ollama.py•4.73 kB
from __future__ import annotations as _annotations
import os
import httpx
from openai import AsyncOpenAI
from pydantic_ai import ModelProfile
from pydantic_ai.exceptions import UserError
from pydantic_ai.models import cached_async_http_client
from pydantic_ai.profiles.cohere import cohere_model_profile
from pydantic_ai.profiles.deepseek import deepseek_model_profile
from pydantic_ai.profiles.google import google_model_profile
from pydantic_ai.profiles.harmony import harmony_model_profile
from pydantic_ai.profiles.meta import meta_model_profile
from pydantic_ai.profiles.mistral import mistral_model_profile
from pydantic_ai.profiles.openai import OpenAIJsonSchemaTransformer, OpenAIModelProfile
from pydantic_ai.profiles.qwen import qwen_model_profile
from pydantic_ai.providers import Provider
try:
from openai import AsyncOpenAI
except ImportError as _import_error: # pragma: no cover
raise ImportError(
'Please install the `openai` package to use the Ollama provider, '
'you can use the `openai` optional group — `pip install "pydantic-ai-slim[openai]"`'
) from _import_error
class OllamaProvider(Provider[AsyncOpenAI]):
"""Provider for local or remote Ollama API."""
@property
def name(self) -> str:
return 'ollama'
@property
def base_url(self) -> str:
return str(self.client.base_url)
@property
def client(self) -> AsyncOpenAI:
return self._client
def model_profile(self, model_name: str) -> ModelProfile | None:
prefix_to_profile = {
'llama': meta_model_profile,
'gemma': google_model_profile,
'qwen': qwen_model_profile,
'qwq': qwen_model_profile,
'deepseek': deepseek_model_profile,
'mistral': mistral_model_profile,
'command': cohere_model_profile,
'gpt-oss': harmony_model_profile,
}
profile = None
for prefix, profile_func in prefix_to_profile.items():
model_name = model_name.lower()
if model_name.startswith(prefix):
profile = profile_func(model_name)
# As OllamaProvider is always used with OpenAIChatModel, which used to unconditionally use OpenAIJsonSchemaTransformer,
# we need to maintain that behavior unless json_schema_transformer is set explicitly
return OpenAIModelProfile(json_schema_transformer=OpenAIJsonSchemaTransformer).update(profile)
def __init__(
self,
base_url: str | None = None,
api_key: str | None = None,
openai_client: AsyncOpenAI | None = None,
http_client: httpx.AsyncClient | None = None,
) -> None:
"""Create a new Ollama provider.
Args:
base_url: The base url for the Ollama requests. If not provided, the `OLLAMA_BASE_URL` environment variable
will be used if available.
api_key: The API key to use for authentication, if not provided, the `OLLAMA_API_KEY` environment variable
will be used if available.
openai_client: An existing
[`AsyncOpenAI`](https://github.com/openai/openai-python?tab=readme-ov-file#async-usage)
client to use. If provided, `base_url`, `api_key`, and `http_client` must be `None`.
http_client: An existing `httpx.AsyncClient` to use for making HTTP requests.
"""
if openai_client is not None:
assert base_url is None, 'Cannot provide both `openai_client` and `base_url`'
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`'
self._client = openai_client
else:
base_url = base_url or os.getenv('OLLAMA_BASE_URL')
if not base_url:
raise UserError(
'Set the `OLLAMA_BASE_URL` environment variable or pass it via `OllamaProvider(base_url=...)`'
'to use the Ollama provider.'
)
# This is a workaround for the OpenAI client requiring an API key, whilst locally served,
# openai compatible models do not always need an API key, but a placeholder (non-empty) key is required.
api_key = api_key or os.getenv('OLLAMA_API_KEY') or 'api-key-not-set'
if http_client is not None:
self._client = AsyncOpenAI(base_url=base_url, api_key=api_key, http_client=http_client)
else:
http_client = cached_async_http_client(provider='ollama')
self._client = AsyncOpenAI(base_url=base_url, api_key=api_key, http_client=http_client)