Tools

import os
from typing import Any

from astrapy import Collection, DataAPIClient, Database
from langchain.pydantic_v1 import BaseModel, Field, create_model
from langchain_core.tools import StructuredTool

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.io import DictInput, IntInput, SecretStrInput, StrInput
from langflow.schema import Data


class AstraDBToolComponent(LCToolComponent):
    display_name: str = "Astra DB Tool"
    description: str = "Create a tool to get transactional data from DataStax Astra DB Collection"
    documentation: str = "https://docs.langflow.org/Components/components-tools#astra-db-tool"
    icon: str = "AstraDB"

    inputs = [
        StrInput(
            name="tool_name",
            display_name="Tool Name",
            info="The name of the tool.",
            required=True,
        ),
        StrInput(
            name="tool_description",
            display_name="Tool Description",
            info="The description of the tool.",
            required=True,
        ),
        StrInput(
            name="namespace",
            display_name="Namespace Name",
            info="The name of the namespace within Astra where the collection is be stored.",
            value="default_keyspace",
            advanced=True,
        ),
        StrInput(
            name="collection_name",
            display_name="Collection Name",
            info="The name of the collection within Astra DB where the vectors will be stored.",
            required=True,
        ),
        SecretStrInput(
            name="token",
            display_name="Astra DB Application Token",
            info="Authentication token for accessing Astra DB.",
            value="ASTRA_DB_APPLICATION_TOKEN",
            required=True,
        ),
        SecretStrInput(
            name="api_endpoint",
            display_name="Database" if os.getenv("ASTRA_ENHANCED", "false").lower() == "true" else "API Endpoint",
            info="API endpoint URL for the Astra DB service.",
            value="ASTRA_DB_API_ENDPOINT",
            required=True,
        ),
        StrInput(
            name="projection_attributes",
            display_name="Projection Attributes",
            info="Attributes to return separated by comma.",
            required=True,
            value="*",
            advanced=True,
        ),
        DictInput(
            name="tool_params",
            info="Attributes to filter and description to the model. Add ! for mandatory (e.g: !customerId)",
            display_name="Tool params",
            is_list=True,
        ),
        DictInput(
            name="static_filters",
            info="Attributes to filter and correspoding value",
            display_name="Static filters",
            advanced=True,
            is_list=True,
        ),
        IntInput(
            name="number_of_results",
            display_name="Number of Results",
            info="Number of results to return.",
            advanced=True,
            value=5,
        ),
    ]

    _cached_client: DataAPIClient | None = None
    _cached_db: Database | None = None
    _cached_collection: Collection | None = None

    def _build_collection(self):
        if self._cached_collection:
            return self._cached_collection

        cached_client = DataAPIClient(self.token)
        cached_db = cached_client.get_database(self.api_endpoint, namespace=self.namespace)
        self._cached_collection = cached_db.get_collection(self.collection_name)
        return self._cached_collection

    def create_args_schema(self) -> dict[str, BaseModel]:
        args: dict[str, tuple[Any, Field] | list[str]] = {}

        for key in self.tool_params:
            if key.startswith("!"):  # Mandatory
                args[key[1:]] = (str, Field(description=self.tool_params[key]))
            else:  # Optional
                args[key] = (str | None, Field(description=self.tool_params[key], default=None))

        model = create_model("ToolInput", **args, __base__=BaseModel)
        return {"ToolInput": model}

    def build_tool(self) -> StructuredTool:
        """Builds an Astra DB Collection tool.

        Returns:
            Tool: The built Astra DB tool.
        """
        schema_dict = self.create_args_schema()

        tool = StructuredTool.from_function(
            name=self.tool_name,
            args_schema=schema_dict["ToolInput"],
            description=self.tool_description,
            func=self.run_model,
            return_direct=False,
        )
        self.status = "Astra DB Tool created"

        return tool

    def projection_args(self, input_str: str) -> dict:
        elements = input_str.split(",")
        result = {}

        for element in elements:
            if element.startswith("!"):
                result[element[1:]] = False
            else:
                result[element] = True

        return result

    def run_model(self, **args) -> Data | list[Data]:
        collection = self._build_collection()
        results = collection.find(
            ({**args, **self.static_filters}),
            projection=self.projection_args(self.projection_attributes),
            limit=self.number_of_results,
        )

        data: list[Data] = [Data(data=doc) for doc in results]
        self.status = data
        return data

import urllib
from http import HTTPStatus
from typing import Any

import requests
from langchain.pydantic_v1 import BaseModel, Field, create_model
from langchain_core.tools import StructuredTool

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.io import DictInput, IntInput, SecretStrInput, StrInput
from langflow.schema import Data


class AstraDBCQLToolComponent(LCToolComponent):
    display_name: str = "Astra DB CQL"
    description: str = "Create a tool to get transactional data from DataStax Astra DB CQL Table"
    documentation: str = "https://docs.langflow.org/Components/components-tools#astra-db-cql-tool"
    icon: str = "AstraDB"

    inputs = [
        StrInput(name="tool_name", display_name="Tool Name", info="The name of the tool.", required=True),
        StrInput(
            name="tool_description",
            display_name="Tool Description",
            info="The tool description to be passed to the model.",
            required=True,
        ),
        StrInput(
            name="keyspace",
            display_name="Keyspace",
            value="default_keyspace",
            info="The keyspace name within Astra DB where the data is stored.",
            required=True,
            advanced=True,
        ),
        StrInput(
            name="table_name",
            display_name="Table Name",
            info="The name of the table within Astra DB where the data is stored.",
            required=True,
        ),
        SecretStrInput(
            name="token",
            display_name="Astra DB Application Token",
            info="Authentication token for accessing Astra DB.",
            value="ASTRA_DB_APPLICATION_TOKEN",
            required=True,
        ),
        StrInput(
            name="api_endpoint",
            display_name="API Endpoint",
            info="API endpoint URL for the Astra DB service.",
            value="ASTRA_DB_API_ENDPOINT",
            required=True,
        ),
        StrInput(
            name="projection_fields",
            display_name="Projection fields",
            info="Attributes to return separated by comma.",
            required=True,
            value="*",
            advanced=True,
        ),
        DictInput(
            name="partition_keys",
            display_name="Partition Keys",
            is_list=True,
            info="Field name and description to the model",
            required=True,
        ),
        DictInput(
            name="clustering_keys",
            display_name="Clustering Keys",
            is_list=True,
            info="Field name and description to the model",
        ),
        DictInput(
            name="static_filters",
            display_name="Static Filters",
            is_list=True,
            advanced=True,
            info="Field name and value. When filled, it will not be generated by the LLM.",
        ),
        IntInput(
            name="number_of_results",
            display_name="Number of Results",
            info="Number of results to return.",
            advanced=True,
            value=5,
        ),
    ]

    def astra_rest(self, args):
        headers = {"Accept": "application/json", "X-Cassandra-Token": f"{self.token}"}
        astra_url = f"{self.api_endpoint}/api/rest/v2/keyspaces/{self.keyspace}/{self.table_name}/"
        key = []

        # Partition keys are mandatory
        key = [self.partition_keys[k] for k in self.partition_keys]

        # Clustering keys are optional
        for k in self.clustering_keys:
            if k in args:
                key.append(args[k])
            elif self.static_filters[k] is not None:
                key.append(self.static_filters[k])

        url = f"{astra_url}{'/'.join(key)}?page-size={self.number_of_results}"

        if self.projection_fields != "*":
            url += f"&fields={urllib.parse.quote(self.projection_fields.replace(' ', ''))}"

        res = requests.request("GET", url=url, headers=headers, timeout=10)

        if int(res.status_code) >= HTTPStatus.BAD_REQUEST:
            return res.text

        try:
            res_data = res.json()
            return res_data["data"]
        except ValueError:
            return res.status_code

    def create_args_schema(self) -> dict[str, BaseModel]:
        args: dict[str, tuple[Any, Field]] = {}

        for key in self.partition_keys:
            # Partition keys are mandatory is it doesn't have a static filter
            if key not in self.static_filters:
                args[key] = (str, Field(description=self.partition_keys[key]))

        for key in self.clustering_keys:
            # Partition keys are mandatory if has the exclamation mark and doesn't have a static filter
            if key not in self.static_filters:
                if key.startswith("!"):  # Mandatory
                    args[key[1:]] = (str, Field(description=self.clustering_keys[key]))
                else:  # Optional
                    args[key] = (str | None, Field(description=self.clustering_keys[key], default=None))

        model = create_model("ToolInput", **args, __base__=BaseModel)
        return {"ToolInput": model}

    def build_tool(self) -> StructuredTool:
        """Builds a Astra DB CQL Table tool.

        Args:
            name (str, optional): The name of the tool.

        Returns:
            Tool: The built AstraDB tool.
        """
        schema_dict = self.create_args_schema()
        return StructuredTool.from_function(
            name=self.tool_name,
            args_schema=schema_dict["ToolInput"],
            description=self.tool_description,
            func=self.run_model,
            return_direct=False,
        )

    def projection_args(self, input_str: str) -> dict:
        elements = input_str.split(",")
        result = {}

        for element in elements:
            if element.startswith("!"):
                result[element[1:]] = False
            else:
                result[element] = True

        return result

    def run_model(self, **args) -> Data | list[Data]:
        results = self.astra_rest(args)
        data: list[Data] = [Data(data=doc) for doc in results]
        self.status = data
        return results

from typing import cast

from langchain_community.tools.bing_search import BingSearchResults
from langchain_community.utilities import BingSearchAPIWrapper

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import IntInput, MessageTextInput, MultilineInput, SecretStrInput
from langflow.schema import Data


class BingSearchAPIComponent(LCToolComponent):
    display_name = "Bing Search API"
    description = "Call the Bing Search API."
    name = "BingSearchAPI"
    icon = "Bing"

    inputs = [
        SecretStrInput(name="bing_subscription_key", display_name="Bing Subscription Key"),
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        MessageTextInput(name="bing_search_url", display_name="Bing Search URL", advanced=True),
        IntInput(name="k", display_name="Number of results", value=4, required=True),
    ]

    def run_model(self) -> list[Data]:
        if self.bing_search_url:
            wrapper = BingSearchAPIWrapper(
                bing_search_url=self.bing_search_url, bing_subscription_key=self.bing_subscription_key
            )
        else:
            wrapper = BingSearchAPIWrapper(bing_subscription_key=self.bing_subscription_key)
        results = wrapper.results(query=self.input_value, num_results=self.k)
        data = [Data(data=result, text=result["snippet"]) for result in results]
        self.status = data
        return data

    def build_tool(self) -> Tool:
        if self.bing_search_url:
            wrapper = BingSearchAPIWrapper(
                bing_search_url=self.bing_search_url, bing_subscription_key=self.bing_subscription_key
            )
        else:
            wrapper = BingSearchAPIWrapper(bing_subscription_key=self.bing_subscription_key)
        return cast("Tool", BingSearchResults(api_wrapper=wrapper, num_results=self.k))

import ast
import operator

from langchain.tools import StructuredTool
from langchain_core.tools import ToolException
from loguru import logger
from pydantic import BaseModel, Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import MessageTextInput
from langflow.schema import Data


class CalculatorToolComponent(LCToolComponent):
    display_name = "Calculator [DEPRECATED]"
    description = "Perform basic arithmetic operations on a given expression."
    icon = "calculator"
    name = "CalculatorTool"
    legacy = True

    inputs = [
        MessageTextInput(
            name="expression",
            display_name="Expression",
            info="The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').",
        ),
    ]

    class CalculatorToolSchema(BaseModel):
        expression: str = Field(..., description="The arithmetic expression to evaluate.")

    def run_model(self) -> list[Data]:
        return self._evaluate_expression(self.expression)

    def build_tool(self) -> Tool:
        return StructuredTool.from_function(
            name="calculator",
            description="Evaluate basic arithmetic expressions. Input should be a string containing the expression.",
            func=self._eval_expr_with_error,
            args_schema=self.CalculatorToolSchema,
        )

    def _eval_expr(self, node):
        if isinstance(node, ast.Num):
            return node.n
        if isinstance(node, ast.BinOp):
            left_val = self._eval_expr(node.left)
            right_val = self._eval_expr(node.right)
            return self.operators[type(node.op)](left_val, right_val)
        if isinstance(node, ast.UnaryOp):
            operand_val = self._eval_expr(node.operand)
            return self.operators[type(node.op)](operand_val)
        if isinstance(node, ast.Call):
            msg = (
                "Function calls like sqrt(), sin(), cos() etc. are not supported. "
                "Only basic arithmetic operations (+, -, *, /, **) are allowed."
            )
            raise TypeError(msg)
        msg = f"Unsupported operation or expression type: {type(node).__name__}"
        raise TypeError(msg)

    def _eval_expr_with_error(self, expression: str) -> list[Data]:
        try:
            return self._evaluate_expression(expression)
        except Exception as e:
            raise ToolException(str(e)) from e

    def _evaluate_expression(self, expression: str) -> list[Data]:
        try:
            # Parse the expression and evaluate it
            tree = ast.parse(expression, mode="eval")
            result = self._eval_expr(tree.body)

            # Format the result to a reasonable number of decimal places
            formatted_result = f"{result:.6f}".rstrip("0").rstrip(".")

            self.status = formatted_result
            return [Data(data={"result": formatted_result})]

        except (SyntaxError, TypeError, KeyError) as e:
            error_message = f"Invalid expression: {e}"
            self.status = error_message
            return [Data(data={"error": error_message, "input": expression})]
        except ZeroDivisionError:
            error_message = "Error: Division by zero"
            self.status = error_message
            return [Data(data={"error": error_message, "input": expression})]
        except Exception as e:  # noqa: BLE001
            logger.opt(exception=True).debug("Error evaluating expression")
            error_message = f"Error: {e}"
            self.status = error_message
            return [Data(data={"error": error_message, "input": expression})]

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.operators = {
            ast.Add: operator.add,
            ast.Sub: operator.sub,
            ast.Mult: operator.mul,
            ast.Div: operator.truediv,
            ast.Pow: operator.pow,
        }

import json
from typing import Any
from urllib.parse import urljoin

import httpx
from langchain_core.tools import StructuredTool, ToolException
from pydantic import BaseModel
from pydantic.v1 import Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import IntInput, MultilineInput, NestedDictInput, SecretStrInput, StrInput
from langflow.schema import Data


class GleanSearchAPISchema(BaseModel):
    query: str = Field(..., description="The search query")
    page_size: int = Field(10, description="Maximum number of results to return")
    request_options: dict[str, Any] | None = Field(default_factory=dict, description="Request Options")


class GleanAPIWrapper(BaseModel):
    """Wrapper around Glean API."""

    glean_api_url: str
    glean_access_token: str
    act_as: str = "langflow-component@datastax.com"  # TODO: Detect this

    def _prepare_request(
        self,
        query: str,
        page_size: int = 10,
        request_options: dict[str, Any] | None = None,
    ) -> dict:
        # Ensure there's a trailing slash
        url = self.glean_api_url
        if not url.endswith("/"):
            url += "/"

        return {
            "url": urljoin(url, "search"),
            "headers": {
                "Authorization": f"Bearer {self.glean_access_token}",
                "X-Scio-ActAs": self.act_as,
            },
            "payload": {
                "query": query,
                "pageSize": page_size,
                "requestOptions": request_options,
            },
        }

    def results(self, query: str, **kwargs: Any) -> list[dict[str, Any]]:
        results = self._search_api_results(query, **kwargs)

        if len(results) == 0:
            msg = "No good Glean Search Result was found"
            raise AssertionError(msg)

        return results

    def run(self, query: str, **kwargs: Any) -> list[dict[str, Any]]:
        try:
            results = self.results(query, **kwargs)

            processed_results = []
            for result in results:
                if "title" in result:
                    result["snippets"] = result.get("snippets", [{"snippet": {"text": result["title"]}}])
                    if "text" not in result["snippets"][0]:
                        result["snippets"][0]["text"] = result["title"]

                processed_results.append(result)
        except Exception as e:
            error_message = f"Error in Glean Search API: {e!s}"
            raise ToolException(error_message) from e

        return processed_results

    def _search_api_results(self, query: str, **kwargs: Any) -> list[dict[str, Any]]:
        request_details = self._prepare_request(query, **kwargs)

        response = httpx.post(
            request_details["url"],
            json=request_details["payload"],
            headers=request_details["headers"],
        )

        response.raise_for_status()
        response_json = response.json()

        return response_json.get("results", [])

    @staticmethod
    def _result_as_string(result: dict) -> str:
        return json.dumps(result, indent=4)


class GleanSearchAPIComponent(LCToolComponent):
    display_name = "Glean Search API"
    description = "Call Glean Search API"
    name = "GleanAPI"
    icon = "Glean"

    inputs = [
        StrInput(
            name="glean_api_url",
            display_name="Glean API URL",
            required=True,
        ),
        SecretStrInput(name="glean_access_token", display_name="Glean Access Token", required=True),
        MultilineInput(name="query", display_name="Query", required=True),
        IntInput(name="page_size", display_name="Page Size", value=10),
        NestedDictInput(name="request_options", display_name="Request Options", required=False),
    ]

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper(
            glean_api_url=self.glean_api_url,
            glean_access_token=self.glean_access_token,
        )

        tool = StructuredTool.from_function(
            name="glean_search_api",
            description="Search Glean for relevant results.",
            func=wrapper.run,
            args_schema=GleanSearchAPISchema,
        )

        self.status = "Glean Search API Tool for Langchain"

        return tool

    def run_model(self) -> list[Data]:
        tool = self.build_tool()

        results = tool.run(
            {
                "query": self.query,
                "page_size": self.page_size,
                "request_options": self.request_options,
            }
        )

        # Build the data
        data = [Data(data=result, text=result["snippets"][0]["text"]) for result in results]
        self.status = data  # type: ignore[assignment]

        return data

    def _build_wrapper(
        self,
        glean_api_url: str,
        glean_access_token: str,
    ):
        return GleanAPIWrapper(
            glean_api_url=glean_api_url,
            glean_access_token=glean_access_token,
        )

from langchain_core.tools import Tool

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.inputs import IntInput, MultilineInput, SecretStrInput
from langflow.schema import Data


class GoogleSearchAPIComponent(LCToolComponent):
    display_name = "Google Search API"
    description = "Call Google Search API."
    name = "GoogleSearchAPI"
    icon = "Google"
    inputs = [
        SecretStrInput(name="google_api_key", display_name="Google API Key", required=True),
        SecretStrInput(name="google_cse_id", display_name="Google CSE ID", required=True),
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        IntInput(name="k", display_name="Number of results", value=4, required=True),
    ]

    def run_model(self) -> Data | list[Data]:
        wrapper = self._build_wrapper()
        results = wrapper.results(query=self.input_value, num_results=self.k)
        data = [Data(data=result, text=result["snippet"]) for result in results]
        self.status = data
        return data

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper()
        return Tool(
            name="google_search",
            description="Search Google for recent results.",
            func=wrapper.run,
        )

    def _build_wrapper(self):
        try:
            from langchain_google_community import GoogleSearchAPIWrapper
        except ImportError as e:
            msg = "Please install langchain-google-community to use GoogleSearchAPIWrapper."
            raise ImportError(msg) from e
        return GoogleSearchAPIWrapper(google_api_key=self.google_api_key, google_cse_id=self.google_cse_id, k=self.k)

from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import IntInput, MultilineInput, SecretStrInput
from langflow.schema import Data


class GoogleSerperAPIComponent(LCToolComponent):
    display_name = "Google Serper API"
    description = "Call the Serper.dev Google Search API."
    name = "GoogleSerperAPI"
    icon = "Google"
    inputs = [
        SecretStrInput(name="serper_api_key", display_name="Serper API Key", required=True),
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        IntInput(name="k", display_name="Number of results", value=4, required=True),
    ]

    def run_model(self) -> Data | list[Data]:
        wrapper = self._build_wrapper()
        results = wrapper.results(query=self.input_value)
        list_results = results.get("organic", [])
        data = [Data(data=result, text=result["snippet"]) for result in list_results]
        self.status = data
        return data

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper()
        return Tool(
            name="google_search",
            description="Search Google for recent results.",
            func=wrapper.run,
        )

    def _build_wrapper(self):
        return GoogleSerperAPIWrapper(serper_api_key=self.serper_api_key, k=self.k)

import ast
import json
from typing import Any

from langchain.agents import Tool
from langchain_core.tools import StructuredTool
from loguru import logger
from pydantic.v1 import Field, create_model
from pydantic.v1.fields import Undefined
from typing_extensions import override

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.inputs.inputs import (
    BoolInput,
    DropdownInput,
    FieldTypes,
    HandleInput,
    MessageTextInput,
    MultilineInput,
)
from langflow.io import Output
from langflow.schema import Data
from langflow.schema.dotdict import dotdict


class PythonCodeStructuredTool(LCToolComponent):
    DEFAULT_KEYS = [
        "code",
        "_type",
        "text_key",
        "tool_code",
        "tool_name",
        "tool_description",
        "return_direct",
        "tool_function",
        "global_variables",
        "_classes",
        "_functions",
    ]
    display_name = "Python Code Structured"
    description = "structuredtool dataclass code to tool"
    documentation = "https://python.langchain.com/docs/modules/tools/custom_tools/#structuredtool-dataclass"
    name = "PythonCodeStructuredTool"
    icon = "Python"
    field_order = ["name", "description", "tool_code", "return_direct", "tool_function"]
    legacy: bool = True

    inputs = [
        MultilineInput(
            name="tool_code",
            display_name="Tool Code",
            info="Enter the dataclass code.",
            placeholder="def my_function(args):\n    pass",
            required=True,
            real_time_refresh=True,
            refresh_button=True,
        ),
        MessageTextInput(
            name="tool_name",
            display_name="Tool Name",
            info="Enter the name of the tool.",
            required=True,
        ),
        MessageTextInput(
            name="tool_description",
            display_name="Description",
            info="Enter the description of the tool.",
            required=True,
        ),
        BoolInput(
            name="return_direct",
            display_name="Return Directly",
            info="Should the tool return the function output directly?",
        ),
        DropdownInput(
            name="tool_function",
            display_name="Tool Function",
            info="Select the function for additional expressions.",
            options=[],
            required=True,
            real_time_refresh=True,
            refresh_button=True,
        ),
        HandleInput(
            name="global_variables",
            display_name="Global Variables",
            info="Enter the global variables or Create Data Component.",
            input_types=["Data"],
            field_type=FieldTypes.DICT,
            is_list=True,
        ),
        MessageTextInput(name="_classes", display_name="Classes", advanced=True),
        MessageTextInput(name="_functions", display_name="Functions", advanced=True),
    ]

    outputs = [
        Output(display_name="Tool", name="result_tool", method="build_tool"),
    ]

    @override
    async def update_build_config(
        self, build_config: dotdict, field_value: Any, field_name: str | None = None
    ) -> dotdict:
        if field_name is None:
            return build_config

        if field_name not in {"tool_code", "tool_function"}:
            return build_config

        try:
            named_functions = {}
            [classes, functions] = self._parse_code(build_config["tool_code"]["value"])
            existing_fields = {}
            if len(build_config) > len(self.DEFAULT_KEYS):
                for key in build_config.copy():
                    if key not in self.DEFAULT_KEYS:
                        existing_fields[key] = build_config.pop(key)

            names = []
            for func in functions:
                named_functions[func["name"]] = func
                names.append(func["name"])

                for arg in func["args"]:
                    field_name = f"{func['name']}|{arg['name']}"
                    if field_name in existing_fields:
                        build_config[field_name] = existing_fields[field_name]
                        continue

                    field = MessageTextInput(
                        display_name=f"{arg['name']}: Description",
                        name=field_name,
                        info=f"Enter the description for {arg['name']}",
                        required=True,
                    )
                    build_config[field_name] = field.to_dict()
            build_config["_functions"]["value"] = json.dumps(named_functions)
            build_config["_classes"]["value"] = json.dumps(classes)
            build_config["tool_function"]["options"] = names
        except Exception as e:  # noqa: BLE001
            self.status = f"Failed to extract names: {e}"
            logger.opt(exception=True).debug(self.status)
            build_config["tool_function"]["options"] = ["Failed to parse", str(e)]
        return build_config

    async def build_tool(self) -> Tool:
        local_namespace = {}  # type: ignore[var-annotated]
        modules = self._find_imports(self.tool_code)
        import_code = ""
        for module in modules["imports"]:
            import_code += f"global {module}\nimport {module}\n"
        for from_module in modules["from_imports"]:
            for alias in from_module.names:
                import_code += f"global {alias.name}\n"
            import_code += (
                f"from {from_module.module} import {', '.join([alias.name for alias in from_module.names])}\n"
            )
        exec(import_code, globals())
        exec(self.tool_code, globals(), local_namespace)

        class PythonCodeToolFunc:
            params: dict = {}

            def run(**kwargs):
                for key, arg in kwargs.items():
                    if key not in PythonCodeToolFunc.params:
                        PythonCodeToolFunc.params[key] = arg
                return local_namespace[self.tool_function](**PythonCodeToolFunc.params)

        globals_ = globals()
        local = {}
        local[self.tool_function] = PythonCodeToolFunc
        globals_.update(local)

        if isinstance(self.global_variables, list):
            for data in self.global_variables:
                if isinstance(data, Data):
                    globals_.update(data.data)
        elif isinstance(self.global_variables, dict):
            globals_.update(self.global_variables)

        classes = json.loads(self._attributes["_classes"])
        for class_dict in classes:
            exec("\n".join(class_dict["code"]), globals_)

        named_functions = json.loads(self._attributes["_functions"])
        schema_fields = {}

        for attr in self._attributes:
            if attr in self.DEFAULT_KEYS:
                continue

            func_name = attr.split("|")[0]
            field_name = attr.split("|")[1]
            func_arg = self._find_arg(named_functions, func_name, field_name)
            if func_arg is None:
                msg = f"Failed to find arg: {field_name}"
                raise ValueError(msg)

            field_annotation = func_arg["annotation"]
            field_description = self._get_value(self._attributes[attr], str)

            if field_annotation:
                exec(f"temp_annotation_type = {field_annotation}", globals_)
                schema_annotation = globals_["temp_annotation_type"]
            else:
                schema_annotation = Any
            schema_fields[field_name] = (
                schema_annotation,
                Field(
                    default=func_arg.get("default", Undefined),
                    description=field_description,
                ),
            )

        if "temp_annotation_type" in globals_:
            globals_.pop("temp_annotation_type")

        python_code_tool_schema = None
        if schema_fields:
            python_code_tool_schema = create_model("PythonCodeToolSchema", **schema_fields)

        return StructuredTool.from_function(
            func=local[self.tool_function].run,
            args_schema=python_code_tool_schema,
            name=self.tool_name,
            description=self.tool_description,
            return_direct=self.return_direct,
        )

    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):
        """This function is called after the code validation is done."""
        frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)
        frontend_node["template"] = await self.update_build_config(
            frontend_node["template"],
            frontend_node["template"]["tool_code"]["value"],
            "tool_code",
        )
        frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)
        for key in frontend_node["template"]:
            if key in self.DEFAULT_KEYS:
                continue
            frontend_node["template"] = await self.update_build_config(
                frontend_node["template"], frontend_node["template"][key]["value"], key
            )
            frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)
        return frontend_node

    def _parse_code(self, code: str) -> tuple[list[dict], list[dict]]:
        parsed_code = ast.parse(code)
        lines = code.split("\n")
        classes = []
        functions = []
        for node in parsed_code.body:
            if isinstance(node, ast.ClassDef):
                class_lines = lines[node.lineno - 1 : node.end_lineno]
                class_lines[-1] = class_lines[-1][: node.end_col_offset]
                class_lines[0] = class_lines[0][node.col_offset :]
                classes.append(
                    {
                        "name": node.name,
                        "code": class_lines,
                    }
                )
                continue

            if not isinstance(node, ast.FunctionDef):
                continue

            func = {"name": node.name, "args": []}
            for arg in node.args.args:
                if arg.lineno != arg.end_lineno:
                    msg = "Multiline arguments are not supported"
                    raise ValueError(msg)

                func_arg = {
                    "name": arg.arg,
                    "annotation": None,
                }

                for default in node.args.defaults:
                    if (
                        arg.lineno > default.lineno
                        or arg.col_offset > default.col_offset
                        or (
                            arg.end_lineno is not None
                            and default.end_lineno is not None
                            and arg.end_lineno < default.end_lineno
                        )
                        or (
                            arg.end_col_offset is not None
                            and default.end_col_offset is not None
                            and arg.end_col_offset < default.end_col_offset
                        )
                    ):
                        continue

                    if isinstance(default, ast.Name):
                        func_arg["default"] = default.id
                    elif isinstance(default, ast.Constant):
                        func_arg["default"] = default.value

                if arg.annotation:
                    annotation_line = lines[arg.annotation.lineno - 1]
                    annotation_line = annotation_line[: arg.annotation.end_col_offset]
                    annotation_line = annotation_line[arg.annotation.col_offset :]
                    func_arg["annotation"] = annotation_line
                    if isinstance(func_arg["annotation"], str) and func_arg["annotation"].count("=") > 0:
                        func_arg["annotation"] = "=".join(func_arg["annotation"].split("=")[:-1]).strip()
                if isinstance(func["args"], list):
                    func["args"].append(func_arg)
            functions.append(func)

        return classes, functions

    def _find_imports(self, code: str) -> dotdict:
        imports: list[str] = []
        from_imports = []
        parsed_code = ast.parse(code)
        for node in parsed_code.body:
            if isinstance(node, ast.Import):
                imports.extend(alias.name for alias in node.names)
            elif isinstance(node, ast.ImportFrom):
                from_imports.append(node)
        return dotdict({"imports": imports, "from_imports": from_imports})

    def _get_value(self, value: Any, annotation: Any) -> Any:
        return value if isinstance(value, annotation) else value["value"]

    def _find_arg(self, named_functions: dict, func_name: str, arg_name: str) -> dict | None:
        for arg in named_functions[func_name]["args"]:
            if arg["name"] == arg_name:
                return arg
        return None

import importlib

from langchain.tools import StructuredTool
from langchain_core.tools import ToolException
from langchain_experimental.utilities import PythonREPL
from loguru import logger
from pydantic import BaseModel, Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import StrInput
from langflow.schema import Data


class PythonREPLToolComponent(LCToolComponent):
    display_name = "Python REPL"
    description = "A tool for running Python code in a REPL environment."
    name = "PythonREPLTool"
    icon = "Python"

    inputs = [
        StrInput(
            name="name",
            display_name="Tool Name",
            info="The name of the tool.",
            value="python_repl",
        ),
        StrInput(
            name="description",
            display_name="Tool Description",
            info="A description of the tool.",
            value="A Python shell. Use this to execute python commands. "
            "Input should be a valid python command. "
            "If you want to see the output of a value, you should print it out with `print(...)`.",
        ),
        StrInput(
            name="global_imports",
            display_name="Global Imports",
            info="A comma-separated list of modules to import globally, e.g. 'math,numpy'.",
            value="math",
        ),
        StrInput(
            name="code",
            display_name="Python Code",
            info="The Python code to execute.",
            value="print('Hello, World!')",
        ),
    ]

    class PythonREPLSchema(BaseModel):
        code: str = Field(..., description="The Python code to execute.")

    def get_globals(self, global_imports: str | list[str]) -> dict:
        global_dict = {}
        if isinstance(global_imports, str):
            modules = [module.strip() for module in global_imports.split(",")]
        elif isinstance(global_imports, list):
            modules = global_imports
        else:
            msg = "global_imports must be either a string or a list"
            raise TypeError(msg)

        for module in modules:
            try:
                imported_module = importlib.import_module(module)
                global_dict[imported_module.__name__] = imported_module
            except ImportError as e:
                msg = f"Could not import module {module}"
                raise ImportError(msg) from e
        return global_dict

    def build_tool(self) -> Tool:
        globals_ = self.get_globals(self.global_imports)
        python_repl = PythonREPL(_globals=globals_)

        def run_python_code(code: str) -> str:
            try:
                return python_repl.run(code)
            except Exception as e:
                logger.opt(exception=True).debug("Error running Python code")
                raise ToolException(str(e)) from e

        tool = StructuredTool.from_function(
            name=self.name,
            description=self.description,
            func=run_python_code,
            args_schema=self.PythonREPLSchema,
        )

        self.status = f"Python REPL Tool created with global imports: {self.global_imports}"
        return tool

    def run_model(self) -> list[Data]:
        tool = self.build_tool()
        result = tool.run(self.code)
        return [Data(data={"result": result})]

from langchain_core.tools import create_retriever_tool

from langflow.custom import CustomComponent
from langflow.field_typing import BaseRetriever, Tool


class RetrieverToolComponent(CustomComponent):
    display_name = "RetrieverTool"
    description = "Tool for interacting with retriever"
    name = "RetrieverTool"
    legacy = True
    icon = "LangChain"

    def build_config(self):
        return {
            "retriever": {
                "display_name": "Retriever",
                "info": "Retriever to interact with",
                "type": BaseRetriever,
                "input_types": ["Retriever"],
            },
            "name": {"display_name": "Name", "info": "Name of the tool"},
            "description": {"display_name": "Description", "info": "Description of the tool"},
        }

    def build(self, retriever: BaseRetriever, name: str, description: str, **kwargs) -> Tool:
        _ = kwargs
        return create_retriever_tool(
            retriever=retriever,
            name=name,
            description=description,
        )

import json
from collections.abc import Sequence
from typing import Any

import requests
from langchain.agents import Tool
from langchain_core.tools import StructuredTool
from loguru import logger
from pydantic.v1 import Field, create_model

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.inputs import DropdownInput, IntInput, MessageTextInput, MultiselectInput
from langflow.io import Output
from langflow.schema.dotdict import dotdict


class SearXNGToolComponent(LCToolComponent):
    search_headers: dict = {}
    display_name = "SearXNG Search"
    description = "A component that searches for tools using SearXNG."
    name = "SearXNGTool"
    legacy: bool = True

    inputs = [
        MessageTextInput(
            name="url",
            display_name="URL",
            value="http://localhost",
            required=True,
            refresh_button=True,
        ),
        IntInput(
            name="max_results",
            display_name="Max Results",
            value=10,
            required=True,
        ),
        MultiselectInput(
            name="categories",
            display_name="Categories",
            options=[],
            value=[],
        ),
        DropdownInput(
            name="language",
            display_name="Language",
            options=[],
        ),
    ]

    outputs = [
        Output(display_name="Tool", name="result_tool", method="build_tool"),
    ]

    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:
        if field_name is None:
            return build_config

        if field_name != "url":
            return build_config

        try:
            url = f"{field_value}/config"

            response = requests.get(url=url, headers=self.search_headers.copy(), timeout=10)
            data = None
            if response.headers.get("Content-Encoding") == "zstd":
                data = json.loads(response.content)
            else:
                data = response.json()
            build_config["categories"]["options"] = data["categories"].copy()
            for selected_category in build_config["categories"]["value"]:
                if selected_category not in build_config["categories"]["options"]:
                    build_config["categories"]["value"].remove(selected_category)
            languages = list(data["locales"])
            build_config["language"]["options"] = languages.copy()
        except Exception as e:  # noqa: BLE001
            self.status = f"Failed to extract names: {e}"
            logger.opt(exception=True).debug(self.status)
            build_config["categories"]["options"] = ["Failed to parse", str(e)]
        return build_config

    def build_tool(self) -> Tool:
        class SearxSearch:
            _url: str = ""
            _categories: list[str] = []
            _language: str = ""
            _headers: dict = {}
            _max_results: int = 10

            @staticmethod
            def search(query: str, categories: Sequence[str] = ()) -> list:
                if not SearxSearch._categories and not categories:
                    msg = "No categories provided."
                    raise ValueError(msg)
                all_categories = SearxSearch._categories + list(set(categories) - set(SearxSearch._categories))
                try:
                    url = f"{SearxSearch._url}/"
                    headers = SearxSearch._headers.copy()
                    response = requests.get(
                        url=url,
                        headers=headers,
                        params={
                            "q": query,
                            "categories": ",".join(all_categories),
                            "language": SearxSearch._language,
                            "format": "json",
                        },
                        timeout=10,
                    ).json()

                    num_results = min(SearxSearch._max_results, len(response["results"]))
                    return [response["results"][i] for i in range(num_results)]
                except Exception as e:  # noqa: BLE001
                    logger.opt(exception=True).debug("Error running SearXNG Search")
                    return [f"Failed to search: {e}"]

        SearxSearch._url = self.url
        SearxSearch._categories = self.categories.copy()
        SearxSearch._language = self.language
        SearxSearch._headers = self.search_headers.copy()
        SearxSearch._max_results = self.max_results

        globals_ = globals()
        local = {}
        local["SearxSearch"] = SearxSearch
        globals_.update(local)

        schema_fields = {
            "query": (str, Field(..., description="The query to search for.")),
            "categories": (
                list[str],
                Field(default=[], description="The categories to search in."),
            ),
        }

        searx_search_schema = create_model("SearxSearchSchema", **schema_fields)

        return StructuredTool.from_function(
            func=local["SearxSearch"].search,
            args_schema=searx_search_schema,
            name="searxng_search_tool",
            description="A tool that searches for tools using SearXNG.\nThe available categories are: "
            + ", ".join(self.categories),
        )

from typing import Any

from langchain.tools import StructuredTool
from langchain_community.utilities.searchapi import SearchApiAPIWrapper
from pydantic import BaseModel, Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import DictInput, IntInput, MessageTextInput, MultilineInput, SecretStrInput
from langflow.schema import Data


class SearchAPIComponent(LCToolComponent):
    display_name: str = "Search API"
    description: str = "Call the searchapi.io API with result limiting"
    name = "SearchAPI"
    documentation: str = "https://www.searchapi.io/docs/google"
    icon = "SearchAPI"

    inputs = [
        MessageTextInput(name="engine", display_name="Engine", value="google"),
        SecretStrInput(name="api_key", display_name="SearchAPI API Key", required=True),
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        DictInput(name="search_params", display_name="Search parameters", advanced=True, is_list=True),
        IntInput(name="max_results", display_name="Max Results", value=5, advanced=True),
        IntInput(name="max_snippet_length", display_name="Max Snippet Length", value=100, advanced=True),
    ]

    class SearchAPISchema(BaseModel):
        query: str = Field(..., description="The search query")
        params: dict[str, Any] = Field(default_factory=dict, description="Additional search parameters")
        max_results: int = Field(5, description="Maximum number of results to return")
        max_snippet_length: int = Field(100, description="Maximum length of each result snippet")

    def _build_wrapper(self):
        return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper()

        def search_func(
            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100
        ) -> list[dict[str, Any]]:
            params = params or {}
            full_results = wrapper.results(query=query, **params)
            organic_results = full_results.get("organic_results", [])[:max_results]

            limited_results = []
            for result in organic_results:
                limited_result = {
                    "title": result.get("title", "")[:max_snippet_length],
                    "link": result.get("link", ""),
                    "snippet": result.get("snippet", "")[:max_snippet_length],
                }
                limited_results.append(limited_result)

            return limited_results

        tool = StructuredTool.from_function(
            name="search_api",
            description="Search for recent results using searchapi.io with result limiting",
            func=search_func,
            args_schema=self.SearchAPISchema,
        )

        self.status = f"Search API Tool created with engine: {self.engine}"
        return tool

    def run_model(self) -> list[Data]:
        tool = self.build_tool()
        results = tool.run(
            {
                "query": self.input_value,
                "params": self.search_params or {},
                "max_results": self.max_results,
                "max_snippet_length": self.max_snippet_length,
            }
        )

        data_list = [Data(data=result, text=result.get("snippet", "")) for result in results]

        self.status = data_list
        return data_list

from typing import Any

from langchain.tools import StructuredTool
from langchain_community.utilities.serpapi import SerpAPIWrapper
from langchain_core.tools import ToolException
from loguru import logger
from pydantic import BaseModel, Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import DictInput, IntInput, MultilineInput, SecretStrInput
from langflow.schema import Data


class SerpAPISchema(BaseModel):
    """Schema for SerpAPI search parameters."""

    query: str = Field(..., description="The search query")
    params: dict[str, Any] | None = Field(
        default={
            "engine": "google",
            "google_domain": "google.com",
            "gl": "us",
            "hl": "en",
        },
        description="Additional search parameters",
    )
    max_results: int = Field(5, description="Maximum number of results to return")
    max_snippet_length: int = Field(100, description="Maximum length of each result snippet")


class SerpAPIComponent(LCToolComponent):
    display_name = "Serp Search API"
    description = "Call Serp Search API with result limiting"
    name = "SerpAPI"
    icon = "SerpSearch"

    inputs = [
        SecretStrInput(name="serpapi_api_key", display_name="SerpAPI API Key", required=True),
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        DictInput(name="search_params", display_name="Parameters", advanced=True, is_list=True),
        IntInput(name="max_results", display_name="Max Results", value=5, advanced=True),
        IntInput(name="max_snippet_length", display_name="Max Snippet Length", value=100, advanced=True),
    ]

    def _build_wrapper(self, params: dict[str, Any] | None = None) -> SerpAPIWrapper:
        """Build a SerpAPIWrapper with the provided parameters."""
        params = params or {}
        if params:
            return SerpAPIWrapper(
                serpapi_api_key=self.serpapi_api_key,
                params=params,
            )
        return SerpAPIWrapper(serpapi_api_key=self.serpapi_api_key)

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper(self.search_params)

        def search_func(
            query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100
        ) -> list[dict[str, Any]]:
            try:
                local_wrapper = wrapper
                if params:
                    local_wrapper = self._build_wrapper(params)

                full_results = local_wrapper.results(query)
                organic_results = full_results.get("organic_results", [])[:max_results]

                limited_results = []
                for result in organic_results:
                    limited_result = {
                        "title": result.get("title", "")[:max_snippet_length],
                        "link": result.get("link", ""),
                        "snippet": result.get("snippet", "")[:max_snippet_length],
                    }
                    limited_results.append(limited_result)

            except Exception as e:
                error_message = f"Error in SerpAPI search: {e!s}"
                logger.debug(error_message)
                raise ToolException(error_message) from e
            return limited_results

        tool = StructuredTool.from_function(
            name="serp_search_api",
            description="Search for recent results using SerpAPI with result limiting",
            func=search_func,
            args_schema=SerpAPISchema,
        )

        self.status = "SerpAPI Tool created"
        return tool

    def run_model(self) -> list[Data]:
        tool = self.build_tool()
        try:
            results = tool.run(
                {
                    "query": self.input_value,
                    "params": self.search_params or {},
                    "max_results": self.max_results,
                    "max_snippet_length": self.max_snippet_length,
                }
            )

            data_list = [Data(data=result, text=result.get("snippet", "")) for result in results]

        except Exception as e:  # noqa: BLE001
            logger.opt(exception=True).debug("Error running SerpAPI")
            self.status = f"Error: {e}"
            return [Data(data={"error": str(e)}, text=str(e))]

        self.status = data_list  # type: ignore[assignment]
        return data_list

from enum import Enum

import httpx
from langchain.tools import StructuredTool
from langchain_core.tools import ToolException
from loguru import logger
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 TavilySearchDepth(Enum):
    BASIC = "basic"
    ADVANCED = "advanced"


class TavilySearchTopic(Enum):
    GENERAL = "general"
    NEWS = "news"


class TavilySearchSchema(BaseModel):
    query: str = Field(..., description="The search query you want to execute with Tavily.")
    search_depth: TavilySearchDepth = Field(TavilySearchDepth.BASIC, description="The depth of the search.")
    topic: TavilySearchTopic = Field(TavilySearchTopic.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(default=False, description="Include a list of query-related images in the response.")
    include_answer: bool = Field(default=False, description="Include a short answer to original query.")


class TavilySearchToolComponent(LCToolComponent):
    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.

Note: Check 'Advanced' for all options.
"""
    icon = "TavilyIcon"
    name = "TavilyAISearch"
    documentation = "https://docs.tavily.com/"
    legacy = True

    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=list(TavilySearchDepth),
            value=TavilySearchDepth.ADVANCED,
            advanced=True,
        ),
        DropdownInput(
            name="topic",
            display_name="Search Topic",
            info="The category of the search.",
            options=list(TavilySearchTopic),
            value=TavilySearchTopic.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,
        ),
    ]

    def run_model(self) -> list[Data]:
        # Convert string values to enum instances with validation
        try:
            search_depth_enum = (
                self.search_depth
                if isinstance(self.search_depth, TavilySearchDepth)
                else TavilySearchDepth(str(self.search_depth).lower())
            )
        except ValueError as e:
            error_message = f"Invalid search depth value: {e!s}"
            self.status = error_message
            return [Data(data={"error": error_message})]

        try:
            topic_enum = (
                self.topic if isinstance(self.topic, TavilySearchTopic) else TavilySearchTopic(str(self.topic).lower())
            )
        except ValueError as e:
            error_message = f"Invalid topic value: {e!s}"
            self.status = error_message
            return [Data(data={"error": error_message})]

        return self._tavily_search(
            self.query,
            search_depth=search_depth_enum,
            topic=topic_enum,
            max_results=self.max_results,
            include_images=self.include_images,
            include_answer=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=TavilySearchSchema,
        )

    def _tavily_search(
        self,
        query: str,
        *,
        search_depth: TavilySearchDepth = TavilySearchDepth.BASIC,
        topic: TavilySearchTopic = TavilySearchTopic.GENERAL,
        max_results: int = 5,
        include_images: bool = False,
        include_answer: bool = False,
    ) -> list[Data]:
        # Validate enum values
        if not isinstance(search_depth, TavilySearchDepth):
            msg = f"Invalid search_depth value: {search_depth}"
            raise TypeError(msg)
        if not isinstance(topic, TavilySearchTopic):
            msg = f"Invalid topic value: {topic}"
            raise TypeError(msg)

        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.value,
                "topic": topic.value,
                "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  # type: ignore[assignment]

        except httpx.HTTPStatusError as e:
            error_message = f"HTTP error: {e.response.status_code} - {e.response.text}"
            logger.debug(error_message)
            self.status = error_message
            raise ToolException(error_message) from e
        except Exception as e:
            error_message = f"Unexpected error: {e}"
            logger.opt(exception=True).debug("Error running Tavily Search")
            self.status = error_message
            raise ToolException(error_message) from e
        return data_results

from langchain_community.utilities.wikipedia import WikipediaAPIWrapper

from langflow.custom import Component
from langflow.inputs import BoolInput, IntInput, MessageTextInput, MultilineInput
from langflow.io import Output
from langflow.schema import Data
from langflow.schema.message import Message


class WikipediaAPIComponent(Component):
    display_name = "Wikipedia API"
    description = "Call Wikipedia API."
    name = "WikipediaAPI"
    icon = "Wikipedia"

    inputs = [
        MultilineInput(
            name="input_value",
            display_name="Input",
            tool_mode=True,
        ),
        MessageTextInput(name="lang", display_name="Language", value="en"),
        IntInput(name="k", display_name="Number of results", value=4, required=True),
        BoolInput(name="load_all_available_meta", display_name="Load all available meta", value=False, advanced=True),
        IntInput(
            name="doc_content_chars_max", display_name="Document content characters max", value=4000, 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]:
        wrapper = self._build_wrapper()
        docs = wrapper.load(self.input_value)
        data = [Data.from_document(doc) for doc in docs]
        self.status = data
        return data

    def fetch_content_text(self) -> Message:
        data = self.fetch_content()
        result_string = ""
        for item in data:
            result_string += item.text + "\n"
        self.status = result_string
        return Message(text=result_string)

    def _build_wrapper(self) -> WikipediaAPIWrapper:
        return WikipediaAPIWrapper(
            top_k_results=self.k,
            lang=self.lang,
            load_all_available_meta=self.load_all_available_meta,
            doc_content_chars_max=self.doc_content_chars_max,
        )

from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import MultilineInput, SecretStrInput
from langflow.schema import Data


class WolframAlphaAPIComponent(LCToolComponent):
    display_name = "WolframAlpha API"
    description = """Enables queries to Wolfram Alpha for computational data, facts, and calculations across various \
topics, delivering structured responses."""
    name = "WolframAlphaAPI"

    inputs = [
        MultilineInput(
            name="input_value", display_name="Input Query", info="Example query: 'What is the population of France?'"
        ),
        SecretStrInput(name="app_id", display_name="App ID", required=True),
    ]

    icon = "WolframAlphaAPI"

    def run_model(self) -> list[Data]:
        wrapper = self._build_wrapper()
        result_str = wrapper.run(self.input_value)
        data = [Data(text=result_str)]
        self.status = data
        return data

    def build_tool(self) -> Tool:
        wrapper = self._build_wrapper()
        return Tool(name="wolfram_alpha_api", description="Answers mathematical questions.", func=wrapper.run)

    def _build_wrapper(self) -> WolframAlphaAPIWrapper:
        return WolframAlphaAPIWrapper(wolfram_alpha_appid=self.app_id)

import ast
import pprint
from enum import Enum

import yfinance as yf
from langchain.tools import StructuredTool
from langchain_core.tools import ToolException
from loguru import logger
from pydantic import BaseModel, Field

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.field_typing import Tool
from langflow.inputs import DropdownInput, IntInput, MessageTextInput
from langflow.schema import Data


class YahooFinanceMethod(Enum):
    GET_INFO = "get_info"
    GET_NEWS = "get_news"
    GET_ACTIONS = "get_actions"
    GET_ANALYSIS = "get_analysis"
    GET_BALANCE_SHEET = "get_balance_sheet"
    GET_CALENDAR = "get_calendar"
    GET_CASHFLOW = "get_cashflow"
    GET_INSTITUTIONAL_HOLDERS = "get_institutional_holders"
    GET_RECOMMENDATIONS = "get_recommendations"
    GET_SUSTAINABILITY = "get_sustainability"
    GET_MAJOR_HOLDERS = "get_major_holders"
    GET_MUTUALFUND_HOLDERS = "get_mutualfund_holders"
    GET_INSIDER_PURCHASES = "get_insider_purchases"
    GET_INSIDER_TRANSACTIONS = "get_insider_transactions"
    GET_INSIDER_ROSTER_HOLDERS = "get_insider_roster_holders"
    GET_DIVIDENDS = "get_dividends"
    GET_CAPITAL_GAINS = "get_capital_gains"
    GET_SPLITS = "get_splits"
    GET_SHARES = "get_shares"
    GET_FAST_INFO = "get_fast_info"
    GET_SEC_FILINGS = "get_sec_filings"
    GET_RECOMMENDATIONS_SUMMARY = "get_recommendations_summary"
    GET_UPGRADES_DOWNGRADES = "get_upgrades_downgrades"
    GET_EARNINGS = "get_earnings"
    GET_INCOME_STMT = "get_income_stmt"


class YahooFinanceSchema(BaseModel):
    symbol: str = Field(..., description="The stock symbol to retrieve data for.")
    method: YahooFinanceMethod = Field(YahooFinanceMethod.GET_INFO, description="The type of data to retrieve.")
    num_news: int | None = Field(5, description="The number of news articles to retrieve.")


class YfinanceToolComponent(LCToolComponent):
    display_name = "Yahoo Finance"
    description = """Uses [yfinance](https://pypi.org/project/yfinance/) (unofficial package) \
to access financial data and market information from Yahoo Finance."""
    icon = "trending-up"
    name = "YahooFinanceTool"

    inputs = [
        MessageTextInput(
            name="symbol",
            display_name="Stock Symbol",
            info="The stock symbol to retrieve data for (e.g., AAPL, GOOG).",
        ),
        DropdownInput(
            name="method",
            display_name="Data Method",
            info="The type of data to retrieve.",
            options=list(YahooFinanceMethod),
            value="get_news",
        ),
        IntInput(
            name="num_news",
            display_name="Number of News",
            info="The number of news articles to retrieve (only applicable for get_news).",
            value=5,
        ),
    ]

    def run_model(self) -> list[Data]:
        return self._yahoo_finance_tool(
            self.symbol,
            self.method,
            self.num_news,
        )

    def build_tool(self) -> Tool:
        return StructuredTool.from_function(
            name="yahoo_finance",
            description="Access financial data and market information from Yahoo Finance.",
            func=self._yahoo_finance_tool,
            args_schema=YahooFinanceSchema,
        )

    def _yahoo_finance_tool(
        self,
        symbol: str,
        method: YahooFinanceMethod,
        num_news: int | None = 5,
    ) -> list[Data]:
        ticker = yf.Ticker(symbol)

        try:
            if method == YahooFinanceMethod.GET_INFO:
                result = ticker.info
            elif method == YahooFinanceMethod.GET_NEWS:
                result = ticker.news[:num_news]
            else:
                result = getattr(ticker, method.value)()

            result = pprint.pformat(result)

            if method == YahooFinanceMethod.GET_NEWS:
                data_list = [Data(data=article) for article in ast.literal_eval(result)]
            else:
                data_list = [Data(data={"result": result})]

        except Exception as e:
            error_message = f"Error retrieving data: {e}"
            logger.debug(error_message)
            self.status = error_message
            raise ToolException(error_message) from e

        return data_list