fix: Remove dataframe function (#8251)

remove dataframe function

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

@@ -5,7 +5,6 @@ from xml.etree.ElementTree import Element
 from defusedxml.ElementTree import fromstring
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.io import DropdownInput, IntInput, MessageTextInput, Output
 from langflow.schema import Data, DataFrame
 
@@ -160,4 +159,4 @@ class ArXivComponent(Component):
             DataFrame: A DataFrame containing the search results.
         """
         data = self.search_papers()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -5,7 +5,6 @@ from langchain_community.utilities import BingSearchAPIWrapper
 
 from langflow.base.langchain_utilities.model import LCToolComponent
 from langflow.field_typing import Tool
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import IntInput, MessageTextInput, MultilineInput, SecretStrInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -49,7 +48,7 @@ class BingSearchAPIComponent(LCToolComponent):
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)
 
     def build_tool(self) -> Tool:
         if self.bing_search_url:

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

@@ -1,7 +1,6 @@
 from langchain_community.tools import DuckDuckGoSearchRun
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import IntInput, MessageTextInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -89,4 +88,4 @@ class DuckDuckGoSearchComponent(Component):
             DataFrame: A DataFrame containing the search results.
         """
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -9,7 +9,6 @@ from pydantic.v1 import Field
 
 from langflow.base.langchain_utilities.model import LCToolComponent
 from langflow.field_typing import Tool
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import IntInput, MultilineInput, NestedDictInput, SecretStrInput, StrInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -170,4 +169,4 @@ class GleanSearchAPIComponent(LCToolComponent):
             DataFrame: A DataFrame containing the search results.
         """
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -3,7 +3,6 @@ from typing import Any
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -76,4 +75,4 @@ class SearchComponent(Component):
             DataFrame: A DataFrame containing the search results.
         """
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -3,7 +3,6 @@ from httpx import HTTPError
 from langchain_core.tools import ToolException
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.io import MultilineInput, Output
 from langflow.schema import Data, DataFrame
 
@@ -82,4 +81,4 @@ class WikidataComponent(Component):
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -1,7 +1,6 @@
 from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import BoolInput, IntInput, MessageTextInput, MultilineInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -50,4 +49,4 @@ class WikipediaComponent(Component):
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -2,7 +2,6 @@ from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 
 from langflow.base.langchain_utilities.model import LCToolComponent
 from langflow.field_typing import Tool
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import MultilineInput, SecretStrInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -51,4 +50,4 @@ topics, delivering structured responses."""
             DataFrame: A DataFrame containing the query results.
         """
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -8,7 +8,6 @@ from loguru import logger
 from pydantic import BaseModel, Field
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.inputs import DropdownInput, IntInput, MessageTextInput
 from langflow.io import Output
 from langflow.schema import Data, DataFrame
@@ -134,4 +133,4 @@ to access financial data and market information from Yahoo Finance."""
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -2,7 +2,6 @@ import httpx
 from loguru import logger
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.io import BoolInput, DropdownInput, MessageTextInput, Output, SecretStrInput
 from langflow.schema import Data
 from langflow.schema.dataframe import DataFrame
@@ -115,4 +114,4 @@ class TavilyExtractComponent(Component):
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

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

@@ -2,7 +2,6 @@ import httpx
 from loguru import logger
 
 from langflow.custom import Component
-from langflow.helpers.data import data_to_dataframe
 from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput
 from langflow.schema import Data
 from langflow.schema.dataframe import DataFrame
@@ -209,4 +208,4 @@ class TavilySearchComponent(Component):
 
     def fetch_content_dataframe(self) -> DataFrame:
         data = self.fetch_content()
-        return data_to_dataframe(data)
+        return DataFrame(data)

src/backend/base/langflow/helpers/data.py

@@ -2,7 +2,7 @@ from collections import defaultdict
 
 from langchain_core.documents import Document
 
-from langflow.schema import Data, DataFrame
+from langflow.schema import Data
 from langflow.schema.message import Message
 
 
@@ -139,17 +139,3 @@ def messages_to_text(template: str, messages: Message | list[Message]) -> str:
 
     formated_messages = [template.format(data=message.model_dump(), **message.model_dump()) for message in messages_]
     return "\n".join(formated_messages)
-
-
-def data_to_dataframe(data: Data | list[Data]) -> DataFrame:
-    """Converts a Data object or a list of Data objects to a DataFrame.
-
-    Args:
-        data (Data | list[Data]): The Data object or list of Data objects to convert.
-
-    Returns:
-        DataFrame: The converted DataFrame.
-    """
-    if isinstance(data, Data):
-        return DataFrame([data.data])
-    return DataFrame(data=[d.data for d in data])

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

@@ -1462,7 +1462,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import data_to_dataframe\nfrom langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema import Data, 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 data_to_dataframe(data)\n"
+                "value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom import Component\nfrom langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema import Data, 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"
               },
               "engine": {
                 "_input_type": "DropdownInput",