Xubin Ren 306958d6e6 add native Bedrock Converse provider
Made-with: Cursor
2026-05-01 18:52:03 +08:00

130 lines
4.6 KiB
Python

"""Create LLM providers from config."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from nanobot.config.schema import Config
from nanobot.providers.base import GenerationSettings, LLMProvider
from nanobot.providers.registry import find_by_name
@dataclass(frozen=True)
class ProviderSnapshot:
provider: LLMProvider
model: str
context_window_tokens: int
signature: tuple[object, ...]
def make_provider(config: Config) -> LLMProvider:
"""Create the LLM provider implied by config."""
model = config.agents.defaults.model
provider_name = config.get_provider_name(model)
p = config.get_provider(model)
spec = find_by_name(provider_name) if provider_name else None
backend = spec.backend if spec else "openai_compat"
if backend == "azure_openai":
if not p or not p.api_key or not p.api_base:
raise ValueError("Azure OpenAI requires api_key and api_base in config.")
elif backend == "openai_compat" and not model.startswith("bedrock/"):
needs_key = not (p and p.api_key)
exempt = spec and (spec.is_oauth or spec.is_local or spec.is_direct)
if needs_key and not exempt:
raise ValueError(f"No API key configured for provider '{provider_name}'.")
if backend == "openai_codex":
from nanobot.providers.openai_codex_provider import OpenAICodexProvider
provider = OpenAICodexProvider(default_model=model)
elif backend == "azure_openai":
from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
provider = AzureOpenAIProvider(
api_key=p.api_key,
api_base=p.api_base,
default_model=model,
)
elif backend == "github_copilot":
from nanobot.providers.github_copilot_provider import GitHubCopilotProvider
provider = GitHubCopilotProvider(default_model=model)
elif backend == "anthropic":
from nanobot.providers.anthropic_provider import AnthropicProvider
provider = AnthropicProvider(
api_key=p.api_key if p else None,
api_base=config.get_api_base(model),
default_model=model,
extra_headers=p.extra_headers if p else None,
)
elif backend == "bedrock":
from nanobot.providers.bedrock_provider import BedrockProvider
provider = BedrockProvider(
api_key=p.api_key if p else None,
api_base=p.api_base if p else None,
default_model=model,
region=getattr(p, "region", None) if p else None,
profile=getattr(p, "profile", None) if p else None,
extra_body=p.extra_body if p else None,
)
else:
from nanobot.providers.openai_compat_provider import OpenAICompatProvider
provider = OpenAICompatProvider(
api_key=p.api_key if p else None,
api_base=config.get_api_base(model),
default_model=model,
extra_headers=p.extra_headers if p else None,
spec=spec,
extra_body=p.extra_body if p else None,
)
defaults = config.agents.defaults
provider.generation = GenerationSettings(
temperature=defaults.temperature,
max_tokens=defaults.max_tokens,
reasoning_effort=defaults.reasoning_effort,
)
return provider
def provider_signature(config: Config) -> tuple[object, ...]:
"""Return the config fields that affect the primary LLM provider."""
model = config.agents.defaults.model
defaults = config.agents.defaults
p = config.get_provider(model)
return (
model,
defaults.provider,
config.get_provider_name(model),
config.get_api_key(model),
config.get_api_base(model),
p.extra_headers if p else None,
p.extra_body if p else None,
getattr(p, "region", None) if p else None,
getattr(p, "profile", None) if p else None,
defaults.max_tokens,
defaults.temperature,
defaults.reasoning_effort,
defaults.context_window_tokens,
)
def build_provider_snapshot(config: Config) -> ProviderSnapshot:
return ProviderSnapshot(
provider=make_provider(config),
model=config.agents.defaults.model,
context_window_tokens=config.agents.defaults.context_window_tokens,
signature=provider_signature(config),
)
def load_provider_snapshot(config_path: Path | None = None) -> ProviderSnapshot:
from nanobot.config.loader import load_config, resolve_config_env_vars
return build_provider_snapshot(resolve_config_env_vars(load_config(config_path)))