Semantic Vector Cache
Instead of blind string matching, the gateway embeds prompts into 384-dim vectors to catch semantically similar queries.
- HuggingFace embeddings (fastembed)
- Sub-100ms response on hits
For teams running chat, RAG, support, or coding agents. Semantic Gateway removes low-value context before inference, so your existing models do more with the tokens you already pay for.
Auto-detect context similarity. The gateway intercepts huge context dumps before they burn tokens, routing via Groq Llama 3 or falling back to local Ollama.
Install the gateway See how it works ↗Paste the same context a production AI feature would send: chat history, retrieval output, support logs. The gateway optimizes it before the request reaches the model.
Instead of blind string matching, the gateway embeds prompts into 384-dim vectors to catch semantically similar queries.
Simple queries go to fast, cheap models (Llama 8B). Complex queries scale up to 70B automatically.
Run locally or in production. If the primary Groq API fails, the gateway fails over to Ollama instantly.
Runs locally with the Semantic Gateway. The user request defines what matters, so duplicate history, irrelevant chunks, and noise are caught via cache or routed cheaply.