Claude
faacda7079
- spring-ai-starter-model-ollama autoconfigures ChatModel/EmbeddingModel from spring.ai.ollama.* properties alone; no API key anywhere in this module. - org.testcontainers:ollama and org.testcontainers:junit-jupiter were both renamed in the Testcontainers 2.x line -- to org.testcontainers:testcontainers-ollama and org.testcontainers:testcontainers-junit-jupiter respectively -- confirmed by reading the real testcontainers-bom-2.0.5.pom that Spring Boot 4.1.1 imports (spring-boot-dependencies -> testcontainers.version=2.0.5). The pre-rename artifact IDs still exist on Maven Central but are stuck on the 1.x line. - Unlike every other module in this series, tests drive a real local model (qwen2.5:0.5b chat, all-minilm embeddings) via a Testcontainers-managed OllamaContainer started from a baked image (scripts/bake-image.sh), not a ScriptedChatModel -- the whole point of this post is a real model answering a real prompt. - LocalChatAndEmbeddingTest forces a genuine cold state with Ollama's keep_alive: 0 option (set via ChatModel.call(Prompt) -- ChatClient.options() does not carry a keepAlive override through to the request in this version) and confirms the unload actually happened via /api/ps before measuring a reload, rather than trusting whichever call happens to run first. - On this quiet sandbox host, even a confirmed-cold reload of the 500MB model came back in single-digit milliseconds once the underlying image layers were cached -- eval (generation) time dominates total latency here, not loading. Captured, not asserted as universal: readers get scripts/bake-image.sh to get their own numbers. - Embedding dimension (384, all-minilm) asserted deterministically. Co-Authored-By: Claude Sonnet 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01FtpJvZfg4nvLvtzgJTDWpB
10 lines
447 B
Plaintext
10 lines
447 B
Plaintext
/api/ps immediately after the unload call: {"models":[]}
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prompt: "Reply with a single short sentence: why do developers like small local models?"
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response: Developers often prefer small local models because they are more efficient, faster, and easier to deploy and train.
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total-duration: 901ms
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load-duration: 1ms
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prompt-eval-count: 44, prompt-eval-duration: 39ms
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eval-count: 23, eval-duration: 856ms
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26.87 tokens/sec (eval-count / eval-duration) |