warmup_semantic_search
Initialize the embedding model and vector store for semantic search to prevent timeout on first query. Downloads and loads the Nomic model, runs a test embedding, and sets up the vector store connection.
Instructions
Pre-load the embedding model and vector store for semantic search.
Call this before your first semantic search to avoid timeout. Downloads and initializes the Nomic embedding model (~270MB) and creates the vector store connection.
The warmup process:
Downloads the embedding model (if not cached)
Loads the model into memory
Runs a test embedding to warm up inference
Initializes the vector store connection
Returns: Dictionary with warmup status: - status: "ready" if successful - model: Name of the embedding model loaded - dimensionality: Embedding dimensions configured - inference_mode: How embeddings are computed (local/remote) - test_embedding_size: Size of test embedding (confirms model works) - warmup_time_seconds: Time taken to warm up
Note: - First call downloads ~270MB model (takes 30-60 seconds) - Subsequent calls are instant (model cached on disk) - Model stays in memory for fast inference after warmup
Input Schema
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Output Schema
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