Ollama support

docs/api/models/ollama.md

@@ -0,0 +1,40 @@
+# `pydantic_ai.models.ollama`
+
+## Setup
+
+For details on how to set up authentication with this model, see [model configuration for Ollama](../../install.md#ollama).
+
+## Example usage
+
+With `ollama` installed, you can run the server with the model you want to use:
+
+```bash title="terminal-run-ollama"
+ollama run llama3.2
+```
+(this will pull the `llama3.2` model if you don't already have it downloaded)
+
+Then run your code, here's a minimal example:
+
+```py title="ollama_example.py"
+from pydantic import BaseModel
+
+from pydantic_ai import Agent
+
+
+class CityLocation(BaseModel):
+    city: str
+    country: str
+
+
+agent = Agent('ollama:llama3.2', result_type=CityLocation)
+
+result = agent.run_sync('Where the olympics held in 2012?')
+print(result.data)
+#> city='London' country='United Kingdom'
+print(result.cost())
+#> Cost(request_tokens=56, response_tokens=8, total_tokens=64, details=None)
+```
+
+See [`OllamaModel`][pydantic_ai.models.ollama.OllamaModel] for more information
+
+::: pydantic_ai.models.ollama

docs/install.md

@@ -323,3 +323,9 @@ model = GroqModel('llama-3.1-70b-versatile', api_key='your-api-key')
 agent = Agent(model)
 ...
 ```
+
+### Ollama
+
+To use [Ollama](https://ollama.com/), you must first download the Ollama client, and then download a model.
+
+You must also ensure the Ollama server is running when trying to make requests to it. For more information, please see the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs)

mkdocs.yml

@@ -38,6 +38,7 @@ nav:
     - api/exceptions.md
     - api/models/base.md
     - api/models/openai.md
+    - api/models/ollama.md
     - api/models/gemini.md
     - api/models/vertexai.md
     - api/models/groq.md

pydantic_ai_slim/pydantic_ai/models/__init__.py

@@ -49,6 +49,23 @@
     'gemini-1.5-pro',
     'vertexai:gemini-1.5-flash',
     'vertexai:gemini-1.5-pro',
+    'ollama:codellama',
+    'ollama:gemma',
+    'ollama:gemma2',
+    'ollama:llama3',
+    'ollama:llama3.1',
+    'ollama:llama3.2',
+    'ollama:llama3.2-vision',
+    'ollama:llama3.3',
+    'ollama:mistral',
+    'ollama:mistral-nemo',
+    'ollama:mixtral',
+    'ollama:phi3',
+    'ollama:qwq',
+    'ollama:qwen',
+    'ollama:qwen2',
+    'ollama:qwen2.5',
+    'ollama:starcoder2',
     'test',
 ]
 """Known model names that can be used with the `model` parameter of [`Agent`][pydantic_ai.Agent].
@@ -239,7 +256,7 @@ def infer_model(model: Model | KnownModelName) -> Model:
     elif model.startswith('openai:'):
         from .openai import OpenAIModel
 
-        return OpenAIModel(model[7:])  # pyright: ignore[reportArgumentType]
+        return OpenAIModel(model[7:])
     elif model.startswith('gemini'):
         from .gemini import GeminiModel
 
@@ -253,6 +270,10 @@ def infer_model(model: Model | KnownModelName) -> Model:
         from .vertexai import VertexAIModel
 
         return VertexAIModel(model[9:])  # pyright: ignore[reportArgumentType]
+    elif model.startswith('ollama:'):
+        from .ollama import OllamaModel
+
+        return OllamaModel(model[7:])
     else:
         raise UserError(f'Unknown model: {model}')
 

pydantic_ai_slim/pydantic_ai/models/ollama.py

@@ -0,0 +1,118 @@
+from __future__ import annotations as _annotations
+
+from dataclasses import dataclass
+from typing import Literal, Union
+
+from httpx import AsyncClient as AsyncHTTPClient
+
+from ..tools import ToolDefinition
+from . import (
+    AgentModel,
+    Model,
+    cached_async_http_client,
+)
+
+try:
+    from openai import AsyncOpenAI
+except ImportError as e:
+    raise ImportError(
+        'Please install `openai` to use the OpenAI model, '
+        "you can use the `openai` optional group — `pip install 'pydantic-ai[openai]'`"
+    ) from e
+
+
+from .openai import OpenAIModel
+
+CommonOllamaModelNames = Literal[
+    'codellama',
+    'gemma',
+    'gemma2',
+    'llama3',
+    'llama3.1',
+    'llama3.2',
+    'llama3.2-vision',
+    'llama3.3',
+    'mistral',
+    'mistral-nemo',
+    'mixtral',
+    'phi3',
+    'qwq',
+    'qwen',
+    'qwen2',
+    'qwen2.5',
+    'starcoder2',
+]
+"""This contains just the most common ollama models.
+
+For a full list see [ollama.com/library](https://ollama.com/library).
+"""
+OllamaModelName = Union[CommonOllamaModelNames, str]
+"""Possible ollama models.
+
+Since Ollama supports hundreds of models, we explicitly list the most models but
+allow any name in the type hints.
+"""
+
+
+@dataclass(init=False)
+class OllamaModel(Model):
+    """A model that implements Ollama using the OpenAI API.
+
+    Internally, this uses the [OpenAI Python client](https://github.com/openai/openai-python) to interact with the Ollama server.
+
+    Apart from `__init__`, all methods are private or match those of the base class.
+    """
+
+    model_name: OllamaModelName
+    openai_model: OpenAIModel
+
+    def __init__(
+        self,
+        model_name: OllamaModelName,
+        *,
+        base_url: str | None = 'http://localhost:11434/v1/',
+        openai_client: AsyncOpenAI | None = None,
+        http_client: AsyncHTTPClient | None = None,
+    ):
+        """Initialize an Ollama model.
+
+        Ollama has built-in compatability for the OpenAI chat completions API ([source](https://ollama.com/blog/openai-compatibility)), so we reuse the
+        [`OpenAIModel`][pydantic_ai.models.openai.OpenAIModel] here.
+
+        Args:
+            model_name: The name of the Ollama model to use. List of models available [here](https://ollama.com/library)
+                You must first download the model (`ollama pull <MODEL-NAME>`) in order to use the model
+            base_url: The base url for the ollama requests. The default value is the ollama default
+            openai_client: An existing
+                [`AsyncOpenAI`](https://github.com/openai/openai-python?tab=readme-ov-file#async-usage)
+                client to use, if provided, `base_url` and `http_client` must be `None`.
+            http_client: An existing `httpx.AsyncClient` to use for making HTTP requests.
+        """
+        self.model_name = model_name
+        if openai_client is not None:
+            assert base_url is None, 'Cannot provide both `openai_client` and `base_url`'
+            self.openai_model = OpenAIModel(model_name=model_name, openai_client=openai_client, http_client=http_client)
+        elif http_client is not None:
+            # API key is not required for ollama but a value is required to create the client
+            oai_client = AsyncOpenAI(base_url=base_url, api_key='ollama', http_client=http_client)
+            self.openai_model = OpenAIModel(model_name=model_name, openai_client=oai_client, http_client=http_client)
+        else:
+            # API key is not required for ollama but a value is required to create the client
+            oai_client = AsyncOpenAI(base_url=base_url, api_key='ollama', http_client=cached_async_http_client())
+            self.openai_model = OpenAIModel(model_name=model_name, openai_client=oai_client, http_client=http_client)
+
+    async def agent_model(
+        self,
+        *,
+        function_tools: list[ToolDefinition],
+        allow_text_result: bool,
+        result_tools: list[ToolDefinition],
+    ) -> AgentModel:
+        return await self.openai_model.agent_model(
+            function_tools=function_tools,
+            allow_text_result=allow_text_result,
+            result_tools=result_tools,
+        )
+
+    def name(self) -> str:
+        return f'ollama:{self.model_name}'

pydantic_ai_slim/pydantic_ai/models/openai.py

@@ -4,7 +4,7 @@
 from contextlib import asynccontextmanager
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
-from typing import Literal, overload
+from typing import Literal, Union, overload
 
 from httpx import AsyncClient as AsyncHTTPClient
 from typing_extensions import assert_never
@@ -43,6 +43,12 @@
         "you can use the `openai` optional group — `pip install 'pydantic-ai[openai]'`"
     ) from e
 
+OpenAIModelName = Union[ChatModel, str]
+"""
+Using this more broad type for the model name instead of the ChatModel definition
+allows this model to be used more easily with other model types (ie, Ollama)
+"""
+
 
 @dataclass(init=False)
 class OpenAIModel(Model):
@@ -53,12 +59,12 @@ class OpenAIModel(Model):
     Apart from `__init__`, all methods are private or match those of the base class.
     """
 
-    model_name: ChatModel
+    model_name: OpenAIModelName
     client: AsyncOpenAI = field(repr=False)
 
     def __init__(
         self,
-        model_name: ChatModel,
+        model_name: OpenAIModelName,
         *,
         api_key: str | None = None,
         openai_client: AsyncOpenAI | None = None,
@@ -77,7 +83,7 @@ def __init__(
                 client to use, if provided, `api_key` and `http_client` must be `None`.
             http_client: An existing `httpx.AsyncClient` to use for making HTTP requests.
         """
-        self.model_name: ChatModel = model_name
+        self.model_name: OpenAIModelName = model_name
         if openai_client is not None:
             assert http_client is None, 'Cannot provide both `openai_client` and `http_client`'
             assert api_key is None, 'Cannot provide both `openai_client` and `api_key`'
@@ -125,7 +131,7 @@ class OpenAIAgentModel(AgentModel):
     """Implementation of `AgentModel` for OpenAI models."""
 
     client: AsyncOpenAI
-    model_name: ChatModel
+    model_name: OpenAIModelName
     allow_text_result: bool
     tools: list[chat.ChatCompletionToolParam]
 

tests/models/test_ollama.py

@@ -0,0 +1,61 @@
+from __future__ import annotations as _annotations
+
+from datetime import datetime, timezone
+
+import pytest
+from inline_snapshot import snapshot
+
+from pydantic_ai import Agent
+from pydantic_ai.messages import (
+    ModelTextResponse,
+    UserPrompt,
+)
+from pydantic_ai.result import Cost
+
+from ..conftest import IsNow, try_import
+
+with try_import() as imports_successful:
+    from openai.types.chat.chat_completion_message import ChatCompletionMessage
+
+    from pydantic_ai.models.ollama import OllamaModel
+
+    from .test_openai import MockOpenAI, completion_message
+
+pytestmark = [
+    pytest.mark.skipif(not imports_successful(), reason='openai not installed'),
+    pytest.mark.anyio,
+]
+
+
+def test_init():
+    m = OllamaModel('llama3.2', base_url='foobar/')
+    assert m.openai_model.client.api_key == 'ollama'
+    assert m.openai_model.client.base_url == 'foobar/'
+    assert m.name() == 'ollama:llama3.2'
+
+
+async def test_request_simple_success(allow_model_requests: None):
+    c = completion_message(ChatCompletionMessage(content='world', role='assistant'))
+    mock_client = MockOpenAI.create_mock(c)
+    print('here')
+    m = OllamaModel('llama3.2', openai_client=mock_client, base_url=None)
+    agent = Agent(m)
+
+    result = await agent.run('hello')
+    assert result.data == 'world'
+    assert result.cost() == snapshot(Cost())
+
+    # reset the index so we get the same response again
+    mock_client.index = 0  # type: ignore
+
+    result = await agent.run('hello', message_history=result.new_messages())
+    assert result.data == 'world'
+    assert result.cost() == snapshot(Cost())
+    assert result.all_messages() == snapshot(
+        [
+            UserPrompt(content='hello', timestamp=IsNow(tz=timezone.utc)),
+            ModelTextResponse(content='world', timestamp=datetime(2024, 1, 1, 0, 0, tzinfo=timezone.utc)),
+            UserPrompt(content='hello', timestamp=IsNow(tz=timezone.utc)),
+            ModelTextResponse(content='world', timestamp=datetime(2024, 1, 1, 0, 0, tzinfo=timezone.utc)),
+        ]
+    )