Lightning-fast semantic search. Billions of vectors, millisecond response. GPU-parallel computation for instant retrieval.
GPU-accelerated semantic similarity search with real-time embeddings
Embeddings transform text into 1536-dimensional vectors. Each dimension captures semantic meaning.
# Text becomes a point in vector space
embedding = model.encode("memory content")
# Result: [0.023, -0.184, 0.291, ...]Vectors are indexed on the GPU for parallel search. CUDA cores enable simultaneous distance calculations.
Query vector finds nearest neighbors by distance. Similar meaning = close vectors = relevant results.
Search memory with semantic similarity
semantic_search(
query: string, // Search text
top_k: number // Results (default: 5)
)Add new memory with auto-embedding
add_memory(
content: string, // Memory text
metadata: object // Optional tags
)Get tether health and statistics
get_status()
// Returns memory count, GPU, uptimeGPU-accelerated vector search. Rapid deployment.
Tell us what you're building. We'll tell you what you need.
We configure the system for your specific requirements.
Lightning-fast semantic search. Production-ready, running, yours.