feat: Add dataframe output to data list components (#6987)
* feat: Add dataframe output to data list components * [autofix.ci] apply automated fixes * Update src/backend/base/langflow/components/data/api_request.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix: make function async * fix tests * fix tests * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: italojohnny <italojohnnydosanjos@gmail.com> Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>
This commit is contained in:
Rodrigo Nader
GitHub
parent
86502232a1
commit
202b707400
17 changed files with 655 additions and 401 deletions
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@ -27,6 +27,7 @@ from langflow.io import (
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TableInput,
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)
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from langflow.schema import Data
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from langflow.schema.dataframe import DataFrame
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from langflow.schema.dotdict import dotdict
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@ -156,6 +157,7 @@ class APIRequestComponent(Component):
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outputs = [
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Output(display_name="Data", name="data", method="make_requests"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def _parse_json_value(self, value: Any) -> Any:
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@ -641,3 +643,12 @@ class APIRequestComponent(Component):
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return {} # Return empty dictionary instead of None
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return processed_headers
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return {}
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async def as_dataframe(self) -> DataFrame:
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"""Convert the API response data into a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the API response data.
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"""
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data = await self.make_requests()
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return DataFrame(data)
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@ -1,6 +1,7 @@
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from langflow.custom import Component
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from langflow.inputs import StrInput
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from langflow.schema import Data
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from langflow.schema.dataframe import DataFrame
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from langflow.template import Output
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@ -22,9 +23,18 @@ class CreateListComponent(Component):
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outputs = [
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Output(display_name="Data List", name="list", method="create_list"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def create_list(self) -> list[Data]:
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data = [Data(text=text) for text in self.texts]
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self.status = data
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return data
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def as_dataframe(self) -> DataFrame:
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"""Convert the list of Data objects into a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the list data.
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"""
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return DataFrame(self.create_list())
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@ -4,6 +4,7 @@ from langflow.inputs import HandleInput
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from langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output
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from langflow.memory import aget_messages
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from langflow.schema import Data
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from langflow.schema.dataframe import DataFrame
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from langflow.schema.message import Message
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from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER
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@ -70,6 +71,7 @@ class MemoryComponent(Component):
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outputs = [
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Output(display_name="Data", name="messages", method="retrieve_messages"),
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Output(display_name="Message", name="messages_text", method="retrieve_messages_as_text"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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async def retrieve_messages(self) -> Data:
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@ -116,3 +118,12 @@ class MemoryComponent(Component):
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stored_text = data_to_text(self.template, await self.retrieve_messages())
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self.status = stored_text
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return Message(text=stored_text)
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async def as_dataframe(self) -> DataFrame:
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"""Convert the retrieved messages into a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the message data.
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"""
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messages = await self.retrieve_messages()
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return DataFrame(messages)
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@ -1,7 +1,7 @@
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from langflow.custom import Component
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from langflow.inputs import MessageTextInput
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from langflow.io import HandleInput, NestedDictInput, Output, StrInput
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from langflow.schema import Data
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from langflow.schema import Data, DataFrame
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class AlterMetadataComponent(Component):
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@ -48,6 +48,12 @@ class AlterMetadataComponent(Component):
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info="List of Input objects each with added Metadata",
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method="process_output",
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),
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Output(
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display_name="DataFrame",
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name="dataframe",
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info="Data objects as a DataFrame, with metadata as columns",
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method="as_dataframe",
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),
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]
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def _as_clean_dict(self, obj):
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@ -88,3 +94,13 @@ class AlterMetadataComponent(Component):
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# Set the status for tracking/debugging purposes
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self.status = data_objects
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return data_objects
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def as_dataframe(self) -> DataFrame:
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"""Convert the processed data objects into a DataFrame.
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Returns:
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DataFrame: A DataFrame where each row corresponds to a Data object,
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with metadata fields as columns.
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"""
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data_list = self.process_output()
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return DataFrame(data_list)
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@ -6,7 +6,7 @@ from defusedxml.ElementTree import fromstring
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from langflow.custom import Component
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from langflow.io import DropdownInput, IntInput, MessageTextInput, Output
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from langflow.schema import Data
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from langflow.schema import Data, DataFrame
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class ArXivComponent(Component):
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@ -37,7 +37,8 @@ class ArXivComponent(Component):
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]
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outputs = [
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Output(display_name="Papers", name="papers", method="search_papers"),
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Output(display_name="Data", name="data", method="search_papers"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def build_query_url(self) -> str:
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@ -148,3 +149,14 @@ class ArXivComponent(Component):
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return [error_data]
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else:
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return results
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def as_dataframe(self) -> DataFrame:
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"""Convert the Arxiv search results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the search results.
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"""
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data = self.search_papers()
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if isinstance(data, list):
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return DataFrame(data=[d.data for d in data])
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return DataFrame(data=[data.data])
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@ -10,7 +10,8 @@ from pydantic.v1 import Field
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from langflow.base.langchain_utilities.model import LCToolComponent
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from langflow.field_typing import Tool
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from langflow.inputs import IntInput, MultilineInput, NestedDictInput, SecretStrInput, StrInput
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from langflow.schema import Data
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from langflow.io import Output
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from langflow.schema import Data, DataFrame
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class GleanSearchAPISchema(BaseModel):
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@ -97,10 +98,15 @@ class GleanAPIWrapper(BaseModel):
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class GleanSearchAPIComponent(LCToolComponent):
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display_name = "Glean Search API"
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description = "Call Glean Search API"
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name = "GleanAPI"
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icon = "Glean"
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display_name: str = "Glean Search API"
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description: str = "Search using Glean's API."
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documentation: str = "https://docs.langflow.org/Components/components-tools#glean-search-api"
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icon: str = "GleanSearch"
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outputs = [
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Output(display_name="Data", name="data", method="run_model"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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inputs = [
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StrInput(
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@ -157,3 +163,14 @@ class GleanSearchAPIComponent(LCToolComponent):
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glean_api_url=glean_api_url,
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glean_access_token=glean_access_token,
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)
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def as_dataframe(self) -> DataFrame:
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"""Convert the Glean search results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the search results.
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"""
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data = self.run_model()
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if isinstance(data, list):
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return DataFrame(data=[d.data for d in data])
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return DataFrame(data=[data.data])
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@ -5,7 +5,7 @@ from langchain_community.utilities.searchapi import SearchApiAPIWrapper
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from langflow.custom import Component
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from langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput
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from langflow.io import Output
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from langflow.schema import Data
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from langflow.schema import Data, DataFrame
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from langflow.schema.message import Message
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@ -31,6 +31,7 @@ class SearchComponent(Component):
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outputs = [
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Output(display_name="Data", name="data", method="fetch_content"),
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Output(display_name="Text", name="text", method="fetch_content_text"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def _build_wrapper(self):
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@ -77,3 +78,12 @@ class SearchComponent(Component):
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result_string += item.text + "\n"
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self.status = result_string
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return Message(text=result_string)
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def as_dataframe(self) -> DataFrame:
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"""Convert the search results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the search results.
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"""
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data = self.fetch_content()
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return DataFrame(data)
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@ -3,7 +3,7 @@ from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
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from langflow.custom import Component
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from langflow.inputs import BoolInput, IntInput, MessageTextInput, MultilineInput
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from langflow.io import Output
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from langflow.schema import Data
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from langflow.schema import Data, DataFrame
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from langflow.schema.message import Message
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@ -28,7 +28,7 @@ class WikipediaComponent(Component):
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outputs = [
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Output(display_name="Data", name="data", method="fetch_content"),
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Output(display_name="Text", name="text", method="fetch_content_text"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def fetch_content(self) -> list[Data]:
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@ -53,3 +53,12 @@ class WikipediaComponent(Component):
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load_all_available_meta=self.load_all_available_meta,
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doc_content_chars_max=self.doc_content_chars_max,
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)
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def as_dataframe(self) -> DataFrame:
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"""Convert the Wikipedia results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the Wikipedia results.
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"""
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data = self.fetch_content()
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return DataFrame(data)
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@ -3,7 +3,8 @@ from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
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from langflow.base.langchain_utilities.model import LCToolComponent
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from langflow.field_typing import Tool
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from langflow.inputs import MultilineInput, SecretStrInput
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from langflow.schema import Data
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from langflow.io import Output
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from langflow.schema import Data, DataFrame
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class WolframAlphaAPIComponent(LCToolComponent):
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@ -12,6 +13,11 @@ class WolframAlphaAPIComponent(LCToolComponent):
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topics, delivering structured responses."""
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name = "WolframAlphaAPI"
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outputs = [
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Output(display_name="Data", name="data", method="run_model"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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inputs = [
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MultilineInput(
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name="input_value", display_name="Input Query", info="Example query: 'What is the population of France?'"
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@ -34,3 +40,12 @@ topics, delivering structured responses."""
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def _build_wrapper(self) -> WolframAlphaAPIWrapper:
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return WolframAlphaAPIWrapper(wolfram_alpha_appid=self.app_id)
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def as_dataframe(self) -> DataFrame:
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"""Convert the Wolfram Alpha results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the query results.
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"""
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data = self.run_model()
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return DataFrame(data)
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@ -10,7 +10,7 @@ from pydantic import BaseModel, Field
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from langflow.custom import Component
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from langflow.inputs import DropdownInput, IntInput, MessageTextInput
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from langflow.io import Output
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from langflow.schema import Data
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from langflow.schema import Data, DataFrame
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from langflow.schema.message import Message
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@ -79,6 +79,7 @@ to access financial data and market information from Yahoo Finance."""
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outputs = [
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Output(display_name="Data", name="data", method="fetch_content"),
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Output(display_name="Text", name="text", method="fetch_content_text"),
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Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
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]
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def run_model(self) -> list[Data]:
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@ -140,3 +141,12 @@ to access financial data and market information from Yahoo Finance."""
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data_list = [Data(text=result, data={"result": result})]
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return data_list
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def as_dataframe(self) -> DataFrame:
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"""Convert the Yahoo search results to a DataFrame.
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Returns:
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DataFrame: A DataFrame containing the search results.
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"""
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data = self.fetch_content()
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return DataFrame(data)
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@ -7,7 +7,7 @@
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"data": {
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"sourceHandle": {
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"dataType": "Agent",
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"id": "Agent-e5naw",
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"id": "Agent-PKpSO",
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"name": "response",
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"output_types": [
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"Message"
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@ -15,7 +15,7 @@
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "ChatOutput-NdG8s",
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"id": "ChatOutput-8np0X",
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"inputTypes": [
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"Data",
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"DataFrame",
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@ -24,11 +24,12 @@
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"type": "str"
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}
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},
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"id": "reactflow__edge-Agent-e5naw{œdataTypeœ:œAgentœ,œidœ:œAgent-e5nawœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-NdG8s{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-NdG8sœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œstrœ}",
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"source": "Agent-e5naw",
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"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-e5nawœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
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"target": "ChatOutput-NdG8s",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-NdG8sœ, œinputTypesœ: [œDataœ, œDataFrameœ, œMessageœ], œtypeœ: œstrœ}"
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"id": "reactflow__edge-Agent-PKpSO{œdataTypeœ:œAgentœ,œidœ:œAgent-PKpSOœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-8np0X{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-8np0Xœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œstrœ}",
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"selected": false,
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"source": "Agent-PKpSO",
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"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-PKpSOœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
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"target": "ChatOutput-8np0X",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-8np0Xœ, œinputTypesœ: [œDataœ, œDataFrameœ, œMessageœ], œtypeœ: œstrœ}"
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},
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{
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"animated": false,
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@ -36,7 +37,7 @@
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"data": {
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"sourceHandle": {
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"dataType": "Agent",
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"id": "Agent-PWZOu",
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"id": "Agent-zOYup",
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"name": "response",
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"output_types": [
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"Message"
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@ -44,18 +45,19 @@
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "Agent-e5naw",
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"id": "Agent-PKpSO",
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"inputTypes": [
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"Message"
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],
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"type": "str"
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}
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},
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"id": "reactflow__edge-Agent-PWZOu{œdataTypeœ:œAgentœ,œidœ:œAgent-PWZOuœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Agent-e5naw{œfieldNameœ:œinput_valueœ,œidœ:œAgent-e5nawœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
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"source": "Agent-PWZOu",
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"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-PWZOuœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
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"target": "Agent-e5naw",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-e5nawœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
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"id": "reactflow__edge-Agent-zOYup{œdataTypeœ:œAgentœ,œidœ:œAgent-zOYupœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Agent-PKpSO{œfieldNameœ:œinput_valueœ,œidœ:œAgent-PKpSOœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
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"selected": false,
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"source": "Agent-zOYup",
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"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-zOYupœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
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"target": "Agent-PKpSO",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-PKpSOœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"animated": false,
|
||||
|
|
@ -63,7 +65,7 @@
|
|||
"data": {
|
||||
"sourceHandle": {
|
||||
"dataType": "Agent",
|
||||
"id": "Agent-3H5qa",
|
||||
"id": "Agent-7K58a",
|
||||
"name": "response",
|
||||
"output_types": [
|
||||
"Message"
|
||||
|
|
@ -71,18 +73,19 @@
|
|||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "Agent-PWZOu",
|
||||
"id": "Agent-zOYup",
|
||||
"inputTypes": [
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-Agent-3H5qa{œdataTypeœ:œAgentœ,œidœ:œAgent-3H5qaœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Agent-PWZOu{œfieldNameœ:œinput_valueœ,œidœ:œAgent-PWZOuœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
|
||||
"source": "Agent-3H5qa",
|
||||
"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-3H5qaœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
|
||||
"target": "Agent-PWZOu",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-PWZOuœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
"id": "reactflow__edge-Agent-7K58a{œdataTypeœ:œAgentœ,œidœ:œAgent-7K58aœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Agent-zOYup{œfieldNameœ:œinput_valueœ,œidœ:œAgent-zOYupœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
|
||||
"selected": false,
|
||||
"source": "Agent-7K58a",
|
||||
"sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-7K58aœ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
|
||||
"target": "Agent-zOYup",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-zOYupœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"animated": false,
|
||||
|
|
@ -90,7 +93,7 @@
|
|||
"data": {
|
||||
"sourceHandle": {
|
||||
"dataType": "ChatInput",
|
||||
"id": "ChatInput-LIxJY",
|
||||
"id": "ChatInput-iZoDa",
|
||||
"name": "message",
|
||||
"output_types": [
|
||||
"Message"
|
||||
|
|
@ -98,18 +101,19 @@
|
|||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "input_value",
|
||||
"id": "Agent-3H5qa",
|
||||
"id": "Agent-7K58a",
|
||||
"inputTypes": [
|
||||
"Message"
|
||||
],
|
||||
"type": "str"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-ChatInput-LIxJY{œdataTypeœ:œChatInputœ,œidœ:œChatInput-LIxJYœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Agent-3H5qa{œfieldNameœ:œinput_valueœ,œidœ:œAgent-3H5qaœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
|
||||
"source": "ChatInput-LIxJY",
|
||||
"sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-LIxJYœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
|
||||
"target": "Agent-3H5qa",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-3H5qaœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
"id": "reactflow__edge-ChatInput-iZoDa{œdataTypeœ:œChatInputœ,œidœ:œChatInput-iZoDaœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Agent-7K58a{œfieldNameœ:œinput_valueœ,œidœ:œAgent-7K58aœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
|
||||
"selected": false,
|
||||
"source": "ChatInput-iZoDa",
|
||||
"sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-iZoDaœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
|
||||
"target": "Agent-7K58a",
|
||||
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-7K58aœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
|
||||
},
|
||||
{
|
||||
"animated": false,
|
||||
|
|
@ -117,7 +121,7 @@
|
|||
"data": {
|
||||
"sourceHandle": {
|
||||
"dataType": "URL",
|
||||
"id": "URL-XDhvs",
|
||||
"id": "URL-j9slU",
|
||||
"name": "component_as_tool",
|
||||
"output_types": [
|
||||
"Tool"
|
||||
|
|
@ -125,18 +129,19 @@
|
|||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "tools",
|
||||
"id": "Agent-PWZOu",
|
||||
"id": "Agent-zOYup",
|
||||
"inputTypes": [
|
||||
"Tool"
|
||||
],
|
||||
"type": "other"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-URL-XDhvs{œdataTypeœ:œURLœ,œidœ:œURL-XDhvsœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-PWZOu{œfieldNameœ:œtoolsœ,œidœ:œAgent-PWZOuœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"source": "URL-XDhvs",
|
||||
"sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-XDhvsœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-PWZOu",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-PWZOuœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
"id": "reactflow__edge-URL-j9slU{œdataTypeœ:œURLœ,œidœ:œURL-j9slUœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-zOYup{œfieldNameœ:œtoolsœ,œidœ:œAgent-zOYupœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"selected": false,
|
||||
"source": "URL-j9slU",
|
||||
"sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-j9slUœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-zOYup",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-zOYupœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
},
|
||||
{
|
||||
"animated": false,
|
||||
|
|
@ -144,7 +149,7 @@
|
|||
"data": {
|
||||
"sourceHandle": {
|
||||
"dataType": "CalculatorComponent",
|
||||
"id": "CalculatorComponent-N83tJ",
|
||||
"id": "CalculatorComponent-L3y5A",
|
||||
"name": "component_as_tool",
|
||||
"output_types": [
|
||||
"Tool"
|
||||
|
|
@ -152,18 +157,19 @@
|
|||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "tools",
|
||||
"id": "Agent-e5naw",
|
||||
"id": "Agent-PKpSO",
|
||||
"inputTypes": [
|
||||
"Tool"
|
||||
],
|
||||
"type": "other"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-CalculatorComponent-N83tJ{œdataTypeœ:œCalculatorComponentœ,œidœ:œCalculatorComponent-N83tJœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-e5naw{œfieldNameœ:œtoolsœ,œidœ:œAgent-e5nawœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"source": "CalculatorComponent-N83tJ",
|
||||
"sourceHandle": "{œdataTypeœ: œCalculatorComponentœ, œidœ: œCalculatorComponent-N83tJœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-e5naw",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-e5nawœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
"id": "reactflow__edge-CalculatorComponent-L3y5A{œdataTypeœ:œCalculatorComponentœ,œidœ:œCalculatorComponent-L3y5Aœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-PKpSO{œfieldNameœ:œtoolsœ,œidœ:œAgent-PKpSOœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"selected": false,
|
||||
"source": "CalculatorComponent-L3y5A",
|
||||
"sourceHandle": "{œdataTypeœ: œCalculatorComponentœ, œidœ: œCalculatorComponent-L3y5Aœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-PKpSO",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-PKpSOœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
},
|
||||
{
|
||||
"animated": false,
|
||||
|
|
@ -171,7 +177,7 @@
|
|||
"data": {
|
||||
"sourceHandle": {
|
||||
"dataType": "SearchComponent",
|
||||
"id": "SearchComponent-fF95b",
|
||||
"id": "SearchComponent-8lQPB",
|
||||
"name": "component_as_tool",
|
||||
"output_types": [
|
||||
"Tool"
|
||||
|
|
@ -179,24 +185,25 @@
|
|||
},
|
||||
"targetHandle": {
|
||||
"fieldName": "tools",
|
||||
"id": "Agent-3H5qa",
|
||||
"id": "Agent-7K58a",
|
||||
"inputTypes": [
|
||||
"Tool"
|
||||
],
|
||||
"type": "other"
|
||||
}
|
||||
},
|
||||
"id": "reactflow__edge-SearchComponent-fF95b{œdataTypeœ:œSearchComponentœ,œidœ:œSearchComponent-fF95bœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-3H5qa{œfieldNameœ:œtoolsœ,œidœ:œAgent-3H5qaœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"source": "SearchComponent-fF95b",
|
||||
"sourceHandle": "{œdataTypeœ: œSearchComponentœ, œidœ: œSearchComponent-fF95bœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-3H5qa",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-3H5qaœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
"id": "reactflow__edge-SearchComponent-8lQPB{œdataTypeœ:œSearchComponentœ,œidœ:œSearchComponent-8lQPBœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-7K58a{œfieldNameœ:œtoolsœ,œidœ:œAgent-7K58aœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
|
||||
"selected": false,
|
||||
"source": "SearchComponent-8lQPB",
|
||||
"sourceHandle": "{œdataTypeœ: œSearchComponentœ, œidœ: œSearchComponent-8lQPBœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
|
||||
"target": "Agent-7K58a",
|
||||
"targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-7K58aœ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
|
||||
}
|
||||
],
|
||||
"nodes": [
|
||||
{
|
||||
"data": {
|
||||
"id": "ChatInput-LIxJY",
|
||||
"id": "ChatInput-iZoDa",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Message"
|
||||
|
|
@ -467,7 +474,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 262,
|
||||
"id": "ChatInput-LIxJY",
|
||||
"id": "ChatInput-iZoDa",
|
||||
"measured": {
|
||||
"height": 262,
|
||||
"width": 360
|
||||
|
|
@ -488,7 +495,7 @@
|
|||
"data": {
|
||||
"description": "Display a chat message in the Playground.",
|
||||
"display_name": "Chat Output",
|
||||
"id": "ChatOutput-NdG8s",
|
||||
"id": "ChatOutput-8np0X",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Message"
|
||||
|
|
@ -770,7 +777,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 262,
|
||||
"id": "ChatOutput-NdG8s",
|
||||
"id": "ChatOutput-8np0X",
|
||||
"measured": {
|
||||
"height": 262,
|
||||
"width": 360
|
||||
|
|
@ -791,7 +798,7 @@
|
|||
"data": {
|
||||
"description": "Define the agent's instructions, then enter a task to complete using tools.",
|
||||
"display_name": "City Selection Agent",
|
||||
"id": "Agent-3H5qa",
|
||||
"id": "Agent-7K58a",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Message"
|
||||
|
|
@ -1390,7 +1397,7 @@
|
|||
},
|
||||
"dragging": true,
|
||||
"height": 725,
|
||||
"id": "Agent-3H5qa",
|
||||
"id": "Agent-7K58a",
|
||||
"measured": {
|
||||
"height": 725,
|
||||
"width": 360
|
||||
|
|
@ -1411,7 +1418,7 @@
|
|||
"data": {
|
||||
"description": "Define the agent's instructions, then enter a task to complete using tools.",
|
||||
"display_name": "Local Expert Agent",
|
||||
"id": "Agent-PWZOu",
|
||||
"id": "Agent-zOYup",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Message"
|
||||
|
|
@ -2010,7 +2017,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 725,
|
||||
"id": "Agent-PWZOu",
|
||||
"id": "Agent-zOYup",
|
||||
"measured": {
|
||||
"height": 725,
|
||||
"width": 360
|
||||
|
|
@ -2031,7 +2038,7 @@
|
|||
"data": {
|
||||
"description": "Define the agent's instructions, then enter a task to complete using tools.",
|
||||
"display_name": "Travel Concierge Agent",
|
||||
"id": "Agent-e5naw",
|
||||
"id": "Agent-PKpSO",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Message"
|
||||
|
|
@ -2630,7 +2637,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 725,
|
||||
"id": "Agent-e5naw",
|
||||
"id": "Agent-PKpSO",
|
||||
"measured": {
|
||||
"height": 725,
|
||||
"width": 360
|
||||
|
|
@ -2649,7 +2656,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "note-qLmmh",
|
||||
"id": "note-gUKzO",
|
||||
"node": {
|
||||
"description": "# Travel Planning Agents \n\nThe travel planning system is a smart setup that uses several specialized \"agents\" to help plan incredible trips. Imagine each agent as a travel expert focusing on a part of your journey. Here's how it works:\n\n- **User-Friendly Start:** You start by telling the system about your travel needs—where you want to go and what you love to do.\n\n- **Data Collection:** The agents uses its tools to gather current info about various destinations, like the best travel times, weather, and costs.\n\n- **Three Key Agents:**\n - **City Selection Agent:** Picks the best places to visit based on your likes and current data.\n - **Local Expert Agent:** Gathers interesting details about what to do and see in the chosen city.\n - **Travel Concierge Agent:** Builds a day-by-day plan that includes where to stay, eat, and explore!\n\n- **Tools and Data:** Each agent uses tools to find and organize the latest information so you get recommendations that are both accurate and exciting.\n\n- **Final Plan:** Once everything is put together, you receive a complete, easy-to-follow travel itinerary, perfect for your adventure!\n",
|
||||
"display_name": "",
|
||||
|
|
@ -2660,7 +2667,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 603,
|
||||
"id": "note-qLmmh",
|
||||
"id": "note-gUKzO",
|
||||
"measured": {
|
||||
"height": 603,
|
||||
"width": 328
|
||||
|
|
@ -2684,7 +2691,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "note-lZk3i",
|
||||
"id": "note-a0pJv",
|
||||
"node": {
|
||||
"description": "# **City Selection Agent**\n - **Purpose:** This agent evaluates potential travel destinations based on user input and external data sources.\n - **Core Functions:** Analyzes factors such as weather, local events, and travel costs to recommend optimal cities.\n - **Tools Utilized:** Employs APIs and data-fetching tools to gather real-time information for decision-making.\n",
|
||||
"display_name": "",
|
||||
|
|
@ -2697,7 +2704,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 334,
|
||||
"id": "note-lZk3i",
|
||||
"id": "note-a0pJv",
|
||||
"measured": {
|
||||
"height": 334,
|
||||
"width": 328
|
||||
|
|
@ -2721,7 +2728,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "note-twqCP",
|
||||
"id": "note-ylJy2",
|
||||
"node": {
|
||||
"description": "# **Local Expert Agent**\n - **Purpose:** Focused on gathering and providing an in-depth guide to the selected city.\n - **Core Functions:** Compiles insights into cultural attractions, local customs, and unique experiences.\n - **Tools Utilized:** Uses web content fetchers and data APIs to collect detailed local insights and enhance the user understanding with hidden gems.\n",
|
||||
"display_name": "",
|
||||
|
|
@ -2734,7 +2741,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 342,
|
||||
"id": "note-twqCP",
|
||||
"id": "note-ylJy2",
|
||||
"measured": {
|
||||
"height": 342,
|
||||
"width": 328
|
||||
|
|
@ -2758,7 +2765,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "note-bhmU0",
|
||||
"id": "note-LZBEx",
|
||||
"node": {
|
||||
"description": "# **Travel Concierge Agent**\n - **Purpose:** Crafts detailed travel itineraries that are customized to the traveler's interests and needs.\n - **Core Functions:** Offers a comprehensive daily schedule, including accommodations, dining spots, and activities.\n - **Tools Utilized:** Integrates calculators and data tools for accurate budget planning and itinerary logistics.",
|
||||
"display_name": "",
|
||||
|
|
@ -2771,7 +2778,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 336,
|
||||
"id": "note-bhmU0",
|
||||
"id": "note-LZBEx",
|
||||
"measured": {
|
||||
"height": 336,
|
||||
"width": 328
|
||||
|
|
@ -2795,7 +2802,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "note-pFFw3",
|
||||
"id": "note-4IUQQ",
|
||||
"node": {
|
||||
"description": "## Configure the agent by obtaining your OpenAI API key from [platform.openai.com](https://platform.openai.com). Under \"Model Provider\", choose:\n- OpenAI: Default, requires only API key\n- Anthropic/Azure/Groq/NVIDIA: Each requires their own API keys\n- Custom: Use your own model endpoint + authentication\n\nSelect model and input API key before running the flow.",
|
||||
"display_name": "",
|
||||
|
|
@ -2808,7 +2815,7 @@
|
|||
},
|
||||
"dragging": false,
|
||||
"height": 324,
|
||||
"id": "note-pFFw3",
|
||||
"id": "note-4IUQQ",
|
||||
"measured": {
|
||||
"height": 324,
|
||||
"width": 328
|
||||
|
|
@ -2830,7 +2837,7 @@
|
|||
"data": {
|
||||
"description": "Load and retrieve data from specified URLs. Supports output in plain text, raw HTML, or JSON, with options for cleaning and separating multiple outputs.",
|
||||
"display_name": "URL",
|
||||
"id": "URL-XDhvs",
|
||||
"id": "URL-j9slU",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Data",
|
||||
|
|
@ -3091,7 +3098,7 @@
|
|||
"type": "URL"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "URL-XDhvs",
|
||||
"id": "URL-j9slU",
|
||||
"measured": {
|
||||
"height": 660,
|
||||
"width": 360
|
||||
|
|
@ -3105,7 +3112,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "CalculatorComponent-N83tJ",
|
||||
"id": "CalculatorComponent-L3y5A",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Data"
|
||||
|
|
@ -3282,7 +3289,7 @@
|
|||
"type": "CalculatorComponent"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "CalculatorComponent-N83tJ",
|
||||
"id": "CalculatorComponent-L3y5A",
|
||||
"measured": {
|
||||
"height": 374,
|
||||
"width": 360
|
||||
|
|
@ -3296,14 +3303,16 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"id": "SearchComponent-fF95b",
|
||||
"description": "Call the searchapi.io API with result limiting",
|
||||
"display_name": "Search API",
|
||||
"id": "SearchComponent-8lQPB",
|
||||
"node": {
|
||||
"base_classes": [
|
||||
"Data",
|
||||
"DataFrame",
|
||||
"Message"
|
||||
],
|
||||
"beta": false,
|
||||
"category": "tools",
|
||||
"conditional_paths": [],
|
||||
"custom_fields": {},
|
||||
"description": "Call the searchapi.io API with result limiting",
|
||||
|
|
@ -3320,9 +3329,7 @@
|
|||
],
|
||||
"frozen": false,
|
||||
"icon": "SearchAPI",
|
||||
"key": "SearchComponent",
|
||||
"legacy": false,
|
||||
"lf_version": "1.1.1",
|
||||
"metadata": {},
|
||||
"minimized": false,
|
||||
"output_types": [],
|
||||
|
|
@ -3334,6 +3341,7 @@
|
|||
"hidden": null,
|
||||
"method": "to_toolkit",
|
||||
"name": "component_as_tool",
|
||||
"options": null,
|
||||
"required_inputs": null,
|
||||
"selected": "Tool",
|
||||
"tool_mode": true,
|
||||
|
|
@ -3344,7 +3352,6 @@
|
|||
}
|
||||
],
|
||||
"pinned": false,
|
||||
"score": 0.0030458160338519237,
|
||||
"template": {
|
||||
"_type": "Component",
|
||||
"api_key": {
|
||||
|
|
@ -3356,7 +3363,7 @@
|
|||
"input_types": [
|
||||
"Message"
|
||||
],
|
||||
"load_from_db": true,
|
||||
"load_from_db": false,
|
||||
"name": "api_key",
|
||||
"password": true,
|
||||
"placeholder": "",
|
||||
|
|
@ -3382,7 +3389,7 @@
|
|||
"show": true,
|
||||
"title_case": false,
|
||||
"type": "code",
|
||||
"value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom import Component\nfrom langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass SearchComponent(Component):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n icon = \"SearchAPI\"\n\n inputs = [\n DropdownInput(name=\"engine\", display_name=\"Engine\", value=\"google\", options=[\"google\", \"bing\", \"duckduckgo\"]),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n tool_mode=True,\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n ]\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def run_model(self) -> list[Data]:\n return self.fetch_content()\n\n def fetch_content(self) -> list[Data]:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> list[Data]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n return [\n Data(\n text=result.get(\"snippet\", \"\"),\n data={\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n },\n )\n for result in organic_results\n ]\n\n results = search_func(\n self.input_value,\n self.search_params or {},\n self.max_results,\n self.max_snippet_length,\n )\n self.status = results\n return results\n\n def fetch_content_text(self) -> Message:\n data = self.fetch_content()\n result_string = \"\"\n for item in data:\n result_string += item.text + \"\\n\"\n self.status = result_string\n return Message(text=result_string)\n"
|
||||
"value": "from typing import Any\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom import Component\nfrom langflow.inputs import DictInput, DropdownInput, IntInput, MultilineInput, SecretStrInput\nfrom langflow.io import Output\nfrom langflow.schema import Data, DataFrame\nfrom langflow.schema.message import Message\n\n\nclass SearchComponent(Component):\n display_name: str = \"Search API\"\n description: str = \"Call the searchapi.io API with result limiting\"\n documentation: str = \"https://www.searchapi.io/docs/google\"\n icon = \"SearchAPI\"\n\n inputs = [\n DropdownInput(name=\"engine\", display_name=\"Engine\", value=\"google\", options=[\"google\", \"bing\", \"duckduckgo\"]),\n SecretStrInput(name=\"api_key\", display_name=\"SearchAPI API Key\", required=True),\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input\",\n tool_mode=True,\n ),\n DictInput(name=\"search_params\", display_name=\"Search parameters\", advanced=True, is_list=True),\n IntInput(name=\"max_results\", display_name=\"Max Results\", value=5, advanced=True),\n IntInput(name=\"max_snippet_length\", display_name=\"Max Snippet Length\", value=100, advanced=True),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n Output(display_name=\"Text\", name=\"text\", method=\"fetch_content_text\"),\n Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n ]\n\n def _build_wrapper(self):\n return SearchApiAPIWrapper(engine=self.engine, searchapi_api_key=self.api_key)\n\n def run_model(self) -> list[Data]:\n return self.fetch_content()\n\n def fetch_content(self) -> list[Data]:\n wrapper = self._build_wrapper()\n\n def search_func(\n query: str, params: dict[str, Any] | None = None, max_results: int = 5, max_snippet_length: int = 100\n ) -> list[Data]:\n params = params or {}\n full_results = wrapper.results(query=query, **params)\n organic_results = full_results.get(\"organic_results\", [])[:max_results]\n\n return [\n Data(\n text=result.get(\"snippet\", \"\"),\n data={\n \"title\": result.get(\"title\", \"\")[:max_snippet_length],\n \"link\": result.get(\"link\", \"\"),\n \"snippet\": result.get(\"snippet\", \"\")[:max_snippet_length],\n },\n )\n for result in organic_results\n ]\n\n results = search_func(\n self.input_value,\n self.search_params or {},\n self.max_results,\n self.max_snippet_length,\n )\n self.status = results\n return results\n\n def fetch_content_text(self) -> Message:\n data = self.fetch_content()\n result_string = \"\"\n for item in data:\n result_string += item.text + \"\\n\"\n self.status = result_string\n return Message(text=result_string)\n\n def as_dataframe(self) -> DataFrame:\n \"\"\"Convert the search results to a DataFrame.\n\n Returns:\n DataFrame: A DataFrame containing the search results.\n \"\"\"\n data = self.fetch_content()\n return DataFrame(data)\n"
|
||||
},
|
||||
"engine": {
|
||||
"_input_type": "DropdownInput",
|
||||
|
|
@ -3411,6 +3418,7 @@
|
|||
"input_value": {
|
||||
"_input_type": "MultilineInput",
|
||||
"advanced": false,
|
||||
"copy_field": false,
|
||||
"display_name": "Input",
|
||||
"dynamic": false,
|
||||
"info": "",
|
||||
|
|
@ -3558,6 +3566,19 @@
|
|||
"name": "tags",
|
||||
"sortable": false,
|
||||
"type": "str"
|
||||
},
|
||||
{
|
||||
"default": true,
|
||||
"description": "Indicates whether the tool is currently active. Set to True to activate this tool.",
|
||||
"disable_edit": false,
|
||||
"display_name": "Enable",
|
||||
"edit_mode": "popover",
|
||||
"filterable": true,
|
||||
"formatter": "boolean",
|
||||
"hidden": false,
|
||||
"name": "status",
|
||||
"sortable": true,
|
||||
"type": "boolean"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
|
@ -3571,6 +3592,7 @@
|
|||
{
|
||||
"description": "fetch_content(api_key: Message) - Call the searchapi.io API with result limiting",
|
||||
"name": "SearchComponent-fetch_content",
|
||||
"status": true,
|
||||
"tags": [
|
||||
"SearchComponent-fetch_content"
|
||||
]
|
||||
|
|
@ -3578,9 +3600,18 @@
|
|||
{
|
||||
"description": "fetch_content_text(api_key: Message) - Call the searchapi.io API with result limiting",
|
||||
"name": "SearchComponent-fetch_content_text",
|
||||
"status": true,
|
||||
"tags": [
|
||||
"SearchComponent-fetch_content_text"
|
||||
]
|
||||
},
|
||||
{
|
||||
"description": "as_dataframe(api_key: Message) - Call the searchapi.io API with result limiting",
|
||||
"name": "SearchComponent-as_dataframe",
|
||||
"status": true,
|
||||
"tags": [
|
||||
"SearchComponent-as_dataframe"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -3591,7 +3622,7 @@
|
|||
"type": "SearchComponent"
|
||||
},
|
||||
"dragging": false,
|
||||
"id": "SearchComponent-fF95b",
|
||||
"id": "SearchComponent-8lQPB",
|
||||
"measured": {
|
||||
"height": 536,
|
||||
"width": 360
|
||||
|
|
@ -3605,9 +3636,9 @@
|
|||
}
|
||||
],
|
||||
"viewport": {
|
||||
"x": -473.30523608095336,
|
||||
"y": 166.93251319338754,
|
||||
"zoom": 0.41164535038506667
|
||||
"x": -422.4287774794177,
|
||||
"y": 323.63311823261427,
|
||||
"zoom": 0.36739682737950574
|
||||
}
|
||||
},
|
||||
"description": "Create a travel planning chatbot that uses specialized agents to craft personalized trip itineraries.",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue