feat: add integrations category in the sidebar (#3843)
* feat: Add skipFallback parameter to ForwardedIconComponent This commit adds a new skipFallback parameter to the ForwardedIconComponent in the genericIconComponent module. The skipFallback parameter allows developers to skip rendering the fallback component when using Suspense. This can be useful in cases where the fallback component is not needed or should be handled differently. * feat: Add skipFallback parameter to IconComponentProps * feat: Add skipFallback parameter to IconComponentProps and ForwardedIconComponent This commit adds the skipFallback parameter to the IconComponentProps and ForwardedIconComponent. This parameter allows skipping the fallback icon rendering when the specified icon is not found. The skipFallback parameter is used in the ParentDisclosureComponent to conditionally render the chevron-down or chevron-right icon based on the component's state. The commit also updates the defaultOpen value in the ParentDisclosureComponent in the ExtraSidebarComponent. The defaultOpen value is set to true, ensuring that the "Integrations" section is always expanded when the component is rendered. * refactor: Update ExtraSidebarComponent layout and styling * [autofix.ci] apply automated fixes * move file * move file * move file * move file * move file * Update NotionLogo size * Update size of AssemblyAI logo * Update colors and names for AssemblyAI in styleUtils.ts * Update BUNDLES_SIDEBAR_FOLDER_NAMES in constants.ts * [autofix.ci] apply automated fixes * add changes from pr #3934 * chore: add cache.db to .gitignore --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
anovazzi1
GitHub
parent
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commit
0d796db02c
18 changed files with 1483 additions and 45 deletions
1
.gitignore
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vendored
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@ -275,3 +275,4 @@ src/frontend/temp
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.history
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.dspy_cache/
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cache.db
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File diff suppressed because one or more lines are too long
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@ -45,7 +45,7 @@ This component allows you to submit an audio or video file for transcription.
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- Audio File: The audio or video file to transcribe.
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- Speech Model (Optional): Select the class of models. Default is *Best*. See [speech models](https://www.assemblyai.com/docs/speech-to-text/speech-recognition#select-the-speech-model-with-best-and-nano) for more info.
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- Automatic Language Detection (Optional): Enable automatic language detection.
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- Language (Optional): The language of the audio file. Can be set manually if automatic language detection is disabled.
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- Language (Optional): The language of the audio file. Can be set manually if automatic language detection is disabled.
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See [supported languages](https://www.assemblyai.com/docs/getting-started/supported-languages) for a list of supported language codes.
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- Enable Speaker Labels (Optional): Detect speakers in an audio file and what each speaker said.
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- Expected Number of Speakers (Optional): Set the expected number of speakers, if Speaker Labels is enabled.
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@ -163,4 +163,3 @@ If you encounter issues:
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4. Review the Langflow logs for any error messages.
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For more advanced usage, refer to the [AssemblyAI API documentation](https://www.assemblyai.com/docs/). If you need more help, you can reach out to the [AssemblyAI support](https://www.assemblyai.com/contact/support).
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@ -0,0 +1,79 @@
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import assemblyai as aai
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from langflow.custom import Component
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from langflow.io import DataInput, DropdownInput, IntInput, Output, SecretStrInput
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from langflow.schema import Data
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class AssemblyAIGetSubtitles(Component):
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display_name = "AssemblyAI Get Subtitles"
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description = "Export your transcript in SRT or VTT format for subtitles and closed captions"
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documentation = "https://www.assemblyai.com/docs"
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icon = "AssemblyAI"
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inputs = [
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SecretStrInput(
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name="api_key",
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display_name="Assembly API Key",
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info="Your AssemblyAI API key. You can get one from https://www.assemblyai.com/",
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),
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DataInput(
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name="transcription_result",
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display_name="Transcription Result",
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info="The transcription result from AssemblyAI",
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),
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DropdownInput(
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name="subtitle_format",
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display_name="Subtitle Format",
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options=["srt", "vtt"],
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value="srt",
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info="The format of the captions (SRT or VTT)",
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),
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IntInput(
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name="chars_per_caption",
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display_name="Characters per Caption",
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info="The maximum number of characters per caption (0 for no limit)",
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value=0,
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advanced=True,
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),
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]
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outputs = [
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Output(display_name="Subtitles", name="subtitles", method="get_subtitles"),
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]
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def get_subtitles(self) -> Data:
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aai.settings.api_key = self.api_key
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# check if it's an error message from the previous step
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if self.transcription_result.data.get("error"):
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self.status = self.transcription_result.data["error"]
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return self.transcription_result
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try:
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transcript_id = self.transcription_result.data["id"]
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transcript = aai.Transcript.get_by_id(transcript_id)
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except Exception as e:
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error = f"Getting transcription failed: {str(e)}"
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self.status = error
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return Data(data={"error": error})
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if transcript.status == aai.TranscriptStatus.completed:
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subtitles = None
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chars_per_caption = self.chars_per_caption if self.chars_per_caption > 0 else None
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if self.subtitle_format == "srt":
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subtitles = transcript.export_subtitles_srt(chars_per_caption)
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else:
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subtitles = transcript.export_subtitles_vtt(chars_per_caption)
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result = Data(
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subtitles=subtitles,
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format=self.subtitle_format,
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transcript_id=transcript_id,
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chars_per_caption=chars_per_caption,
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)
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self.status = result
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return result
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self.status = transcript.error
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return Data(data={"error": transcript.error})
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@ -0,0 +1,182 @@
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import assemblyai as aai
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from langflow.custom import Component
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from langflow.io import DataInput, DropdownInput, FloatInput, IntInput, MultilineInput, Output, SecretStrInput
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from langflow.schema import Data
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class AssemblyAILeMUR(Component):
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display_name = "AssemblyAI LeMUR"
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description = "Apply Large Language Models to spoken data using the AssemblyAI LeMUR framework"
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documentation = "https://www.assemblyai.com/docs/lemur"
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icon = "AssemblyAI"
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inputs = [
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SecretStrInput(
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name="api_key",
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display_name="Assembly API Key",
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info="Your AssemblyAI API key. You can get one from https://www.assemblyai.com/",
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advanced=False,
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),
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DataInput(
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name="transcription_result",
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display_name="Transcription Result",
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info="The transcription result from AssemblyAI",
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),
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MultilineInput(
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name="prompt",
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display_name="Input Prompt",
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info="The text to prompt the model",
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),
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DropdownInput(
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name="final_model",
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display_name="Final Model",
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options=["claude3_5_sonnet", "claude3_opus", "claude3_haiku", "claude3_sonnet"],
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value="claude3_5_sonnet",
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info="The model that is used for the final prompt after compression is performed",
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advanced=True,
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),
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FloatInput(
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name="temperature",
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display_name="Temperature",
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advanced=True,
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value=0.0,
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info="The temperature to use for the model",
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),
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IntInput(
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name="max_output_size",
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display_name=" Max Output Size",
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advanced=True,
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value=2000,
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info="Max output size in tokens, up to 4000",
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),
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DropdownInput(
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name="endpoint",
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display_name="Endpoint",
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options=["task", "summary", "question-answer"],
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value="task",
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info=(
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"The LeMUR endpoint to use. For 'summary' and 'question-answer',"
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" no prompt input is needed. See https://www.assemblyai.com/docs/api-reference/lemur/ for more info."
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),
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advanced=True,
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),
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MultilineInput(
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name="questions",
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display_name="Questions",
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info="Comma-separated list of your questions. Only used if Endpoint is 'question-answer'",
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advanced=True,
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),
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MultilineInput(
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name="transcript_ids",
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display_name="Transcript IDs",
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info=(
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"Comma-separated list of transcript IDs. LeMUR can perform actions over multiple transcripts."
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" If provided, the Transcription Result is ignored."
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),
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advanced=True,
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),
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]
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outputs = [
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Output(display_name="LeMUR Response", name="lemur_response", method="run_lemur"),
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]
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def run_lemur(self) -> Data:
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"""Use the LeMUR task endpoint to input the LLM prompt."""
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aai.settings.api_key = self.api_key
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if not self.transcription_result and not self.transcript_ids:
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error = "Either a Transcription Result or Transcript IDs must be provided"
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self.status = error
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return Data(data={"error": error})
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if self.transcription_result and self.transcription_result.data.get("error"):
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# error message from the previous step
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self.status = self.transcription_result.data["error"]
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return self.transcription_result
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if self.endpoint == "task" and not self.prompt:
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self.status = "No prompt specified for the task endpoint"
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return Data(data={"error": "No prompt specified"})
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if self.endpoint == "question-answer" and not self.questions:
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error = "No Questions were provided for the question-answer endpoint"
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self.status = error
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return Data(data={"error": error})
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# Check for valid transcripts
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transcript_ids = None
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if self.transcription_result and "id" in self.transcription_result.data:
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transcript_ids = [self.transcription_result.data["id"]]
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elif self.transcript_ids:
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transcript_ids = self.transcript_ids.split(",") or []
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transcript_ids = [t.strip() for t in transcript_ids]
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if not transcript_ids:
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error = "Either a valid Transcription Result or valid Transcript IDs must be provided"
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self.status = error
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return Data(data={"error": error})
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# Get TranscriptGroup and check if there is any error
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transcript_group = aai.TranscriptGroup(transcript_ids=transcript_ids)
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transcript_group, failures = transcript_group.wait_for_completion(return_failures=True)
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if failures:
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error = f"Getting transcriptions failed: {failures[0]}"
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self.status = error
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return Data(data={"error": error})
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for t in transcript_group.transcripts:
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if t.status == aai.TranscriptStatus.error:
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self.status = t.error
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return Data(data={"error": t.error})
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# Perform LeMUR action
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try:
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response = self.perform_lemur_action(transcript_group, self.endpoint)
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result = Data(data=response)
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self.status = result
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return result
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except Exception as e:
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error = f"An Error happened: {str(e)}"
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self.status = error
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return Data(data={"error": error})
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def perform_lemur_action(self, transcript_group: aai.TranscriptGroup, endpoint: str) -> dict:
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print("Endpoint:", endpoint, type(endpoint))
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if endpoint == "task":
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result = transcript_group.lemur.task(
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prompt=self.prompt,
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final_model=self.get_final_model(self.final_model),
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temperature=self.temperature,
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max_output_size=self.max_output_size,
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)
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elif endpoint == "summary":
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result = transcript_group.lemur.summarize(
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final_model=self.get_final_model(self.final_model),
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temperature=self.temperature,
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max_output_size=self.max_output_size,
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)
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elif endpoint == "question-answer":
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questions = self.questions.split(",")
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questions = [aai.LemurQuestion(question=q) for q in questions]
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result = transcript_group.lemur.question(
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questions=questions,
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final_model=self.get_final_model(self.final_model),
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temperature=self.temperature,
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max_output_size=self.max_output_size,
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)
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else:
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msg = f"Endpoint not supported: {endpoint}"
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raise ValueError(msg)
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return result.dict()
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def get_final_model(self, model_name: str) -> aai.LemurModel:
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if model_name == "claude3_5_sonnet":
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return aai.LemurModel.claude3_5_sonnet
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if model_name == "claude3_opus":
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return aai.LemurModel.claude3_opus
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if model_name == "claude3_haiku":
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return aai.LemurModel.claude3_haiku
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if model_name == "claude3_sonnet":
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return aai.LemurModel.claude3_sonnet
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msg = f"Model name not supported: {model_name}"
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raise ValueError(msg)
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@ -0,0 +1,91 @@
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import assemblyai as aai
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from langflow.custom import Component
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from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput
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from langflow.schema import Data
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class AssemblyAIListTranscripts(Component):
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display_name = "AssemblyAI List Transcripts"
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description = "Retrieve a list of transcripts from AssemblyAI with filtering options"
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documentation = "https://www.assemblyai.com/docs"
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icon = "AssemblyAI"
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inputs = [
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SecretStrInput(
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name="api_key",
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display_name="Assembly API Key",
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info="Your AssemblyAI API key. You can get one from https://www.assemblyai.com/",
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),
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IntInput(
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name="limit",
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display_name="Limit",
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info="Maximum number of transcripts to retrieve (default: 20, use 0 for all)",
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value=20,
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),
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DropdownInput(
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name="status_filter",
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display_name="Status Filter",
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options=["all", "queued", "processing", "completed", "error"],
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value="all",
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info="Filter by transcript status",
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advanced=True,
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),
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MessageTextInput(
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name="created_on",
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display_name="Created On",
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info="Only get transcripts created on this date (YYYY-MM-DD)",
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advanced=True,
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),
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BoolInput(
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name="throttled_only",
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display_name="Throttled Only",
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info="Only get throttled transcripts, overrides the status filter",
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advanced=True,
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),
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]
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outputs = [
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Output(display_name="Transcript List", name="transcript_list", method="list_transcripts"),
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]
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def list_transcripts(self) -> list[Data]:
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aai.settings.api_key = self.api_key
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params = aai.ListTranscriptParameters()
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if self.limit:
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params.limit = self.limit
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if self.status_filter != "all":
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params.status = self.status_filter
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if self.created_on and self.created_on.text:
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params.created_on = self.created_on.text
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if self.throttled_only:
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params.throttled_only = True
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try:
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transcriber = aai.Transcriber()
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def convert_page_to_data_list(page):
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return [Data(**t.dict()) for t in page.transcripts]
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if self.limit == 0:
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# paginate over all pages
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params.limit = 100
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page = transcriber.list_transcripts(params)
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transcripts = convert_page_to_data_list(page)
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while page.page_details.before_id_of_prev_url is not None:
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params.before_id = page.page_details.before_id_of_prev_url
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page = transcriber.list_transcripts(params)
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transcripts.extend(convert_page_to_data_list(page))
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else:
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# just one page
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page = transcriber.list_transcripts(params)
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transcripts = convert_page_to_data_list(page)
|
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|
||||
self.status = transcripts
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return transcripts
|
||||
except Exception as e:
|
||||
error_data = Data(data={"error": f"An error occurred: {str(e)}"})
|
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self.status = [error_data]
|
||||
return [error_data]
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
import assemblyai as aai
|
||||
|
||||
from langflow.custom import Component
|
||||
from langflow.io import DataInput, FloatInput, Output, SecretStrInput
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class AssemblyAITranscriptionJobPoller(Component):
|
||||
display_name = "AssemblyAI Poll Transcript"
|
||||
description = "Poll for the status of a transcription job using AssemblyAI"
|
||||
documentation = "https://www.assemblyai.com/docs"
|
||||
icon = "AssemblyAI"
|
||||
|
||||
inputs = [
|
||||
SecretStrInput(
|
||||
name="api_key",
|
||||
display_name="Assembly API Key",
|
||||
info="Your AssemblyAI API key. You can get one from https://www.assemblyai.com/",
|
||||
),
|
||||
DataInput(
|
||||
name="transcript_id",
|
||||
display_name="Transcript ID",
|
||||
info="The ID of the transcription job to poll",
|
||||
),
|
||||
FloatInput(
|
||||
name="polling_interval",
|
||||
display_name="Polling Interval",
|
||||
value=3.0,
|
||||
info="The polling interval in seconds",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
|
||||
outputs = [
|
||||
Output(display_name="Transcription Result", name="transcription_result", method="poll_transcription_job"),
|
||||
]
|
||||
|
||||
def poll_transcription_job(self) -> Data:
|
||||
"""Polls the transcription status until completion and returns the Data."""
|
||||
aai.settings.api_key = self.api_key
|
||||
aai.settings.polling_interval = self.polling_interval
|
||||
|
||||
# check if it's an error message from the previous step
|
||||
if self.transcript_id.data.get("error"):
|
||||
self.status = self.transcript_id.data["error"]
|
||||
return self.transcript_id
|
||||
|
||||
try:
|
||||
transcript = aai.Transcript.get_by_id(self.transcript_id.data["transcript_id"])
|
||||
except Exception as e:
|
||||
error = f"Getting transcription failed: {str(e)}"
|
||||
self.status = error
|
||||
return Data(data={"error": error})
|
||||
|
||||
if transcript.status == aai.TranscriptStatus.completed:
|
||||
json_response = transcript.json_response
|
||||
text = json_response.pop("text", None)
|
||||
utterances = json_response.pop("utterances", None)
|
||||
transcript_id = json_response.pop("id", None)
|
||||
sorted_data = {"text": text, "utterances": utterances, "id": transcript_id}
|
||||
sorted_data.update(json_response)
|
||||
data = Data(data=sorted_data)
|
||||
self.status = data
|
||||
return data
|
||||
self.status = transcript.error
|
||||
return Data(data={"error": transcript.error})
|
||||
|
|
@ -0,0 +1,185 @@
|
|||
import os
|
||||
|
||||
import assemblyai as aai
|
||||
from loguru import logger
|
||||
|
||||
from langflow.custom import Component
|
||||
from langflow.io import BoolInput, DropdownInput, FileInput, MessageTextInput, Output, SecretStrInput
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class AssemblyAITranscriptionJobCreator(Component):
|
||||
display_name = "AssemblyAI Start Transcript"
|
||||
description = "Create a transcription job for an audio file using AssemblyAI with advanced options"
|
||||
documentation = "https://www.assemblyai.com/docs"
|
||||
icon = "AssemblyAI"
|
||||
|
||||
inputs = [
|
||||
SecretStrInput(
|
||||
name="api_key",
|
||||
display_name="Assembly API Key",
|
||||
info="Your AssemblyAI API key. You can get one from https://www.assemblyai.com/",
|
||||
),
|
||||
FileInput(
|
||||
name="audio_file",
|
||||
display_name="Audio File",
|
||||
file_types=[
|
||||
"3ga",
|
||||
"8svx",
|
||||
"aac",
|
||||
"ac3",
|
||||
"aif",
|
||||
"aiff",
|
||||
"alac",
|
||||
"amr",
|
||||
"ape",
|
||||
"au",
|
||||
"dss",
|
||||
"flac",
|
||||
"flv",
|
||||
"m4a",
|
||||
"m4b",
|
||||
"m4p",
|
||||
"m4r",
|
||||
"mp3",
|
||||
"mpga",
|
||||
"ogg",
|
||||
"oga",
|
||||
"mogg",
|
||||
"opus",
|
||||
"qcp",
|
||||
"tta",
|
||||
"voc",
|
||||
"wav",
|
||||
"wma",
|
||||
"wv",
|
||||
"webm",
|
||||
"mts",
|
||||
"m2ts",
|
||||
"ts",
|
||||
"mov",
|
||||
"mp2",
|
||||
"mp4",
|
||||
"m4p",
|
||||
"m4v",
|
||||
"mxf",
|
||||
],
|
||||
info="The audio file to transcribe",
|
||||
),
|
||||
MessageTextInput(
|
||||
name="audio_file_url",
|
||||
display_name="Audio File URL",
|
||||
info="The URL of the audio file to transcribe (Can be used instead of a File)",
|
||||
advanced=True,
|
||||
),
|
||||
DropdownInput(
|
||||
name="speech_model",
|
||||
display_name="Speech Model",
|
||||
options=[
|
||||
"best",
|
||||
"nano",
|
||||
],
|
||||
value="best",
|
||||
info="The speech model to use for the transcription",
|
||||
advanced=True,
|
||||
),
|
||||
BoolInput(
|
||||
name="language_detection",
|
||||
display_name="Automatic Language Detection",
|
||||
info="Enable automatic language detection",
|
||||
advanced=True,
|
||||
),
|
||||
MessageTextInput(
|
||||
name="language_code",
|
||||
display_name="Language",
|
||||
info=(
|
||||
"""
|
||||
The language of the audio file. Can be set manually if automatic language detection is disabled.
|
||||
See https://www.assemblyai.com/docs/getting-started/supported-languages """
|
||||
"for a list of supported language codes."
|
||||
),
|
||||
advanced=True,
|
||||
),
|
||||
BoolInput(
|
||||
name="speaker_labels",
|
||||
display_name="Enable Speaker Labels",
|
||||
info="Enable speaker diarization",
|
||||
),
|
||||
MessageTextInput(
|
||||
name="speakers_expected",
|
||||
display_name="Expected Number of Speakers",
|
||||
info="Set the expected number of speakers (optional, enter a number)",
|
||||
advanced=True,
|
||||
),
|
||||
BoolInput(
|
||||
name="punctuate",
|
||||
display_name="Punctuate",
|
||||
info="Enable automatic punctuation",
|
||||
advanced=True,
|
||||
value=True,
|
||||
),
|
||||
BoolInput(
|
||||
name="format_text",
|
||||
display_name="Format Text",
|
||||
info="Enable text formatting",
|
||||
advanced=True,
|
||||
value=True,
|
||||
),
|
||||
]
|
||||
|
||||
outputs = [
|
||||
Output(display_name="Transcript ID", name="transcript_id", method="create_transcription_job"),
|
||||
]
|
||||
|
||||
def create_transcription_job(self) -> Data:
|
||||
aai.settings.api_key = self.api_key
|
||||
|
||||
# Convert speakers_expected to int if it's not empty
|
||||
speakers_expected = None
|
||||
if self.speakers_expected and self.speakers_expected.strip():
|
||||
try:
|
||||
speakers_expected = int(self.speakers_expected)
|
||||
except ValueError:
|
||||
self.status = "Error: Expected Number of Speakers must be a valid integer"
|
||||
return Data(data={"error": "Error: Expected Number of Speakers must be a valid integer"})
|
||||
|
||||
language_code = self.language_code if self.language_code else None
|
||||
|
||||
config = aai.TranscriptionConfig(
|
||||
speech_model=self.speech_model,
|
||||
language_detection=self.language_detection,
|
||||
language_code=language_code,
|
||||
speaker_labels=self.speaker_labels,
|
||||
speakers_expected=speakers_expected,
|
||||
punctuate=self.punctuate,
|
||||
format_text=self.format_text,
|
||||
)
|
||||
|
||||
audio = None
|
||||
if self.audio_file:
|
||||
if self.audio_file_url:
|
||||
logger.warning("Both an audio file an audio URL were specified. The audio URL was ignored.")
|
||||
|
||||
# Check if the file exists
|
||||
if not os.path.exists(self.audio_file):
|
||||
self.status = "Error: Audio file not found"
|
||||
return Data(data={"error": "Error: Audio file not found"})
|
||||
audio = self.audio_file
|
||||
elif self.audio_file_url:
|
||||
audio = self.audio_file_url
|
||||
else:
|
||||
self.status = "Error: Either an audio file or an audio URL must be specified"
|
||||
return Data(data={"error": "Error: Either an audio file or an audio URL must be specified"})
|
||||
|
||||
try:
|
||||
transcript = aai.Transcriber().submit(audio, config=config)
|
||||
|
||||
if transcript.error:
|
||||
self.status = transcript.error
|
||||
return Data(data={"error": transcript.error})
|
||||
result = Data(data={"transcript_id": transcript.id})
|
||||
self.status = result
|
||||
return result
|
||||
except Exception as e:
|
||||
self.status = f"An error occurred: {str(e)}"
|
||||
return Data(data={"error": f"An error occurred: {str(e)}"})
|
||||
|
|
@ -125,7 +125,6 @@ pytest-flakefinder = "^1.1.0"
|
|||
types-markdown = "^3.7.0.20240822"
|
||||
assemblyai = "^0.33.0"
|
||||
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
minversion = "6.0"
|
||||
#addopts = "-ra"
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ export const ForwardedIconComponent = memo(
|
|||
stroke,
|
||||
strokeWidth,
|
||||
id = "",
|
||||
skipFallback = false,
|
||||
}: IconComponentProps,
|
||||
ref,
|
||||
) => {
|
||||
|
|
@ -56,7 +57,7 @@ export const ForwardedIconComponent = memo(
|
|||
);
|
||||
|
||||
return (
|
||||
<Suspense fallback={fallback}>
|
||||
<Suspense fallback={skipFallback ? undefined : fallback}>
|
||||
<TargetIcon
|
||||
className={className}
|
||||
style={style}
|
||||
|
|
|
|||
|
|
@ -729,7 +729,12 @@ export const PRIORITY_SIDEBAR_ORDER = [
|
|||
"embeddings",
|
||||
];
|
||||
|
||||
export const BUNDLES_SIDEBAR_FOLDER_NAMES = ["notion", "Notion"];
|
||||
export const BUNDLES_SIDEBAR_FOLDER_NAMES = [
|
||||
"notion",
|
||||
"Notion",
|
||||
"AssemblyAI",
|
||||
"assemblyai",
|
||||
];
|
||||
|
||||
export const AUTHORIZED_DUPLICATE_REQUESTS = [
|
||||
"/health",
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
const AssemblyAISVG = (props) => (
|
||||
<svg
|
||||
width="501"
|
||||
height="434"
|
||||
width="1.1em"
|
||||
height="1.1em"
|
||||
viewBox="0 0 501 434"
|
||||
fill="none"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
const SvgNotionLogo = (props) => (
|
||||
<svg
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
width="1em"
|
||||
height="1em"
|
||||
width="1.1em"
|
||||
height="1.1em"
|
||||
fill="none"
|
||||
viewBox="0 0 100 100"
|
||||
{...props}
|
||||
|
|
|
|||
|
|
@ -33,7 +33,8 @@ export default function ParentDisclosureComponent({
|
|||
))}
|
||||
<div>
|
||||
<IconComponent
|
||||
name="ChevronsUpDownIcon"
|
||||
skipFallback
|
||||
name={open ? "chevron-down" : "chevron-right"}
|
||||
className={`${
|
||||
open || defaultOpen ? "" : ""
|
||||
} h-4 w-4 text-foreground`}
|
||||
|
|
|
|||
|
|
@ -245,6 +245,35 @@ export default function ExtraSidebar(): JSX.Element {
|
|||
<div key={index}></div>
|
||||
),
|
||||
)}
|
||||
<>
|
||||
<ParentDisclosureComponent
|
||||
defaultOpen={true}
|
||||
key={`${search.length !== 0}-${getFilterEdge.length !== 0}-Bundle`}
|
||||
button={{
|
||||
title: "Integrations",
|
||||
Icon: nodeIconsLucide.unknown,
|
||||
}}
|
||||
testId="bundle-extended-disclosure"
|
||||
>
|
||||
{Object.keys(dataFilter)
|
||||
.sort(sortKeys)
|
||||
.filter((x) => BUNDLES_SIDEBAR_FOLDER_NAMES.includes(x))
|
||||
.map((SBSectionName: keyof APIObjectType, index) =>
|
||||
Object.keys(dataFilter[SBSectionName]).length > 0 ? (
|
||||
<SidebarCategoryComponent
|
||||
key={`DisclosureComponent${index + search + JSON.stringify(getFilterEdge)}`}
|
||||
search={search}
|
||||
getFilterEdge={getFilterEdge}
|
||||
category={dataFilter[SBSectionName]}
|
||||
name={SBSectionName}
|
||||
onDragStart={onDragStart}
|
||||
/>
|
||||
) : (
|
||||
<div key={index}></div>
|
||||
),
|
||||
)}
|
||||
</ParentDisclosureComponent>
|
||||
</>
|
||||
<ParentDisclosureComponent
|
||||
defaultOpen={search.length !== 0 || getFilterEdge.length !== 0}
|
||||
key={`${search.length !== 0}-${getFilterEdge.length !== 0}-Advanced`}
|
||||
|
|
@ -306,39 +335,6 @@ export default function ExtraSidebar(): JSX.Element {
|
|||
</a>
|
||||
)}
|
||||
</ParentDisclosureComponent>
|
||||
{ENABLE_MVPS && (
|
||||
<>
|
||||
<Separator />
|
||||
|
||||
<ParentDisclosureComponent
|
||||
defaultOpen={search.length !== 0 || getFilterEdge.length !== 0}
|
||||
key={`${search.length !== 0}-${getFilterEdge.length !== 0}-Bundle`}
|
||||
button={{
|
||||
title: "Bundles",
|
||||
Icon: nodeIconsLucide.unknown,
|
||||
}}
|
||||
testId="bundle-extended-disclosure"
|
||||
>
|
||||
{Object.keys(dataFilter)
|
||||
.sort(sortKeys)
|
||||
.filter((x) => BUNDLES_SIDEBAR_FOLDER_NAMES.includes(x))
|
||||
.map((SBSectionName: keyof APIObjectType, index) =>
|
||||
Object.keys(dataFilter[SBSectionName]).length > 0 ? (
|
||||
<SidebarCategoryComponent
|
||||
key={`DisclosureComponent${index + search + JSON.stringify(getFilterEdge)}`}
|
||||
search={search}
|
||||
getFilterEdge={getFilterEdge}
|
||||
category={dataFilter[SBSectionName]}
|
||||
name={SBSectionName}
|
||||
onDragStart={onDragStart}
|
||||
/>
|
||||
) : (
|
||||
<div key={index}></div>
|
||||
),
|
||||
)}
|
||||
</ParentDisclosureComponent>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
|
|
|
|||
|
|
@ -231,7 +231,7 @@
|
|||
@apply fill-chat-trigger-disabled stroke-chat-trigger-disabled stroke-1;
|
||||
}
|
||||
.parent-disclosure-arrangement {
|
||||
@apply flex w-full select-none items-center justify-between bg-background px-5 py-3;
|
||||
@apply flex w-full select-none items-center justify-between bg-background pl-5 pr-3 py-3;
|
||||
}
|
||||
.components-disclosure-arrangement {
|
||||
@apply -mt-px flex w-full select-none items-center justify-between border-y border-y-input bg-muted px-3 py-2;
|
||||
|
|
|
|||
|
|
@ -369,6 +369,7 @@ export type IconComponentProps = {
|
|||
stroke?: string;
|
||||
strokeWidth?: number;
|
||||
id?: string;
|
||||
skipFallback?: boolean;
|
||||
};
|
||||
|
||||
export type InputProps = {
|
||||
|
|
|
|||
|
|
@ -297,6 +297,8 @@ export const nodeColors: { [char: string]: string } = {
|
|||
wrappers: "#E6277A",
|
||||
notion: "#000000",
|
||||
Notion: "#000000",
|
||||
AssemblyAI: "#213ED7",
|
||||
assemblyai: "#213ED7",
|
||||
helpers: "#31A3CC",
|
||||
prototypes: "#E6277A",
|
||||
astra_assistants: "#272541",
|
||||
|
|
@ -325,6 +327,8 @@ export const nodeNames: { [char: string]: string } = {
|
|||
models: "Models",
|
||||
notion: "Notion",
|
||||
Notion: "Notion",
|
||||
AssemblyAI: "AssemblyAI",
|
||||
assemblyai: "AssemblyAI",
|
||||
model_specs: "Model Specs",
|
||||
chains: "Chains",
|
||||
agents: "Agents",
|
||||
|
|
@ -391,6 +395,7 @@ export const nodeIconsLucide: iconsType = {
|
|||
Amazon: AWSIcon,
|
||||
Anthropic: AnthropicIcon,
|
||||
ChatAnthropic: AnthropicIcon,
|
||||
assemblyai: AssemblyAIIcon,
|
||||
AssemblyAI: AssemblyAIIcon,
|
||||
AstraDB: AstraDBIcon,
|
||||
BingSearchAPIWrapper: BingIcon,
|
||||
|
|
|
|||
Loading…
Reference in a new issue