Tools

import urllib.request
from urllib.parse import urlparse
from xml.etree.ElementTree import Element

from defusedxml.ElementTree import fromstring

from langflow.custom import Component
from langflow.io import DropdownInput, IntInput, MessageTextInput, Output
from langflow.schema import Data, DataFrame


class ArXivComponent(Component):
    display_name = "arXiv"
    description = "Search and retrieve papers from arXiv.org"
    icon = "arXiv"

    inputs = [
        MessageTextInput(
            name="search_query",
            display_name="Search Query",
            info="The search query for arXiv papers (e.g., 'quantum computing')",
            tool_mode=True,
        ),
        DropdownInput(
            name="search_type",
            display_name="Search Field",
            info="The field to search in",
            options=["all", "title", "abstract", "author", "cat"],  # cat is for category
            value="all",
        ),
        IntInput(
            name="max_results",
            display_name="Max Results",
            info="Maximum number of results to return",
            value=10,
        ),
    ]

    outputs = [
        Output(display_name="Data", name="data", method="search_papers"),
        Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
    ]

    def build_query_url(self) -> str:
        """Build the arXiv API query URL."""
        base_url = "http://export.arxiv.org/api/query?"

        # Build the search query
        search_query = f"{self.search_type}:{self.search_query}"

        # URL parameters
        params = {
            "search_query": search_query,
            "max_results": str(self.max_results),
        }

        # Convert params to URL query string
        query_string = "&".join([f"{k}={urllib.parse.quote(str(v))}" for k, v in params.items()])

        return base_url + query_string

    def parse_atom_response(self, response_text: str) -> list[dict]:
        """Parse the Atom XML response from arXiv."""
        # Parse XML safely using defusedxml
        root = fromstring(response_text)

        # Define namespace dictionary for XML parsing
        ns = {"atom": "http://www.w3.org/2005/Atom", "arxiv": "http://arxiv.org/schemas/atom"}

        papers = []
        # Process each entry (paper)
        for entry in root.findall("atom:entry", ns):
            paper = {
                "id": self._get_text(entry, "atom:id", ns),
                "title": self._get_text(entry, "atom:title", ns),
                "summary": self._get_text(entry, "atom:summary", ns),
                "published": self._get_text(entry, "atom:published", ns),
                "updated": self._get_text(entry, "atom:updated", ns),
                "authors": [author.find("atom:name", ns).text for author in entry.findall("atom:author", ns)],
                "arxiv_url": self._get_link(entry, "alternate", ns),
                "pdf_url": self._get_link(entry, "related", ns),
                "comment": self._get_text(entry, "arxiv:comment", ns),
                "journal_ref": self._get_text(entry, "arxiv:journal_ref", ns),
                "primary_category": self._get_category(entry, ns),
                "categories": [cat.get("term") for cat in entry.findall("atom:category", ns)],
            }
            papers.append(paper)

        return papers

    def _get_text(self, element: Element, path: str, ns: dict) -> str | None:
        """Safely extract text from an XML element."""
        el = element.find(path, ns)
        return el.text.strip() if el is not None and el.text else None

    def _get_link(self, element: Element, rel: str, ns: dict) -> str | None:
        """Get link URL based on relation type."""
        for link in element.findall("atom:link", ns):
            if link.get("rel") == rel:
                return link.get("href")
        return None

    def _get_category(self, element: Element, ns: dict) -> str | None:
        """Get primary category."""
        cat = element.find("arxiv:primary_category", ns)
        return cat.get("term") if cat is not None else None

    def search_papers(self) -> list[Data]:
        """Search arXiv and return results."""
        try:
            # Build the query URL
            url = self.build_query_url()

            # Validate URL scheme and host
            parsed_url = urlparse(url)
            if parsed_url.scheme not in {"http", "https"}:
                error_msg = f"Invalid URL scheme: {parsed_url.scheme}"
                raise ValueError(error_msg)
            if parsed_url.hostname != "export.arxiv.org":
                error_msg = f"Invalid host: {parsed_url.hostname}"
                raise ValueError(error_msg)

            # Create a custom opener that only allows http/https schemes
            class RestrictedHTTPHandler(urllib.request.HTTPHandler):
                def http_open(self, req):
                    return super().http_open(req)

            class RestrictedHTTPSHandler(urllib.request.HTTPSHandler):
                def https_open(self, req):
                    return super().https_open(req)

            # Build opener with restricted handlers
            opener = urllib.request.build_opener(RestrictedHTTPHandler, RestrictedHTTPSHandler)
            urllib.request.install_opener(opener)

            # Make the request with validated URL using restricted opener
            response = opener.open(url)
            response_text = response.read().decode("utf-8")

            # Parse the response
            papers = self.parse_atom_response(response_text)

            # Convert to Data objects
            results = [Data(data=paper) for paper in papers]
            self.status = results
        except (urllib.error.URLError, ValueError) as e:
            error_data = Data(data={"error": f"Request error: {e!s}"})
            self.status = error_data
            return [error_data]
        else:
            return results

    def as_dataframe(self) -> DataFrame:
        """Convert the Arxiv search results to a DataFrame.

        Returns:
            DataFrame: A DataFrame containing the search results.
        """
        data = self.search_papers()
        if isinstance(data, list):
            return DataFrame(data=[d.data for d in data])
        return DataFrame(data=[data.data])

import os
from datetime import datetime, timezone
from typing import Any

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

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.io import BoolInput, DictInput, HandleInput, IntInput, SecretStrInput, StrInput, TableInput
from langflow.logging import logger
from langflow.schema import Data
from langflow.schema.table import EditMode


class AstraDBToolComponent(LCToolComponent):
    display_name: str = "Astra DB Tool"
    description: str = "Tool to run hybrid vector and metadata search on 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 to be passed to the LLM.",
            required=True,
        ),
        StrInput(
            name="tool_description",
            display_name="Tool Description",
            info="Describe the tool to LLM. Add any information that can help the LLM to use the tool.",
            required=True,
        ),
        StrInput(
            name="keyspace",
            display_name="Keyspace Name",
            info="The name of the keyspace within Astra where the collection is 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 be returned by the tool separated by comma.",
            required=True,
            value="*",
            advanced=True,
        ),
        TableInput(
            name="tools_params_v2",
            display_name="Tools Parameters",
            info="Define the structure for the tool parameters. Describe the parameters "
            "in a way the LLM can understand how to use them.",
            required=False,
            table_schema=[
                {
                    "name": "name",
                    "display_name": "Name",
                    "type": "str",
                    "description": "Specify the name of the output field/parameter for the model.",
                    "default": "field",
                    "edit_mode": EditMode.INLINE,
                },
                {
                    "name": "attribute_name",
                    "display_name": "Attribute Name",
                    "type": "str",
                    "description": "Specify the attribute name to be filtered on the collection. "
                    "Leave empty if the attribute name is the same as the name of the field.",
                    "default": "",
                    "edit_mode": EditMode.INLINE,
                },
                {
                    "name": "description",
                    "display_name": "Description",
                    "type": "str",
                    "description": "Describe the purpose of the output field.",
                    "default": "description of field",
                    "edit_mode": EditMode.POPOVER,
                },
                {
                    "name": "metadata",
                    "display_name": "Is Metadata",
                    "type": "boolean",
                    "edit_mode": EditMode.INLINE,
                    "description": ("Indicate if the field is included in the metadata field."),
                    "options": ["True", "False"],
                    "default": "False",
                },
                {
                    "name": "mandatory",
                    "display_name": "Is Mandatory",
                    "type": "boolean",
                    "edit_mode": EditMode.INLINE,
                    "description": ("Indicate if the field is mandatory."),
                    "options": ["True", "False"],
                    "default": "False",
                },
                {
                    "name": "is_timestamp",
                    "display_name": "Is Timestamp",
                    "type": "boolean",
                    "edit_mode": EditMode.INLINE,
                    "description": ("Indicate if the field is a timestamp."),
                    "options": ["True", "False"],
                    "default": "False",
                },
                {
                    "name": "operator",
                    "display_name": "Operator",
                    "type": "str",
                    "description": "Set the operator for the field. "
                    "https://docs.datastax.com/en/astra-db-serverless/api-reference/documents.html#operators",
                    "default": "$eq",
                    "options": ["$gt", "$gte", "$lt", "$lte", "$eq", "$ne", "$in", "$nin", "$exists", "$all", "$size"],
                    "edit_mode": EditMode.INLINE,
                },
            ],
            value=[],
        ),
        DictInput(
            name="tool_params",
            info="DEPRECATED: Attributes to filter and description to the model. "
            "Add ! for mandatory (e.g: !customerId)",
            display_name="Tool params",
            is_list=True,
            advanced=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,
        ),
        BoolInput(
            name="use_search_query",
            display_name="Semantic Search",
            info="When this parameter is activated, the search query parameter will be used to search the collection.",
            advanced=False,
            value=False,
        ),
        BoolInput(
            name="use_vectorize",
            display_name="Use Astra DB Vectorize",
            info="When this parameter is activated, Astra DB Vectorize method will be used to generate the embeddings.",
            advanced=False,
            value=False,
        ),
        HandleInput(name="embedding", display_name="Embedding Model", input_types=["Embeddings"]),
        StrInput(
            name="semantic_search_instruction",
            display_name="Semantic Search Instruction",
            info="The instruction to use for the semantic search.",
            required=True,
            value="Search query to find relevant documents.",
            advanced=True,
        ),
    ]

    _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

        try:
            environment = parse_api_endpoint(self.api_endpoint).environment
            cached_client = DataAPIClient(self.token, environment=environment)
            cached_db = cached_client.get_database(self.api_endpoint, keyspace=self.keyspace)
            self._cached_collection = cached_db.get_collection(self.collection_name)
        except Exception as e:
            msg = f"Error building collection: {e}"
            raise ValueError(msg) from e
        else:
            return self._cached_collection

    def create_args_schema(self) -> dict[str, BaseModel]:
        """DEPRECATED: This method is deprecated. Please use create_args_schema_v2 instead.

        It is keep only for backward compatibility.
        """
        logger.warning("This is the old way to define the tool parameters. Please use the new way.")
        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))

        if self.use_search_query:
            args["search_query"] = (
                str | None,
                Field(description="Search query to find relevant documents.", default=None),
            )

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

    def create_args_schema_v2(self) -> dict[str, BaseModel]:
        """Create the tool input schema using the new tool parameters configuration."""
        args: dict[str, tuple[Any, Field] | list[str]] = {}

        for tool_param in self.tools_params_v2:
            if tool_param["mandatory"]:
                args[tool_param["name"]] = (str, Field(description=tool_param["description"]))
            else:
                args[tool_param["name"]] = (str | None, Field(description=tool_param["description"], default=None))

        if self.use_search_query:
            args["search_query"] = (
                str,
                Field(description=self.semantic_search_instruction),
            )

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

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

        Returns:
            Tool: The built Astra DB tool.
        """
        schema_dict = self.create_args_schema() if len(self.tool_params.keys()) > 0 else self.create_args_schema_v2()

        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 | None:
        """Build the projection arguments for the AstraDB query."""
        elements = input_str.split(",")
        result = {}

        if elements == ["*"]:
            return None

        # Force the projection to exclude the $vector field as it is not required by the tool
        result["$vector"] = False

        # Fields with ! as prefix should be removed from the projection
        for element in elements:
            if element.startswith("!"):
                result[element[1:]] = False
            else:
                result[element] = True

        return result

    def parse_timestamp(self, timestamp_str: str) -> datetime:
        """Parse a timestamp string into Astra DB REST API format.

        Args:
            timestamp_str (str): Input timestamp string

        Returns:
            datetime: Datetime object

        Raises:
            ValueError: If the timestamp cannot be parsed
        """
        # Common datetime formats to try
        formats = [
            "%Y-%m-%d",  # 2024-03-21
            "%Y-%m-%dT%H:%M:%S",  # 2024-03-21T15:30:00
            "%Y-%m-%dT%H:%M:%S%z",  # 2024-03-21T15:30:00+0000
            "%Y-%m-%d %H:%M:%S",  # 2024-03-21 15:30:00
            "%d/%m/%Y",  # 21/03/2024
            "%Y/%m/%d",  # 2024/03/21
        ]

        for fmt in formats:
            try:
                # Parse the date string
                date_obj = datetime.strptime(timestamp_str, fmt).astimezone()

                # If the parsed date has no timezone info, assume UTC
                if date_obj.tzinfo is None:
                    date_obj = date_obj.replace(tzinfo=timezone.utc)

                # Convert to UTC and format
                return date_obj.astimezone(timezone.utc)

            except ValueError:
                continue

        msg = f"Could not parse date: {timestamp_str}"
        logger.error(msg)
        raise ValueError(msg)

    def build_filter(self, args: dict, filter_settings: list) -> dict:
        """Build filter dictionary for AstraDB query.

        Args:
            args: Dictionary of arguments from the tool
            filter_settings: List of filter settings from tools_params_v2
        Returns:
            Dictionary containing the filter conditions
        """
        filters = {**self.static_filters}

        for key, value in args.items():
            # Skip search_query as it's handled separately
            if key == "search_query":
                continue

            filter_setting = next((x for x in filter_settings if x["name"] == key), None)
            if filter_setting and value is not None:
                field_name = filter_setting["attribute_name"] if filter_setting["attribute_name"] else key
                filter_key = field_name if not filter_setting["metadata"] else f"metadata.{field_name}"
                if filter_setting["operator"] == "$exists":
                    filters[filter_key] = {**filters.get(filter_key, {}), filter_setting["operator"]: True}
                elif filter_setting["operator"] in ["$in", "$nin", "$all"]:
                    filters[filter_key] = {
                        **filters.get(filter_key, {}),
                        filter_setting["operator"]: value.split(",") if isinstance(value, str) else value,
                    }
                elif filter_setting["is_timestamp"] == True:  # noqa: E712
                    try:
                        filters[filter_key] = {
                            **filters.get(filter_key, {}),
                            filter_setting["operator"]: self.parse_timestamp(value),
                        }
                    except ValueError as e:
                        msg = f"Error parsing timestamp: {e} - Use the prompt to specify the date in the correct format"
                        logger.error(msg)
                        raise ValueError(msg) from e
                else:
                    filters[filter_key] = {**filters.get(filter_key, {}), filter_setting["operator"]: value}
        return filters

    def run_model(self, **args) -> Data | list[Data]:
        """Run the query to get the data from the AstraDB collection."""
        collection = self._build_collection()
        sort = {}

        # Build filters using the new method
        filters = self.build_filter(args, self.tools_params_v2)

        # Build the vector search on
        if self.use_search_query and args["search_query"] is not None and args["search_query"] != "":
            if self.use_vectorize:
                sort["$vectorize"] = args["search_query"]
            else:
                if self.embedding is None:
                    msg = "Embedding model is not set. Please set the embedding model or use Astra DB Vectorize."
                    logger.error(msg)
                    raise ValueError(msg)
                embedding_query = self.embedding.embed_query(args["search_query"])
                sort["$vector"] = embedding_query
            del args["search_query"]

        find_options = {
            "filter": filters,
            "limit": self.number_of_results,
            "sort": sort,
        }

        projection = self.projection_args(self.projection_attributes)
        if projection and len(projection) > 0:
            find_options["projection"] = projection

        try:
            results = collection.find(**find_options)
        except Exception as e:
            msg = f"Error on Astra DB Tool {self.tool_name} request: {e}"
            logger.error(msg)
            raise ValueError(msg) from e

        logger.info(f"Tool {self.tool_name} executed`")

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

import json
import urllib
from datetime import datetime, timezone
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, Tool

from langflow.base.langchain_utilities.model import LCToolComponent
from langflow.io import DictInput, IntInput, SecretStrInput, StrInput, TableInput
from langflow.logging import logger
from langflow.schema import Data
from langflow.schema.table import EditMode


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,
        ),
        TableInput(
            name="tools_params",
            display_name="Tools Parameters",
            info="Define the structure for the tool parameters. Describe the parameters "
            "in a way the LLM can understand how to use them. Add the parameters "
            "respecting the table schema (Partition Keys, Clustering Keys and Indexed Fields).",
            required=False,
            table_schema=[
                {
                    "name": "name",
                    "display_name": "Name",
                    "type": "str",
                    "description": "Name of the field/parameter to be used by the model.",
                    "default": "field",
                    "edit_mode": EditMode.INLINE,
                },
                {
                    "name": "field_name",
                    "display_name": "Field Name",
                    "type": "str",
                    "description": "Specify the column name to be filtered on the table. "
                    "Leave empty if the attribute name is the same as the name of the field.",
                    "default": "",
                    "edit_mode": EditMode.INLINE,
                },
                {
                    "name": "description",
                    "display_name": "Description",
                    "type": "str",
                    "description": "Describe the purpose of the parameter.",
                    "default": "description of tool parameter",
                    "edit_mode": EditMode.POPOVER,
                },
                {
                    "name": "mandatory",
                    "display_name": "Is Mandatory",
                    "type": "boolean",
                    "edit_mode": EditMode.INLINE,
                    "description": ("Indicate if the field is mandatory."),
                    "options": ["True", "False"],
                    "default": "False",
                },
                {
                    "name": "is_timestamp",
                    "display_name": "Is Timestamp",
                    "type": "boolean",
                    "edit_mode": EditMode.INLINE,
                    "description": ("Indicate if the field is a timestamp."),
                    "options": ["True", "False"],
                    "default": "False",
                },
                {
                    "name": "operator",
                    "display_name": "Operator",
                    "type": "str",
                    "description": "Set the operator for the field. "
                    "https://docs.datastax.com/en/astra-db-serverless/api-reference/documents.html#operators",
                    "default": "$eq",
                    "options": ["$gt", "$gte", "$lt", "$lte", "$eq", "$ne", "$in", "$nin", "$exists", "$all", "$size"],
                    "edit_mode": EditMode.INLINE,
                },
            ],
            value=[],
        ),
        DictInput(
            name="partition_keys",
            display_name="DEPRECATED: Partition Keys",
            is_list=True,
            info="Field name and description to the model",
            required=False,
            advanced=True,
        ),
        DictInput(
            name="clustering_keys",
            display_name="DEPRECATED: Clustering Keys",
            is_list=True,
            info="Field name and description to the model",
            required=False,
            advanced=True,
        ),
        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 parse_timestamp(self, timestamp_str: str) -> str:
        """Parse a timestamp string into Astra DB REST API format.

        Args:
            timestamp_str (str): Input timestamp string

        Returns:
            str: Formatted timestamp string in YYYY-MM-DDTHH:MI:SS.000Z format

        Raises:
            ValueError: If the timestamp cannot be parsed
        """
        # Common datetime formats to try
        formats = [
            "%Y-%m-%d",  # 2024-03-21
            "%Y-%m-%dT%H:%M:%S",  # 2024-03-21T15:30:00
            "%Y-%m-%dT%H:%M:%S%z",  # 2024-03-21T15:30:00+0000
            "%Y-%m-%d %H:%M:%S",  # 2024-03-21 15:30:00
            "%d/%m/%Y",  # 21/03/2024
            "%Y/%m/%d",  # 2024/03/21
        ]

        for fmt in formats:
            try:
                # Parse the date string
                date_obj = datetime.strptime(timestamp_str, fmt).astimezone()

                # If the parsed date has no timezone info, assume UTC
                if date_obj.tzinfo is None:
                    date_obj = date_obj.replace(tzinfo=timezone.utc)

                # Convert to UTC and format
                utc_date = date_obj.astimezone(timezone.utc)
                return utc_date.strftime("%Y-%m-%dT%H:%M:%S.000Z")
            except ValueError:
                continue

        msg = f"Could not parse date: {timestamp_str}"
        logger.error(msg)
        raise ValueError(msg)

    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}/"
        where = {}

        for param in self.tools_params:
            field_name = param["field_name"] if param["field_name"] else param["name"]
            field_value = None

            if field_name in self.static_filters:
                field_value = self.static_filters[field_name]
            elif param["name"] in args:
                field_value = args[param["name"]]

            if field_value is None:
                continue

            if param["is_timestamp"] == True:  # noqa: E712
                try:
                    field_value = self.parse_timestamp(field_value)
                except ValueError as e:
                    msg = f"Error parsing timestamp: {e} - Use the prompt to specify the date in the correct format"
                    logger.error(msg)
                    raise ValueError(msg) from e

            if param["operator"] == "$exists":
                where[field_name] = {**where.get(field_name, {}), param["operator"]: True}
            elif param["operator"] in ["$in", "$nin", "$all"]:
                where[field_name] = {
                    **where.get(field_name, {}),
                    param["operator"]: field_value.split(",") if isinstance(field_value, str) else field_value,
                }
            else:
                where[field_name] = {**where.get(field_name, {}), param["operator"]: field_value}

        url = f"{astra_url}?page-size={self.number_of_results}"
        url += f"&where={json.dumps(where)}"

        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:
            msg = f"Error on Astra DB CQL Tool {self.tool_name} request: {res.text}"
            logger.error(msg)
            raise ValueError(msg)

        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 param in self.tools_params:
            field_name = param["field_name"] if param["field_name"] else param["name"]
            if field_name not in self.static_filters:
                if param["mandatory"]:
                    args[param["name"]] = (str, Field(description=param["description"]))
                else:
                    args[param["name"]] = (str | None, Field(description=param["description"], default=None))

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

    def build_tool(self) -> Tool:
        """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] = []

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

        self.status = data
        return data

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,
        }

from langchain_community.tools import DuckDuckGoSearchRun

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


class DuckDuckGoSearchComponent(Component):
    """Component for performing web searches using DuckDuckGo."""

    display_name = "DuckDuckGo Search"
    description = "Search the web using DuckDuckGo with customizable result limits"
    documentation = "https://python.langchain.com/docs/integrations/tools/ddg"
    icon = "DuckDuckGo"

    inputs = [
        MessageTextInput(
            name="input_value",
            display_name="Search Query",
            required=True,
            info="The search query to execute with DuckDuckGo",
            tool_mode=True,
        ),
        IntInput(
            name="max_results",
            display_name="Max Results",
            value=5,
            required=False,
            advanced=True,
            info="Maximum number of search results to return",
        ),
        IntInput(
            name="max_snippet_length",
            display_name="Max Snippet Length",
            value=100,
            required=False,
            advanced=True,
            info="Maximum length of each result snippet",
        ),
    ]

    outputs = [
        Output(display_name="Data", name="data", method="fetch_content"),
        Output(display_name="Text", name="text", method="fetch_content_text"),
    ]

    def _build_wrapper(self) -> DuckDuckGoSearchRun:
        """Build the DuckDuckGo search wrapper."""
        return DuckDuckGoSearchRun()

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

    def fetch_content(self) -> list[Data]:
        """Execute the search and return results as Data objects."""
        try:
            wrapper = self._build_wrapper()

            full_results = wrapper.run(f"{self.input_value} (site:*)")

            result_list = full_results.split("\n")[: self.max_results]

            data_results = []
            for result in result_list:
                if result.strip():
                    snippet = result[: self.max_snippet_length]
                    data_results.append(
                        Data(
                            text=snippet,
                            data={
                                "content": result,
                                "snippet": snippet,
                            },
                        )
                    )
        except (ValueError, AttributeError) as e:
            error_data = [Data(text=str(e), data={"error": str(e)})]
            self.status = error_data
            return error_data
        else:
            self.status = data_results
            return data_results

    def fetch_content_text(self) -> Message:
        """Return search results as a single text message."""
        data = self.fetch_content()
        result_string = "\n".join(item.text for item in data)
        self.status = result_string
        return Message(text=result_string)

from langchain_core.tools import tool
from metaphor_python import Metaphor

from langflow.custom import Component
from langflow.field_typing import Tool
from langflow.io import BoolInput, IntInput, Output, SecretStrInput


class ExaSearchToolkit(Component):
    display_name = "Exa Search"
    description = "Exa Search toolkit for search and content retrieval"
    documentation = "https://python.langchain.com/docs/integrations/tools/metaphor_search"
    beta = True
    name = "ExaSearch"
    icon = "ExaSearch"

    inputs = [
        SecretStrInput(
            name="metaphor_api_key",
            display_name="Exa Search API Key",
            password=True,
        ),
        BoolInput(
            name="use_autoprompt",
            display_name="Use Autoprompt",
            value=True,
        ),
        IntInput(
            name="search_num_results",
            display_name="Search Number of Results",
            value=5,
        ),
        IntInput(
            name="similar_num_results",
            display_name="Similar Number of Results",
            value=5,
        ),
    ]

    outputs = [
        Output(name="tools", display_name="Tools", method="build_toolkit"),
    ]

    def build_toolkit(self) -> Tool:
        client = Metaphor(api_key=self.metaphor_api_key)

        @tool
        def search(query: str):
            """Call search engine with a query."""
            return client.search(query, use_autoprompt=self.use_autoprompt, num_results=self.search_num_results)

        @tool
        def get_contents(ids: list[str]):
            """Get contents of a webpage.

            The ids passed in should be a list of ids as fetched from `search`.
            """
            return client.get_contents(ids)

        @tool
        def find_similar(url: str):
            """Get search results similar to a given URL.

            The url passed in should be a URL returned from `search`
            """
            return client.find_similar(url, num_results=self.similar_num_results)

        return [search, get_contents, find_similar]

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.io import Output
from langflow.schema import Data, DataFrame


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: str = "Glean Search API"
    description: str = "Search using Glean's API."
    documentation: str = "https://docs.langflow.org/Components/components-tools#glean-search-api"
    icon: str = "Glean"

    outputs = [
        Output(display_name="Data", name="data", method="run_model"),
        Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
    ]

    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, tool_mode=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,
        )

    def as_dataframe(self) -> DataFrame:
        """Convert the Glean search results to a DataFrame.

        Returns:
            DataFrame: A DataFrame containing the search results.
        """
        data = self.run_model()
        if isinstance(data, list):
            return DataFrame(data=[d.data for d in data])
        return DataFrame(data=[data.data])

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 [DEPRECATED]"
    description = "Call Google Search API."
    name = "GoogleSearchAPI"
    icon = "Google"
    legacy = True
    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 typing import Any

from langchain.tools import StructuredTool
from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
from pydantic import BaseModel, Field

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


class QuerySchema(BaseModel):
    query: str = Field(..., description="The query to search for.")
    query_type: str = Field(
        "search",
        description="The type of search to perform (e.g., 'news' or 'search').",
    )
    k: int = Field(4, description="The number of results to return.")
    query_params: dict[str, Any] = Field({}, description="Additional query parameters to pass to the API.")


class GoogleSerperAPIComponent(LCToolComponent):
    display_name = "Google Serper API [DEPRECATED]"
    description = "Call the Serper.dev Google Search API."
    name = "GoogleSerperAPI"
    icon = "Google"
    legacy = True
    inputs = [
        SecretStrInput(name="serper_api_key", display_name="Serper API Key", required=True),
        MultilineInput(
            name="query",
            display_name="Query",
        ),
        IntInput(name="k", display_name="Number of results", value=4, required=True),
        DropdownInput(
            name="query_type",
            display_name="Query Type",
            required=False,
            options=["news", "search"],
            value="search",
        ),
        DictInput(
            name="query_params",
            display_name="Query Params",
            required=False,
            value={
                "gl": "us",
                "hl": "en",
            },
            list=True,
        ),
    ]

    def run_model(self) -> Data | list[Data]:
        wrapper = self._build_wrapper(self.k, self.query_type, self.query_params)
        results = wrapper.results(query=self.query)

        # Adjust the extraction based on the `type`
        if self.query_type == "search":
            list_results = results.get("organic", [])
        elif self.query_type == "news":
            list_results = results.get("news", [])
        else:
            list_results = []

        data_list = []
        for result in list_results:
            result["text"] = result.pop("snippet", "")
            data_list.append(Data(data=result))
        self.status = data_list
        return data_list

    def build_tool(self) -> Tool:
        return StructuredTool.from_function(
            name="google_search",
            description="Search Google for recent results.",
            func=self._search,
            args_schema=self.QuerySchema,
        )

    def _build_wrapper(
        self,
        k: int = 5,
        query_type: str = "search",
        query_params: dict | None = None,
    ) -> GoogleSerperAPIWrapper:
        wrapper_args = {
            "serper_api_key": self.serper_api_key,
            "k": k,
            "type": query_type,
        }

        # Add query_params if provided
        if query_params:
            wrapper_args.update(query_params)  # Merge with additional query params

        # Dynamically pass parameters to the wrapper
        return GoogleSerperAPIWrapper(**wrapper_args)

    def _search(
        self,
        query: str,
        k: int = 5,
        query_type: str = "search",
        query_params: dict | None = None,
    ) -> dict:
        wrapper = self._build_wrapper(k, query_type, query_params)
        return wrapper.results(query=query)

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 [DEPRECATED]"
    description = "A tool for running Python code in a REPL environment."
    name = "PythonREPLTool"
    icon = "Python"
    legacy = True

    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 [DEPRECATED]"
    description: str = "Call the searchapi.io API with result limiting"
    name = "SearchAPI"
    documentation: str = "https://www.searchapi.io/docs/google"
    icon = "SearchAPI"
    legacy = True

    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 [DEPRECATED]"
    description = "Call Serp Search API with result limiting"
    name = "SerpAPI"
    icon = "SerpSearch"
    legacy = True

    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

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,
        ),
        DropdownInput(
            name="time_range",
            display_name="Time Range",
            info="The time range back from the current date to include in the search results.",
            options=["day", "week", "month", "year"],
            value=None,
            advanced=True,
            combobox=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,
                "time_range": self.time_range,
            }

            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)

import httpx
from httpx import HTTPError
from langchain_core.tools import ToolException

from langflow.custom import Component
from langflow.helpers.data import data_to_text
from langflow.io import MultilineInput, Output
from langflow.schema import Data
from langflow.schema.message import Message


class WikidataComponent(Component):
    display_name = "Wikidata"
    description = "Performs a search using the Wikidata API."
    icon = "Wikipedia"

    inputs = [
        MultilineInput(
            name="query",
            display_name="Query",
            info="The text query for similarity search on Wikidata.",
            required=True,
            tool_mode=True,
        ),
    ]

    outputs = [
        Output(display_name="Data", name="data", method="fetch_content"),
        Output(display_name="Message", name="text", method="fetch_content_text"),
    ]

    def fetch_content(self) -> list[Data]:
        try:
            # Define request parameters for Wikidata API
            params = {
                "action": "wbsearchentities",
                "format": "json",
                "search": self.query,
                "language": "en",
            }

            # Send request to Wikidata API
            wikidata_api_url = "https://www.wikidata.org/w/api.php"
            response = httpx.get(wikidata_api_url, params=params)
            response.raise_for_status()
            response_json = response.json()

            # Extract search results
            results = response_json.get("search", [])

            if not results:
                return [Data(data={"error": "No search results found for the given query."})]

            # Transform the API response into Data objects
            data = [
                Data(
                    text=f"{result['label']}: {result.get('description', '')}",
                    data={
                        "label": result["label"],
                        "id": result.get("id"),
                        "url": result.get("url"),
                        "description": result.get("description", ""),
                        "concepturi": result.get("concepturi"),
                    },
                )
                for result in results
            ]

            self.status = data
        except HTTPError as e:
            error_message = f"HTTP Error in Wikidata Search API: {e!s}"
            raise ToolException(error_message) from None
        except KeyError as e:
            error_message = f"Data parsing error in Wikidata API response: {e!s}"
            raise ToolException(error_message) from None
        except ValueError as e:
            error_message = f"Value error in Wikidata API: {e!s}"
            raise ToolException(error_message) from None
        else:
            return data

    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)

from typing import cast

from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper

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


class WikipediaAPIComponent(LCToolComponent):
    display_name = "Wikipedia API [Deprecated]"
    description = "Call Wikipedia API."
    name = "WikipediaAPI"
    icon = "Wikipedia"
    legacy = True

    inputs = [
        MultilineInput(
            name="input_value",
            display_name="Input",
        ),
        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
        ),
    ]

    def run_model(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 build_tool(self) -> Tool:
        wrapper = self._build_wrapper()
        return cast("Tool", WikipediaQueryRun(api_wrapper=wrapper))

    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.io import Output
from langflow.schema import Data, DataFrame


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"

    outputs = [
        Output(display_name="Data", name="data", method="run_model"),
        Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
    ]

    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)

    def as_dataframe(self) -> DataFrame:
        """Convert the Wolfram Alpha results to a DataFrame.

        Returns:
            DataFrame: A DataFrame containing the query results.
        """
        data = self.run_model()
        return DataFrame(data)

import ast
import pprint
from enum import Enum

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

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


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 YfinanceComponent(Component):
    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"

    inputs = [
        MessageTextInput(
            name="symbol",
            display_name="Stock Symbol",
            info="The stock symbol to retrieve data for (e.g., AAPL, GOOG).",
            tool_mode=True,
        ),
        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,
        ),
    ]

    outputs = [
        Output(display_name="Data", name="data", method="fetch_content"),
        Output(display_name="Text", name="text", method="fetch_content_text"),
        Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
    ]

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

    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 _fetch_yfinance_data(self, ticker: yf.Ticker, method: YahooFinanceMethod, num_news: int | None) -> str:
        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)()
            return pprint.pformat(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

    def fetch_content(self) -> list[Data]:
        try:
            return self._yahoo_finance_tool(
                self.symbol,
                YahooFinanceMethod(self.method),
                self.num_news,
            )
        except ToolException:
            raise
        except Exception as e:
            error_message = f"Unexpected error: {e}"
            logger.debug(error_message)
            self.status = error_message
            raise ToolException(error_message) from e

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

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

        return data_list

    def as_dataframe(self) -> DataFrame:
        """Convert the Yahoo search results to a DataFrame.

        Returns:
            DataFrame: A DataFrame containing the search results.
        """
        data = self.fetch_content()
        return DataFrame(data)