> ## Documentation Index > Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Decomposition > How Mixpeek turns raw objects into searchable documents and queryable features Decomposition: raw objects are split into documents, each with extracted features like embeddings, transcripts, and metadata Decomposition is the core transformation in Mixpeek. A raw file (video, PDF, image, audio) goes in as one **object**. It comes out as many **documents**, each with its own **features**. This is what makes sub-file search possible — you search *within* a video at the segment level, not *for* the video as a whole. ## Three Primitives | Primitive | What It Is | Role | | ------------ | -------------------------------------------------------------------------------------- | --------------------------------------------------------------- | | **Object** | Raw file or record in a bucket (video, PDF, JSON row, image). | The input boundary. You upload objects. | | **Document** | One row of output in a collection, produced by decomposition. | The query boundary. You search documents. | | **Feature** | A named output attached to a document (embedding, transcript, OCR text, label, score). | The composition boundary. Retrievers reference features by URI. | The pipeline is always: ``` Object (bucket) → Decomposition → Document (collection) → Features (MVS + MongoDB) ``` ## What Decomposition Decides The feature extractor controls *how* an object is decomposed into documents. The strategy depends on the content type: | Content Type | Decomposition Strategy | Result | | ------------------- | ------------------------------------------------- | -------------------------------------------------------------------------------- | | **Video** | Time intervals, scene boundaries, or silence gaps | Each segment = 1 document with visual embedding + transcript + scene description | | **Audio** | Silence boundaries or fixed intervals | Each segment = 1 document with transcript + transcript embedding | | **PDF / Document** | Page, paragraph, or sentence boundaries | Each chunk = 1 document with text content + text embedding | | **Image** | No split (1:1) | 1 image = 1 document with visual embedding + OCR + description | | **Structured data** | Row-level (1:1) | 1 row = 1 document with field-level features | ## Why It Matters **Without decomposition**, a 30-minute video is one record. Searching for "the moment the CEO mentions revenue" means scanning the entire video. There's no way to return a specific timestamp. **With decomposition**, that video becomes \~180 ten-second segments, each with its own transcript embedding, visual embedding, and scene description. A search returns the exact segment at 14:30 where the CEO says "revenue grew 22%." The same applies to documents: a 200-page PDF becomes 200 searchable chunks instead of one monolithic record. ## Feature URIs Every feature produced by decomposition gets a URI that uniquely identifies it: ``` mixpeek://multimodal_extractor@v1/vertex_multimodal_embedding mixpeek://face_identity_extractor@v1/insightface__arcface mixpeek://my_custom_plugin@1.0.0/domain_embedding ``` Retrievers, taxonomies, and clusters reference features by URI. This is the composition boundary — you can build a retriever that searches `multimodal_embedding` in one stage and `face_embedding` in another, even though they were produced by different extractors. ## Configuring Decomposition Decomposition is configured via the `feature_extractor` field on a collection: ```json theme={null} { "collection_name": "video-library", "source": { "type": "bucket", "bucket_ids": ["bkt_videos"] }, "feature_extractor": { "feature_extractor_name": "multimodal_extractor", "version": "v1", "input_mappings": { "video": "video_url" }, "settings": { "video_segmentation": { "type": "time", "interval_sec": 10 }, "run_transcription": true, "run_scene_description": true } } } ``` The `settings` object controls the decomposition strategy. Each extractor has its own settings — see the extractor-specific pages for details: Time, scene, and silence segmentation strategies Visual embeddings, OCR, and structured extraction Silence-boundary segmentation and transcription Page, paragraph, and sentence chunking ## Multi-Tier Decomposition When a single extraction pass isn't enough — e.g., you need to transcribe audio *then* embed the transcription *then* classify each chunk — you chain collections. Each tier reads the output of the previous one, forming a DAG: ``` Tier 1: raw video → segments with transcripts Tier 2: tier-1 docs → text chunks with embeddings Tier 3: tier-2 docs → classifications per chunk ``` The engine resolves tiers automatically and executes them in dependency order. See [Multi-Tier Feature Extraction](/processing/multi-tier-extractors) for the full guide. ## Lineage Every document tracks its lineage back to the original object: ```json theme={null} { "root_object_id": "obj_video_123", "root_bucket_id": "bkt_marketing", "source_collection_id": "col_segments", "lineage_path": "bkt_marketing/col_segments/col_chunks" } ``` This lets you trace any search result back through tiers to the original file. Use the [Lineage API](/api-reference/document-lineage/get-document-lineage) to visualize the decomposition tree.