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>

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

@@ -0,0 +1,157 @@
+import httpx
+from langchain.tools import StructuredTool
+from pydantic import BaseModel, Field
+
+from langflow.base.langchain_utilities.model import LCToolComponent
+from langflow.field_typing import Tool
+from langflow.inputs import BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput
+from langflow.schema import Data
+
+
+class TavilySearchToolComponent(LCToolComponent):
+    display_name = "Tavily AI Search"
+    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.
+
+Note: Check 'Advanced' for all options.
+"""
+    icon = "TavilyIcon"
+    name = "TavilyAISearch"
+    documentation = "https://docs.tavily.com/"
+
+    inputs = [
+        SecretStrInput(
+            name="api_key",
+            display_name="Tavily API Key",
+            required=True,
+            info="Your Tavily API Key.",
+        ),
+        MessageTextInput(
+            name="query",
+            display_name="Search Query",
+            info="The search query you want to execute with Tavily.",
+        ),
+        DropdownInput(
+            name="search_depth",
+            display_name="Search Depth",
+            info="The depth of the search.",
+            options=["basic", "advanced"],
+            value="advanced",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="topic",
+            display_name="Search Topic",
+            info="The category of the search.",
+            options=["general", "news"],
+            value="general",
+            advanced=True,
+        ),
+        IntInput(
+            name="max_results",
+            display_name="Max Results",
+            info="The maximum number of search results to return.",
+            value=5,
+            advanced=True,
+        ),
+        BoolInput(
+            name="include_images",
+            display_name="Include Images",
+            info="Include a list of query-related images in the response.",
+            value=True,
+            advanced=True,
+        ),
+        BoolInput(
+            name="include_answer",
+            display_name="Include Answer",
+            info="Include a short answer to original query.",
+            value=True,
+            advanced=True,
+        ),
+    ]
+
+    class TavilySearchSchema(BaseModel):
+        query: str = Field(..., description="The search query you want to execute with Tavily.")
+        search_depth: str = Field("basic", description="The depth of the search.")
+        topic: str = Field("general", description="The category of the search.")
+        max_results: int = Field(5, description="The maximum number of search results to return.")
+        include_images: bool = Field(False, description="Include a list of query-related images in the response.")
+        include_answer: bool = Field(False, description="Include a short answer to original query.")
+
+    def run_model(self) -> list[Data]:
+        return self._tavily_search(
+            self.query,
+            self.search_depth,
+            self.topic,
+            self.max_results,
+            self.include_images,
+            self.include_answer,
+        )
+
+    def build_tool(self) -> Tool:
+        return StructuredTool.from_function(
+            name="tavily_search",
+            description="Perform a web search using the Tavily API.",
+            func=self._tavily_search,
+            args_schema=self.TavilySearchSchema,
+        )
+
+    def _tavily_search(
+        self,
+        query: str,
+        search_depth: str = "basic",
+        topic: str = "general",
+        max_results: int = 5,
+        include_images: bool = False,
+        include_answer: bool = False,
+    ) -> list[Data]:
+        try:
+            url = "https://api.tavily.com/search"
+            headers = {
+                "content-type": "application/json",
+                "accept": "application/json",
+            }
+            payload = {
+                "api_key": self.api_key,
+                "query": query,
+                "search_depth": search_depth,
+                "topic": topic,
+                "max_results": max_results,
+                "include_images": include_images,
+                "include_answer": include_answer,
+            }
+
+            with httpx.Client() as client:
+                response = client.post(url, json=payload, headers=headers)
+
+            response.raise_for_status()
+            search_results = response.json()
+
+            data_results = [
+                Data(
+                    data={
+                        "title": result.get("title"),
+                        "url": result.get("url"),
+                        "content": result.get("content"),
+                        "score": result.get("score"),
+                    }
+                )
+                for result in search_results.get("results", [])
+            ]
+
+            if include_answer and search_results.get("answer"):
+                data_results.insert(0, Data(data={"answer": search_results["answer"]}))
+
+            if include_images and search_results.get("images"):
+                data_results.append(Data(data={"images": search_results["images"]}))
+
+            self.status = data_results
+            return data_results
+
+        except httpx.HTTPStatusError as e:
+            error_message = f"HTTP error: {e.response.status_code} - {e.response.text}"
+            self.status = error_message
+            return [Data(data={"error": error_message})]
+        except Exception as e:
+            error_message = f"Unexpected error: {str(e)}"
+            self.status = error_message
+            return [Data(data={"error": error_message})]