Multimodal Embedding Generation And Semantic Search Across Text Images And Video

via “multi-modal semantic search with unified embedding indexing”

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

Unique: Unifies text, image, audio, and video embeddings in a single FAISS-compatible index within the .mv2 file, enabling cross-modal semantic search without external vector databases. The append-only Smart Frame design ensures new embeddings are indexed immediately without reindexing the entire corpus.

Faster and more portable than Pinecone or Weaviate for multimodal search because embeddings are stored locally in a single file with no network round-trips, and supports offline-first retrieval without API dependencies.