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# Analyzers API Reference |
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::: video_processor.analyzers.diagram_analyzer |
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::: video_processor.analyzers.content_analyzer |
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::: video_processor.analyzers.action_detector |
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--- |
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## Overview |
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The analyzers module contains the core content extraction logic for PlanOpticon. These analyzers process video frames and transcripts to extract structured knowledge: diagrams, key points, action items, and cross-referenced entities. |
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All analyzers accept an optional `ProviderManager` instance. When provided, they use LLM capabilities for richer extraction. Without one, they fall back to heuristic/pattern-based methods where possible. |
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## DiagramAnalyzer |
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```python |
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from video_processor.analyzers.diagram_analyzer import DiagramAnalyzer |
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``` |
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Vision model-based diagram detection and analysis. Classifies video frames as diagrams, slides, screenshots, or other content, then performs full extraction on high-confidence frames. |
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### Constructor |
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```python |
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def __init__( |
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self, |
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provider_manager: Optional[ProviderManager] = None, |
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confidence_threshold: float = 0.3, |
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) |
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``` |
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| Parameter | Type | Default | Description | |
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|---|---|---|---| |
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| `provider_manager` | `Optional[ProviderManager]` | `None` | LLM provider (creates a default if not provided) | |
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| `confidence_threshold` | `float` | `0.3` | Minimum confidence to process a frame at all | |
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### classify_frame() |
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```python |
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def classify_frame(self, image_path: Union[str, Path]) -> dict |
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``` |
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Classify a single frame using a vision model. Determines whether the frame contains a diagram, slide, or other visual content worth extracting. |
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**Parameters:** |
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| Parameter | Type | Description | |
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|---|---|---| |
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| `image_path` | `Union[str, Path]` | Path to the frame image file | |
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**Returns:** `dict` with the following keys: |
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| Key | Type | Description | |
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|---|---|---| |
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| `is_diagram` | `bool` | Whether the frame contains extractable content | |
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| `diagram_type` | `str` | One of: `flowchart`, `sequence`, `architecture`, `whiteboard`, `chart`, `table`, `slide`, `screenshot`, `unknown` | |
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| `confidence` | `float` | Detection confidence from 0.0 to 1.0 | |
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| `content_type` | `str` | Content category: `slide`, `diagram`, `document`, `screen_share`, `whiteboard`, `chart`, `person`, `other` | |
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| `brief_description` | `str` | One-sentence description of the frame content | |
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**Important:** Frames showing people, webcam feeds, or video conference participant views return `confidence: 0.0`. The classifier is tuned to detect only shared/presented content. |
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```python |
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analyzer = DiagramAnalyzer() |
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result = analyzer.classify_frame("/path/to/frame_042.jpg") |
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if result["confidence"] >= 0.7: |
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print(f"Diagram detected: {result['diagram_type']}") |
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``` |
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### analyze_diagram_single_pass() |
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```python |
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def analyze_diagram_single_pass(self, image_path: Union[str, Path]) -> dict |
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``` |
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Full single-pass diagram analysis. Extracts description, text content, elements, relationships, Mermaid syntax, and chart data in a single LLM call. |
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**Returns:** `dict` with the following keys: |
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| Key | Type | Description | |
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|---|---|---| |
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| `diagram_type` | `str` | Diagram classification | |
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| `description` | `str` | Detailed description of the visual content | |
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| `text_content` | `str` | All visible text, preserving structure | |
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| `elements` | `list[str]` | Identified elements/components | |
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| `relationships` | `list[str]` | Relationships in `"A -> B: label"` format | |
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| `mermaid` | `str` | Valid Mermaid diagram syntax | |
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| `chart_data` | `dict \| None` | Chart data with `labels`, `values`, `chart_type` (only for data charts) | |
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Returns an empty `dict` on failure. |
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### caption_frame() |
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```python |
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def caption_frame(self, image_path: Union[str, Path]) -> str |
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``` |
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Get a brief 1-2 sentence caption for a frame. Used as a fallback when full diagram analysis is not warranted. |
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**Returns:** `str` -- a brief description of the frame content. |
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### process_frames() |
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```python |
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def process_frames( |
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frame_paths: List[Union[str, Path]], |
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diagrams_dir: Optional[Path] = None, |
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captures_dir: Optional[Path] = None, |
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) -> Tuple[List[DiagramResult], List[ScreenCapture]] |
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``` |
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Process a batch of extracted video frames through the full classification and analysis pipeline. |
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**Parameters:** |
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| Parameter | Type | Default | Description | |
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|---|---|---|---| |
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| `frame_paths` | `List[Union[str, Path]]` | *required* | Paths to frame images | |
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| `diagrams_dir` | `Optional[Path]` | `None` | Output directory for diagram files (images, mermaid, JSON) | |
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| `captures_dir` | `Optional[Path]` | `None` | Output directory for screengrab fallback files | |
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**Returns:** `Tuple[List[DiagramResult], List[ScreenCapture]]` |
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**Confidence thresholds:** |
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| Confidence Range | Action | |
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|---|---| |
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| >= 0.7 | Full diagram analysis -- extracts elements, relationships, Mermaid syntax | |
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| 0.3 to 0.7 | Screengrab fallback -- saves frame with a brief caption | |
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| < 0.3 | Skipped entirely | |
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**Output files (when directories are provided):** |
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For diagrams (`diagrams_dir`): |
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- `diagram_N.jpg` -- original frame image |
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- `diagram_N.mermaid` -- Mermaid source (if generated) |
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- `diagram_N.json` -- full DiagramResult as JSON |
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For screen captures (`captures_dir`): |
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- `capture_N.jpg` -- original frame image |
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- `capture_N.json` -- ScreenCapture metadata as JSON |
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```python |
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from pathlib import Path |
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from video_processor.analyzers.diagram_analyzer import DiagramAnalyzer |
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from video_processor.providers.manager import ProviderManager |
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analyzer = DiagramAnalyzer( |
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provider_manager=ProviderManager(), |
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confidence_threshold=0.3, |
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) |
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frame_paths = list(Path("output/frames").glob("*.jpg")) |
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diagrams, captures = analyzer.process_frames( |
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frame_paths, |
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diagrams_dir=Path("output/diagrams"), |
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captures_dir=Path("output/captures"), |
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) |
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print(f"Found {len(diagrams)} diagrams, {len(captures)} screengrabs") |
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for d in diagrams: |
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print(f" [{d.diagram_type.value}] {d.description}") |
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``` |
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## ContentAnalyzer |
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```python |
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from video_processor.analyzers.content_analyzer import ContentAnalyzer |
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``` |
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Cross-references transcript and diagram entities for richer knowledge extraction. Merges entities found in different sources and enriches key points with diagram links. |
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### Constructor |
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```python |
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def __init__(self, provider_manager: Optional[ProviderManager] = None) |
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``` |
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| Parameter | Type | Default | Description | |
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| `provider_manager` | `Optional[ProviderManager]` | `None` | Required for LLM-based fuzzy matching | |
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### cross_reference() |
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```python |
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def cross_reference( |
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transcript_entities: List[Entity], |
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diagram_entities: List[Entity], |
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) -> List[Entity] |
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``` |
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Merge entities from transcripts and diagrams into a unified list with source attribution. |
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**Merge strategy:** |
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1. Index all transcript entities by lowercase name, marked with `source="transcript"` |
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2. Merge diagram entities: if a name matches, set `source="both"` and combine descriptions/occurrences; otherwise add as `source="diagram"` |
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3. If a `ProviderManager` is available, use LLM fuzzy matching to find additional matches among unmatched entities (e.g., "PostgreSQL" from transcript matching "Postgres" from diagram) |
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**Parameters:** |
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| Parameter | Type | Description | |
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| `transcript_entities` | `List[Entity]` | Entities extracted from transcript | |
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| `diagram_entities` | `List[Entity]` | Entities extracted from diagrams | |
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**Returns:** `List[Entity]` -- merged entity list with `source` attribution. |
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```python |
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from video_processor.analyzers.content_analyzer import ContentAnalyzer |
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from video_processor.models import Entity |
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analyzer = ContentAnalyzer(provider_manager=pm) |
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transcript_entities = [ |
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Entity(name="PostgreSQL", type="technology"), |
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Entity(name="Alice", type="person"), |
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] |
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diagram_entities = [ |
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Entity(name="Postgres", type="technology"), |
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Entity(name="Redis", type="technology"), |
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] |
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merged = analyzer.cross_reference(transcript_entities, diagram_entities) |
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# "PostgreSQL" and "Postgres" may be fuzzy-matched and merged |
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``` |
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### enrich_key_points() |
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```python |
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def enrich_key_points( |
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self, |
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key_points: List[KeyPoint], |
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diagrams: list, |
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transcript_text: str, |
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) -> List[KeyPoint] |
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``` |
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Link key points to relevant diagrams by entity overlap. Examines word overlap between key point text and diagram elements/text content. |
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**Parameters:** |
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| Parameter | Type | Description | |
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|---|---|---| |
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| `key_points` | `List[KeyPoint]` | Key points to enrich | |
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| `diagrams` | `list` | List of `DiagramResult` objects or dicts | |
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| `transcript_text` | `str` | Full transcript text (reserved for future use) | |
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**Returns:** `List[KeyPoint]` -- key points with `related_diagrams` indices populated. |
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A key point is linked to a diagram when they share 2 or more words (excluding short words) between the key point text/details and the diagram's elements/text content. |
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--- |
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## ActionDetector |
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```python |
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from video_processor.analyzers.action_detector import ActionDetector |
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``` |
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Detects action items from transcripts and diagram content using LLM extraction with a regex pattern fallback. |
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### Constructor |
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```python |
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def __init__(self, provider_manager: Optional[ProviderManager] = None) |
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``` |
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| Parameter | Type | Default | Description | |
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|---|---|---|---| |
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| `provider_manager` | `Optional[ProviderManager]` | `None` | Required for LLM-based extraction | |
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### detect_from_transcript() |
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```python |
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def detect_from_transcript( |
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self, |
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text: str, |
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segments: Optional[List[TranscriptSegment]] = None, |
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) -> List[ActionItem] |
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``` |
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Detect action items from transcript text. |
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**Parameters:** |
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| Parameter | Type | Default | Description | |
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|---|---|---|---| |
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| `text` | `str` | *required* | Transcript text to analyze | |
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| `segments` | `Optional[List[TranscriptSegment]]` | `None` | Transcript segments for timestamp attachment | |
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**Returns:** `List[ActionItem]` -- detected action items with `source="transcript"`. |
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**Extraction modes:** |
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- **LLM mode** (when `provider_manager` is set): Sends the transcript to the LLM with a structured extraction prompt. Extracts action, assignee, deadline, priority, and context. |
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- **Pattern mode** (fallback): Matches sentences against regex patterns for action-oriented language. |
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**Pattern matching** detects sentences containing: |
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- "need/needs to", "should/must/shall" |
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- "will/going to", "action item/todo/follow-up" |
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- "assigned to/responsible for", "deadline/due by" |
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- "let's/let us", "make sure/ensure" |
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- "can you/could you/please" |
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**Timestamp attachment:** When `segments` are provided, each action item is matched to the most relevant transcript segment (by word overlap, minimum 3 matching words), and a timestamp is added to `context`. |
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### detect_from_diagrams() |
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```python |
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def detect_from_diagrams(self, diagrams: list) -> List[ActionItem] |
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``` |
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Extract action items from diagram text content and elements. Processes each diagram's combined text using either LLM or pattern extraction. |
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**Parameters:** |
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| Parameter | Type | Description | |
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|---|---|---| |
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| `diagrams` | `list` | List of `DiagramResult` objects or dicts | |
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**Returns:** `List[ActionItem]` -- action items with `source="diagram"`. |
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### merge_action_items() |
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```python |
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def merge_action_items( |
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self, |
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transcript_items: List[ActionItem], |
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diagram_items: List[ActionItem], |
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) -> List[ActionItem] |
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``` |
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Merge action items from multiple sources, deduplicating by action text (case-insensitive, whitespace-normalized). |
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**Returns:** `List[ActionItem]` -- deduplicated merged list. |
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### Usage example |
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```python |
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from video_processor.analyzers.action_detector import ActionDetector |
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from video_processor.providers.manager import ProviderManager |
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detector = ActionDetector(provider_manager=ProviderManager()) |
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# From transcript |
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transcript_items = detector.detect_from_transcript( |
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text="Alice needs to update the API docs by Friday. " |
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"Bob should review the PR before merging.", |
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segments=transcript_segments, |
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) |
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# From diagrams |
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diagram_items = detector.detect_from_diagrams(diagram_results) |
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# Merge and deduplicate |
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all_items = detector.merge_action_items(transcript_items, diagram_items) |
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for item in all_items: |
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print(f"[{item.priority or 'unset'}] {item.action}") |
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if item.assignee: |
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print(f" Assignee: {item.assignee}") |
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if item.deadline: |
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print(f" Deadline: {item.deadline}") |
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``` |
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### Pattern fallback (no LLM) |
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```python |
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# Works without any API keys |
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detector = ActionDetector() # No provider_manager |
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items = detector.detect_from_transcript( |
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"We need to finalize the database schema. " |
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"Please update the deployment scripts." |
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) |
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# Returns ActionItems matched by regex patterns |
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``` |