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: llama3.1
  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: 12000.0  # Time elapsed until ollama times out the request. Default is 120s. Format is float.

vectorstore:
  database: qdrant

qdrant:
  path: local_data/private_gpt/qdrant