> ## Documentation Index > Fetch the complete documentation index at: https://docs.videodb.io/llms.txt > Use this file to discover all available pages before exploring further. # Chess Match Montage Generator > Automatically create highlight reels from long chess matches with AI-powered move detection

## The Challenge A chess match can last 4+ hours. As a content creator, you want to turn that into a punchy highlight reel, but manually scrubbing through and identifying key moments is tedious. What if AI could watch the entire match, detect every move, and automatically compile a highlight montage? ## What You'll Build Turn a long chess match into a **punchy highlight reel** — automatically! You'll: 1. Upload chess video + background music 2. Index scenes with simple move detection 3. Extract timestamps automatically using AI 4. Build a montage with transitions and effects 5. Output a professional highlight reel All powered by **VideoDB's Editor SDK** — no manual frame-by-frame editing needed. ## Setup ### Install Dependencies ```bash theme={null} pip install videodb ``` ### Connect to VideoDB ```python theme={null} import videodb # Connect to VideoDB api_key = "your_api_key" conn = videodb.connect(api_key=api_key) coll = conn.get_collection() ``` ## Implementation ### Step 1: Upload Chess Video and Music ```python theme={null} from videodb import MediaType # Upload the chess match video chess_video = coll.upload(url="https://www.youtube.com/watch?v=dhDe-RcoyAU") # Upload background music for the montage bg_music = coll.upload( url="https://www.youtube.com/watch?v=S19UcWdOA-I", media_type=MediaType.audio ) ``` ### Step 2: Index Scenes with Move Detection Create a scene index with a **binary prompt** to detect moves: ```python theme={null} from videodb import SceneExtractionType moves_index_id = chess_video.index_scenes( extraction_type=SceneExtractionType.time_based, extraction_config={"time": 8, "frame_count": 5}, prompt="""Look at this chess scene and focus on the chess board. Your task is to detect when pieces are moved. Respond with ONLY one of these two keywords: - "Player Moved" — if a chess piece was moved - "No Move" — if no move occurred (same position, paused, talking, etc.) Be strict. Only say "Player Moved" if you clearly see a chess piece moved.""", name="Chess_Move_Detection" ) # Get all scenes with descriptions moves_scenes = chess_video.get_scene_index(moves_index_id) ``` ### Step 3: Extract Move Timestamps Using AI Feed the scene index to the LLM to extract timestamps: ```python theme={null} import json prompt = f"""Analyze the scene descriptions from this chess video. Find EVERY scene where the description says "Player Moved". Return a JSON array containing ONLY the start timestamps (in seconds) of those scenes. Example output format: [0, 8, 16, 24, 40, 48] Rules: - Return ONLY the JSON array, nothing else - No descriptions, no explanations, just timestamps Moves Index : "{moves_scenes}" """ response = coll.generate_text( prompt=prompt, response_type="json", model_name="pro" ) # Parse timestamps from LLM response timestamps = response["output"] if isinstance(timestamps, str): timestamps = json.loads(timestamps) ``` Initialize timeline and sample moves: ```python theme={null} from videodb.editor import ( Timeline, Track, Clip, VideoAsset, AudioAsset, Filter, Transition, TextAsset, Font ) CLIP_DURATION = 5 # seconds per clip TARGET_CLIPS = 10 # how many clips we want # Sample timestamps evenly total_detected = len(timestamps) step = max(1, total_detected // TARGET_CLIPS) sampled_timestamps = timestamps[::step][:TARGET_CLIPS] # Initialize timeline timeline = Timeline(conn) timeline.background = "#000000" # Create intro text intro_text = TextAsset( text="Let the Match Begin", font=Font(family="Clear Sans", size=56, color="#FFFFFF"), ) intro_clip = Clip( asset=intro_text, duration=3, transition=Transition(in_="fade", out="fade", duration=0.5) ) intro_track = Track() intro_track.add_clip(0, intro_clip) timeline.add_track(intro_track) ``` ### Step 5: Add Video Clips with Transitions ```python theme={null} # Add video clips video_track = Track() timeline_position = 3 for i, start_time in enumerate(sampled_timestamps): clip = Clip( asset=VideoAsset( id=chess_video.id, start=start_time-1, volume=0 # Muting the original audio ), duration=CLIP_DURATION, filter=Filter.contrast, transition=Transition(in_="fade", out="fade", duration=1) ) video_track.add_clip(timeline_position, clip) timeline_position += CLIP_DURATION timeline.add_track(video_track) total_duration = len(sampled_timestamps) * CLIP_DURATION + 3 ``` ### Step 6: Add Background Music ```python theme={null} # Add music track music_clip = Clip( asset=AudioAsset( id=bg_music.id, start=0, volume=0.7 ), duration=total_duration ) audio_track = Track() audio_track.add_clip(0, music_clip) timeline.add_track(audio_track) ``` ### Step 7: Render Montage ```python theme={null} # Generate final montage stream stream_url = timeline.generate_stream() ``` ## What You Get A professional highlight reel with: * AI-detected key moves * Evenly sampled clips for pacing * Smooth fade transitions * Enhanced contrast for visual impact * Background music * Professional polish in seconds Here's the final chess montage: