refactor(component): Convert Tavily Search to standard component patt…

…ern (#5430)

* refactor(components): Convert Tavily Search to standard component pattern

* [autofix.ci] apply automated fixes

* fix: improve error handling in tavily component

* fix: Fix linting error in __init__.py

* fix: rename TavilyComponent to TavilySearchToolComponent maintaining backward compatibility

* fix: rename component class to avoid conflict with legacy version

---------

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

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

@@ -16,6 +16,7 @@
 from .search_api import SearchAPIComponent
 from .searxng import SearXNGToolComponent
 from .serp_api import SerpAPIComponent
+from .tavily import TavilySearchComponent
 from .tavily_search import TavilySearchToolComponent
 from .wikidata_api import WikidataAPIComponent
 from .wikipedia_api import WikipediaAPIComponent
@@ -45,6 +46,7 @@
     "SearXNGToolComponent",
     "SearchAPIComponent",
     "SerpAPIComponent",
+    "TavilySearchComponent",
     "TavilySearchToolComponent",
     "WikidataAPIComponent",
     "WikipediaAPIComponent",

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

@@ -0,0 +1,138 @@
+import httpx
+from loguru import logger
+
+from langflow.custom import Component
+from langflow.helpers.data import data_to_text
+from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput
+from langflow.schema import Data
+from langflow.schema.message import Message
+
+
+class TavilySearchComponent(Component):
+    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."""
+    icon = "TavilyIcon"
+
+    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.",
+            tool_mode=True,
+        ),
+        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,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Data", name="data", method="fetch_content"),
+        Output(display_name="Text", name="text", method="fetch_content_text"),
+    ]
+
+    def fetch_content(self) -> list[Data]:
+        try:
+            url = "https://api.tavily.com/search"
+            headers = {
+                "content-type": "application/json",
+                "accept": "application/json",
+            }
+            payload = {
+                "api_key": self.api_key,
+                "query": self.query,
+                "search_depth": self.search_depth,
+                "topic": self.topic,
+                "max_results": self.max_results,
+                "include_images": self.include_images,
+                "include_answer": self.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 = []
+
+            if self.include_answer and search_results.get("answer"):
+                data_results.append(Data(text=search_results["answer"]))
+
+            for result in search_results.get("results", []):
+                content = result.get("content", "")
+                data_results.append(
+                    Data(
+                        text=content,
+                        data={
+                            "title": result.get("title"),
+                            "url": result.get("url"),
+                            "content": content,
+                            "score": result.get("score"),
+                        },
+                    )
+                )
+
+            if self.include_images and search_results.get("images"):
+                data_results.append(Data(text="Images found", data={"images": search_results["images"]}))
+        except httpx.HTTPStatusError as exc:
+            error_message = f"HTTP error occurred: {exc.response.status_code} - {exc.response.text}"
+            logger.error(error_message)
+            return [Data(text=error_message, data={"error": error_message})]
+        except httpx.RequestError as exc:
+            error_message = f"Request error occurred: {exc}"
+            logger.error(error_message)
+            return [Data(text=error_message, data={"error": error_message})]
+        except ValueError as exc:
+            error_message = f"Invalid response format: {exc}"
+            logger.error(error_message)
+            return [Data(text=error_message, data={"error": error_message})]
+        else:
+            self.status = data_results
+            return data_results
+
+    def fetch_content_text(self) -> Message:
+        data = self.fetch_content()
+        result_string = data_to_text("{text}", data)
+        self.status = result_string
+        return Message(text=result_string)

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

@@ -32,7 +32,7 @@ class TavilySearchSchema(BaseModel):
 
 
 class TavilySearchToolComponent(LCToolComponent):
-    display_name = "Tavily AI Search"
+    display_name = "Tavily AI Search [DEPRECATED]"
     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.
 
@@ -41,6 +41,7 @@ class TavilySearchToolComponent(LCToolComponent):
     icon = "TavilyIcon"
     name = "TavilyAISearch"
     documentation = "https://docs.tavily.com/"
+    legacy = True
 
     inputs = [
         SecretStrInput(