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Running
Running
| server: | |
| env_name: ${APP_ENV:ollama} | |
| llm: | |
| mode: ollama | |
| max_new_tokens: 512 | |
| context_window: 3900 | |
| 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) | |
| embedding: | |
| mode: ollama | |
| ollama: | |
| llm_model: mistral | |
| embedding_model: nomic-embed-text | |
| api_base: http://localhost:11434 | |
| embedding_api_base: http://localhost:11434 # change if your embedding model runs on another ollama | |
| keep_alive: 5m | |
| 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. | |
| 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) | |
| 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) | |
| repeat_last_n: 64 # Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | |
| 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) | |
| request_timeout: 120.0 # Time elapsed until ollama times out the request. Default is 120s. Format is float. | |
| vectorstore: | |
| database: qdrant | |
| qdrant: | |
| path: local_data/private_gpt/qdrant | |