OllamaEmbeddings

Model name to use.

Whether to validate the model exists in ollama locally on initialization.

Base url the model is hosted under.

If none, defaults to the Ollama client default.

Supports userinfo auth in the format http://username:password@localhost:11434. Useful if your Ollama server is behind a proxy.

Additional kwargs to pass to the httpx clients. Pass headers in here.

These arguments are passed to both synchronous and async clients.

Use sync_client_kwargs and async_client_kwargs to pass different arguments to synchronous and asynchronous clients.

Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)

Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)

Sets the size of the context window used to generate the next token. (Default: 2048)

The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.

Controls how long the model will stay loaded into memory following the request (default: 5m)

Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).

Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)

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)

The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)

Sets the stop tokens to use.

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. (default: 1)

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)

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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