strands.models.ollama

Ollama model provider.

• Docs: https://ollama.com/

OllamaModel

class OllamaModel(Model)

Defined in: src/strands/models/ollama.py:26

Ollama model provider implementation.

The implementation handles Ollama-specific features such as:

• Local model invocation
• Streaming responses
• Tool/function calling

OllamaConfig

class OllamaConfig(TypedDict)

Defined in: src/strands/models/ollama.py:36

Configuration parameters for Ollama models.

Attributes:

• additional_args - Any additional arguments to include in the request.
• keep_alive - Controls how long the model will stay loaded into memory following the request (default: “5m”).
• max_tokens - Maximum number of tokens to generate in the response.
• model_id - Ollama model ID (e.g., “llama3”, “mistral”, “phi3”).
• options - Additional model parameters (e.g., top_k).
• stop_sequences - List of sequences that will stop generation when encountered.
• temperature - Controls randomness in generation (higher = more random).
• top_p - Controls diversity via nucleus sampling (alternative to temperature).

__init__

def __init__(host: str | None,
             *,
             ollama_client_args: dict[str, Any] | None = None,
             **model_config: Unpack[OllamaConfig]) -> None

Defined in: src/strands/models/ollama.py:59

Initialize provider instance.

Arguments:

• host - The address of the Ollama server hosting the model.
• ollama_client_args - Additional arguments for the Ollama client.
• **model_config - Configuration options for the Ollama model.

update_config

@override
def update_config(**model_config: Unpack[OllamaConfig]) -> None

Defined in: src/strands/models/ollama.py:81

Update the Ollama Model configuration with the provided arguments.

Arguments:

• **model_config - Configuration overrides.

get_config

@override
def get_config() -> OllamaConfig

Defined in: src/strands/models/ollama.py:91

Get the Ollama model configuration.

Returns:

The Ollama model configuration.

format_request

def format_request(messages: Messages,
                   tool_specs: list[ToolSpec] | None = None,
                   system_prompt: str | None = None) -> dict[str, Any]

Defined in: src/strands/models/ollama.py:174

Format an Ollama chat streaming request.

Arguments:

• messages - List of message objects to be processed by the model.
• tool_specs - List of tool specifications to make available to the model.
• system_prompt - System prompt to provide context to the model.

Returns:

An Ollama chat streaming request.

Raises:

• TypeError - If a message contains a content block type that cannot be converted to an Ollama-compatible format.

format_chunk

def format_chunk(event: dict[str, Any]) -> StreamEvent

Defined in: src/strands/models/ollama.py:227

Format the Ollama response events into standardized message chunks.

Arguments:

• event - A response event from the Ollama model.

Returns:

The formatted chunk.

Raises:

• RuntimeError - If chunk_type is not recognized. This error should never be encountered as we control chunk_type in the stream method.

stream

@override
async def stream(messages: Messages,
                 tool_specs: list[ToolSpec] | None = None,
                 system_prompt: str | None = None,
                 *,
                 tool_choice: ToolChoice | None = None,
                 **kwargs: Any) -> AsyncGenerator[StreamEvent, None]

Defined in: src/strands/models/ollama.py:290

Stream conversation with the Ollama model.

Arguments:

• messages - List of message objects to be processed by the model.
• tool_specs - List of tool specifications to make available to the model.
• system_prompt - System prompt to provide context to the model.
• tool_choice - Selection strategy for tool invocation. Note: This parameter is accepted for interface consistency but is currently ignored for this model provider.
• **kwargs - Additional keyword arguments for future extensibility.

Yields:

Formatted message chunks from the model.

structured_output

@override
async def structured_output(
        output_model: type[T],
        prompt: Messages,
        system_prompt: str | None = None,
        **kwargs: Any) -> AsyncGenerator[dict[str, T | Any], None]

Defined in: src/strands/models/ollama.py:346

Get structured output from the model.

Arguments:

• output_model - The output model to use for the agent.
• prompt - The prompt messages to use for the agent.
• system_prompt - System prompt to provide context to the model.
• **kwargs - Additional keyword arguments for future extensibility.

Yields:

Model events with the last being the structured output.