Software Design Document (SDD)
Video-to-Knowledge Pipeline System
Version: 1.0
Date: 2026-02-05
Status: Draft
1. Executive Summary
The Video-to-Knowledge Pipeline System automates the transformation of video content into structured, searchable, and actionable knowledge artifacts. The system downloads videos from platforms (YouTube, etc.), extracts audio for transcription, samples video frames, deduplicates visual content, and generates comprehensive documentation including SDD, TDD, ADR, C4 diagrams, and glossary artifacts.
1.1 Key Capabilities
| Capability | Description |
|---|---|
| Multi-Source Ingestion | Download from YouTube, Vimeo, direct URLs via yt-dlp |
| Audio Intelligence | Extract audio → MP3 → Markdown transcription (Whisper/STT) |
| Visual Analysis | Frame extraction at configurable rates with perceptual deduplication |
| Content Synthesis | Merge transcript + visual analysis into unified markdown |
| Artifact Generation | Auto-generate SDD, TDD, ADR, C4 diagrams, glossary |
| Knowledge Expansion | Web search integration for context enrichment |
2. System Overview
2.1 High-Level Architecture
2.2 Data Flow
┌─────────────┐ ┌─────────────┐ ┌─────────────────┐
│ Video URL │────▶│ yt-dlp │────▶│ Video + Audio │
└─────────────┘ └─────────────┘ └─────────────────┘
│
┌────────────────────────────┼────────────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Audio Pipeline │ │ Video Pipeline │ │ Metadata │
│ ───────────── │ │ ───────────── │ │ Extraction │
│ • MP3 extract │ │ • Frame sample │ └─────────────────┘
│ • Transcribe │ │ • Deduplicate │
│ • Timestamp │ │ • Vision LLM │
└────────┬────────┘ └────────┬────────┘
│ │
└────────────┬───────────────┘
▼
┌─────────────────────┐
│ Content Synthesis │
│ ───────────────── │
│ • Align timestamps │
│ • Merge contexts │
│ • Chunk by topic │
└──────────┬──────────┘
▼
┌─────────────────────┐
│ Artifact Generation│
│ ───────────────── │
│ • SDD/TDD/ADR │
│ • C4 Models │
│ • Glossary │
│ • Web expansion │
└──────────┬──────────┘
▼
┌─────────────────────┐
│ Document Inventory │
└─────────────────────┘
3. Component Design
3.1 Component Catalog
| Component | Responsibility | Technology |
|---|---|---|
| VideoDownloader | Download videos from URLs | yt-dlp Python API |
| AudioExtractor | Extract audio streams | FFmpeg |
| TranscriptionEngine | Speech-to-text conversion | Whisper (OpenAI) |
| FrameSampler | Extract frames at intervals | FFmpeg, OpenCV |
| DeduplicationEngine | Remove perceptually similar frames | ImageHash, SSIM |
| VisionAnalyzer | Describe image content | GPT-4V / Claude Vision |
| ContentMerger | Synthesize transcript + vision | LLM with context window |
| ArtifactGenerator | Generate structured documents | Template engine + LLM |
| WebSearchExpander | Enrich content with web data | Serper/Google Search API |
3.2 Component Interfaces
# Core interfaces (pseudo-code)
class VideoDownloader:
def download(url: str, options: DownloadOptions) -> VideoAsset: ...
class AudioExtractor:
def extract(video: VideoAsset, format: AudioFormat) -> AudioAsset: ...
class TranscriptionEngine:
def transcribe(audio: AudioAsset) -> Transcript: ...
class FrameSampler:
def sample(video: VideoAsset, fps: float) -> List[Frame]: ...
class DeduplicationEngine:
def deduplicate(frames: List[Frame], threshold: float) -> List[Frame]: ...
class VisionAnalyzer:
def analyze(frame: Frame, context: str) -> ImageAnalysis: ...
class ArtifactGenerator:
def generate_sdd(content: SynthesizedContent) -> SDDDocument: ...
def generate_tdd(content: SynthesizedContent) -> TDDDocument: ...
def generate_adr(content: SynthesizedContent) -> List[ADRDocument]: ...
def generate_c4(content: SynthesizedContent) -> C4Model: ...
4. Data Model
4.1 Core Entities
5. Processing Pipeline
5.1 Stage 1: Ingestion
| Step | Input | Output | Tool |
|---|---|---|---|
| 1.1 URL Validation | Raw URL | Validated URL | URL parser |
| 1.2 Download | Validated URL | Video file | yt-dlp |
| 1.3 Metadata Extraction | Video file | Title, duration, description | ffprobe |
5.2 Stage 2: Audio Processing
| Step | Input | Output | Tool |
|---|---|---|---|
| 2.1 Audio Extraction | Video file | MP3 | FFmpeg |
| 2.2 Transcription | MP3 | Raw transcript | Whisper |
| 2.3 Segmentation | Raw transcript | Timestamped segments | Whisper + post-processing |
5.3 Stage 3: Visual Processing
| Step | Input | Output | Tool |
|---|---|---|---|
| 3.1 Frame Extraction | Video file | Frame images | FFmpeg |
| 3.2 Feature Extraction | Frame images | Perceptual hashes | imagehash |
| 3.3 Deduplication | Frames + hashes | Unique frames | Hamming distance threshold |
| 3.4 Vision Analysis | Unique frames | Image descriptions | GPT-4V |
5.4 Stage 4: Synthesis
| Step | Input | Output | Tool |
|---|---|---|---|
| 4.1 Timestamp Alignment | Transcript + Frames | Aligned content | Time-matching |
| 4.2 Semantic Chunking | Aligned content | Topic chunks | LLM + embeddings |
| 4.3 Context Enrichment | Topic chunks | Enriched chunks | Web search API |
5.5 Stage 5: Artifact Generation
| Step | Input | Output | Tool |
|---|---|---|---|
| 5.1 Executive Summary | Enriched chunks | Summary doc | LLM |
| 5.2 Outline Generation | Enriched chunks | Structured outline | LLM |
| 5.3 SDD Generation | Outline + chunks | SDD document | Template + LLM |
| 5.4 TDD Generation | Outline + chunks | TDD document | Template + LLM |
| 5.5 ADR Generation | Technical decisions | ADR documents | Template + LLM |
| 5.6 C4 Generation | Architecture info | C4 models | Template + LLM |
| 5.7 Glossary Extraction | All content | Glossary | Term extraction + LLM |
| 5.8 Inventory Generation | All artifacts | Inventory document | Metadata aggregation |
6. Configuration
6.1 Pipeline Settings
pipeline:
ingestion:
download_quality: "best"
extract_audio: true
extract_subtitles: true
audio:
format: "mp3"
bitrate: "192k"
sample_rate: 44100
transcription_backend: "local" # "local" | "openai-api"
whisper_model: "auto" # "tiny" | "base" | "small" | "medium" | "large-v3" | "auto"
whisper_priority: "balanced" # "speed" | "quality" | "balanced"
language: "auto-detect"
visual:
frame_rate: 0.5 # frames per second
scale: "960:-1" # width:height
quality: 2 # jpeg quality
dedup_threshold: 10 # hamming distance
vision_model: "gpt-4-vision-preview"
synthesis:
chunk_size: 4000 # tokens
overlap: 200 # tokens
embedding_model: "text-embedding-3-large"
generation:
llm_model: "claude-3-5-sonnet"
web_search_enabled: true
max_search_results: 5
7. Error Handling
| Error Type | Handling Strategy |
|---|---|
| Download failure | Retry with fallback quality, log error |
| Transcription failure | Try alternative STT model |
| Vision API failure | Retry with exponential backoff |
| Deduplication failure | Process all frames (safe fallback) |
| LLM rate limiting | Queue with exponential backoff |
8. Monitoring & Observability
| Metric | Description |
|---|---|
| Pipeline duration | Total time from URL to artifacts |
| Stage latency | Time per processing stage |
| Deduplication ratio | % of frames removed |
| API costs | Token usage and API call costs |
| Artifact quality | LLM-based quality scores |
9. Security Considerations
- URL Validation: Prevent SSRF, validate domains
- File Sanitization: Scan downloads for malware
- API Key Management: Use environment variables, rotate keys
- Data Retention: Define retention policies for video/audio
- PII Handling: Detect and optionally redact personal information
10. Future Enhancements
| Feature | Description |
|---|---|
| Real-time processing | Stream processing vs batch |
| Multi-video synthesis | Cross-video knowledge graphs |
| Video QA | Question answering on content |
| Translation | Multi-language artifact generation |
| Voice cloning | Generate audio summaries |
Appendices
A. Glossary
See ../glossary/glossary.md
B. C4 Architecture
See ../c4/c4_model.md
C. ADRs
See ../adr/ directory
D. Document Inventory
See ../inventory/document_inventory.md