From 327c0fd7917fc316479303361fc1ce392281c2b8 Mon Sep 17 00:00:00 2001 From: Will Date: Tue, 13 May 2025 11:56:29 -0700 Subject: [PATCH] feat: Twelve Labs Bundle (#7837) * feat: add twelve labs components * fix: fix uv.lock for twelve labs components --- pyproject.toml | 1 + .../components/twelvelabs/__init__.py | 17 + .../twelvelabs/convert_astra_results.py | 84 + .../components/twelvelabs/pegasus_index.py | 311 ++ .../components/twelvelabs/split_video.py | 291 ++ .../components/twelvelabs/text_embeddings.py | 57 + .../twelvelabs/twelvelabs_pegasus.py | 408 ++ .../components/twelvelabs/video_embeddings.py | 100 + .../components/twelvelabs/video_file.py | 179 + src/frontend/package-lock.json | 4 +- .../src/icons/TwelveLabs/TL-Symbol.svg | 38 + .../src/icons/TwelveLabs/TwelveLabsLogo.jsx | 249 ++ src/frontend/src/icons/TwelveLabs/index.tsx | 9 + src/frontend/src/icons/eagerIconImports.ts | 230 + src/frontend/src/icons/lazyIconImports.ts | 6 + src/frontend/src/utils/styleUtils.ts | 1 + uv.lock | 3768 +++++++++-------- 17 files changed, 4038 insertions(+), 1715 deletions(-) create mode 100644 src/backend/base/langflow/components/twelvelabs/__init__.py create mode 100644 src/backend/base/langflow/components/twelvelabs/convert_astra_results.py create mode 100644 src/backend/base/langflow/components/twelvelabs/pegasus_index.py create mode 100644 src/backend/base/langflow/components/twelvelabs/split_video.py create mode 100644 src/backend/base/langflow/components/twelvelabs/text_embeddings.py create mode 100644 src/backend/base/langflow/components/twelvelabs/twelvelabs_pegasus.py create mode 100644 src/backend/base/langflow/components/twelvelabs/video_embeddings.py create mode 100644 src/backend/base/langflow/components/twelvelabs/video_file.py create mode 100644 src/frontend/src/icons/TwelveLabs/TL-Symbol.svg create mode 100644 src/frontend/src/icons/TwelveLabs/TwelveLabsLogo.jsx create mode 100644 src/frontend/src/icons/TwelveLabs/index.tsx create mode 100644 src/frontend/src/icons/eagerIconImports.ts diff --git a/pyproject.toml b/pyproject.toml index a625b6846..f35564d21 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -123,6 +123,7 @@ dependencies = [ "langchain-ibm>=0.3.8", "opik>=1.6.3", "openai>=1.68.2", + "twelvelabs>=0.4.7", ] [dependency-groups] diff --git a/src/backend/base/langflow/components/twelvelabs/__init__.py b/src/backend/base/langflow/components/twelvelabs/__init__.py new file mode 100644 index 000000000..d276f6153 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/__init__.py @@ -0,0 +1,17 @@ +from .convert_astra_results import ConvertAstraToTwelveLabs +from .pegasus_index import PegasusIndexVideo +from .split_video import SplitVideoComponent +from .text_embeddings import TwelveLabsTextEmbeddingsComponent +from .twelvelabs_pegasus import TwelveLabsPegasus +from .video_embeddings import TwelveLabsVideoEmbeddingsComponent +from .video_file import VideoFileComponent + +__all__ = [ + "ConvertAstraToTwelveLabs", + "PegasusIndexVideo", + "SplitVideoComponent", + "TwelveLabsPegasus", + "TwelveLabsTextEmbeddingsComponent", + "TwelveLabsVideoEmbeddingsComponent", + "VideoFileComponent", +] diff --git a/src/backend/base/langflow/components/twelvelabs/convert_astra_results.py b/src/backend/base/langflow/components/twelvelabs/convert_astra_results.py new file mode 100644 index 000000000..a54f673f3 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/convert_astra_results.py @@ -0,0 +1,84 @@ +from typing import Any + +from langflow.custom import Component +from langflow.io import HandleInput, Output +from langflow.schema import Data +from langflow.schema.message import Message + + +class ConvertAstraToTwelveLabs(Component): + """Convert AstraDB search results to TwelveLabs Pegasus inputs.""" + + display_name = "Convert AstraDB to Pegasus Input" + description = "Converts AstraDB search results to inputs compatible with TwelveLabs Pegasus." + icon = "TwelveLabs" + name = "ConvertAstraToTwelveLabs" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + inputs = [ + HandleInput( + name="astra_results", + display_name="AstraDB Results", + input_types=["Data"], + info="Search results from AstraDB component", + required=True, + is_list=True, + ) + ] + + outputs = [ + Output( + name="index_id", + display_name="Index ID", + type_=Message, + method="get_index_id", + ), + Output( + name="video_id", + display_name="Video ID", + type_=Message, + method="get_video_id", + ), + ] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._video_id = None + self._index_id = None + + def build(self, **kwargs: Any) -> None: # noqa: ARG002 - Required for parent class compatibility + """Process the AstraDB results and extract TwelveLabs index information.""" + if not self.astra_results: + return + + # Convert to list if single item + results = self.astra_results if isinstance(self.astra_results, list) else [self.astra_results] + + # Try to extract index information from metadata + for doc in results: + if not isinstance(doc, Data): + continue + + # Get the metadata, handling the nested structure + metadata = {} + if hasattr(doc, "metadata") and isinstance(doc.metadata, dict): + # Handle nested metadata using .get() method + metadata = doc.metadata.get("metadata", doc.metadata) + + # Extract index_id and video_id + self._index_id = metadata.get("index_id") + self._video_id = metadata.get("video_id") + + # If we found both, we can stop searching + if self._index_id and self._video_id: + break + + def get_video_id(self) -> Message: + """Return the extracted video ID as a Message.""" + self.build() + return Message(text=self._video_id if self._video_id else "") + + def get_index_id(self) -> Message: + """Return the extracted index ID as a Message.""" + self.build() + return Message(text=self._index_id if self._index_id else "") diff --git a/src/backend/base/langflow/components/twelvelabs/pegasus_index.py b/src/backend/base/langflow/components/twelvelabs/pegasus_index.py new file mode 100644 index 000000000..c5552e9c2 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/pegasus_index.py @@ -0,0 +1,311 @@ +import time +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from typing import Any + +from tenacity import retry, stop_after_attempt, wait_exponential +from twelvelabs import TwelveLabs + +from langflow.custom import Component +from langflow.inputs import DataInput, DropdownInput, SecretStrInput, StrInput +from langflow.io import Output +from langflow.schema import Data + + +class TwelveLabsError(Exception): + """Base exception for Twelve Labs errors.""" + + +class IndexCreationError(TwelveLabsError): + """Error raised when there's an issue with an index.""" + + +class TaskError(TwelveLabsError): + """Error raised when a task fails.""" + + +class TaskTimeoutError(TwelveLabsError): + """Error raised when a task times out.""" + + +class PegasusIndexVideo(Component): + """Indexes videos using Twelve Labs Pegasus API and adds the video ID to metadata.""" + + display_name = "Twelve Labs Pegasus Index Video" + description = "Index videos using Twelve Labs and add the video_id to metadata." + icon = "TwelveLabs" + name = "TwelveLabsPegasusIndexVideo" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + inputs = [ + DataInput( + name="videodata", + display_name="Video Data", + info="Video Data objects (from VideoFile or SplitVideo)", + is_list=True, + required=True, + ), + SecretStrInput( + name="api_key", display_name="Twelve Labs API Key", info="Enter your Twelve Labs API Key.", required=True + ), + DropdownInput( + name="model_name", + display_name="Model", + info="Pegasus model to use for indexing", + options=["pegasus1.2"], + value="pegasus1.2", + advanced=False, + ), + StrInput( + name="index_name", + display_name="Index Name", + info="Name of the index to use. If the index doesn't exist, it will be created.", + required=False, + ), + StrInput( + name="index_id", + display_name="Index ID", + info="ID of an existing index to use. If provided, index_name will be ignored.", + required=False, + ), + ] + + outputs = [ + Output( + display_name="Indexed Data", name="indexed_data", method="index_videos", output_types=["Data"], is_list=True + ), + ] + + def _get_or_create_index(self, client: TwelveLabs) -> tuple[str, str]: + """Get existing index or create new one. + + Returns (index_id, index_name). + """ + # First check if index_id is provided and valid + if hasattr(self, "index_id") and self.index_id: + try: + index = client.index.retrieve(id=self.index_id) + except (ValueError, KeyError) as e: + if not hasattr(self, "index_name") or not self.index_name: + error_msg = "Invalid index ID provided and no index name specified for fallback" + raise IndexCreationError(error_msg) from e + else: + return self.index_id, index.name + + # If index_name is provided, try to find it + if hasattr(self, "index_name") and self.index_name: + try: + # List all indexes and find by name + indexes = client.index.list() + for idx in indexes: + if idx.name == self.index_name: + return idx.id, idx.name + + # If we get here, index wasn't found - create it + index = client.index.create( + name=self.index_name, + models=[ + { + "name": self.model_name if hasattr(self, "model_name") else "pegasus1.2", + "options": ["visual", "audio"], + } + ], + ) + except (ValueError, KeyError) as e: + error_msg = f"Error with index name {self.index_name}" + raise IndexCreationError(error_msg) from e + else: + return index.id, index.name + + # If we get here, neither index_id nor index_name was provided + error_msg = "Either index_name or index_id must be provided" + raise IndexCreationError(error_msg) + + def on_task_update(self, task: Any, video_path: str) -> None: + """Callback for task status updates. + + Updates the component status with the current task status. + """ + video_name = Path(video_path).name + status_msg = f"Indexing {video_name}... Status: {task.status}" + self.status = status_msg + + @retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=5, max=60), reraise=True) + def _check_task_status( + self, + client: TwelveLabs, + task_id: str, + video_path: str, + ) -> Any: + """Check task status once. + + Makes a single API call to check the status of a task. + """ + task = client.task.retrieve(id=task_id) + self.on_task_update(task, video_path) + return task + + def _wait_for_task_completion( + self, client: TwelveLabs, task_id: str, video_path: str, max_retries: int = 120, sleep_time: int = 10 + ) -> Any: + """Wait for task completion with timeout and improved error handling. + + Polls the task status until completion or timeout. + """ + retries = 0 + consecutive_errors = 0 + max_consecutive_errors = 5 + video_name = Path(video_path).name + + while retries < max_retries: + try: + self.status = f"Checking task status for {video_name} (attempt {retries + 1})" + task = self._check_task_status(client, task_id, video_path) + + if task.status == "ready": + self.status = f"Indexing for {video_name} completed successfully!" + return task + if task.status == "failed": + error_msg = f"Task failed for {video_name}: {getattr(task, 'error', 'Unknown error')}" + self.status = error_msg + raise TaskError(error_msg) + if task.status == "error": + error_msg = f"Task encountered an error for {video_name}: {getattr(task, 'error', 'Unknown error')}" + self.status = error_msg + raise TaskError(error_msg) + + time.sleep(sleep_time) + retries += 1 + elapsed_time = retries * sleep_time + self.status = f"Indexing {video_name}... {elapsed_time}s elapsed" + + except (ValueError, KeyError) as e: + consecutive_errors += 1 + error_msg = f"Error checking task status for {video_name}: {e!s}" + self.status = error_msg + + if consecutive_errors >= max_consecutive_errors: + too_many_errors = f"Too many consecutive errors checking task status for {video_name}" + raise TaskError(too_many_errors) from e + + time.sleep(sleep_time * (2**consecutive_errors)) + continue + + timeout_msg = f"Timeout waiting for indexing of {video_name} after {max_retries * sleep_time} seconds" + self.status = timeout_msg + raise TaskTimeoutError(timeout_msg) + + def _upload_video(self, client: TwelveLabs, video_path: str, index_id: str) -> str: + """Upload a single video and return its task ID. + + Uploads a video file to the specified index and returns the task ID. + """ + video_name = Path(video_path).name + with Path(video_path).open("rb") as video_file: + self.status = f"Uploading {video_name} to index {index_id}..." + task = client.task.create(index_id=index_id, file=video_file) + task_id = task.id + self.status = f"Upload complete for {video_name}. Task ID: {task_id}" + return task_id + + def index_videos(self) -> list[Data]: + """Indexes each video and adds the video_id to its metadata.""" + if not self.videodata: + self.status = "No video data provided." + return [] + + if not self.api_key: + error_msg = "Twelve Labs API Key is required" + raise IndexCreationError(error_msg) + + if not (hasattr(self, "index_name") and self.index_name) and not (hasattr(self, "index_id") and self.index_id): + error_msg = "Either index_name or index_id must be provided" + raise IndexCreationError(error_msg) + + client = TwelveLabs(api_key=self.api_key) + indexed_data_list: list[Data] = [] + + # Get or create the index + try: + index_id, index_name = self._get_or_create_index(client) + self.status = f"Using index: {index_name} (ID: {index_id})" + except IndexCreationError as e: + self.status = f"Failed to get/create Twelve Labs index: {e!s}" + raise + + # First, validate all videos and create a list of valid ones + valid_videos: list[tuple[Data, str]] = [] + for video_data_item in self.videodata: + if not isinstance(video_data_item, Data): + self.status = f"Skipping invalid data item: {video_data_item}" + continue + + video_info = video_data_item.data + if not isinstance(video_info, dict): + self.status = f"Skipping item with invalid data structure: {video_info}" + continue + + video_path = video_info.get("text") + if not video_path or not isinstance(video_path, str): + self.status = f"Skipping item with missing or invalid video path: {video_info}" + continue + + if not Path(video_path).exists(): + self.status = f"Video file not found, skipping: {video_path}" + continue + + valid_videos.append((video_data_item, video_path)) + + if not valid_videos: + self.status = "No valid videos to process." + return [] + + # Upload all videos first and collect their task IDs + upload_tasks: list[tuple[Data, str, str]] = [] # (data_item, video_path, task_id) + for data_item, video_path in valid_videos: + try: + task_id = self._upload_video(client, video_path, index_id) + upload_tasks.append((data_item, video_path, task_id)) + except (ValueError, KeyError) as e: + self.status = f"Failed to upload {video_path}: {e!s}" + continue + + # Now check all tasks in parallel using a thread pool + with ThreadPoolExecutor(max_workers=min(10, len(upload_tasks))) as executor: + futures = [] + for data_item, video_path, task_id in upload_tasks: + future = executor.submit(self._wait_for_task_completion, client, task_id, video_path) + futures.append((data_item, video_path, future)) + + # Process results as they complete + for data_item, video_path, future in futures: + try: + completed_task = future.result() + if completed_task.status == "ready": + video_id = completed_task.video_id + video_name = Path(video_path).name + self.status = f"Video {video_name} indexed successfully. Video ID: {video_id}" + + # Add video_id to the metadata + video_info = data_item.data + if "metadata" not in video_info: + video_info["metadata"] = {} + elif not isinstance(video_info["metadata"], dict): + self.status = f"Warning: Overwriting non-dict metadata for {video_path}" + video_info["metadata"] = {} + + video_info["metadata"].update( + {"video_id": video_id, "index_id": index_id, "index_name": index_name} + ) + + updated_data_item = Data(data=video_info) + indexed_data_list.append(updated_data_item) + except (TaskError, TaskTimeoutError) as e: + self.status = f"Failed to process {video_path}: {e!s}" + + if not indexed_data_list: + self.status = "No videos were successfully indexed." + else: + self.status = f"Finished indexing {len(indexed_data_list)}/{len(self.videodata)} videos." + + return indexed_data_list diff --git a/src/backend/base/langflow/components/twelvelabs/split_video.py b/src/backend/base/langflow/components/twelvelabs/split_video.py new file mode 100644 index 000000000..4c89949a3 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/split_video.py @@ -0,0 +1,291 @@ +import hashlib +import math +import subprocess +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from langflow.custom import Component +from langflow.inputs import BoolInput, DropdownInput, HandleInput, IntInput +from langflow.schema import Data +from langflow.template import Output + + +class SplitVideoComponent(Component): + """A component that splits a video into multiple clips of specified duration using FFmpeg.""" + + display_name = "Split Video" + description = "Split a video into multiple clips of specified duration." + icon = "TwelveLabs" + name = "SplitVideo" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + inputs = [ + HandleInput( + name="videodata", + display_name="Video Data", + info="Input video data from VideoFile component", + required=True, + input_types=["Data"], + ), + IntInput( + name="clip_duration", + display_name="Clip Duration (seconds)", + info="Duration of each clip in seconds", + required=True, + value=30, + ), + DropdownInput( + name="last_clip_handling", + display_name="Last Clip Handling", + info=( + "How to handle the final clip when it would be shorter than the specified duration:\n" + "- Truncate: Skip the final clip entirely if it's shorter than the specified duration\n" + "- Overlap Previous: Start the final clip earlier to maintain full duration, " + "overlapping with previous clip\n" + "- Keep Short: Keep the final clip at its natural length, even if shorter than specified duration" + ), + options=["Truncate", "Overlap Previous", "Keep Short"], + value="Overlap Previous", + required=True, + ), + BoolInput( + name="include_original", + display_name="Include Original Video", + info="Whether to include the original video in the output", + value=False, + ), + ] + + outputs = [ + Output( + name="clips", + display_name="Video Clips", + method="process", + output_types=["Data"], + ), + ] + + def get_video_duration(self, video_path: str) -> float: + """Get video duration using FFmpeg.""" + try: + # Validate video path to prevent shell injection + if not isinstance(video_path, str) or any(c in video_path for c in ";&|`$(){}[]<>*?!#~"): + error_msg = "Invalid video path" + raise ValueError(error_msg) + + cmd = [ + "ffprobe", + "-v", + "error", + "-show_entries", + "format=duration", + "-of", + "default=noprint_wrappers=1:nokey=1", + video_path, + ] + result = subprocess.run( # noqa: S603 + cmd, + capture_output=True, + text=True, + check=False, + shell=False, # Explicitly set shell=False for security + ) + if result.returncode != 0: + error_msg = f"FFprobe error: {result.stderr}" + raise RuntimeError(error_msg) + return float(result.stdout.strip()) + except Exception as e: + self.log(f"Error getting video duration: {e!s}", "ERROR") + raise + + def get_output_dir(self, video_path: str) -> str: + """Create a unique output directory for clips based on video name and timestamp.""" + # Get the video filename without extension + path_obj = Path(video_path) + base_name = path_obj.stem + + # Create a timestamp + timestamp = datetime.now(tz=timezone.utc).strftime("%Y-%m-%d_%H-%M-%S") + + # Create a unique hash from the video path + path_hash = hashlib.sha256(video_path.encode()).hexdigest()[:8] + + # Create the output directory path + output_dir = Path(path_obj.parent) / f"clips_{base_name}_{timestamp}_{path_hash}" + + # Create the directory if it doesn't exist + output_dir.mkdir(parents=True, exist_ok=True) + + return str(output_dir) + + def process_video(self, video_path: str, clip_duration: int, *, include_original: bool) -> list[Data]: + """Process video and split it into clips using FFmpeg.""" + try: + # Get video duration + total_duration = self.get_video_duration(video_path) + + # Calculate number of clips (ceiling to include partial clip) + num_clips = math.ceil(total_duration / clip_duration) + self.log( + f"Total duration: {total_duration}s, Clip duration: {clip_duration}s, Number of clips: {num_clips}" + ) + + # Create output directory for clips + output_dir = self.get_output_dir(video_path) + + # Get original video info + path_obj = Path(video_path) + original_filename = path_obj.name + original_name = path_obj.stem + + # List to store all video paths (including original if requested) + video_paths: list[Data] = [] + + # Add original video if requested + if include_original: + original_data: dict[str, Any] = { + "text": video_path, + "metadata": { + "source": video_path, + "type": "video", + "clip_index": -1, # -1 indicates original video + "duration": int(total_duration), # Convert to int + "original_video": { + "name": original_name, + "filename": original_filename, + "path": video_path, + "duration": int(total_duration), # Convert to int + "total_clips": int(num_clips), + "clip_duration": int(clip_duration), + }, + }, + } + video_paths.append(Data(data=original_data)) + + # Split video into clips + for i in range(int(num_clips)): # Convert num_clips to int for range + start_time = float(i * clip_duration) # Convert to float for time calculations + end_time = min(float((i + 1) * clip_duration), total_duration) + duration = end_time - start_time + + # Handle last clip if it's shorter + if i == int(num_clips) - 1 and duration < clip_duration: # Convert num_clips to int for comparison + if self.last_clip_handling == "Truncate": + # Skip if the last clip would be too short + continue + if self.last_clip_handling == "Overlap Previous" and i > 0: + # Start from earlier to make full duration + start_time = total_duration - clip_duration + duration = clip_duration + # For "Keep Short", we use the original start_time and duration + + # Skip if duration is too small (less than 1 second) + if duration < 1: + continue + + # Generate output path + output_path = Path(output_dir) / f"clip_{i:03d}.mp4" + output_path_str = str(output_path) + + try: + # Use FFmpeg to split the video + cmd = [ + "ffmpeg", + "-i", + video_path, + "-ss", + str(start_time), + "-t", + str(duration), + "-c:v", + "libx264", + "-c:a", + "aac", + "-y", # Overwrite output file if it exists + output_path_str, + ] + + result = subprocess.run( # noqa: S603 + cmd, + capture_output=True, + text=True, + check=False, + shell=False, # Explicitly set shell=False for security + ) + if result.returncode != 0: + error_msg = f"FFmpeg error: {result.stderr}" + raise RuntimeError(error_msg) + + # Create timestamp string for metadata + start_min = int(start_time // 60) + start_sec = int(start_time % 60) + end_min = int(end_time // 60) + end_sec = int(end_time % 60) + timestamp_str = f"{start_min:02d}:{start_sec:02d} - {end_min:02d}:{end_sec:02d}" + + # Create Data object for the clip + clip_data: dict[str, Any] = { + "text": output_path_str, + "metadata": { + "source": video_path, + "type": "video", + "clip_index": i, + "start_time": float(start_time), + "end_time": float(end_time), + "duration": float(duration), + "original_video": { + "name": original_name, + "filename": original_filename, + "path": video_path, + "duration": int(total_duration), + "total_clips": int(num_clips), + "clip_duration": int(clip_duration), + }, + "clip": { + "index": i, + "total": int(num_clips), + "duration": float(duration), + "start_time": float(start_time), + "end_time": float(end_time), + "timestamp": timestamp_str, + }, + }, + } + video_paths.append(Data(data=clip_data)) + + except Exception as e: + self.log(f"Error processing clip {i}: {e!s}", "ERROR") + raise + + self.log(f"Created {len(video_paths)} clips in {output_dir}") + except Exception as e: + self.log(f"Error processing video: {e!s}", "ERROR") + raise + else: + return video_paths + + def process(self) -> list[Data]: + """Process the input video and return a list of Data objects containing the clips.""" + try: + # Get the input video path from the previous component + if not hasattr(self, "videodata") or not isinstance(self.videodata, list) or len(self.videodata) != 1: + error_msg = "Please provide exactly one video" + raise ValueError(error_msg) + + video_path = self.videodata[0].data.get("text") + if not video_path or not Path(video_path).exists(): + error_msg = "Invalid video path" + raise ValueError(error_msg) + + # Validate video path to prevent shell injection + if not isinstance(video_path, str) or any(c in video_path for c in ";&|`$(){}[]<>*?!#~"): + error_msg = "Invalid video path contains unsafe characters" + raise ValueError(error_msg) + + # Process the video + return self.process_video(video_path, self.clip_duration, include_original=self.include_original) + + except Exception as e: + self.log(f"Error in split video component: {e!s}", "ERROR") + raise diff --git a/src/backend/base/langflow/components/twelvelabs/text_embeddings.py b/src/backend/base/langflow/components/twelvelabs/text_embeddings.py new file mode 100644 index 000000000..40668346d --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/text_embeddings.py @@ -0,0 +1,57 @@ +from twelvelabs import TwelveLabs + +from langflow.base.embeddings.model import LCEmbeddingsModel +from langflow.field_typing import Embeddings +from langflow.io import DropdownInput, FloatInput, IntInput, SecretStrInput + + +class TwelveLabsTextEmbeddings(Embeddings): + def __init__(self, api_key: str, model: str) -> None: + self.client = TwelveLabs(api_key=api_key) + self.model = model + + def embed_documents(self, texts: list[str]) -> list[list[float]]: + all_embeddings: list[list[float]] = [] + for text in texts: + if not text: + continue + + result = self.client.embed.create(model_name=self.model, text=text) + + if result.text_embedding and result.text_embedding.segments: + for segment in result.text_embedding.segments: + all_embeddings.append([float(x) for x in segment.embeddings_float]) + break # Only take first segment for now + + return all_embeddings + + def embed_query(self, text: str) -> list[float]: + result = self.client.embed.create(model_name=self.model, text=text) + + if result.text_embedding and result.text_embedding.segments: + return [float(x) for x in result.text_embedding.segments[0].embeddings_float] + return [] + + +class TwelveLabsTextEmbeddingsComponent(LCEmbeddingsModel): + display_name = "Twelve Labs Text Embeddings" + description = "Generate embeddings using Twelve Labs text embedding models." + icon = "TwelveLabs" + name = "TwelveLabsTextEmbeddings" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + inputs = [ + SecretStrInput(name="api_key", display_name="Twelve Labs API Key", value="TWELVELABS_API_KEY", required=True), + DropdownInput( + name="model", + display_name="Model", + advanced=False, + options=["Marengo-retrieval-2.7"], + value="Marengo-retrieval-2.7", + ), + IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True), + FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True), + ] + + def build_embeddings(self) -> Embeddings: + return TwelveLabsTextEmbeddings(api_key=self.api_key, model=self.model) diff --git a/src/backend/base/langflow/components/twelvelabs/twelvelabs_pegasus.py b/src/backend/base/langflow/components/twelvelabs/twelvelabs_pegasus.py new file mode 100644 index 000000000..04818e349 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/twelvelabs_pegasus.py @@ -0,0 +1,408 @@ +import json +import subprocess +import time +from pathlib import Path +from typing import Any + +from tenacity import retry, stop_after_attempt, wait_exponential +from twelvelabs import TwelveLabs + +from langflow.custom import Component +from langflow.field_typing.range_spec import RangeSpec +from langflow.inputs import DataInput, DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput +from langflow.io import Output +from langflow.schema.message import Message + + +class TaskError(Exception): + """Error raised when a task fails.""" + + +class TaskTimeoutError(Exception): + """Error raised when a task times out.""" + + +class IndexCreationError(Exception): + """Error raised when there's an issue with an index.""" + + +class ApiRequestError(Exception): + """Error raised when an API request fails.""" + + +class VideoValidationError(Exception): + """Error raised when video validation fails.""" + + +class TwelveLabsPegasus(Component): + display_name = "Twelve Labs Pegasus" + description = "Chat with videos using Twelve Labs Pegasus API." + icon = "TwelveLabs" + name = "TwelveLabsPegasus" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + inputs = [ + DataInput(name="videodata", display_name="Video Data", info="Video Data", is_list=True), + SecretStrInput( + name="api_key", display_name="Twelve Labs API Key", info="Enter your Twelve Labs API Key.", required=True + ), + MessageInput( + name="video_id", + display_name="Pegasus Video ID", + info="Enter a Video ID for a previously indexed video.", + ), + MessageInput( + name="index_name", + display_name="Index Name", + info="Name of the index to use. If the index doesn't exist, it will be created.", + required=False, + ), + MessageInput( + name="index_id", + display_name="Index ID", + info="ID of an existing index to use. If provided, index_name will be ignored.", + required=False, + ), + DropdownInput( + name="model_name", + display_name="Model", + info="Pegasus model to use for indexing", + options=["pegasus1.2"], + value="pegasus1.2", + advanced=False, + ), + MultilineInput( + name="message", + display_name="Prompt", + info="Message to chat with the video.", + required=True, + ), + SliderInput( + name="temperature", + display_name="Temperature", + value=0.7, + range_spec=RangeSpec(min=0, max=1, step=0.01), + info=( + "Controls randomness in responses. Lower values are more deterministic, " + "higher values are more creative." + ), + ), + ] + + outputs = [ + Output( + display_name="Message", + name="response", + method="process_video", + type_=Message, + ), + Output( + display_name="Video ID", + name="processed_video_id", + method="get_video_id", + type_=Message, + ), + ] + + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + + self._task_id: str | None = None + self._video_id: str | None = None + self._index_id: str | None = None + self._index_name: str | None = None + self._message: str | None = None + + def _get_or_create_index(self, client: TwelveLabs) -> tuple[str, str]: + """Get existing index or create new one. + + Returns (index_id, index_name). + """ + # First check if index_id is provided and valid + if hasattr(self, "_index_id") and self._index_id: + try: + index = client.index.retrieve(id=self._index_id) + self.log(f"Found existing index with ID: {self._index_id}") + except (ValueError, KeyError) as e: + self.log(f"Error retrieving index with ID {self._index_id}: {e!s}", "WARNING") + else: + return self._index_id, index.name + + # If index_name is provided, try to find it + if hasattr(self, "_index_name") and self._index_name: + try: + # List all indexes and find by name + indexes = client.index.list() + for idx in indexes: + if idx.name == self._index_name: + self.log(f"Found existing index: {self._index_name} (ID: {idx.id})") + return idx.id, idx.name + + # If we get here, index wasn't found - create it + self.log(f"Creating new index: {self._index_name}") + index = client.index.create( + name=self._index_name, + models=[ + { + "name": self.model_name if hasattr(self, "model_name") else "pegasus1.2", + "options": ["visual", "audio"], + } + ], + ) + except (ValueError, KeyError) as e: + self.log(f"Error with index name {self._index_name}: {e!s}", "ERROR") + error_message = f"Error with index name {self._index_name}" + raise IndexCreationError(error_message) from e + else: + return index.id, index.name + + # If neither is provided, create a new index with timestamp + try: + index_name = f"index_{int(time.time())}" + self.log(f"Creating new index: {index_name}") + index = client.index.create( + name=index_name, + models=[ + { + "name": self.model_name if hasattr(self, "model_name") else "pegasus1.2", + "options": ["visual", "audio"], + } + ], + ) + except (ValueError, KeyError) as e: + self.log(f"Failed to create new index: {e!s}", "ERROR") + error_message = "Failed to create new index" + raise IndexCreationError(error_message) from e + else: + return index.id, index.name + + @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), reraise=True) + async def _make_api_request(self, method: Any, *args: Any, **kwargs: Any) -> Any: + """Make API request with retry logic. + + Retries failed requests with exponential backoff. + """ + try: + return await method(*args, **kwargs) + except (ValueError, KeyError) as e: + self.log(f"API request failed: {e!s}", "ERROR") + error_message = "API request failed" + raise ApiRequestError(error_message) from e + + def wait_for_task_completion( + self, client: TwelveLabs, task_id: str, max_retries: int = 120, sleep_time: int = 5 + ) -> Any: + """Wait for task completion with timeout and improved error handling. + + Polls the task status until completion or timeout. + """ + retries = 0 + consecutive_errors = 0 + max_consecutive_errors = 3 + + while retries < max_retries: + try: + self.log(f"Checking task status (attempt {retries + 1})") + result = client.task.retrieve(id=task_id) + consecutive_errors = 0 # Reset error counter on success + + if result.status == "ready": + self.log("Task completed successfully!") + return result + if result.status == "failed": + error_msg = f"Task failed with status: {result.status}" + self.log(error_msg, "ERROR") + raise TaskError(error_msg) + if result.status == "error": + error_msg = f"Task encountered an error: {getattr(result, 'error', 'Unknown error')}" + self.log(error_msg, "ERROR") + raise TaskError(error_msg) + + time.sleep(sleep_time) + retries += 1 + status_msg = f"Processing video... {retries * sleep_time}s elapsed" + self.status = status_msg + self.log(status_msg) + + except (ValueError, KeyError) as e: + consecutive_errors += 1 + error_msg = f"Error checking task status: {e!s}" + self.log(error_msg, "WARNING") + + if consecutive_errors >= max_consecutive_errors: + too_many_errors = "Too many consecutive errors" + raise TaskError(too_many_errors) from e + + time.sleep(sleep_time * 2) + continue + + timeout_msg = f"Timeout after {max_retries * sleep_time} seconds" + self.log(timeout_msg, "ERROR") + raise TaskTimeoutError(timeout_msg) + + def validate_video_file(self, filepath: str) -> tuple[bool, str]: + """Validate video file using ffprobe. + + Returns (is_valid, error_message). + """ + # Ensure filepath is a string and doesn't contain shell metacharacters + if not isinstance(filepath, str) or any(c in filepath for c in ";&|`$(){}[]<>*?!#~"): + return False, "Invalid filepath" + + try: + cmd = [ + "ffprobe", + "-loglevel", + "error", + "-show_entries", + "stream=codec_type,codec_name", + "-of", + "default=nw=1", + "-print_format", + "json", + "-show_format", + filepath, + ] + + # Use subprocess with a list of arguments to avoid shell injection + # We need to skip the S603 warning here as we're taking proper precautions + # with input validation and using shell=False + result = subprocess.run( # noqa: S603 + cmd, + capture_output=True, + text=True, + check=False, + shell=False, # Explicitly set shell=False for security + ) + + if result.returncode != 0: + return False, f"FFprobe error: {result.stderr}" + + probe_data = json.loads(result.stdout) + + has_video = any(stream.get("codec_type") == "video" for stream in probe_data.get("streams", [])) + + if not has_video: + return False, "No video stream found in file" + + self.log(f"Video validation successful: {json.dumps(probe_data, indent=2)}") + except subprocess.SubprocessError as e: + return False, f"FFprobe process error: {e!s}" + except json.JSONDecodeError as e: + return False, f"FFprobe output parsing error: {e!s}" + except (ValueError, OSError) as e: + return False, f"Validation error: {e!s}" + else: + return True, "" + + def on_task_update(self, task: Any) -> None: + """Callback for task status updates. + + Updates the component status with the current task status. + """ + self.status = f"Processing video... Status: {task.status}" + self.log(self.status) + + def process_video(self) -> Message: + """Process video using Pegasus and generate response if message is provided. + + Handles video indexing and question answering using the Twelve Labs API. + """ + # Check and initialize inputs + if hasattr(self, "index_id") and self.index_id: + self._index_id = self.index_id.text if hasattr(self.index_id, "text") else self.index_id + + if hasattr(self, "index_name") and self.index_name: + self._index_name = self.index_name.text if hasattr(self.index_name, "text") else self.index_name + + if hasattr(self, "video_id") and self.video_id: + self._video_id = self.video_id.text if hasattr(self.video_id, "text") else self.video_id + + if hasattr(self, "message") and self.message: + self._message = self.message.text if hasattr(self.message, "text") else self.message + + try: + # If we have a message and already processed video, use existing video_id + if self._message and self._video_id and self._video_id != "": + self.status = f"Have video id: {self._video_id}" + + client = TwelveLabs(api_key=self.api_key) + + self.status = f"Processing query (w/ video ID): {self._video_id} {self._message}" + self.log(self.status) + + response = client.generate.text( + video_id=self._video_id, + prompt=self._message, + temperature=self.temperature, + ) + return Message(text=response.data) + + # Otherwise process new video + if not self.videodata or not isinstance(self.videodata, list) or len(self.videodata) != 1: + return Message(text="Please provide exactly one video") + + video_path = self.videodata[0].data.get("text") + if not video_path or not Path(video_path).exists(): + return Message(text="Invalid video path") + + if not self.api_key: + return Message(text="No API key provided") + + client = TwelveLabs(api_key=self.api_key) + + # Get or create index + try: + index_id, index_name = self._get_or_create_index(client) + self.status = f"Using index: {index_name} (ID: {index_id})" + self.log(f"Using index: {index_name} (ID: {index_id})") + self._index_id = index_id + self._index_name = index_name + except IndexCreationError as e: + return Message(text=f"Failed to get/create index: {e}") + + with Path(video_path).open("rb") as video_file: + task = client.task.create(index_id=self._index_id, file=video_file) + self._task_id = task.id + + # Wait for processing to complete + task.wait_for_done(sleep_interval=5, callback=self.on_task_update) + + if task.status != "ready": + return Message(text=f"Processing failed with status {task.status}") + + # Store video_id for future use + self._video_id = task.video_id + + # Generate response if message provided + if self._message: + self.status = f"Processing query: {self._message}" + self.log(self.status) + + response = client.generate.text( + video_id=self._video_id, + prompt=self._message, + temperature=self.temperature, + ) + return Message(text=response.data) + + success_msg = ( + f"Video processed successfully. You can now ask questions about the video. Video ID: {self._video_id}" + ) + return Message(text=success_msg) + + except (ValueError, KeyError, IndexCreationError, TaskError, TaskTimeoutError) as e: + self.log(f"Error: {e!s}", "ERROR") + # Clear stored IDs on error + self._video_id = None + self._index_id = None + self._task_id = None + return Message(text=f"Error: {e!s}") + + def get_video_id(self) -> Message: + """Return the video ID of the processed video as a Message. + + Returns an empty string if no video has been processed. + """ + video_id = self._video_id or "" + return Message(text=video_id) diff --git a/src/backend/base/langflow/components/twelvelabs/video_embeddings.py b/src/backend/base/langflow/components/twelvelabs/video_embeddings.py new file mode 100644 index 000000000..af7e25997 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/video_embeddings.py @@ -0,0 +1,100 @@ +import time +from pathlib import Path +from typing import Any, cast + +from twelvelabs import TwelveLabs + +from langflow.base.embeddings.model import LCEmbeddingsModel +from langflow.field_typing import Embeddings +from langflow.io import DropdownInput, IntInput, SecretStrInput + + +class TwelveLabsVideoEmbeddings(Embeddings): + def __init__(self, api_key: str, model_name: str = "Marengo-retrieval-2.7") -> None: + self.client = TwelveLabs(api_key=api_key) + self.model_name = model_name + + def _wait_for_task_completion(self, task_id: str) -> Any: + while True: + result = self.client.embed.task.retrieve(id=task_id) + if result.status == "ready": + return result + time.sleep(5) + + def embed_documents(self, texts: list[str]) -> list[list[float]]: + embeddings: list[list[float]] = [] + for text in texts: + video_path = text.page_content if hasattr(text, "page_content") else str(text) + result = self.embed_video(video_path) + + # First try to use video embedding, then fall back to clip embedding if available + if result["video_embedding"] is not None: + embeddings.append(cast(list[float], result["video_embedding"])) + elif result["clip_embeddings"] and len(result["clip_embeddings"]) > 0: + embeddings.append(cast(list[float], result["clip_embeddings"][0])) + else: + # If neither is available, raise an error + error_msg = "No embeddings were generated for the video" + raise ValueError(error_msg) + + return embeddings + + def embed_query(self, text: str) -> list[float]: + video_path = text.page_content if hasattr(text, "page_content") else str(text) + result = self.embed_video(video_path) + + # First try to use video embedding, then fall back to clip embedding if available + if result["video_embedding"] is not None: + return cast(list[float], result["video_embedding"]) + if result["clip_embeddings"] and len(result["clip_embeddings"]) > 0: + return cast(list[float], result["clip_embeddings"][0]) + # If neither is available, raise an error + error_msg = "No embeddings were generated for the video" + raise ValueError(error_msg) + + def embed_video(self, video_path: str) -> dict[str, list[float] | list[list[float]]]: + file_path = Path(video_path) + with file_path.open("rb") as video_file: + task = self.client.embed.task.create( + model_name=self.model_name, + video_file=video_file, + video_embedding_scopes=["video", "clip"], + ) + + result = self._wait_for_task_completion(task.id) + + video_embedding: dict[str, list[float] | list[list[float]]] = { + "video_embedding": [], # Initialize as empty list instead of None + "clip_embeddings": [], + } + + if hasattr(result.video_embedding, "segments") and result.video_embedding.segments: + for seg in result.video_embedding.segments: + # Check for embeddings_float attribute (this is the correct attribute name) + if hasattr(seg, "embeddings_float") and seg.embedding_scope == "video": + # Convert to list of floats + video_embedding["video_embedding"] = [float(x) for x in seg.embeddings_float] + + return video_embedding + + +class TwelveLabsVideoEmbeddingsComponent(LCEmbeddingsModel): + display_name = "Twelve Labs Video Embeddings" + description = "Generate embeddings from videos using Twelve Labs video embedding models." + name = "TwelveLabsVideoEmbeddings" + icon = "TwelveLabs" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + inputs = [ + SecretStrInput(name="api_key", display_name="API Key", required=True), + DropdownInput( + name="model_name", + display_name="Model", + advanced=False, + options=["Marengo-retrieval-2.7"], + value="Marengo-retrieval-2.7", + ), + IntInput(name="request_timeout", display_name="Request Timeout", advanced=True), + ] + + def build_embeddings(self) -> Embeddings: + return TwelveLabsVideoEmbeddings(api_key=self.api_key, model_name=self.model_name) diff --git a/src/backend/base/langflow/components/twelvelabs/video_file.py b/src/backend/base/langflow/components/twelvelabs/video_file.py new file mode 100644 index 000000000..ffe0a8f72 --- /dev/null +++ b/src/backend/base/langflow/components/twelvelabs/video_file.py @@ -0,0 +1,179 @@ +from pathlib import Path + +from langflow.base.data import BaseFileComponent +from langflow.io import FileInput +from langflow.schema import Data + + +class VideoFileComponent(BaseFileComponent): + """Handles loading and processing of video files. + + This component supports processing video files in common video formats. + """ + + display_name = "Video File" + description = "Load a video file in common video formats." + icon = "TwelveLabs" + name = "VideoFile" + documentation = "https://github.com/twelvelabs-io/twelvelabs-developer-experience/blob/main/integrations/Langflow/TWELVE_LABS_COMPONENTS_README.md" + + VALID_EXTENSIONS = [ + # Common video formats + "mp4", + "avi", + "mov", + "mkv", + "webm", + "flv", + "wmv", + "mpg", + "mpeg", + "m4v", + "3gp", + "3g2", + "m2v", + # Professional video formats + "mxf", + "dv", + "vob", + # Additional video formats + "ogv", + "rm", + "rmvb", + "amv", + "divx", + "m2ts", + "mts", + "ts", + "qt", + "yuv", + "y4m", + ] + + inputs = [ + FileInput( + display_name="Video File", + name="file_path", + file_types=[ + # Common video formats + "mp4", + "avi", + "mov", + "mkv", + "webm", + "flv", + "wmv", + "mpg", + "mpeg", + "m4v", + "3gp", + "3g2", + "m2v", + # Professional video formats + "mxf", + "dv", + "vob", + # Additional video formats + "ogv", + "rm", + "rmvb", + "amv", + "divx", + "m2ts", + "mts", + "ts", + "qt", + "yuv", + "y4m", + ], + required=True, + info="Upload a video file in any common video format supported by ffmpeg", + ), + ] + + outputs = [ + *BaseFileComponent._base_outputs, + ] + + def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]: + """Process video files.""" + self.log(f"DEBUG: Processing video files: {len(file_list)}") + + if not file_list: + msg = "No files to process." + raise ValueError(msg) + + processed_files = [] + for file in file_list: + try: + file_path = str(file.path) + self.log(f"DEBUG: Processing video file: {file_path}") + + # Verify file exists + file_path_obj = Path(file_path) + if not file_path_obj.exists(): + error_msg = f"Video file not found: {file_path}" + raise FileNotFoundError(error_msg) + + # Verify extension + if not file_path.lower().endswith(tuple(self.VALID_EXTENSIONS)): + error_msg = f"Invalid file type. Expected: {', '.join(self.VALID_EXTENSIONS)}" + raise ValueError(error_msg) + + # Create a dictionary instead of a Document + doc_data = {"text": file_path, "metadata": {"source": file_path, "type": "video"}} + + # Pass the dictionary to Data + file.data = Data(data=doc_data) + + self.log(f"DEBUG: Created data: {doc_data}") + processed_files.append(file) + + except Exception as e: + self.log(f"Error processing video file: {e!s}", "ERROR") + raise + + return processed_files + + def load_files(self) -> list[Data]: + """Load video files and return a list of Data objects.""" + try: + self.log("DEBUG: Starting video file load") + if not hasattr(self, "file_path") or not self.file_path: + self.log("DEBUG: No video file path provided") + return [] + + self.log(f"DEBUG: Loading video from path: {self.file_path}") + + # Verify file exists + file_path_obj = Path(self.file_path) + if not file_path_obj.exists(): + self.log(f"DEBUG: Video file not found at path: {self.file_path}") + return [] + + # Verify file size + file_size = file_path_obj.stat().st_size + self.log(f"DEBUG: Video file size: {file_size} bytes") + + # Create a proper Data object with the video path + video_data = { + "text": self.file_path, + "metadata": {"source": self.file_path, "type": "video", "size": file_size}, + } + + self.log(f"DEBUG: Created video data: {video_data}") + result = [Data(data=video_data)] + + # Log the result to verify it's a proper Data object + self.log("DEBUG: Returning list with Data objects") + except (FileNotFoundError, PermissionError, OSError) as e: + self.log(f"DEBUG: File error in video load_files: {e!s}", "ERROR") + return [] + except ImportError as e: + self.log(f"DEBUG: Import error in video load_files: {e!s}", "ERROR") + return [] + except (ValueError, TypeError) as e: + self.log(f"DEBUG: Value or type error in video load_files: {e!s}", "ERROR") + return [] + else: + return result diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index ee356ed62..aa49dd664 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -1,12 +1,12 @@ { "name": "langflow", - "version": "1.4.1", + "version": "1.3.4", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "langflow", - "version": "1.4.1", + "version": "1.3.4", "dependencies": { "@chakra-ui/number-input": "^2.1.2", "@headlessui/react": "^2.0.4", diff --git a/src/frontend/src/icons/TwelveLabs/TL-Symbol.svg b/src/frontend/src/icons/TwelveLabs/TL-Symbol.svg new file mode 100644 index 000000000..40d0a0b2e --- /dev/null +++ b/src/frontend/src/icons/TwelveLabs/TL-Symbol.svg @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/src/frontend/src/icons/TwelveLabs/TwelveLabsLogo.jsx b/src/frontend/src/icons/TwelveLabs/TwelveLabsLogo.jsx new file mode 100644 index 000000000..4e715c61e --- /dev/null +++ b/src/frontend/src/icons/TwelveLabs/TwelveLabsLogo.jsx @@ -0,0 +1,249 @@ +const SvgTwelveLogo = (props) => ( + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +); + +export default SvgTwelveLogo; diff --git a/src/frontend/src/icons/TwelveLabs/index.tsx b/src/frontend/src/icons/TwelveLabs/index.tsx new file mode 100644 index 000000000..8105dfcc0 --- /dev/null +++ b/src/frontend/src/icons/TwelveLabs/index.tsx @@ -0,0 +1,9 @@ +import React, { forwardRef } from "react"; +import SvgTwelveLogo from "./TwelveLabsLogo"; + +export const TwelveLabsIcon = forwardRef< + SVGSVGElement, + React.PropsWithChildren<{}> +>((props, ref) => { + return ; +}); diff --git a/src/frontend/src/icons/eagerIconImports.ts b/src/frontend/src/icons/eagerIconImports.ts new file mode 100644 index 000000000..5182adc54 --- /dev/null +++ b/src/frontend/src/icons/eagerIconImports.ts @@ -0,0 +1,230 @@ +import { AgentQLIcon } from "@/icons/AgentQL"; +import { AIMLIcon } from "@/icons/AIML"; +import { AirbyteIcon } from "@/icons/Airbyte"; +import { AnthropicIcon } from "@/icons/Anthropic"; +import { ApifyIcon, ApifyWhiteIcon } from "@/icons/Apify"; +import { ArizeIcon } from "@/icons/Arize"; +import { ArXivIcon } from "@/icons/ArXiv"; +import { AssemblyAIIcon } from "@/icons/AssemblyAI"; +import { AstraDBIcon } from "@/icons/AstraDB"; +import { AthenaIcon } from "@/icons/athena/index"; +import { AWSIcon } from "@/icons/AWS"; +import { AWSInvertedIcon } from "@/icons/AWSInverted"; +import { AzureIcon } from "@/icons/Azure"; +import { BingIcon } from "@/icons/Bing"; +import { BotMessageSquareIcon } from "@/icons/BotMessageSquare"; +import { BWPythonIcon } from "@/icons/BW python"; +import { CassandraIcon } from "@/icons/Cassandra"; +import { ChromaIcon } from "@/icons/ChromaIcon"; +import { ClickhouseIcon } from "@/icons/Clickhouse"; +import { CloudflareIcon } from "@/icons/Cloudflare"; +import { CohereIcon } from "@/icons/Cohere"; +import { ComposioIcon } from "@/icons/Composio"; +import { ConfluenceIcon } from "@/icons/Confluence"; +import { CouchbaseIcon } from "@/icons/Couchbase"; +import { CrewAiIcon } from "@/icons/CrewAI"; +import { DeepSeekIcon } from "@/icons/DeepSeek"; +import { DropboxIcon } from "@/icons/Dropbox"; +import { DuckDuckGoIcon } from "@/icons/DuckDuckGo"; +import { ElasticsearchIcon } from "@/icons/ElasticsearchStore"; +import { EvernoteIcon } from "@/icons/Evernote"; +import { ExaIcon } from "@/icons/Exa"; +import { FBIcon } from "@/icons/FacebookMessenger"; +import { FirecrawlIcon } from "@/icons/Firecrawl"; +import { freezeAllIcon } from "@/icons/freezeAll"; +import { GitBookIcon } from "@/icons/GitBook"; +import { GitLoaderIcon } from "@/icons/GitLoader"; +import { GleanIcon } from "@/icons/Glean"; +import { GlobeOkIcon } from "@/icons/globe-ok"; +import { GmailIcon } from "@/icons/gmail"; +import { GoogleIcon } from "@/icons/Google"; +import { GoogleDriveIcon } from "@/icons/GoogleDrive"; +import { GoogleGenerativeAIIcon } from "@/icons/GoogleGenerativeAI"; +import { + GradientInfinity, + GradientSave, + GradientUngroup, +} from "@/icons/GradientSparkles"; +import { GridHorizontalIcon } from "@/icons/GridHorizontal"; +import { GroqIcon } from "@/icons/Groq"; +import { HackerNewsIcon } from "@/icons/hackerNews"; +import { HCDIcon } from "@/icons/HCD"; +import { HomeAssistantIcon } from "@/icons/HomeAssistant"; +import { HuggingFaceIcon } from "@/icons/HuggingFace"; +import { WatsonxAiIcon } from "@/icons/IBMWatsonx"; +import { IcosaIcon } from "@/icons/Icosa"; +import { IFixIcon } from "@/icons/IFixIt"; +import { JSIcon } from "@/icons/JSicon"; +import { LangChainIcon } from "@/icons/LangChain"; +import { LangwatchIcon } from "@/icons/Langwatch"; +import { LMStudioIcon } from "@/icons/LMStudio"; +import { MaritalkIcon } from "@/icons/Maritalk"; +import { Mem0 } from "@/icons/Mem0"; +import { MetaIcon } from "@/icons/Meta"; +import { MidjourneyIcon } from "@/icons/Midjorney"; +import { MilvusIcon } from "@/icons/Milvus"; +import { MistralIcon } from "@/icons/mistral"; +import { MongoDBIcon } from "@/icons/MongoDB"; +import { NeedleIcon } from "@/icons/Needle"; +import { NotDiamondIcon } from "@/icons/NotDiamond"; +import { NotionIcon } from "@/icons/Notion"; +import { NovitaIcon } from "@/icons/Novita"; +import { NvidiaIcon } from "@/icons/Nvidia"; +import { OlivyaIcon } from "@/icons/Olivya"; +import { OllamaIcon } from "@/icons/Ollama"; +import { OneDriveIcon } from "@/icons/OneDrive"; +import { OpenAiIcon } from "@/icons/OpenAi"; +import { OpenRouterIcon } from "@/icons/OpenRouter"; +import { OpenSearch } from "@/icons/OpenSearch"; +import { PerplexityIcon } from "@/icons/Perplexity"; +import { PineconeIcon } from "@/icons/Pinecone"; +import { PostgresIcon } from "@/icons/Postgres"; +import { PythonIcon } from "@/icons/Python"; +import { QDrantIcon } from "@/icons/QDrant"; +import { QianFanChatIcon } from "@/icons/QianFanChat"; +import { RedisIcon } from "@/icons/Redis"; +import { SambaNovaIcon } from "@/icons/SambaNova"; +import { ScrapeGraph } from "@/icons/ScrapeGraphAI"; +import { SearchAPIIcon } from "@/icons/SearchAPI"; +import { SearchHybridIcon } from "@/icons/SearchHybrid"; +import { SearchLexicalIcon } from "@/icons/SearchLexical"; +import { SearchVectorIcon } from "@/icons/SearchVector"; +import { SearxIcon } from "@/icons/Searx"; +import { SerperIcon } from "@/icons/Serper"; +import { SerpSearchIcon } from "@/icons/SerpSearch"; +import { ShareIcon } from "@/icons/Share"; +import { Share2Icon } from "@/icons/Share2"; +import { SlackIcon } from "@/icons/Slack"; +import { SpiderIcon } from "@/icons/Spider"; +import { Streamlit } from "@/icons/Streamlit"; +import { SupabaseIcon } from "@/icons/supabase"; +import { TavilyIcon } from "@/icons/Tavily"; +import { ThumbDownIconCustom, ThumbUpIconCustom } from "@/icons/thumbs"; +import { TwelveLabsIcon } from "@/icons/TwelveLabs"; +import { UnstructuredIcon } from "@/icons/Unstructured"; +import { UpstashSvgIcon } from "@/icons/Upstash"; +import { VectaraIcon } from "@/icons/VectaraIcon"; +import { VertexAIIcon } from "@/icons/VertexAI"; +import { WeaviateIcon } from "@/icons/Weaviate"; +import { WikipediaIcon } from "@/icons/Wikipedia"; +import { WolframIcon } from "@/icons/Wolfram"; +import { XAIIcon } from "@/icons/xAI"; +import { YouTubeSvgIcon as YouTubeIcon } from "@/icons/Youtube"; +import { ZepMemoryIcon } from "@/icons/ZepMemory"; + +// Export the eagerly loaded icons map +export const eagerIconsMapping = { + "AI/ML": AIMLIcon, + AgentQL: AgentQLIcon, + Airbyte: AirbyteIcon, + Anthropic: AnthropicIcon, + Apify: ApifyIcon, + ApifyWhite: ApifyWhiteIcon, + ArXiv: ArXivIcon, + Arize: ArizeIcon, + AssemblyAI: AssemblyAIIcon, + AstraDB: AstraDBIcon, + Athena: AthenaIcon, + AWS: AWSIcon, + AWSInverted: AWSInvertedIcon, + Azure: AzureIcon, + Bing: BingIcon, + BotMessageSquare: BotMessageSquareIcon, + BWPython: BWPythonIcon, + Cassandra: CassandraIcon, + Chroma: ChromaIcon, + Clickhouse: ClickhouseIcon, + Cloudflare: CloudflareIcon, + Cohere: CohereIcon, + Composio: ComposioIcon, + Confluence: ConfluenceIcon, + Couchbase: CouchbaseIcon, + CrewAI: CrewAiIcon, + DeepSeek: DeepSeekIcon, + Dropbox: DropboxIcon, + DuckDuckGo: DuckDuckGoIcon, + ElasticsearchStore: ElasticsearchIcon, + Evernote: EvernoteIcon, + Exa: ExaIcon, + FacebookMessenger: FBIcon, + Firecrawl: FirecrawlIcon, + FreezeAll: freezeAllIcon, + GitBook: GitBookIcon, + GitLoader: GitLoaderIcon, + Glean: GleanIcon, + GlobeOk: GlobeOkIcon, + Google: GoogleIcon, + GoogleDrive: GoogleDriveIcon, + GoogleGenerativeAI: GoogleGenerativeAIIcon, + Gmail: GmailIcon, + GradientInfinity: GradientInfinity, + GradientSave: GradientSave, + GradientUngroup: GradientUngroup, + GridHorizontal: GridHorizontalIcon, + Groq: GroqIcon, + HackerNews: HackerNewsIcon, + HCD: HCDIcon, + HomeAssistant: HomeAssistantIcon, + HuggingFace: HuggingFaceIcon, + Icosa: IcosaIcon, + IFixIt: IFixIcon, + javascript: JSIcon, + LangChain: LangChainIcon, + Langwatch: LangwatchIcon, + LMStudio: LMStudioIcon, + Maritalk: MaritalkIcon, + Mem0: Mem0, + Meta: MetaIcon, + Midjourney: MidjourneyIcon, + Milvus: MilvusIcon, + Mistral: MistralIcon, + MongoDB: MongoDBIcon, + Needle: NeedleIcon, + NotDiamond: NotDiamondIcon, + Notion: NotionIcon, + Novita: NovitaIcon, + NVIDIA: NvidiaIcon, + Olivya: OlivyaIcon, + Ollama: OllamaIcon, + OneDrive: OneDriveIcon, + OpenAI: OpenAiIcon, + OpenRouter: OpenRouterIcon, + OpenSearch: OpenSearch, + Perplexity: PerplexityIcon, + Pinecone: PineconeIcon, + Postgres: PostgresIcon, + Python: PythonIcon, + QDrant: QDrantIcon, + QianFanChat: QianFanChatIcon, + Redis: RedisIcon, + SambaNova: SambaNovaIcon, + ScrapeGraph: ScrapeGraph, + SearchAPI: SearchAPIIcon, + SearchLexical: SearchLexicalIcon, + SearchHybrid: SearchHybridIcon, + SearchVector: SearchVectorIcon, + Searx: SearxIcon, + SerpSearch: SerpSearchIcon, + Serper: SerperIcon, + Share: ShareIcon, + Share2: Share2Icon, + Slack: SlackIcon, + Spider: SpiderIcon, + Streamlit: Streamlit, + Supabase: SupabaseIcon, + Tavily: TavilyIcon, + ThumbDownCustom: ThumbDownIconCustom, + ThumbUpCustom: ThumbUpIconCustom, + TwelveLabs: TwelveLabsIcon, + Unstructured: UnstructuredIcon, + Upstash: UpstashSvgIcon, + Vectara: VectaraIcon, + VertexAI: VertexAIIcon, + WatsonxAI: WatsonxAiIcon, + Weaviate: WeaviateIcon, + Wikipedia: WikipediaIcon, + Wolfram: WolframIcon, + xAI: XAIIcon, + YouTube: YouTubeIcon, + ZepMemory: ZepMemoryIcon, +}; diff --git a/src/frontend/src/icons/lazyIconImports.ts b/src/frontend/src/icons/lazyIconImports.ts index 8cec685eb..78ddf87e2 100644 --- a/src/frontend/src/icons/lazyIconImports.ts +++ b/src/frontend/src/icons/lazyIconImports.ts @@ -1,3 +1,5 @@ +import { TwelveLabsIcon } from "./TwelveLabs"; + // Export the lazy loading mapping for icons export const lazyIconsMapping = { "AI/ML": () => @@ -258,6 +260,10 @@ export const lazyIconsMapping = { import("@/icons/thumbs").then((mod) => ({ default: mod.ThumbUpIconCustom, })), + TwelveLabs: () => + import("@/icons/TwelveLabs").then((mod) => ({ + default: mod.TwelveLabsIcon, + })), TwitterX: () => import("@/icons/Twitter X").then((mod) => ({ default: mod.TwitterXIcon, diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts index abfc03514..01f12463d 100644 --- a/src/frontend/src/utils/styleUtils.ts +++ b/src/frontend/src/utils/styleUtils.ts @@ -265,6 +265,7 @@ export const SIDEBAR_BUNDLES = [ { display_name: "Mem0", name: "mem0", icon: "Mem0" }, { display_name: "Youtube", name: "youtube", icon: "YouTube" }, { display_name: "ScrapeGraph AI", name: "scrapegraph", icon: "ScrapeGraph" }, + { display_name: "Twelve Labs", name: "twelvelabs", icon: "TwelveLabs" }, { display_name: "Home Assistant", name: "homeassistant", diff --git a/uv.lock b/uv.lock index f7eb2b05b..252ae6198 100644 --- a/uv.lock +++ b/uv.lock @@ -178,16 +178,16 @@ wheels = [ [[package]] name = "alembic" -version = "1.15.1" +version = "1.15.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mako" 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