Joins
Cross-Modal Join & Correlation
Join collections across shared embedding spaces or time overlap. Enables investigations, analytics, and multi-source RAG.
video
image
text
Multi-Stage
45.0K runs
Deploy RecipeWhy This Matters
Joins are infrastructure operations—not ML models. Once collections share embedding spaces, you can query across them.
from mixpeek import Mixpeekclient = Mixpeek(api_key="your-api-key")# Create collections with shared embedding spacevideo_collection = client.collections.create(collection_name="video_library",feature_extractor={"feature_extractor_name": "multimodal_extractor","version": "v1"})transcript_collection = client.collections.create(collection_name="transcripts",feature_extractor={"feature_extractor_name": "text_extractor","version": "v1"})# Cross-modal search across bothresults = client.retrievers.execute(retriever_id="cross-modal-retriever",inputs={"query_text": "security incident","collections": ["video_library", "transcripts"],"time_range": {"start": "2024-12-01T00:00:00Z","end": "2024-12-31T23:59:59Z"}})
Retrieval Flow
1
feature search(search)
Search across multiple collections
2
attribute filter(filter)
Time-based overlap filtering
3
compose(compose)
Merge results from multiple collections
Feature Extractors
Feature Extractors
Text Embedding
Extract semantic embeddings from documents, transcripts and text content
827K runs
Image Embedding
Generate visual embeddings for similarity search and clustering
752K runs
Video Embedding
Generate vector embeddings for video content
610K runs
Retriever Stages
feature search
Search collections using multimodal embeddings
search
attribute filter
Filter documents by metadata attributes
filter
compose
Compose multiple retriever pipelines together
compose
