litellm.py•5.07 kB
from __future__ import annotations as _annotations
from typing import overload
from httpx import AsyncClient as AsyncHTTPClient
from openai import AsyncOpenAI
from pydantic_ai import ModelProfile
from pydantic_ai.models import cached_async_http_client
from pydantic_ai.profiles.amazon import amazon_model_profile
from pydantic_ai.profiles.anthropic import anthropic_model_profile
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.grok import grok_model_profile
from pydantic_ai.profiles.groq import groq_model_profile
from pydantic_ai.profiles.meta import meta_model_profile
from pydantic_ai.profiles.mistral import mistral_model_profile
from pydantic_ai.profiles.moonshotai import moonshotai_model_profile
from pydantic_ai.profiles.openai import OpenAIJsonSchemaTransformer, OpenAIModelProfile, openai_model_profile
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 LiteLLM provider, '
'you can use the `openai` optional group — `pip install "pydantic-ai-slim[openai]"`'
) from _import_error
class LiteLLMProvider(Provider[AsyncOpenAI]):
"""Provider for LiteLLM API."""
@property
def name(self) -> str:
return 'litellm'
@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:
# Map provider prefixes to their profile functions
provider_to_profile = {
'anthropic': anthropic_model_profile,
'openai': openai_model_profile,
'google': google_model_profile,
'mistralai': mistral_model_profile,
'mistral': mistral_model_profile,
'cohere': cohere_model_profile,
'amazon': amazon_model_profile,
'bedrock': amazon_model_profile,
'meta-llama': meta_model_profile,
'meta': meta_model_profile,
'groq': groq_model_profile,
'deepseek': deepseek_model_profile,
'moonshotai': moonshotai_model_profile,
'x-ai': grok_model_profile,
'qwen': qwen_model_profile,
}
profile = None
# Check if model name contains a provider prefix (e.g., "anthropic/claude-3")
if '/' in model_name:
provider_prefix, model_suffix = model_name.split('/', 1)
if provider_prefix in provider_to_profile:
profile = provider_to_profile[provider_prefix](model_suffix)
# If no profile found, default to OpenAI profile
if profile is None:
profile = openai_model_profile(model_name)
# As LiteLLMProvider is used with OpenAIModel, which uses OpenAIJsonSchemaTransformer,
# we maintain that behavior
return OpenAIModelProfile(json_schema_transformer=OpenAIJsonSchemaTransformer).update(profile)
@overload
def __init__(
self,
*,
api_key: str | None = None,
api_base: str | None = None,
) -> None: ...
@overload
def __init__(
self,
*,
api_key: str | None = None,
api_base: str | None = None,
http_client: AsyncHTTPClient,
) -> None: ...
@overload
def __init__(self, *, openai_client: AsyncOpenAI) -> None: ...
def __init__(
self,
*,
api_key: str | None = None,
api_base: str | None = None,
openai_client: AsyncOpenAI | None = None,
http_client: AsyncHTTPClient | None = None,
) -> None:
"""Initialize a LiteLLM provider.
Args:
api_key: API key for the model provider. If None, LiteLLM will try to get it from environment variables.
api_base: Base URL for the model provider. Use this for custom endpoints or self-hosted models.
openai_client: Pre-configured OpenAI client. If provided, other parameters are ignored.
http_client: Custom HTTP client to use.
"""
if openai_client is not None:
self._client = openai_client
return
# Create OpenAI client that will be used with LiteLLM's completion function
# The actual API calls will be intercepted and routed through LiteLLM
if http_client is not None:
self._client = AsyncOpenAI(
base_url=api_base, api_key=api_key or 'litellm-placeholder', http_client=http_client
)
else:
http_client = cached_async_http_client(provider='litellm')
self._client = AsyncOpenAI(
base_url=api_base, api_key=api_key or 'litellm-placeholder', http_client=http_client
)