feat(tools): add TavilyAI search tool for enhanced LLM search results (#3774)
* add tavily new icon * feat(tools): add TavilyAI search tool for enhanced LLM search results * [autofix.ci] apply automated fixes * add tavily icon * [autofix.ci] apply automated fixes --------- Co-authored-by: cristhianzl <cristhian.lousa@gmail.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
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157
src/backend/base/langflow/components/tools/TavilyAISearch.py
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157
src/backend/base/langflow/components/tools/TavilyAISearch.py
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@ -0,0 +1,157 @@
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import httpx
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from langchain.tools import StructuredTool
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from pydantic import BaseModel, Field
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from langflow.base.langchain_utilities.model import LCToolComponent
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from langflow.field_typing import Tool
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from langflow.inputs import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput
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from langflow.schema import Data
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class TavilySearchToolComponent(LCToolComponent):
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display_name = "Tavily AI Search"
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description = """**Tavily AI** is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.
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Note: Check 'Advanced' for all options.
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"""
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icon = "TavilyIcon"
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name = "TavilyAISearch"
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documentation = "https://docs.tavily.com/"
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inputs = [
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SecretStrInput(
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name="api_key",
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display_name="Tavily API Key",
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required=True,
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info="Your Tavily API Key.",
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),
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MessageTextInput(
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name="query",
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display_name="Search Query",
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info="The search query you want to execute with Tavily.",
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),
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DropdownInput(
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name="search_depth",
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display_name="Search Depth",
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info="The depth of the search.",
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options=["basic", "advanced"],
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value="advanced",
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advanced=True,
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),
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DropdownInput(
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name="topic",
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display_name="Search Topic",
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info="The category of the search.",
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options=["general", "news"],
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value="general",
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advanced=True,
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),
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IntInput(
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name="max_results",
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display_name="Max Results",
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info="The maximum number of search results to return.",
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value=5,
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advanced=True,
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),
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BoolInput(
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name="include_images",
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display_name="Include Images",
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info="Include a list of query-related images in the response.",
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value=True,
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advanced=True,
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),
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BoolInput(
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name="include_answer",
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display_name="Include Answer",
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info="Include a short answer to original query.",
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value=True,
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advanced=True,
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),
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]
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class TavilySearchSchema(BaseModel):
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query: str = Field(..., description="The search query you want to execute with Tavily.")
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search_depth: str = Field("basic", description="The depth of the search.")
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topic: str = Field("general", description="The category of the search.")
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max_results: int = Field(5, description="The maximum number of search results to return.")
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include_images: bool = Field(False, description="Include a list of query-related images in the response.")
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include_answer: bool = Field(False, description="Include a short answer to original query.")
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def run_model(self) -> list[Data]:
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return self._tavily_search(
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self.query,
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self.search_depth,
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self.topic,
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self.max_results,
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self.include_images,
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self.include_answer,
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)
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def build_tool(self) -> Tool:
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return StructuredTool.from_function(
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name="tavily_search",
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description="Perform a web search using the Tavily API.",
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func=self._tavily_search,
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args_schema=self.TavilySearchSchema,
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)
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def _tavily_search(
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self,
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query: str,
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search_depth: str = "basic",
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topic: str = "general",
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max_results: int = 5,
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include_images: bool = False,
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include_answer: bool = False,
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) -> list[Data]:
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try:
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url = "https://api.tavily.com/search"
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headers = {
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"content-type": "application/json",
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"accept": "application/json",
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}
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payload = {
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"api_key": self.api_key,
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"query": query,
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"search_depth": search_depth,
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"topic": topic,
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"max_results": max_results,
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"include_images": include_images,
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"include_answer": include_answer,
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}
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with httpx.Client() as client:
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response = client.post(url, json=payload, headers=headers)
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response.raise_for_status()
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search_results = response.json()
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data_results = [
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Data(
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data={
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"title": result.get("title"),
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"url": result.get("url"),
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"content": result.get("content"),
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"score": result.get("score"),
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}
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)
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for result in search_results.get("results", [])
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]
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if include_answer and search_results.get("answer"):
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data_results.insert(0, Data(data={"answer": search_results["answer"]}))
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if include_images and search_results.get("images"):
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data_results.append(Data(data={"images": search_results["images"]}))
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self.status = data_results
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return data_results
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except httpx.HTTPStatusError as e:
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error_message = f"HTTP error: {e.response.status_code} - {e.response.text}"
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self.status = error_message
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return [Data(data={"error": error_message})]
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except Exception as e:
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error_message = f"Unexpected error: {str(e)}"
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self.status = error_message
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return [Data(data={"error": error_message})]
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69
src/frontend/src/icons/Tavily/Tavily.jsx
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69
src/frontend/src/icons/Tavily/Tavily.jsx
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@ -0,0 +1,69 @@
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const Tavily = (props) => (
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<svg
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xmlns="http://www.w3.org/2000/svg"
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width="1em"
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height="1em"
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viewBox="0 0 375 375"
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{...props}
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>
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<defs>
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<clipPath id="a">
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<path d="M109.379 231.133h37.105v37.105H109.38Zm0 0" />
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</clipPath>
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<clipPath id="b">
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<path d="M127.934 231.133c-10.246 0-18.555 8.304-18.555 18.554 0 10.247 8.308 18.551 18.555 18.551 10.246 0 18.55-8.304 18.55-18.55 0-10.25-8.304-18.555-18.55-18.555Zm0 0" />
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</clipPath>
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</defs>
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<path
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fill="none"
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stroke="#f25022"
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strokeLinecap="round"
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strokeWidth={28.360419999999998}
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d="M127.926 239.96V50.165"
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/>
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<path
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fill="none"
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stroke="#f25022"
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strokeLinecap="round"
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strokeLinejoin="round"
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strokeWidth={28.360419999999998}
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d="m85.387 99.793 42.539-56.719 42.539 56.719"
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/>
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<path
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fill="none"
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stroke="#ffb901"
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strokeLinecap="round"
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strokeWidth={28.360419999999998}
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d="m141.012 254.168 172.476-.02"
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/>
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<path
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fill="none"
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stroke="#ffb901"
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strokeLinecap="round"
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strokeLinejoin="round"
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strokeWidth={28.360419999999998}
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d="m263.855 211.613 56.723 42.535-56.715 42.547"
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/>
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<path
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fill="none"
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stroke="#04a3ec"
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strokeLinecap="round"
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strokeWidth={23.20398}
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d="m117.852 259.344-57.446 66.015"
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/>
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<path
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fill="none"
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stroke="#04a3ec"
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strokeLinecap="round"
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strokeLinejoin="round"
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strokeWidth={23.20398}
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d="m113.32 317.574-56.718 12.16 4.207-57.859"
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/>
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<g clipPath="url(#a)">
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<g clipPath="url(#b)">
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<path fill="#32b37f" d="M109.379 231.133h37.105v37.105H109.38Zm0 0" />
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</g>
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</g>
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</svg>
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);
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export default Tavily;
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9
src/frontend/src/icons/Tavily/index.tsx
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9
src/frontend/src/icons/Tavily/index.tsx
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@ -0,0 +1,9 @@
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import React, { forwardRef } from "react";
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import Tavily from "./Tavily";
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export const TavilyIcon = forwardRef<
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SVGSVGElement,
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React.PropsWithChildren<{}>
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>((props, ref) => {
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return <Tavily ref={ref} {...props} />;
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});
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1
src/frontend/src/icons/Tavily/tavily.svg
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1
src/frontend/src/icons/Tavily/tavily.svg
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@ -0,0 +1 @@
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="500" zoomAndPan="magnify" viewBox="0 0 375 374.999991" height="500" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="d0348dc115"><path d="M 109.378906 231.132812 L 146.484375 231.132812 L 146.484375 268.238281 L 109.378906 268.238281 Z M 109.378906 231.132812 " clip-rule="nonzero"/></clipPath><clipPath id="a28b194a7a"><path d="M 127.933594 231.132812 C 117.6875 231.132812 109.378906 239.4375 109.378906 249.6875 C 109.378906 259.933594 117.6875 268.238281 127.933594 268.238281 C 138.179688 268.238281 146.484375 259.933594 146.484375 249.6875 C 146.484375 239.4375 138.179688 231.132812 127.933594 231.132812 Z M 127.933594 231.132812 " clip-rule="nonzero"/></clipPath></defs><path stroke-linecap="round" transform="matrix(0, -2.578223, 2.578223, 0, 113.745458, 254.140061)" fill="none" stroke-linejoin="miter" d="M 5.499573 5.500038 L 79.114962 5.500038 " stroke="#f25022" stroke-width="11" stroke-opacity="1" stroke-miterlimit="4"/><path stroke-linecap="round" transform="matrix(0, -2.578223, 2.578223, 0, 113.745458, 254.140061)" fill="none" stroke-linejoin="round" d="M 59.865692 -10.999336 L 81.864858 5.500038 L 59.865692 21.999412 " stroke="#f25022" stroke-width="11" stroke-opacity="1" stroke-miterlimit="4"/><path stroke-linecap="round" transform="matrix(2.578223, -0.000251357, 0.000251357, 2.578223, 126.828174, 239.987372)" fill="none" stroke-linejoin="miter" d="M 5.500751 5.50068 L 72.398214 5.499627 " stroke="#ffb901" stroke-width="11" stroke-opacity="1" stroke-miterlimit="4"/><path stroke-linecap="round" transform="matrix(2.578223, -0.000251357, 0.000251357, 2.578223, 126.828174, 239.987372)" fill="none" stroke-linejoin="round" d="M 53.149037 -11.000109 L 75.148109 5.499895 L 53.14885 22.000154 " stroke="#ffb901" stroke-width="11" stroke-opacity="1" stroke-miterlimit="4"/><path stroke-linecap="round" transform="matrix(-1.692446, 1.944957, -1.944957, -1.692446, 134.219043, 258.208373)" fill="none" stroke-linejoin="miter" d="M 4.499518 4.49999 L 38.441562 4.500107 " stroke="#04a3ec" stroke-width="9" stroke-opacity="1" stroke-miterlimit="4"/><path stroke-linecap="round" transform="matrix(-1.692446, 1.944957, -1.944957, -1.692446, 134.219043, 258.208373)" fill="none" stroke-linejoin="round" d="M 22.691248 -9.000192 L 40.69038 4.49943 L 22.68978 17.999994 " stroke="#04a3ec" stroke-width="9" stroke-opacity="1" stroke-miterlimit="4"/><g clip-path="url(#d0348dc115)"><g clip-path="url(#a28b194a7a)"><path fill="#32b37f" d="M 109.378906 231.132812 L 146.484375 231.132812 L 146.484375 268.238281 L 109.378906 268.238281 Z M 109.378906 231.132812 " fill-opacity="1" fill-rule="nonzero"/></g></g></svg>
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After Width: | Height: | Size: 2.7 KiB |
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@ -1,6 +1,7 @@
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import { AIMLIcon } from "@/icons/AIML";
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import { DuckDuckGoIcon } from "@/icons/DuckDuckGo";
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import Perplexity from "@/icons/Perplexity/Perplexity";
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import { TavilyIcon } from "@/icons/Tavily";
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import { UnstructuredIcon } from "@/icons/Unstructured";
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import { AthenaIcon } from "@/icons/athena/index";
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import { freezeAllIcon } from "@/icons/freezeAll";
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@ -636,6 +637,7 @@ export const nodeIconsLucide: iconsType = {
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OptionIcon: OptionIcon,
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Option: OptionIcon,
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Perplexity,
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TavilyIcon,
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DuckDuckGo: DuckDuckGoIcon,
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OpenSearch,
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};
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