming

* Updated Wolfram

* Updated Yahoo naming

* Updated Yahoo Finance name

* Updated Yahoo Finance

* [autofix.ci] apply automated fixes

* Update google serper bundle

* updated ai ml icon name

* Updated maritalk

* Changed ai ml api name

* removed openai from base url

* Revert components-bundles changes

* revert changes on components-vector-stores

* Revert changes on deployment-hugging-face-spaces

* Revert changes on integrations-nvidia-ingest

* Revert changes on release-notes

* Update changes on sequential agent

* [autofix.ci] apply automated fixes

* fixed filterSidebar test

* updated filter edge test

* updated shard 11 test with new sidebar names

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

.cursor/rules/icons.mdc

@@ -8,10 +8,10 @@ globs:
 alwaysApply: false
 ---
 
-
 # Component Icon Rules
 
 ## Purpose
+
 To ensure consistent, clear, and functional icon usage for components, covering both backend (Python) and frontend (React/TypeScript) steps.
 
 ---
@@ -36,17 +36,19 @@ To ensure consistent, clear, and functional icon usage for components, covering
 - **Where:**
   In a new directory for your icon, e.g., `src/frontend/src/icons/AstraDB/`.
 - **How:**
+
   - Add your SVG as a React component, e.g., `AstraSVG` in `AstraDB.jsx`.
     ```jsx
     const AstraSVG = (props) => (
       <svg {...props}>
         <path
-          // ...
+        // ...
         />
       </svg>
     );
     ```
   - Create an `index.tsx` that exports your icon using `forwardRef`:
+
     ```tsx
     import React, { forwardRef } from "react";
     import AstraSVG from "./AstraDB";
@@ -118,6 +120,7 @@ To ensure consistent, clear, and functional icon usage for components, covering
 ---
 
 **Example for AstraDB:**
+
 - Backend:
   ```python
   icon = "AstraDB"

src/backend/base/langflow/base/huggingface/model_bridge.py

@@ -3,19 +3,19 @@ from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolCall
 from langchain_core.tools import BaseTool
 
-# Import HuggingFace Model base
+# Import Hugging Face Model base
 from smolagents import Model, Tool
 from smolagents.models import ChatMessage, ChatMessageToolCall, ChatMessageToolCallDefinition
 
 
 def _lc_tool_call_to_hf_tool_call(tool_call: ToolCall) -> ChatMessageToolCall:
-    """Convert a LangChain ToolCall to a HuggingFace ChatMessageToolCall.
+    """Convert a LangChain ToolCall to a Hugging Face ChatMessageToolCall.
 
     Args:
         tool_call (ToolCall): LangChain tool call to convert
 
     Returns:
-        ChatMessageToolCall: Equivalent HuggingFace tool call
+        ChatMessageToolCall: Equivalent Hugging Face tool call
     """
     return ChatMessageToolCall(
         function=ChatMessageToolCallDefinition(name=tool_call.name, arguments=tool_call.args),
@@ -24,24 +24,24 @@ def _lc_tool_call_to_hf_tool_call(tool_call: ToolCall) -> ChatMessageToolCall:
 
 
 def _hf_tool_to_lc_tool(tool) -> BaseTool:
-    """Convert a HuggingFace Tool to a LangChain BaseTool.
+    """Convert a Hugging Face Tool to a LangChain BaseTool.
 
     Args:
-        tool (Tool): HuggingFace tool to convert
+        tool (Tool): Hugging Face tool to convert
 
     Returns:
         BaseTool: Equivalent LangChain tool
     """
     if not hasattr(tool, "langchain_tool"):
-        msg = "HuggingFace Tool does not have a langchain_tool attribute"
+        msg = "Hugging Face Tool does not have a langchain_tool attribute"
         raise ValueError(msg)
     return tool.langchain_tool
 
 
 class LangChainHFModel(Model):
-    """A class bridging HuggingFace's `Model` interface with a LangChain `BaseChatModel`.
+    """A class bridging Hugging Face's `Model` interface with a LangChain `BaseChatModel`.
 
-    This adapter allows using any LangChain chat model with the HuggingFace interface.
+    This adapter allows using any LangChain chat model with the Hugging Face interface.
     It handles conversion of message formats and tool calls between the two frameworks.
 
     Usage:
@@ -68,7 +68,7 @@ class LangChainHFModel(Model):
         tools_to_call_from: list[Tool] | None = None,
         **kwargs,
     ) -> ChatMessage:
-        """Process messages through the LangChain model and return HuggingFace format.
+        """Process messages through the LangChain model and return Hugging Face format.
 
         Args:
             messages: List of message dictionaries with 'role' and 'content' keys
@@ -78,7 +78,7 @@ class LangChainHFModel(Model):
             **kwargs: Additional arguments passed to the LangChain model
 
         Returns:
-            ChatMessage: Response in HuggingFace format
+            ChatMessage: Response in Hugging Face format
         """
         if grammar:
             msg = "Grammar is not yet supported."

src/backend/base/langflow/base/models/aiml_constants.py

@@ -17,10 +17,10 @@ class AimlModels:
                 response = client.get("https://api.aimlapi.com/models")
                 response.raise_for_status()
         except httpx.RequestError as e:
-            msg = "Failed to connect to the AIML API."
+            msg = "Failed to connect to the AI/ML API."
             raise APIConnectionError(msg) from e
         except httpx.HTTPStatusError as e:
-            msg = f"AIML API responded with status code: {e.response.status_code}"
+            msg = f"AI/ML API responded with status code: {e.response.status_code}"
             raise APIError(
                 message=msg,
                 body=None,
@@ -31,7 +31,7 @@ class AimlModels:
             models = response.json().get("data", [])
             self.separate_models_by_type(models)
         except (ValueError, KeyError, TypeError) as e:
-            msg = "Failed to parse response data from AIML API. The format may be incorrect."
+            msg = "Failed to parse response data from AI/ML API. The format may be incorrect."
             raise ValueError(msg) from e
 
     def separate_models_by_type(self, models):

src/backend/base/langflow/base/models/groq_constants.py

@@ -97,7 +97,7 @@ GROQ_MODELS_DETAILED = [
     create_model_metadata(  # OpenAI
         provider="Groq", name="whisper-large-v3-turbo", icon="Groq", not_supported=True
     ),
-    create_model_metadata(  # HuggingFace
+    create_model_metadata(  # Hugging Face
         provider="Groq", name="distil-whisper-large-v3-en", icon="Groq", not_supported=True
     ),
 ]

src/backend/base/langflow/components/aiml/aiml.py

@@ -17,8 +17,8 @@ from langflow.inputs.inputs import (
 
 
 class AIMLModelComponent(LCModelComponent):
-    display_name = "AIML"
+    display_name = "AI/ML API"
-    description = "Generates text using AIML LLMs."
+    description = "Generates text using AI/ML API LLMs."
     icon = "AIML"
     name = "AIMLModel"
     documentation = "https://docs.aimlapi.com/api-reference"
@@ -42,15 +42,15 @@ class AIMLModelComponent(LCModelComponent):
         ),
         StrInput(
             name="aiml_api_base",
-            display_name="AIML API Base",
+            display_name="AI/ML API Base",
             advanced=True,
-            info="The base URL of the OpenAI API. Defaults to https://api.aimlapi.com . "
+            info="The base URL of the API. Defaults to https://api.aimlapi.com . "
             "You can change this to use other APIs like JinaChat, LocalAI and Prem.",
         ),
         SecretStrInput(
             name="api_key",
-            display_name="AIML API Key",
+            display_name="AI/ML API Key",
-            info="The AIML API Key to use for the OpenAI model.",
+            info="The AI/ML API Key to use for the OpenAI model.",
             advanced=False,
             value="AIML_API_KEY",
             required=True,

src/backend/base/langflow/components/aiml/aiml_embeddings.py

@@ -6,9 +6,9 @@ from langflow.io import SecretStrInput
 
 
 class AIMLEmbeddingsComponent(LCEmbeddingsModel):
-    display_name = "AI/ML Embeddings"
+    display_name = "AI/ML API Embeddings"
     description = "Generate embeddings using the AI/ML API."
-    icon = "AI/ML"
+    icon = "AIML"
     name = "AIMLEmbeddings"
 
     inputs = [

src/backend/base/langflow/components/anthropic/anthropic.py

@@ -19,7 +19,7 @@ from langflow.schema.dotdict import dotdict
 
 class AnthropicModelComponent(LCModelComponent):
     display_name = "Anthropic"
-    description = "Generate text using Anthropic Chat&Completion LLMs with prefill support."
+    description = "Generate text using Anthropic's Messages API and models."
     icon = "Anthropic"
     name = "AnthropicModel"
 
@@ -74,9 +74,6 @@ class AnthropicModelComponent(LCModelComponent):
             value=False,
             real_time_refresh=True,
         ),
-        MessageTextInput(
-            name="prefill", display_name="Prefill", info="Prefill text to guide the model's response.", advanced=True
-        ),
     ]
 
     def build_model(self) -> LanguageModel:  # type: ignore[type-var]

src/backend/base/langflow/components/datastax/astra_vectorize.py

@@ -12,6 +12,7 @@ class AstraVectorizeComponent(Component):
         "This component is deprecated. Please use the Astra DB Component directly."
     )
     documentation: str = "https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html"
+    legacy = True
     icon = "AstraDB"
     name = "AstraVectorize"
 

src/backend/base/langflow/components/datastax/astradb_cql.py

@@ -277,7 +277,7 @@ class AstraDBCQLToolComponent(LCToolComponent):
             name (str, optional): The name of the tool.
 
         Returns:
-            Tool: The built AstraDB tool.
+            Tool: The built Astra DB tool.
         """
         schema_dict = self.create_args_schema()
         return StructuredTool.from_function(

src/backend/base/langflow/components/datastax/astradb_tool.py

@@ -268,7 +268,7 @@ class AstraDBToolComponent(LCToolComponent):
         return tool
 
     def projection_args(self, input_str: str) -> dict | None:
-        """Build the projection arguments for the AstraDB query."""
+        """Build the projection arguments for the Astra DB query."""
         elements = input_str.split(",")
         result = {}
 
@@ -329,7 +329,7 @@ class AstraDBToolComponent(LCToolComponent):
         raise ValueError(msg)
 
     def build_filter(self, args: dict, filter_settings: list) -> dict:
-        """Build filter dictionary for AstraDB query.
+        """Build filter dictionary for Astra DB query.
 
         Args:
             args: Dictionary of arguments from the tool
@@ -370,7 +370,7 @@ class AstraDBToolComponent(LCToolComponent):
         return filters
 
     def run_model(self, **args) -> Data | list[Data]:
-        """Run the query to get the data from the AstraDB collection."""
+        """Run the query to get the data from the Astra DB collection."""
         collection = self._build_collection()
         sort = {}
 

src/backend/base/langflow/components/datastax/cassandra.py

@@ -13,28 +13,28 @@ class CassandraChatMemory(LCChatMemoryComponent):
         MessageTextInput(
             name="database_ref",
             display_name="Contact Points / Astra Database ID",
-            info="Contact points for the database (or AstraDB database ID)",
+            info="Contact points for the database (or Astra DB database ID)",
             required=True,
         ),
         MessageTextInput(
-            name="username", display_name="Username", info="Username for the database (leave empty for AstraDB)."
+            name="username", display_name="Username", info="Username for the database (leave empty for Astra DB)."
         ),
         SecretStrInput(
             name="token",
-            display_name="Password / AstraDB Token",
+            display_name="Password / Astra DB Token",
-            info="User password for the database (or AstraDB token).",
+            info="User password for the database (or Astra DB token).",
             required=True,
         ),
         MessageTextInput(
             name="keyspace",
             display_name="Keyspace",
-            info="Table Keyspace (or AstraDB namespace).",
+            info="Table Keyspace (or Astra DB namespace).",
             required=True,
         ),
         MessageTextInput(
             name="table_name",
             display_name="Table Name",
-            info="The name of the table (or AstraDB collection) where vectors will be stored.",
+            info="The name of the table (or Astra DB collection) where vectors will be stored.",
             required=True,
         ),
         MessageTextInput(

src/backend/base/langflow/components/datastax/getenvvar.py

@@ -7,20 +7,20 @@ from langflow.template.field.base import Output
 
 
 class GetEnvVar(Component):
-    display_name = "Get env var"
+    display_name = "Get Environment Variable"
-    description = "Get env var"
+    description = "Gets the value of an environment variable from the system."
     icon = "AstraDB"
 
     inputs = [
         StrInput(
             name="env_var_name",
-            display_name="Env var name",
+            display_name="Environment Variable Name",
             info="Name of the environment variable to get",
         )
     ]
 
     outputs = [
-        Output(display_name="Env var value", name="env_var_value", method="process_inputs"),
+        Output(display_name="Environment Variable Value", name="env_var_value", method="process_inputs"),
     ]
 
     def process_inputs(self) -> Message:

src/backend/base/langflow/components/firecrawl/firecrawl_crawl_api.py

@@ -6,8 +6,8 @@ from langflow.schema.data import Data
 
 
 class FirecrawlCrawlApi(Component):
-    display_name: str = "FirecrawlCrawlApi"
+    display_name: str = "Firecrawl Crawl API"
-    description: str = "Firecrawl Crawl API."
+    description: str = "Crawls a URL and returns the results."
     name = "FirecrawlCrawlApi"
 
     documentation: str = "https://docs.firecrawl.dev/v1/api-reference/endpoint/crawl-post"

src/backend/base/langflow/components/firecrawl/firecrawl_extract_api.py

@@ -12,8 +12,8 @@ from langflow.schema.data import Data
 
 
 class FirecrawlExtractApi(Component):
-    display_name: str = "FirecrawlExtractApi"
+    display_name: str = "Firecrawl Extract API"
-    description: str = "Firecrawl Extract API."
+    description: str = "Extracts data from a URL."
     name = "FirecrawlExtractApi"
 
     documentation: str = "https://docs.firecrawl.dev/api-reference/endpoint/extract"

src/backend/base/langflow/components/firecrawl/firecrawl_map_api.py

@@ -9,8 +9,8 @@ from langflow.schema.data import Data
 
 
 class FirecrawlMapApi(Component):
-    display_name: str = "FirecrawlMapApi"
+    display_name: str = "Firecrawl Map API"
-    description: str = "Firecrawl Map API."
+    description: str = "Maps a URL and returns the results."
     name = "FirecrawlMapApi"
 
     documentation: str = "https://docs.firecrawl.dev/api-reference/endpoint/map"

src/backend/base/langflow/components/firecrawl/firecrawl_scrape_api.py

@@ -10,8 +10,8 @@ from langflow.schema.data import Data
 
 
 class FirecrawlScrapeApi(Component):
-    display_name: str = "FirecrawlScrapeApi"
+    display_name: str = "Firecrawl Scrape API"
-    description: str = "Firecrawl Scrape API."
+    description: str = "Scrapes a URL and returns the results."
     name = "FirecrawlScrapeApi"
 
     documentation: str = "https://docs.firecrawl.dev/api-reference/endpoint/scrape"

src/backend/base/langflow/components/google/google_generative_ai_embeddings.py

@@ -24,7 +24,7 @@ class GoogleGenerativeAIEmbeddingsComponent(Component):
         "found in the langchain-google-genai package."
     )
     documentation: str = "https://python.langchain.com/v0.2/docs/integrations/text_embedding/google_generative_ai/"
-    icon = "Google"
+    icon = "GoogleGenerativeAI"
     name = "Google Generative AI Embeddings"
 
     inputs = [

src/backend/base/langflow/components/homeassistant/list_home_assistant_states.py

@@ -12,7 +12,7 @@ from langflow.schema.data import Data
 
 
 class ListHomeAssistantStates(LCToolComponent):
-    display_name: str = "List HomeAssistant States"
+    display_name: str = "List Home Assistant States"
     description: str = (
         "Retrieve states from Home Assistant. "
         "The agent only needs to specify 'filter_domain' (optional). "

src/backend/base/langflow/components/huggingface/huggingface.py

@@ -16,7 +16,7 @@ DEFAULT_MODEL = "meta-llama/Llama-3.3-70B-Instruct"
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):
-    display_name: str = "HuggingFace"
+    display_name: str = "Hugging Face"
     description: str = "Generate text using Hugging Face Inference APIs."
     icon = "HuggingFace"
     name = "HuggingFaceModel"
@@ -26,7 +26,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
         DropdownInput(
             name="model_id",
             display_name="Model ID",
-            info="Select a model from HuggingFace Hub",
+            info="Select a model from Hugging Face Hub",
             options=[
                 DEFAULT_MODEL,
                 "mistralai/Mixtral-8x7B-Instruct-v0.1",
@@ -44,7 +44,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
         StrInput(
             name="custom_model",
             display_name="Custom Model ID",
-            info="Enter a custom model ID from HuggingFace Hub",
+            info="Enter a custom model ID from Hugging Face Hub",
             value="",
             show=False,
             required=True,
@@ -191,7 +191,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
                 repetition_penalty=repetition_penalty,
             )
         except Exception as e:
-            msg = "Could not connect to HuggingFace Endpoints API."
+            msg = "Could not connect to Hugging Face Endpoints API."
             raise ValueError(msg) from e
 
         return llm

src/backend/base/langflow/components/huggingface/huggingface_inference_api.py

@@ -13,8 +13,8 @@ from langflow.io import MessageTextInput, Output, SecretStrInput
 
 
 class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
-    display_name = "HuggingFace Embeddings Inference"
+    display_name = "Hugging Face Embeddings Inference"
-    description = "Generate embeddings using HuggingFace Text Embeddings Inference (TEI)"
+    description = "Generate embeddings using Hugging Face Text Embeddings Inference (TEI)"
     documentation = "https://huggingface.co/docs/text-embeddings-inference/index"
     icon = "HuggingFace"
     name = "HuggingFaceInferenceAPIEmbeddings"
@@ -66,7 +66,7 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
             raise ValueError(msg) from e
 
         if response.status_code != requests.codes.ok:
-            msg = f"HuggingFace health check failed: {response.status_code}"
+            msg = f"Hugging Face health check failed: {response.status_code}"
             raise ValueError(msg)
         # returning True to solve linting error
         return True
@@ -102,5 +102,5 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(LCEmbeddingsModel):
         try:
             return self.create_huggingface_embeddings(api_key, api_url, self.model_name)
         except Exception as e:
-            msg = "Could not connect to HuggingFace Inference API."
+            msg = "Could not connect to Hugging Face Inference API."
             raise ValueError(msg) from e

src/backend/base/langflow/components/langchain_utilities/character.py

@@ -8,7 +8,7 @@ from langflow.utils.util import unescape_string
 
 
 class CharacterTextSplitterComponent(LCTextSplitterComponent):
-    display_name = "CharacterTextSplitter"
+    display_name = "Character Text Splitter"
     description = "Split text by number of characters."
     documentation = "https://docs.langflow.org/components/text-splitters#charactertextsplitter"
     name = "CharacterTextSplitter"

src/backend/base/langflow/components/langchain_utilities/csv_agent.py

@@ -14,7 +14,7 @@ from langflow.template.field.base import Output
 
 
 class CSVAgentComponent(LCAgentComponent):
-    display_name = "CSVAgent"
+    display_name = "CSV Agent"
     description = "Construct a CSV agent from a CSV and tools."
     documentation = "https://python.langchain.com/docs/modules/agents/toolkits/csv"
     name = "CSVAgent"

src/backend/base/langflow/components/maritalk/maritalk.py

@@ -7,8 +7,8 @@ from langflow.inputs.inputs import DropdownInput, FloatInput, IntInput, SecretSt
 
 
 class MaritalkModelComponent(LCModelComponent):
-    display_name = "Maritalk"
+    display_name = "MariTalk"
-    description = "Generates text using Maritalk LLMs."
+    description = "Generates text using MariTalk LLMs."
     icon = "Maritalk"
     name = "Maritalk"
     inputs = [
@@ -29,8 +29,8 @@ class MaritalkModelComponent(LCModelComponent):
         ),
         SecretStrInput(
             name="api_key",
-            display_name="Maritalk API Key",
+            display_name="MariTalk API Key",
-            info="The Maritalk API Key to use for the OpenAI model.",
+            info="The MariTalk API Key to use for authentication.",
             advanced=False,
         ),
         FloatInput(name="temperature", display_name="Temperature", value=0.1, range_spec=RangeSpec(min=0, max=1)),

src/backend/base/langflow/components/scrapegraph/scrapegraph_markdownify_api.py

@@ -8,14 +8,12 @@ from langflow.schema.data import Data
 
 
 class ScrapeGraphMarkdownifyApi(Component):
-    display_name: str = "ScrapeGraphMarkdownifyApi"
+    display_name: str = "ScrapeGraph Markdownify API"
-    description: str = """ScrapeGraph Markdownify API.
+    description: str = "Given a URL, it will return the markdownified content of the website."
-    Given a URL, it will return the markdownified content of the website.
-    More info at https://docs.scrapegraphai.com/services/markdownify"""
     name = "ScrapeGraphMarkdownifyApi"
 
     output_types: list[str] = ["Document"]
-    documentation: str = "https://docs.scrapegraphai.com/introduction"
+    documentation: str = "https://docs.scrapegraphai.com/services/markdownify"
 
     inputs = [
         SecretStrInput(

src/backend/base/langflow/components/scrapegraph/scrapegraph_search_api.py

@@ -8,13 +8,11 @@ from langflow.schema.data import Data
 
 
 class ScrapeGraphSearchApi(Component):
-    display_name: str = "ScrapeGraphSearchApi"
+    display_name: str = "ScrapeGraph Search API"
-    description: str = """ScrapeGraph Search API.
+    description: str = "Given a search prompt, it will return search results using ScrapeGraph's search functionality."
-    Given a search prompt, it will return search results using ScrapeGraph's search functionality.
-    More info at https://docs.scrapegraphai.com/services/searchscraper"""
     name = "ScrapeGraphSearchApi"
 
-    documentation: str = "https://docs.scrapegraphai.com/introduction"
+    documentation: str = "https://docs.scrapegraphai.com/services/searchscraper"
     icon = "ScrapeGraph"
 
     inputs = [

src/backend/base/langflow/components/scrapegraph/scrapegraph_smart_scraper_api.py

@@ -8,14 +8,12 @@ from langflow.schema.data import Data
 
 
 class ScrapeGraphSmartScraperApi(Component):
-    display_name: str = "ScrapeGraphSmartScraperApi"
+    display_name: str = "ScrapeGraph Smart Scraper API"
-    description: str = """ScrapeGraph Smart Scraper API.
+    description: str = "Given a URL, it will return the structured data of the website."
-    Given a URL, it will return the structured data of the website.
-    More info at https://docs.scrapegraphai.com/services/smartscraper"""
     name = "ScrapeGraphSmartScraperApi"
 
     output_types: list[str] = ["Document"]
-    documentation: str = "https://docs.scrapegraphai.com/introduction"
+    documentation: str = "https://docs.scrapegraphai.com/services/smartscraper"
 
     inputs = [
         SecretStrInput(

src/backend/base/langflow/components/searchapi/search.py

@@ -10,8 +10,8 @@ from langflow.schema.dataframe import DataFrame
 
 
 class SearchComponent(Component):
-    display_name: str = "Search API"
+    display_name: str = "SearchApi"
-    description: str = "Call the searchapi.io API with result limiting"
+    description: str = "Calls the SearchApi API with result limiting. Supports Google, Bing and DuckDuckGo."
     documentation: str = "https://www.searchapi.io/docs/google"
     icon = "SearchAPI"
 

src/backend/base/langflow/components/serper/__init__.py

@@ -0,0 +1,3 @@
+from .google_serper_api_core import GoogleSerperAPICore
+
+__all__ = ["GoogleSerperAPICore"]

src/backend/base/langflow/components/serper/google_serper_api_core.py

@@ -0,0 +1,74 @@
+from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+
+from langflow.custom.custom_component.component import Component
+from langflow.io import IntInput, MultilineInput, Output, SecretStrInput
+from langflow.schema.dataframe import DataFrame
+from langflow.schema.message import Message
+
+
+class GoogleSerperAPICore(Component):
+    display_name = "Serper Google Search API"
+    description = "Calls the Serper.dev Google Search API and fetches the results."
+    icon = "Serper"
+
+    inputs = [
+        SecretStrInput(
+            name="serper_api_key",
+            display_name="Serper API Key",
+            required=True,
+        ),
+        MultilineInput(
+            name="input_value",
+            display_name="Input",
+            tool_mode=True,
+        ),
+        IntInput(
+            name="k",
+            display_name="Number of results",
+            value=4,
+            required=True,
+        ),
+    ]
+
+    outputs = [
+        Output(
+            display_name="Results",
+            name="results",
+            type_=DataFrame,
+            method="search_serper",
+        ),
+    ]
+
+    def search_serper(self) -> DataFrame:
+        try:
+            wrapper = self._build_wrapper()
+            results = wrapper.results(query=self.input_value)
+            list_results = results.get("organic", [])
+
+            # Convert results to DataFrame using list comprehension
+            df_data = [
+                {
+                    "title": result.get("title", ""),
+                    "link": result.get("link", ""),
+                    "snippet": result.get("snippet", ""),
+                }
+                for result in list_results
+            ]
+
+            return DataFrame(df_data)
+        except (ValueError, KeyError, ConnectionError) as e:
+            error_message = f"Error occurred while searching: {e!s}"
+            self.status = error_message
+            # Return DataFrame with error as a list of dictionaries
+            return DataFrame([{"error": error_message}])
+
+    def text_search_serper(self) -> Message:
+        search_results = self.search_serper()
+        text_result = search_results.to_string(index=False) if not search_results.empty else "No results found."
+        return Message(text=text_result)
+
+    def _build_wrapper(self):
+        return GoogleSerperAPIWrapper(serper_api_key=self.serper_api_key, k=self.k)
+
+    def build(self):
+        return self.search_serper

src/backend/base/langflow/components/tools/yahoo_finance.py

@@ -49,9 +49,9 @@ class YahooFinanceSchema(BaseModel):
 
 
 class YfinanceToolComponent(LCToolComponent):
-    display_name = "Yahoo Finance [DEPRECATED]"
+    display_name = "Yahoo! Finance [DEPRECATED]"
     description = """Uses [yfinance](https://pypi.org/project/yfinance/) (unofficial package) \
-to access financial data and market information from Yahoo Finance."""
+to access financial data and market information from Yahoo! Finance."""
     icon = "trending-up"
     name = "YahooFinanceTool"
     legacy = True
@@ -87,7 +87,7 @@ to access financial data and market information from Yahoo Finance."""
     def build_tool(self) -> Tool:
         return StructuredTool.from_function(
             name="yahoo_finance",
-            description="Access financial data and market information from Yahoo Finance.",
+            description="Access financial data and market information from Yahoo! Finance.",
             func=self._yahoo_finance_tool,
             args_schema=YahooFinanceSchema,
         )

src/backend/base/langflow/components/twelvelabs/convert_astra_results.py

@@ -7,10 +7,10 @@ from langflow.schema.message import Message
 
 
 class ConvertAstraToTwelveLabs(Component):
-    """Convert AstraDB search results to TwelveLabs Pegasus inputs."""
+    """Convert Astra DB search results to TwelveLabs Pegasus inputs."""
 
-    display_name = "Convert AstraDB to Pegasus Input"
+    display_name = "Convert Astra DB to Pegasus Input"
-    description = "Converts AstraDB search results to inputs compatible with TwelveLabs Pegasus."
+    description = "Converts Astra DB search results to inputs compatible with TwelveLabs Pegasus."
     icon = "TwelveLabs"
     name = "ConvertAstraToTwelveLabs"
     documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md"
@@ -18,9 +18,9 @@ class ConvertAstraToTwelveLabs(Component):
     inputs = [
         HandleInput(
             name="astra_results",
-            display_name="AstraDB Results",
+            display_name="Astra DB Results",
             input_types=["Data"],
-            info="Search results from AstraDB component",
+            info="Search results from Astra DB component",
             required=True,
             is_list=True,
         )
@@ -47,7 +47,7 @@ class ConvertAstraToTwelveLabs(Component):
         self._index_id = None
 
     def build(self, **kwargs: Any) -> None:  # noqa: ARG002 - Required for parent class compatibility
-        """Process the AstraDB results and extract TwelveLabs index information."""
+        """Process the Astra DB results and extract TwelveLabs index information."""
         if not self.astra_results:
             return
 

src/backend/base/langflow/components/twelvelabs/pegasus_index.py

@@ -13,7 +13,7 @@ from langflow.schema import Data
 
 
 class TwelveLabsError(Exception):
-    """Base exception for Twelve Labs errors."""
+    """Base exception for TwelveLabs errors."""
 
 
 class IndexCreationError(TwelveLabsError):
@@ -29,10 +29,10 @@ class TaskTimeoutError(TwelveLabsError):
 
 
 class PegasusIndexVideo(Component):
-    """Indexes videos using Twelve Labs Pegasus API and adds the video ID to metadata."""
+    """Indexes videos using TwelveLabs Pegasus API and adds the video ID to metadata."""
 
-    display_name = "Twelve Labs Pegasus Index Video"
+    display_name = "TwelveLabs Pegasus Index Video"
-    description = "Index videos using Twelve Labs and add the video_id to metadata."
+    description = "Index videos using TwelveLabs and add the video_id to metadata."
     icon = "TwelveLabs"
     name = "TwelveLabsPegasusIndexVideo"
     documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md"
@@ -46,7 +46,7 @@ class PegasusIndexVideo(Component):
             required=True,
         ),
         SecretStrInput(
-            name="api_key", display_name="Twelve Labs API Key", info="Enter your Twelve Labs API Key.", required=True
+            name="api_key", display_name="TwelveLabs API Key", info="Enter your TwelveLabs API Key.", required=True
         ),
         DropdownInput(
             name="model_name",
@@ -215,7 +215,7 @@ class PegasusIndexVideo(Component):
             return []
 
         if not self.api_key:
-            error_msg = "Twelve Labs API Key is required"
+            error_msg = "TwelveLabs API Key is required"
             raise IndexCreationError(error_msg)
 
         if not (hasattr(self, "index_name") and self.index_name) and not (hasattr(self, "index_id") and self.index_id):
@@ -230,7 +230,7 @@ class PegasusIndexVideo(Component):
             index_id, index_name = self._get_or_create_index(client)
             self.status = f"Using index: {index_name} (ID: {index_id})"
         except IndexCreationError as e:
-            self.status = f"Failed to get/create Twelve Labs index: {e!s}"
+            self.status = f"Failed to get/create TwelveLabs index: {e!s}"
             raise
 
         # First, validate all videos and create a list of valid ones

src/backend/base/langflow/components/twelvelabs/text_embeddings.py

@@ -34,14 +34,14 @@ class TwelveLabsTextEmbeddings(Embeddings):
 
 
 class TwelveLabsTextEmbeddingsComponent(LCEmbeddingsModel):
-    display_name = "Twelve Labs Text Embeddings"
+    display_name = "TwelveLabs Text Embeddings"
-    description = "Generate embeddings using Twelve Labs text embedding models."
+    description = "Generate embeddings using TwelveLabs text embedding models."
     icon = "TwelveLabs"
     name = "TwelveLabsTextEmbeddings"
     documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md"
 
     inputs = [
-        SecretStrInput(name="api_key", display_name="Twelve Labs API Key", value="TWELVELABS_API_KEY", required=True),
+        SecretStrInput(name="api_key", display_name="TwelveLabs API Key", value="TWELVELABS_API_KEY", required=True),
         DropdownInput(
             name="model",
             display_name="Model",

src/backend/base/langflow/components/twelvelabs/twelvelabs_pegasus.py

@@ -35,8 +35,8 @@ class VideoValidationError(Exception):
 
 
 class TwelveLabsPegasus(Component):
-    display_name = "Twelve Labs Pegasus"
+    display_name = "TwelveLabs Pegasus"
-    description = "Chat with videos using Twelve Labs Pegasus API."
+    description = "Chat with videos using TwelveLabs Pegasus API."
     icon = "TwelveLabs"
     name = "TwelveLabsPegasus"
     documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md"
@@ -44,7 +44,7 @@ class TwelveLabsPegasus(Component):
     inputs = [
         DataInput(name="videodata", display_name="Video Data", info="Video Data", is_list=True),
         SecretStrInput(
-            name="api_key", display_name="Twelve Labs API Key", info="Enter your Twelve Labs API Key.", required=True
+            name="api_key", display_name="TwelveLabs API Key", info="Enter your TwelveLabs API Key.", required=True
         ),
         MessageInput(
             name="video_id",
@@ -306,7 +306,7 @@ class TwelveLabsPegasus(Component):
     def process_video(self) -> Message:
         """Process video using Pegasus and generate response if message is provided.
 
-        Handles video indexing and question answering using the Twelve Labs API.
+        Handles video indexing and question answering using the TwelveLabs API.
         """
         # Check and initialize inputs
         if hasattr(self, "index_id") and self.index_id:

src/backend/base/langflow/components/twelvelabs/video_embeddings.py

@@ -79,8 +79,8 @@ class TwelveLabsVideoEmbeddings(Embeddings):
 
 
 class TwelveLabsVideoEmbeddingsComponent(LCEmbeddingsModel):
-    display_name = "Twelve Labs Video Embeddings"
+    display_name = "TwelveLabs Video Embeddings"
-    description = "Generate embeddings from videos using Twelve Labs video embedding models."
+    description = "Generate embeddings from videos using TwelveLabs video embedding models."
     name = "TwelveLabsVideoEmbeddings"
     icon = "TwelveLabs"
     documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md"

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

@@ -24,28 +24,28 @@ class CassandraVectorStoreComponent(LCVectorStoreComponent):
         MessageTextInput(
             name="database_ref",
             display_name="Contact Points / Astra Database ID",
-            info="Contact points for the database (or AstraDB database ID)",
+            info="Contact points for the database (or Astra DB database ID)",
             required=True,
         ),
         MessageTextInput(
-            name="username", display_name="Username", info="Username for the database (leave empty for AstraDB)."
+            name="username", display_name="Username", info="Username for the database (leave empty for Astra DB)."
         ),
         SecretStrInput(
             name="token",
-            display_name="Password / AstraDB Token",
+            display_name="Password / Astra DB Token",
-            info="User password for the database (or AstraDB token).",
+            info="User password for the database (or Astra DB token).",
             required=True,
         ),
         MessageTextInput(
             name="keyspace",
             display_name="Keyspace",
-            info="Table Keyspace (or AstraDB namespace).",
+            info="Table Keyspace (or Astra DB namespace).",
             required=True,
         ),
         MessageTextInput(
             name="table_name",
             display_name="Table Name",
-            info="The name of the table (or AstraDB collection) where vectors will be stored.",
+            info="The name of the table (or Astra DB collection) where vectors will be stored.",
             required=True,
         ),
         IntInput(

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

@@ -25,28 +25,28 @@ class CassandraGraphVectorStoreComponent(LCVectorStoreComponent):
         MessageTextInput(
             name="database_ref",
             display_name="Contact Points / Astra Database ID",
-            info="Contact points for the database (or AstraDB database ID)",
+            info="Contact points for the database (or Astra DB database ID)",
             required=True,
         ),
         MessageTextInput(
-            name="username", display_name="Username", info="Username for the database (leave empty for AstraDB)."
+            name="username", display_name="Username", info="Username for the database (leave empty for Astra DB)."
         ),
         SecretStrInput(
             name="token",
-            display_name="Password / AstraDB Token",
+            display_name="Password / Astra DB Token",
-            info="User password for the database (or AstraDB token).",
+            info="User password for the database (or Astra DB token).",
             required=True,
         ),
         MessageTextInput(
             name="keyspace",
             display_name="Keyspace",
-            info="Table Keyspace (or AstraDB namespace).",
+            info="Table Keyspace (or Astra DB namespace).",
             required=True,
         ),
         MessageTextInput(
             name="table_name",
             display_name="Table Name",
-            info="The name of the table (or AstraDB collection) where vectors will be stored.",
+            info="The name of the table (or Astra DB collection) where vectors will be stored.",
             required=True,
         ),
         DropdownInput(

src/backend/base/langflow/components/vertexai/vertexai_embeddings.py

@@ -4,8 +4,8 @@ from langflow.io import BoolInput, FileInput, FloatInput, IntInput, MessageTextI
 
 
 class VertexAIEmbeddingsComponent(LCModelComponent):
-    display_name = "VertexAI Embeddings"
+    display_name = "Vertex AI Embeddings"
-    description = "Generate embeddings using Google Cloud VertexAI models."
+    description = "Generate embeddings using Google Cloud Vertex AI models."
     icon = "VertexAI"
     name = "VertexAIEmbeddings"
 

src/backend/base/langflow/components/wolframalpha/wolfram_alpha_api.py

@@ -10,7 +10,7 @@ from langflow.schema.dataframe import DataFrame
 
 class WolframAlphaAPIComponent(LCToolComponent):
     display_name = "WolframAlpha API"
-    description = """Enables queries to Wolfram Alpha for computational data, facts, and calculations across various \
+    description = """Enables queries to WolframAlpha for computational data, facts, and calculations across various \
 topics, delivering structured responses."""
     name = "WolframAlphaAPI"
 
@@ -45,7 +45,7 @@ topics, delivering structured responses."""
         return data
 
     def fetch_content_dataframe(self) -> DataFrame:
-        """Convert the Wolfram Alpha results to a DataFrame.
+        """Convert the WolframAlpha results to a DataFrame.
 
         Returns:
             DataFrame: A DataFrame containing the query results.

src/backend/base/langflow/components/yahoosearch/yahoo.py

@@ -49,9 +49,9 @@ class YahooFinanceSchema(BaseModel):
 
 
 class YfinanceComponent(Component):
-    display_name = "Yahoo Finance"
+    display_name = "Yahoo! Finance"
     description = """Uses [yfinance](https://pypi.org/project/yfinance/) (unofficial package) \
-to access financial data and market information from Yahoo Finance."""
+to access financial data and market information from Yahoo! Finance."""
     icon = "trending-up"
 
     inputs = [

src/backend/base/langflow/components/youtube/playlist.py

@@ -8,7 +8,7 @@ from langflow.template.field.base import Output
 
 
 class YouTubePlaylistComponent(Component):
-    display_name = "Youtube Playlist"
+    display_name = "YouTube Playlist"
     description = "Extracts all video URLs from a YouTube playlist."
     icon = "YouTube"  # Replace with a suitable icon
 

src/backend/base/langflow/initial_setup/starter_projects/Search agent.json

@@ -90,7 +90,7 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "ScrapeGraph Search API.\n    Given a search prompt, it will return search results using ScrapeGraph's search functionality.\n    More info at https://docs.scrapegraphai.com/services/searchscraper",
+            "description": "Given a search prompt, it will return search results using ScrapeGraph's search functionality.",
             "display_name": "ScrapeGraphSearchApi",
             "documentation": "https://docs.scrapegraphai.com/introduction",
             "edited": false,
@@ -103,7 +103,7 @@
             "legacy": false,
             "lf_version": "1.1.5",
             "metadata": {
-              "code_hash": "cdea312e9de9",
+              "code_hash": "99b8b89dc4ca",
               "module": "langflow.components.scrapegraph.scrapegraph_search_api.ScrapeGraphSearchApi"
             },
             "minimized": false,
@@ -163,7 +163,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom.custom_component.component import Component\nfrom langflow.io import (\n    MessageTextInput,\n    Output,\n    SecretStrInput,\n)\nfrom langflow.schema.data import Data\n\n\nclass ScrapeGraphSearchApi(Component):\n    display_name: str = \"ScrapeGraphSearchApi\"\n    description: str = \"\"\"ScrapeGraph Search API.\n    Given a search prompt, it will return search results using ScrapeGraph's search functionality.\n    More info at https://docs.scrapegraphai.com/services/searchscraper\"\"\"\n    name = \"ScrapeGraphSearchApi\"\n\n    documentation: str = \"https://docs.scrapegraphai.com/introduction\"\n    icon = \"ScrapeGraph\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"ScrapeGraph API Key\",\n            required=True,\n            password=True,\n            info=\"The API key to use ScrapeGraph API.\",\n        ),\n        MessageTextInput(\n            name=\"user_prompt\",\n            display_name=\"Search Prompt\",\n            tool_mode=True,\n            info=\"The search prompt to use.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"search\"),\n    ]\n\n    def search(self) -> list[Data]:\n        try:\n            from scrapegraph_py import Client\n            from scrapegraph_py.logger import sgai_logger\n        except ImportError as e:\n            msg = \"Could not import scrapegraph-py package. Please install it with `pip install scrapegraph-py`.\"\n            raise ImportError(msg) from e\n\n        # Set logging level\n        sgai_logger.set_logging(level=\"INFO\")\n\n        # Initialize the client with API key\n        sgai_client = Client(api_key=self.api_key)\n\n        try:\n            # SearchScraper request\n            response = sgai_client.searchscraper(\n                user_prompt=self.user_prompt,\n            )\n\n            # Close the client\n            sgai_client.close()\n\n            return Data(data=response)\n        except Exception:\n            sgai_client.close()\n            raise\n"
+                "value": "from langflow.custom.custom_component.component import Component\nfrom langflow.io import (\n    MessageTextInput,\n    Output,\n    SecretStrInput,\n)\nfrom langflow.schema.data import Data\n\n\nclass ScrapeGraphSearchApi(Component):\n    display_name: str = \"ScrapeGraph Search API\"\n    description: str = \"Given a search prompt, it will return search results using ScrapeGraph's search functionality.\"\n    name = \"ScrapeGraphSearchApi\"\n\n    documentation: str = \"https://docs.scrapegraphai.com/services/searchscraper\"\n    icon = \"ScrapeGraph\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"ScrapeGraph API Key\",\n            required=True,\n            password=True,\n            info=\"The API key to use ScrapeGraph API.\",\n        ),\n        MessageTextInput(\n            name=\"user_prompt\",\n            display_name=\"Search Prompt\",\n            tool_mode=True,\n            info=\"The search prompt to use.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"search\"),\n    ]\n\n    def search(self) -> list[Data]:\n        try:\n            from scrapegraph_py import Client\n            from scrapegraph_py.logger import sgai_logger\n        except ImportError as e:\n            msg = \"Could not import scrapegraph-py package. Please install it with `pip install scrapegraph-py`.\"\n            raise ImportError(msg) from e\n\n        # Set logging level\n        sgai_logger.set_logging(level=\"INFO\")\n\n        # Initialize the client with API key\n        sgai_client = Client(api_key=self.api_key)\n\n        try:\n            # SearchScraper request\n            response = sgai_client.searchscraper(\n                user_prompt=self.user_prompt,\n            )\n\n            # Close the client\n            sgai_client.close()\n\n            return Data(data=response)\n        except Exception:\n            sgai_client.close()\n            raise\n"
               },
               "tools_metadata": {
                 "_input_type": "ToolsInput",

src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json

@@ -1417,7 +1417,7 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Call the searchapi.io API with result limiting",
+            "description": "Calls the SearchApi API with result limiting. Supports Google, Bing and DuckDuckGo.",
             "display_name": "Search API",
             "documentation": "https://www.searchapi.io/docs/google",
             "edited": false,
@@ -1434,7 +1434,7 @@
             "legacy": false,
             "lf_version": "1.2.0",
             "metadata": {
-              "code_hash": "727befdc79e7",
+              "code_hash": "c561e416205b",
               "module": "langflow.components.searchapi.search.SearchComponent"
             },
             "minimized": false,
@@ -1494,7 +1494,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\n\n\nclass SearchComponent(Component):\n    display_name: str = \"Search API\"\n    description: str = \"Call the searchapi.io API with result limiting\"\n    documentation: str = \"https://www.searchapi.io/docs/google\"\n    icon = \"SearchAPI\"\n\n    inputs = [\n        DropdownInput(name=\"engine\", display_name=\"Engine\", value=\"google\", options=[\"google\", \"bing\", \"duckduckgo\"]),\n        SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            tool_mode=True,\n        ),\n        DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n        IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n        IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n    ]\n\n    outputs = [\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"fetch_content_dataframe\"),\n    ]\n\n    def _build_wrapper(self):\n        return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n    def run_model(self) -> DataFrame:\n        return self.fetch_content_dataframe()\n\n    def fetch_content(self) -> list[Data]:\n        wrapper = self._build_wrapper()\n\n        def search_func(\n            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n        ) -> list[Data]:\n            params = params or {}\n            full_results = wrapper.results(query=query, **params)\n            organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n            return [\n                Data(\n                    text=result.get(\"snippet\", \"\"),\n                    data={\n                        \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n                        \"link\": result.get(\"link\", \"\"),\n                        \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n                    },\n                )\n                for result in organic_results\n            ]\n\n        results = search_func(\n            self.input_value,\n            self.search_params or {},\n            self.max_results,\n            self.max_snippet_length,\n        )\n        self.status = results\n        return results\n\n    def fetch_content_dataframe(self) -> DataFrame:\n        \"\"\"Convert the search results to a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the search results.\n        \"\"\"\n        data = self.fetch_content()\n        return DataFrame(data)\n"
+                "value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\n\n\nclass SearchComponent(Component):\n    display_name: str = \"SearchApi\"\n    description: str = \"Calls the SearchApi API with result limiting. Supports Google, Bing and DuckDuckGo.\"\n    documentation: str = \"https://www.searchapi.io/docs/google\"\n    icon = \"SearchAPI\"\n\n    inputs = [\n        DropdownInput(name=\"engine\", display_name=\"Engine\", value=\"google\", options=[\"google\", \"bing\", \"duckduckgo\"]),\n        SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            tool_mode=True,\n        ),\n        DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n        IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n        IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n    ]\n\n    outputs = [\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"fetch_content_dataframe\"),\n    ]\n\n    def _build_wrapper(self):\n        return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n    def run_model(self) -> DataFrame:\n        return self.fetch_content_dataframe()\n\n    def fetch_content(self) -> list[Data]:\n        wrapper = self._build_wrapper()\n\n        def search_func(\n            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n        ) -> list[Data]:\n            params = params or {}\n            full_results = wrapper.results(query=query, **params)\n            organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n            return [\n                Data(\n                    text=result.get(\"snippet\", \"\"),\n                    data={\n                        \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n                        \"link\": result.get(\"link\", \"\"),\n                        \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n                    },\n                )\n                for result in organic_results\n            ]\n\n        results = search_func(\n            self.input_value,\n            self.search_params or {},\n            self.max_results,\n            self.max_snippet_length,\n        )\n        self.status = results\n        return results\n\n    def fetch_content_dataframe(self) -> DataFrame:\n        \"\"\"Convert the search results to a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the search results.\n        \"\"\"\n        data = self.fetch_content()\n        return DataFrame(data)\n"
               },
               "engine": {
                 "_input_type": "DropdownInput",

src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json

@@ -9,16 +9,12 @@
             "dataType": "YouTubeCommentsComponent",
             "id": "YouTubeCommentsComponent-y3wJZ",
             "name": "comments",
-            "output_types": [
+            "output_types": ["DataFrame"]
-              "DataFrame"
-            ]
           },
           "targetHandle": {
             "fieldName": "df",
             "id": "BatchRunComponent-30WdR",
-            "inputTypes": [
+            "inputTypes": ["DataFrame"],
-              "DataFrame"
-            ],
             "type": "other"
           }
         },
@@ -37,16 +33,12 @@
             "dataType": "Prompt",
             "id": "Prompt-yqoLt",
             "name": "prompt",
-            "output_types": [
+            "output_types": ["Message"]
-              "Message"
-            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "Agent-JRSRu",
-            "inputTypes": [
+            "inputTypes": ["Message"],
-              "Message"
-            ],
             "type": "str"
           }
         },
@@ -65,18 +57,12 @@
             "dataType": "Agent",
             "id": "Agent-JRSRu",
             "name": "response",
-            "output_types": [
+            "output_types": ["Message"]
-              "Message"
-            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "ChatOutput-vlskP",
-            "inputTypes": [
+            "inputTypes": ["Data", "DataFrame", "Message"],
-              "Data",
-              "DataFrame",
-              "Message"
-            ],
             "type": "str"
           }
         },
@@ -95,16 +81,12 @@
             "dataType": "YouTubeTranscripts",
             "id": "YouTubeTranscripts-TlFcG",
             "name": "component_as_tool",
-            "output_types": [
+            "output_types": ["Tool"]
-              "Tool"
-            ]
           },
           "targetHandle": {
             "fieldName": "tools",
             "id": "Agent-JRSRu",
-            "inputTypes": [
+            "inputTypes": ["Tool"],
-              "Tool"
-            ],
             "type": "other"
           }
         },
@@ -123,17 +105,12 @@
             "dataType": "BatchRunComponent",
             "id": "BatchRunComponent-30WdR",
             "name": "batch_results",
-            "output_types": [
+            "output_types": ["DataFrame"]
-              "DataFrame"
-            ]
           },
           "targetHandle": {
             "fieldName": "input_data",
             "id": "parser-k0Bpy",
-            "inputTypes": [
+            "inputTypes": ["DataFrame", "Data"],
-              "DataFrame",
-              "Data"
-            ],
             "type": "other"
           }
         },
@@ -152,16 +129,12 @@
             "dataType": "parser",
             "id": "parser-k0Bpy",
             "name": "parsed_text",
-            "output_types": [
+            "output_types": ["Message"]
-              "Message"
-            ]
           },
           "targetHandle": {
             "fieldName": "analysis",
             "id": "Prompt-yqoLt",
-            "inputTypes": [
+            "inputTypes": ["Message"],
-              "Message"
-            ],
             "type": "str"
           }
         },
@@ -180,16 +153,12 @@
             "dataType": "LanguageModelComponent",
             "id": "LanguageModelComponent-OvIt5",
             "name": "model_output",
-            "output_types": [
+            "output_types": ["LanguageModel"]
-              "LanguageModel"
-            ]
           },
           "targetHandle": {
             "fieldName": "model",
             "id": "BatchRunComponent-30WdR",
-            "inputTypes": [
+            "inputTypes": ["LanguageModel"],
-              "LanguageModel"
-            ],
             "type": "other"
           }
         },
@@ -208,16 +177,12 @@
             "dataType": "ChatInput",
             "id": "ChatInput-kaWcL",
             "name": "message",
-            "output_types": [
+            "output_types": ["Message"]
-              "Message"
-            ]
           },
           "targetHandle": {
             "fieldName": "video_url",
             "id": "YouTubeCommentsComponent-y3wJZ",
-            "inputTypes": [
+            "inputTypes": ["Message"],
-              "Message"
-            ],
             "type": "str"
           }
         },
@@ -236,16 +201,12 @@
             "dataType": "ChatInput",
             "id": "ChatInput-kaWcL",
             "name": "message",
-            "output_types": [
+            "output_types": ["Message"]
-              "Message"
-            ]
           },
           "targetHandle": {
             "fieldName": "url",
             "id": "Prompt-yqoLt",
-            "inputTypes": [
+            "inputTypes": ["Message"],
-              "Message"
-            ],
             "type": "str"
           }
         },
@@ -262,9 +223,7 @@
         "data": {
           "id": "BatchRunComponent-30WdR",
           "node": {
-            "base_classes": [
+            "base_classes": ["DataFrame"],
-              "DataFrame"
-            ],
             "beta": false,
             "category": "helpers",
             "conditional_paths": [],
@@ -273,12 +232,7 @@
             "display_name": "Batch Run",
             "documentation": "",
             "edited": false,
-            "field_order": [
+            "field_order": ["model", "system_message", "df", "column_name"],
-              "model",
-              "system_message",
-              "df",
-              "column_name"
-            ],
             "frozen": false,
             "icon": "List",
             "key": "BatchRunComponent",
@@ -300,9 +254,7 @@
                 "name": "batch_results",
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": [
+                "types": ["DataFrame"],
-                  "DataFrame"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -353,9 +305,7 @@
                 "display_name": "DataFrame",
                 "dynamic": false,
                 "info": "The DataFrame whose column (specified by 'column_name') we'll treat as text messages.",
-                "input_types": [
+                "input_types": ["DataFrame"],
-                  "DataFrame"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "df",
@@ -393,9 +343,7 @@
                 "display_name": "Language Model",
                 "dynamic": false,
                 "info": "Connect the 'Language Model' output from your LLM component here.",
-                "input_types": [
+                "input_types": ["LanguageModel"],
-                  "LanguageModel"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "model",
@@ -413,9 +361,7 @@
                 "display_name": "Output Column Name",
                 "dynamic": false,
                 "info": "Name of the column where the model's response will be stored.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -436,9 +382,7 @@
                 "display_name": "Instructions",
                 "dynamic": false,
                 "info": "Multi-line system instruction for all rows in the DataFrame.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -478,9 +422,7 @@
         "data": {
           "id": "YouTubeCommentsComponent-y3wJZ",
           "node": {
-            "base_classes": [
+            "base_classes": ["DataFrame"],
-              "DataFrame"
-            ],
             "beta": false,
             "category": "youtube",
             "conditional_paths": [],
@@ -518,9 +460,7 @@
                 "name": "comments",
                 "selected": "DataFrame",
                 "tool_mode": true,
-                "types": [
+                "types": ["DataFrame"],
-                  "DataFrame"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -626,10 +566,7 @@
                 "dynamic": false,
                 "info": "Sort comments by time or relevance.",
                 "name": "sort_by",
-                "options": [
+                "options": ["time", "relevance"],
-                  "time",
-                  "relevance"
-                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -646,9 +583,7 @@
                 "display_name": "Video URL",
                 "dynamic": false,
                 "info": "The URL of the YouTube video to get comments from.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -687,9 +622,7 @@
         "data": {
           "id": "Agent-JRSRu",
           "node": {
-            "base_classes": [
+            "base_classes": ["Message"],
-              "Message"
-            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -740,9 +673,7 @@
                 "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -774,9 +705,7 @@
                 "display_name": "Agent Description [Deprecated]",
                 "dynamic": false,
                 "info": "The description of the agent. This is only used when in Tool Mode. Defaults to 'A helpful assistant with access to the following tools:' and tools are added dynamically. This feature is deprecated and will be removed in future versions.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -897,9 +826,7 @@
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "The input provided by the user for the agent to process.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1107,9 +1034,7 @@
                 "display_name": "Agent Instructions",
                 "dynamic": false,
                 "info": "System Prompt: Initial instructions and context provided to guide the agent's behavior.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1177,9 +1102,7 @@
                 "display_name": "Tools",
                 "dynamic": false,
                 "info": "These are the tools that the agent can use to help with tasks.",
-                "input_types": [
+                "input_types": ["Tool"],
-                  "Tool"
-                ],
                 "list": true,
                 "list_add_label": "Add More",
                 "name": "tools",
@@ -1233,26 +1156,18 @@
         "data": {
           "id": "Prompt-yqoLt",
           "node": {
-            "base_classes": [
+            "base_classes": ["Message"],
-              "Message"
-            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {
-              "template": [
+              "template": ["url", "analysis"]
-                "url",
-                "analysis"
-              ]
             },
             "description": "Create a prompt template with dynamic variables.",
             "display_name": "Prompt",
             "documentation": "",
             "edited": false,
             "error": null,
-            "field_order": [
+            "field_order": ["template", "tool_placeholder"],
-              "template",
-              "tool_placeholder"
-            ],
             "frozen": false,
             "full_path": null,
             "icon": "braces",
@@ -1275,9 +1190,7 @@
                 "name": "prompt",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1292,9 +1205,7 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1348,9 +1259,7 @@
                 "display_name": "Tool Placeholder",
                 "dynamic": false,
                 "info": "A placeholder input for tool mode.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1373,9 +1282,7 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -1411,9 +1318,7 @@
         "data": {
           "id": "ChatOutput-vlskP",
           "node": {
-            "base_classes": [
+            "base_classes": ["Message"],
-              "Message"
-            ],
             "beta": false,
             "category": "outputs",
             "conditional_paths": [],
@@ -1454,9 +1359,7 @@
                 "name": "message",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1470,9 +1373,7 @@
                 "display_name": "Background Color",
                 "dynamic": false,
                 "info": "The background color of the icon.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1493,9 +1394,7 @@
                 "display_name": "Icon",
                 "dynamic": false,
                 "info": "The icon of the message.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1552,9 +1451,7 @@
                 "display_name": "Data Template",
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1575,11 +1472,7 @@
                 "display_name": "Inputs",
                 "dynamic": false,
                 "info": "Message to be passed as output.",
-                "input_types": [
+                "input_types": ["Data", "DataFrame", "Message"],
-                  "Data",
-                  "DataFrame",
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1603,10 +1496,7 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": [
+                "options": ["Machine", "User"],
-                  "Machine",
-                  "User"
-                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -1623,9 +1513,7 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1646,9 +1534,7 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1687,9 +1573,7 @@
                 "display_name": "Text Color",
                 "dynamic": false,
                 "info": "The text color of the name",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1727,11 +1611,7 @@
         "data": {
           "id": "YouTubeTranscripts-TlFcG",
           "node": {
-            "base_classes": [
+            "base_classes": ["Data", "DataFrame", "Message"],
-              "Data",
-              "DataFrame",
-              "Message"
-            ],
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
@@ -1739,11 +1619,7 @@
             "display_name": "YouTube Transcripts",
             "documentation": "",
             "edited": false,
-            "field_order": [
+            "field_order": ["url", "chunk_size_seconds", "translation"],
-              "url",
-              "chunk_size_seconds",
-              "translation"
-            ],
             "frozen": false,
             "icon": "YouTube",
             "last_updated": "2025-07-07T14:52:15.000Z",
@@ -1768,9 +1644,7 @@
                 "required_inputs": null,
                 "selected": "Tool",
                 "tool_mode": true,
-                "types": [
+                "types": ["Tool"],
-                  "Tool"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -1845,9 +1719,7 @@
                     "name": "get_dataframe_output",
                     "readonly": false,
                     "status": true,
-                    "tags": [
+                    "tags": ["get_dataframe_output"]
-                      "get_dataframe_output"
-                    ]
                   },
                   {
                     "args": {
@@ -1863,9 +1735,7 @@
                     "name": "get_message_output",
                     "readonly": false,
                     "status": true,
-                    "tags": [
+                    "tags": ["get_message_output"]
-                      "get_message_output"
-                    ]
                   },
                   {
                     "args": {
@@ -1881,9 +1751,7 @@
                     "name": "get_data_output",
                     "readonly": false,
                     "status": true,
-                    "tags": [
+                    "tags": ["get_data_output"]
-                      "get_data_output"
-                    ]
                   }
                 ]
               },
@@ -1927,9 +1795,7 @@
                 "display_name": "Video URL",
                 "dynamic": false,
                 "info": "Enter the YouTube video URL to get transcripts from.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1969,7 +1835,7 @@
         "data": {
           "id": "note-eKoWw",
           "node": {
-            "description": "# 📖 README\nThis flow performs comprehensive analysis of YouTube videos.\n1.  Extract video comments and transcripts.\n2.  Run sentiment analysis on comments using LLM.\n3.  Combine transcript content and comment sentiment for comprehensive video analysis.\n## Quickstart\n-  Add your **OpenAI API Key** to the **Language Model** and **YT-Insight** Agent Component\n-  Add your **YouTube Data API v3 key**\n- If you don't have a YoutTube API key, create one in the [Google Cloud Console](https://console.cloud.google.com).\n- Ensure the chat input is a valid Youtube video URL. A sample URL is provided in the chat input component.\n",
+            "description": "# 📖 README\nThis flow performs comprehensive analysis of YouTube videos.\n1.  Extract video comments and transcripts.\n2.  Run sentiment analysis on comments using LLM.\n3.  Combine transcript content and comment sentiment for comprehensive video analysis.\n## Quickstart\n-  Add your **OpenAI API Key** to the **Language Model** and **YT-Insight** Agent Component\n-  Add your **YouTube Data API v3 key**\n- If you don't have a YoutTube API key, create one in the [Google Cloud Console](https://console.cloud.google.com).\n- Ensure the chat input is a valid YouTube video URL. A sample URL is provided in the chat input component.\n",
             "display_name": "",
             "documentation": "",
             "template": {
@@ -1998,9 +1864,7 @@
         "data": {
           "id": "parser-k0Bpy",
           "node": {
-            "base_classes": [
+            "base_classes": ["Message"],
-              "Message"
-            ],
             "beta": false,
             "category": "processing",
             "conditional_paths": [],
@@ -2009,12 +1873,7 @@
             "display_name": "Parser",
             "documentation": "",
             "edited": false,
-            "field_order": [
+            "field_order": ["mode", "pattern", "input_data", "sep"],
-              "mode",
-              "pattern",
-              "input_data",
-              "sep"
-            ],
             "frozen": false,
             "icon": "braces",
             "key": "parser",
@@ -2032,9 +1891,7 @@
                 "name": "parsed_text",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2066,10 +1923,7 @@
                 "display_name": "Data or DataFrame",
                 "dynamic": false,
                 "info": "Accepts either a DataFrame or a Data object.",
-                "input_types": [
+                "input_types": ["DataFrame", "Data"],
-                  "DataFrame",
-                  "Data"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "input_data",
@@ -2088,10 +1942,7 @@
                 "dynamic": false,
                 "info": "Convert into raw string instead of using a template.",
                 "name": "mode",
-                "options": [
+                "options": ["Parser", "Stringify"],
-                  "Parser",
-                  "Stringify"
-                ],
                 "placeholder": "",
                 "real_time_refresh": true,
                 "required": false,
@@ -2109,9 +1960,7 @@
                 "display_name": "Template",
                 "dynamic": true,
                 "info": "Use variables within curly brackets to extract column values for DataFrames or key values for Data.For example: `Name: {Name}, Age: {Age}, Country: {Country}`",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2133,9 +1982,7 @@
                 "display_name": "Separator",
                 "dynamic": false,
                 "info": "String used to separate rows/items.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2174,10 +2021,7 @@
         "data": {
           "id": "LanguageModelComponent-OvIt5",
           "node": {
-            "base_classes": [
+            "base_classes": ["LanguageModel", "Message"],
-              "LanguageModel",
-              "Message"
-            ],
             "beta": false,
             "category": "models",
             "conditional_paths": [],
@@ -2223,9 +2067,7 @@
                 "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               },
               {
@@ -2239,9 +2081,7 @@
                 "required_inputs": null,
                 "selected": "LanguageModel",
                 "tool_mode": true,
-                "types": [
+                "types": ["LanguageModel"],
-                  "LanguageModel"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2292,9 +2132,7 @@
                 "display_name": "Input",
                 "dynamic": false,
                 "info": "The input text to send to the model",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2350,11 +2188,7 @@
                 "dynamic": false,
                 "info": "Select the model provider",
                 "name": "provider",
-                "options": [
+                "options": ["OpenAI", "Anthropic", "Google"],
-                  "OpenAI",
-                  "Anthropic",
-                  "Google"
-                ],
                 "options_metadata": [
                   {
                     "icon": "OpenAI"
@@ -2402,9 +2236,7 @@
                 "display_name": "System Message",
                 "dynamic": false,
                 "info": "A system message that helps set the behavior of the assistant",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2472,9 +2304,7 @@
         "data": {
           "id": "ChatInput-kaWcL",
           "node": {
-            "base_classes": [
+            "base_classes": ["Message"],
-              "Message"
-            ],
             "beta": false,
             "category": "input_output",
             "conditional_paths": [],
@@ -2515,9 +2345,7 @@
                 "name": "message",
                 "selected": "Message",
                 "tool_mode": true,
-                "types": [
+                "types": ["Message"],
-                  "Message"
-                ],
                 "value": "__UNDEFINED__"
               }
             ],
@@ -2531,9 +2359,7 @@
                 "display_name": "Background Color",
                 "dynamic": false,
                 "info": "The background color of the icon.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2554,9 +2380,7 @@
                 "display_name": "Icon",
                 "dynamic": false,
                 "info": "The icon of the message.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2665,10 +2489,7 @@
                 "dynamic": false,
                 "info": "Type of sender.",
                 "name": "sender",
-                "options": [
+                "options": ["Machine", "User"],
-                  "Machine",
-                  "User"
-                ],
                 "options_metadata": [],
                 "placeholder": "",
                 "required": false,
@@ -2686,9 +2507,7 @@
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "info": "Name of the sender.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2709,9 +2528,7 @@
                 "display_name": "Session ID",
                 "dynamic": false,
                 "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2750,9 +2567,7 @@
                 "display_name": "Text Color",
                 "dynamic": false,
                 "info": "The text color of the name",
-                "input_types": [
+                "input_types": ["Message"],
-                  "Message"
-                ],
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -2798,9 +2613,6 @@
   "id": "3d1e15c2-b095-46de-8247-949c7a5bda04",
   "is_component": false,
   "last_tested_version": "1.4.3",
-  "name": "Youtube Analysis",
+  "name": "YouTube Analysis",
-  "tags": [
+  "tags": ["agents", "assistants"]
-    "agents",
-    "assistants"
-  ]
 }

src/backend/tests/unit/components/search/test_yfinance_tool.py

@@ -22,7 +22,7 @@ class TestYfinanceComponent:
 
     def test_initialization(self, component_class):
         component = component_class()
-        assert component.display_name == "Yahoo Finance"
+        assert component.display_name == "Yahoo! Finance"
         assert component.icon == "trending-up"
         assert "yfinance" in component.description
 

src/frontend/src/constants/constants.ts

@@ -761,7 +761,7 @@ export const BUNDLES_SIDEBAR_FOLDER_NAMES = [
   "assemblyai",
   "LangWatch",
   "langwatch",
-  "Youtube",
+  "YouTube",
   "youtube",
 ];
 

src/frontend/src/icons/eagerIconImports.ts

@@ -117,7 +117,7 @@ import { WindsurfIcon } from "./Windsurf";
 
 // Export the eagerly loaded icons map
 export const eagerIconsMapping = {
-  "AI/ML": AIMLIcon,
+  AIML: AIMLIcon,
   AgentQL: AgentQLIcon,
   Airbyte: AirbyteIcon,
   Anthropic: AnthropicIcon,

src/frontend/src/icons/lazyIconImports.ts

@@ -1,7 +1,6 @@
 // Export the lazy loading mapping for icons
 export const lazyIconsMapping = {
-  "AI/ML": () =>
+  AIML: () => import("@/icons/AIML").then((mod) => ({ default: mod.AIMLIcon })),
-    import("@/icons/AIML").then((mod) => ({ default: mod.AIMLIcon })),
   AgentQL: () =>
     import("@/icons/AgentQL").then((mod) => ({ default: mod.AgentQLIcon })),
   Airbyte: () =>

src/frontend/src/utils/styleUtils.ts

@@ -230,13 +230,13 @@ export const SIDEBAR_CATEGORIES = [
 ];
 
 export const SIDEBAR_BUNDLES = [
-  { display_name: "AI/ML", name: "aiml", icon: "AI/ML" },
+  { display_name: "AI/ML API", name: "aiml", icon: "AIML" },
   { display_name: "AgentQL", name: "agentql", icon: "AgentQL" },
   { display_name: "Amazon", name: "amazon", icon: "Amazon" },
   { display_name: "Anthropic", name: "anthropic", icon: "Anthropic" },
   { display_name: "Apify", name: "apify", icon: "Apify" },
 
-  { display_name: "Arxiv", name: "arxiv", icon: "arXiv" },
+  { display_name: "arXiv", name: "arxiv", icon: "arXiv" },
   { display_name: "AssemblyAI", name: "assemblyai", icon: "AssemblyAI" },
   { display_name: "Azure", name: "azure", icon: "Azure" },
   { display_name: "Baidu", name: "baidu", icon: "BaiduQianfan" },
@@ -270,7 +270,7 @@ export const SIDEBAR_BUNDLES = [
     name: "homeassistant",
     icon: "HomeAssistant",
   },
-  { display_name: "HuggingFace", name: "huggingface", icon: "HuggingFace" },
+  { display_name: "Hugging Face", name: "huggingface", icon: "HuggingFace" },
   { display_name: "IBM", name: "ibm", icon: "WatsonxAI" },
   { display_name: "Icosa Computing", name: "icosacomputing", icon: "Icosa" },
   { display_name: "JigsawStack", name: "jigsawstack", icon: "JigsawStack" },
@@ -282,7 +282,7 @@ export const SIDEBAR_BUNDLES = [
   { display_name: "Memories", name: "memories", icon: "Cpu" },
   { display_name: "MistralAI", name: "mistral", icon: "MistralAI" },
   { display_name: "Needle", name: "needle", icon: "Needle" },
-  { display_name: "NotDiamond", name: "notdiamond", icon: "NotDiamond" },
+  { display_name: "Not Diamond", name: "notdiamond", icon: "NotDiamond" },
   { display_name: "Notion", name: "Notion", icon: "Notion" },
   { display_name: "Novita", name: "novita", icon: "Novita" },
   { display_name: "NVIDIA", name: "nvidia", icon: "NVIDIA" },
@@ -295,13 +295,14 @@ export const SIDEBAR_BUNDLES = [
   { display_name: "Redis", name: "redis", icon: "Redis" },
   { display_name: "SambaNova", name: "sambanova", icon: "SambaNova" },
   { display_name: "ScrapeGraph AI", name: "scrapegraph", icon: "ScrapeGraph" },
-  { display_name: "SearchAPI", name: "searchapi", icon: "SearchAPI" },
+  { display_name: "SearchApi", name: "searchapi", icon: "SearchAPI" },
   { display_name: "SerpApi", name: "serpapi", icon: "SerpSearch" },
+  { display_name: "Serper", name: "serper", icon: "Serper" },
   { display_name: "Tavily", name: "tavily", icon: "TavilyIcon" },
-  { display_name: "Twelve Labs", name: "twelvelabs", icon: "TwelveLabs" },
+  { display_name: "TwelveLabs", name: "twelvelabs", icon: "TwelveLabs" },
   { display_name: "Unstructured", name: "unstructured", icon: "Unstructured" },
   { display_name: "Vectara", name: "vectara", icon: "Vectara" },
-  { display_name: "VertexAI", name: "vertexai", icon: "VertexAI" },
+  { display_name: "Vertex AI", name: "vertexai", icon: "VertexAI" },
   { display_name: "Wikipedia", name: "wikipedia", icon: "Wikipedia" },
   {
     display_name: "WolframAlpha",
@@ -309,8 +310,8 @@ export const SIDEBAR_BUNDLES = [
     icon: "WolframAlphaAPI",
   },
   { display_name: "xAI", name: "xai", icon: "xAI" },
-  { display_name: "YahooSearch", name: "yahoosearch", icon: "trending-up" },
+  { display_name: "Yahoo! Finance", name: "yahoosearch", icon: "trending-up" },
-  { display_name: "Youtube", name: "youtube", icon: "YouTube" },
+  { display_name: "YouTube", name: "youtube", icon: "YouTube" },
   { display_name: "Zep", name: "zep", icon: "ZepMemory" },
 ];
 
@@ -371,7 +372,7 @@ export const nodeIconToDisplayIconMap: Record<string, string> = {
   ChatOutput: "MessagesSquare",
   //Integration Icons
   Outlook: "Outlook",
-  AIML: "AI/ML",
+  AIML: "AIML",
   AgentQL: "AgentQL",
   LanguageModels: "BrainCircuit",
   EmbeddingModels: "Binary",

src/frontend/tests/core/features/filterSidebar.spec.ts

@@ -73,7 +73,7 @@ test(
     await expect(page.getByTestId("input_outputChat Input")).toBeVisible();
     await expect(page.getByTestId("input_outputChat Output")).toBeVisible();
     await expect(page.getByTestId("processingPrompt Template")).toBeVisible();
-    await expect(page.getByTestId("langchain_utilitiesCSVAgent")).toBeVisible();
+    await expect(page.getByTestId("langchain_utilitiesCSV Agent")).toBeVisible();
     await expect(
       page.getByTestId("langchain_utilitiesConversationChain"),
     ).toBeVisible();

src/frontend/tests/core/integrations/Youtube Analysis.spec.ts

@@ -6,7 +6,7 @@ import { initialGPTsetup } from "../../utils/initialGPTsetup";
 import { withEventDeliveryModes } from "../../utils/withEventDeliveryModes";
 
 withEventDeliveryModes(
-  "Youtube Analysis",
+  "YouTube Analysis",
   { tag: ["@release", "@starter-projects"] },
   async ({ page }) => {
     test.skip(
@@ -27,7 +27,7 @@ withEventDeliveryModes(
     await awaitBootstrapTest(page);
 
     await page.getByTestId("side_nav_options_all-templates").click();
-    await page.getByRole("heading", { name: "Youtube Analysis" }).click();
+    await page.getByRole("heading", { name: "YouTube Analysis" }).click();
 
     await page.waitForSelector('[data-testid="fit_view"]', {
       timeout: 100000,

src/frontend/tests/extended/features/filterEdge-shard-1.spec.ts

@@ -1,5 +1,4 @@
 import { expect, test } from "@playwright/test";
-import { addFlowToTestOnEmptyLangflow } from "../../utils/add-flow-to-test-on-empty-langflow";
 import { addLegacyComponents } from "../../utils/add-legacy-components";
 import { adjustScreenView } from "../../utils/adjust-screen-view";
 import { awaitBootstrapTest } from "../../utils/await-bootstrap-test";
@@ -78,7 +77,7 @@ test(
       "mem0Mem0 Chat Memory",
       "logicCondition",
       "langchain_utilitiesSelf Query Retriever",
-      "langchain_utilitiesCharacterTextSplitter",
+      "langchain_utilitiesCharacter Text Splitter",
     ];
 
     await Promise.all(
@@ -104,7 +103,7 @@ test(
       "cohereCohere Language Models",
       "groqGroq",
       "lmstudioLM Studio",
-      "maritalkMaritalk",
+      "maritalkMariTalk",
       "mistralMistralAI",
       "perplexityPerplexity",
       "baiduQianfan",

src/frontend/tests/extended/regression/generalBugs-shard-11.spec.ts

@@ -49,14 +49,14 @@ test(
 
     await page.getByTestId("sidebar-search-input").click();
     await page.getByTestId("sidebar-search-input").fill("search api");
-    await page.waitForSelector('[data-testid="searchapiSearch API"]', {
+    await page.waitForSelector('[data-testid="searchapiSearchApi"]', {
       timeout: 1000,
     });
 
     await zoomOut(page, 3);
 
     await page
-      .getByTestId("searchapiSearch API")
+      .getByTestId("searchapiSearchApi")
       .dragTo(page.locator('//*[@id="react-flow-id"]'), {
         targetPosition: { x: 100, y: 100 },
       });
@@ -78,7 +78,7 @@ test(
 
     await page.getByTestId("fit_view").click();
 
-    await page.getByTestId("title-Search API").first().click();
+    await page.getByTestId("title-SearchApi").first().click();
     await page.getByTestId("tool-mode-button").click();
 
     //connection