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• quarkus-langchain4j-ollama
• 0.17.0
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• EmbeddingModelConfig.jdp

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io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.jdp Maven / Gradle / Ivy

io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.logRequests=Whether embedding model requests should be logged
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.logResponses=Whether embedding model responses should be logged
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.numPredict=Maximum number of tokens to predict when generating text
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.stop=Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.temperature=The temperature of the model. Increasing the temperature will make the model answer with\nmore variability. A lower temperature will make the model answer more conservatively.
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.topK=Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower\nvalue (e.g. 10) will be more conservative
io.quarkiverse.langchain4j.ollama.runtime.config.EmbeddingModelConfig.topP=Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5)\nwill generate more focused and conservative text