31 lines
1.7 KiB
YAML
31 lines
1.7 KiB
YAML
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server:
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env_name: ${APP_ENV:ollama}
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llm:
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mode: ollama
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max_new_tokens: 512
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context_window: 3900
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temperature: 0.1 #The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual. (Default: 0.1)
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embedding:
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mode: ollama
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ollama:
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llm_model: llama3.1
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embedding_model: nomic-embed-text
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api_base: http://localhost:11434
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embedding_api_base: http://localhost:11434 # change if your embedding model runs on another ollama
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keep_alive: 5m
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tfs_z: 1.0 # Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.
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top_k: 40 # Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)
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top_p: 0.9 # 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) will generate more focused and conservative text. (Default: 0.9)
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repeat_last_n: 64 # Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)
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repeat_penalty: 1.2 # Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)
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request_timeout: 120.0 # Time elapsed until ollama times out the request. Default is 120s. Format is float.
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vectorstore:
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database: qdrant
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qdrant:
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path: local_data/private_gpt/qdrant
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