autogen_ext.models.ollama#
- pydantic 模型 BaseOllamaClientConfigurationConfigModel[源码]#
基类:
CreateArgumentsConfigModel显示 JSON 模式
{ "title": "BaseOllamaClientConfigurationConfigModel", "type": "object", "properties": { "model": { "title": "Model", "type": "string" }, "host": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Host" }, "response_format": { "default": null, "title": "Response Format" }, "follow_redirects": { "default": true, "title": "Follow Redirects", "type": "boolean" }, "timeout": { "default": null, "title": "Timeout" }, "headers": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "default": null, "title": "Headers" }, "model_capabilities": { "anyOf": [ { "$ref": "#/$defs/ModelCapabilities" }, { "type": "null" } ], "default": null }, "model_info": { "anyOf": [ { "$ref": "#/$defs/ModelInfo" }, { "type": "null" } ], "default": null }, "options": { "anyOf": [ { "type": "object" }, { "$ref": "#/$defs/Options" }, { "type": "null" } ], "default": null, "title": "Options" } }, "$defs": { "ModelCapabilities": { "deprecated": true, "properties": { "vision": { "title": "Vision", "type": "boolean" }, "function_calling": { "title": "Function Calling", "type": "boolean" }, "json_output": { "title": "Json Output", "type": "boolean" } }, "required": [ "vision", "function_calling", "json_output" ], "title": "ModelCapabilities", "type": "object" }, "ModelInfo": { "description": "ModelInfo is a dictionary that contains information about a model's properties.\nIt is expected to be used in the model_info property of a model client.\n\nWe are expecting this to grow over time as we add more features.", "properties": { "vision": { "title": "Vision", "type": "boolean" }, "function_calling": { "title": "Function Calling", "type": "boolean" }, "json_output": { "title": "Json Output", "type": "boolean" }, "family": { "anyOf": [ { "enum": [ "gpt-41", "gpt-45", "gpt-4o", "o1", "o3", "o4", "gpt-4", "gpt-35", "r1", "gemini-1.5-flash", "gemini-1.5-pro", "gemini-2.0-flash", "gemini-2.5-pro", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3-5-haiku", "claude-3-5-sonnet", "claude-3-7-sonnet", "unknown" ], "type": "string" }, { "type": "string" } ], "title": "Family" }, "structured_output": { "title": "Structured Output", "type": "boolean" }, "multiple_system_messages": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Multiple System Messages" } }, "required": [ "vision", "function_calling", "json_output", "family", "structured_output" ], "title": "ModelInfo", "type": "object" }, "Options": { "properties": { "numa": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Numa" }, "num_ctx": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Ctx" }, "num_batch": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Batch" }, "num_gpu": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Gpu" }, "main_gpu": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Main Gpu" }, "low_vram": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Low Vram" }, "f16_kv": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "F16 Kv" }, "logits_all": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Logits All" }, "vocab_only": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Vocab Only" }, "use_mmap": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Use Mmap" }, "use_mlock": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Use Mlock" }, "embedding_only": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Embedding Only" }, "num_thread": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Thread" }, "num_keep": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Keep" }, "seed": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Seed" }, "num_predict": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Num Predict" }, "top_k": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Top K" }, "top_p": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Top P" }, "tfs_z": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Tfs Z" }, "typical_p": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Typical P" }, "repeat_last_n": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Repeat Last N" }, "temperature": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Temperature" }, "repeat_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Repeat Penalty" }, "presence_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Presence Penalty" }, "frequency_penalty": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Frequency Penalty" }, "mirostat": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "title": "Mirostat" }, "mirostat_tau": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Mirostat Tau" }, "mirostat_eta": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "default": null, "title": "Mirostat Eta" }, "penalize_newline": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "default": null, "title": "Penalize Newline" }, "stop": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "title": "Stop" } }, "title": "Options", "type": "object" } }, "required": [ "model" ] }
- 字段:
follow_redirects (bool)headers (Mapping[str, str] | None)model_capabilities (autogen_core.models._model_client.ModelCapabilities | None)model_info (autogen_core.models._model_client.ModelInfo | None)options (Mapping[str, Any] | ollama._types.Options | None)timeout (Any)
- 字段 model_capabilities: ModelCapabilities | None = None#
- pydantic 模型 CreateArgumentsConfigModel[源码]#
基类:
BaseModel显示 JSON 模式
{ "title": "CreateArgumentsConfigModel", "type": "object", "properties": { "model": { "title": "Model", "type": "string" }, "host": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "title": "Host" }, "response_format": { "default": null, "title": "Response Format" } }, "required": [ "model" ] }
- 字段:
host (str | None)model (str)response_format (Any)
- 类 OllamaChatCompletionClient(**kwargs: Unpack)[源码]#
基类:
BaseOllamaChatCompletionClient,Component[BaseOllamaClientConfigurationConfigModel]用于 Ollama 托管模型的聊天完成客户端。
必须安装 Ollama 并拉取相应的模型。
- 参数:
注意
只有下载量超过 20 万的模型（截至 2025 年 1 月 21 日）以及 phi4、deepseek-r1 具有预定义的模型信息。 请参阅此文件获取完整列表。 一个模型的条目涵盖该模型的所有参数变体。
要使用此客户端，您必须安装 ollama 扩展
pip install "autogen-ext[ollama]"
以下代码片段展示了如何将客户端与 Ollama 模型一起使用
from autogen_ext.models.ollama import OllamaChatCompletionClient from autogen_core.models import UserMessage ollama_client = OllamaChatCompletionClient( model="llama3", ) result = await ollama_client.create([UserMessage(content="What is the capital of France?", source="user")]) # type: ignore print(result)
要从配置加载客户端，可以使用 load_component 方法
from autogen_core.models import ChatCompletionClient config = { "provider": "OllamaChatCompletionClient", "config": {"model": "llama3"}, } client = ChatCompletionClient.load_component(config)
要输出结构化数据，可以使用 response_format 参数
from autogen_ext.models.ollama import OllamaChatCompletionClient from autogen_core.models import UserMessage from pydantic import BaseModel class StructuredOutput(BaseModel): first_name: str last_name: str ollama_client = OllamaChatCompletionClient( model="llama3", response_format=StructuredOutput, ) result = await ollama_client.create([UserMessage(content="Who was the first man on the moon?", source="user")]) # type: ignore print(result)
注意
Ollama 中的工具使用比 OpenAI 对应项更严格。 虽然 OpenAI 接受 [str, Any] 的映射，但 Ollama 需要 [str, Property] 的映射，其中 Property 是包含
type和description字段的类型化对象。 因此，只有键type和description将从工具模式中的属性 blob 转换。要查看可用配置选项的完整列表，请参阅
OllamaClientConfigurationConfigModel类。- classmethod _from_config(config: BaseOllamaClientConfigurationConfigModel) Self[source]#
从配置对象创建一个组件的新实例。
- 参数:
config (T) – 配置对象。
- 返回值:
Self – 组件的新实例。
- _to_config() BaseOllamaClientConfigurationConfigModel[source]#
转储创建与此实例配置匹配的组件的新实例所需的配置。
- 返回值:
T – 组件的配置。
- component_config_schema#
- component_provider_override: ClassVar[str | None] = 'autogen_ext.models.ollama.OllamaChatCompletionClient'#
覆盖组件的提供程序字符串。 这应该用于防止内部模块名称成为模块名称的一部分。
- component_type: ClassVar[ComponentType] = 'model'#
组件的逻辑类型。