how do i preform content caching for gemini
# ask-aij
jolly-apartment-95291
12/15/2025, 11:02 PMhow do i preform content caching for gemini
t
tall-airline-16756
12/15/2025, 11:02 PMThanks for reaching out, Tyler. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
12/15/2025, 11:02 PMYou're asking how to perform content caching for Gemini — do you mean caching model responses/assets when using one of LiveKit's Gemini integrations (LLM inference, real-time plugin, TTS, or the Gemini Live API)? Which integration are you using, and what exactly do you want to cache (model responses, audio files, prompt/completion pairs, embeddings, or other assets)?
A few quick clarifying questions so I can give precise steps:
• Which Gemini integration are you using? (LLM inference, realtime plugin, TTS, Gemini Live API, or custom)
• Do you control the server-side code (can add a cache layer) or are you working only client-side?
• Expected traffic and cache goals: reduce API calls, lower latency, or persist results long-term?
• Any constraints: storage type (in-memory, Redis, S3), TTL requirements, or privacy/PII concerns?
Tell me that and I’ll provide a concise caching approach tailored to your setup (recommended cache layer, keys/invalidations, and example flow).
Sources: Google Gemini LLM | LiveKit Docs | Gemini Live API plugin | LiveKit docs | Google Gemini LLM plugin guide | LiveKit Docs | Gemini TTS plugin guide | LiveKit Docs | Google AI and LiveKit | LiveKit Docs
j
jolly-apartment-95291
12/15/2025, 11:03 PMi want to perform explicit content caching for tokens for grounidng data
t
tall-airline-16756
12/15/2025, 11:04 PMThanks — that helps. I want to make sure I give a precise, actionable caching plan for "tokens for grounding data." A couple quick clarifying points so I can recommend the right design and examples:
1. Which Gemini integration are you using (LLM inference, realtime plugin, TTS, Gemini Live API, or custom)?
2. By "tokens for grounding data" do you mean:
◦ the model's tokenized prompt/completion data (token-level caching), or
◦ cached embeddings/semantic tokens used as grounding, or
◦ authentication/API tokens?
1. Where can you modify code: server-side (can add Redis/S3/etc.) or only client-side?
2. Desired outcome: reduce API calls, lower latency, support offline lookup, or persist audit/history?
3. Constraints: preferred storage (Redis, memory, S3), TTL requirements, and any privacy/PII rules?
Answer these and I’ll provide a concise caching design (key schema, TTL strategy, invalidation, and a short example using Redis or S3) tailored to your setup.
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